Enhanced Antioxidant and Antimicrobial Activity through mixture design predictive modelling of an Essential Oil Blend from Syzygium aromaticum, Zingiber officinale, and Cananga odorata of Comoros Islands

Enhanced Antioxidant and Antimicrobial Activity through mixture design predictive modelling of an Essential Oil Blend from Syzygium aromaticum, Zingiber officinale, and Cananga odorata of Comoros Islands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhanced Antioxidant and Antimicrobial Activity through mixture design predictive modelling of an Essential Oil Blend from Syzygium aromaticum, Zingiber officinale, and Cananga odorata of Comoros Islands Djanah-Karene Nacer-Eddine, EL Hassania Loukili, Sara Lebrazi, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6184540/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Medicinal and aromatic plants from the Comoros Islands are a potential source of unexploited bioactive compounds. This study investigates the chemical composition and biological properties of essential oils (EOs) from three emblematic Comorian plants: Syzygium aromaticum (L.) Merr. & L.M.Perry (clove), Cananga odorata (Lam.) Hook. f. & Thomson (ylang-ylang), and Zingiber officinale Roscoe (ginger). EOs were characterized using gas chromatography coupled with mass spectrometry (GC-MS). Antioxidant activities were evaluated using the DPPH and molybdenum-reducing power methods, while antimicrobial properties were determined by the microdilution method against various pathogenic strains. A mixture design was applied to optimize EO combinations, identifying significant synergies in their biological activities. GC-MS analysis revealed that the major compounds in S. aromaticum EO were cinnamal (53.23%), eucalyptol (16.37%), and camphene (9.74%), Z. officinale EO was characterized by α-zingiberene (19.33%), eucalyptol (16.37%), β-citral (12.93%), and β-cymene (7.16%), while C. odorata EO contained primarily o-cresol (14.09%), germacrene D (11.16%), and β-linalool (10.47%). Mixture design optimization revealed that the combination of S. aromaticum and Z. officinale EOs significantly enhanced antioxidant activity (DPPH assay). Additionally, Escherichia coli , Candida albicans, and Pseudomonas aeruginosa exhibited the highest susceptibility to a binary mixture of S. aromaticum and C. odorata EOs. Moreover, S. aromaticum EO alone demonstrated the highest total antioxidant activity in the phosphomolybdenum assay. Furthermore, molecular docking analysis of the three main compounds in the EOs revealed strong interactions with the binding sites of various selected proteins, confirming their potential antioxidant and antibacterial properties. This research contributes to the valorization of Comorian natural resources and opens new perspectives for their exploitation in pharmaceutical and environmental sectors. Biological sciences/Plant sciences Physical sciences/Chemistry Comoros Islands Syzygium aromaticum Zingiber officinale Cananga odorata essential oil mixture design antioxidant antimicrobial synergy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction In recent decades, interest in plant-derived products has surged, driven by increasing concerns over synthetic chemical compounds and the demand for sustainable therapeutic alternatives [ 1 ]. Medicinal and aromatic plants have emerged as promising sources of bioactive compounds [ 2 – 3 ], attracting significant attention from researchers and practitioners due to their effectiveness and relatively straightforward extraction processes [ 4 – 5 ]. The Comoros Islands harbor a rich biodiversity of medicinal plants, including three particularly significant species: Syzygium aromaticum (L.) Merr. & L.M.Perry (clove), Cananga odorata (Lam.) Hook. f. & Thomson (ylang-ylang), and Zingiber officinale Roscoe (ginger). C. odorata is distinguished by its high content of sesquiterpene alcohols and esters, contributing to its therapeutic properties [ 6 ]. S. aromaticum contains significant concentrations of eugenol, a compound widely recognized for its antiseptic and analgesic properties [ 7 ]. Studies indicate that Z. officinale , rich in zingiberene and gingerol, exhibits notable anti-inflammatory and stimulant effects [ 8 ]. Despite their traditional use in Comorian medicine, comprehensive scientific research on their bioactive properties, particularly in combination, remains limited. Moreover, the valorization of extraction by-products, such as ginger residues, is an unexplored research area aligned with sustainability goals [ 9 ]. Oxidation and bacterial resistance are significant problems in many scientific and industrial sectors. Oxidation, which involves the loss of electrons, can degrade industrial materials, food products, and pharmaceutical compounds, deteriorating their physicochemical characteristics and decreasing efficacy [ 10 ]. Similarly, bacterial resistance to antimicrobial agents has become a global public health concern, making infection control more difficult and endangering the effectiveness of current treatments. Innovative formulations usually include active ingredients with synergistic properties to combat oxidation and bacterial resistance simultaneously, improving stability and enhancing antimicrobial potency. Mixture design methodologies are invaluable for evaluating interactions between components to achieve desired effects, whether antagonistic, synergistic, or additive. These designs help researchers determine optimal ratios where the combined impact of substances surpasses the sum of their individual effects [ 11 ]. This approach is frequently employed in food science, agrochemicals, and pharmaceutical formulation industries, where precise ingredient balance is necessary to maximize effectiveness while minimizing costs and adverse effects. For more reliable and efficient outcomes, mixture designs aid in streamlining formulation procedures and maximizing experimental trials. The present study uses a mixture design approach to phytochemically characterize essential oils (EOs) and evaluate their antioxidant and antimicrobial properties, both individually and in combination. In addition, it examines their antimicrobial activities at different concentrations using a structured mixing scheme. This research enhances the value of Comorian natural resources while addressing contemporary sustainable development challenges. Through mixture design methodology, this work enhanced the synergistic antibacterial and antioxidant properties of S. aromaticum , Z. officinale , and C. odorata EOs. Molecular interaction analyses of the primary compounds in these EOs demonstrate their strong binding affinity to active sites of key target proteins. This suggests that these EOs have strong antibacterial and antioxidant properties, which supports their possible use in medicinal and pharmaceutical formulations. This integrated approach advances our understanding of these plants' biological properties and explores innovative ways to utilise extraction by-products, promoting a more sustainable use of natural resources. Furthermore, this study addresses the growing need for evidence-based validation of traditional medicinal plants while potentially identifying new applications in pharmaceutical and environmental sectors. Materials and Methods 1. Plant material and EO extraction The plants Syzygium aromaticum (L.) ( Merr. & L.M.Perry), Cananga odorata (Lam.) (Hook. f. & Thomson) and Zingiber officinale were collected in the Comoros Islands (Fig. 1 ). Professor Mohamed ADDI, a researcher and expert botanist at the Laboratory for Improvement of Agricultural Production, Biotechnology, and Environment (LAPABE), Faculty of Sciences, Mohammed Premier University, Oujda, Morocco, taxonomically identified the plants. Voucher specimens (CLP-013, CLP-014, and CLP-015) were placed at the Faculty of Sciences' herbarium in Oujda and assigned to S. aromaticum , Z. officinale and C. odorata , respectively. This study complied with current guidelines and regulations concerning the collection and use of plant resources. Plant samples were dried under continuous ventilation in a dark place at room temperature (25 ± 2°C). EOs were extracted via hydrodistillation of 100 g dried plant material for 180 min using a Clevenger-type apparatus [ 12 ]. 2. Chemical composition of EOs by GC-MS Using a Shimadzu GC system (Kyoto, Japan) with a BPX25 capillary column (30 m, 0.25 mm I.D., and 0.25 µm film thickness), containing 5% diphenyl and 95% dimethylpolysiloxane, and connected to a QP2010 mass spectrometer, the phytochemical profile of the EOs was determined. Helium (99.99%) was used as the carrier gas at a flow rate of 1.69 L/min. The interface, ion source, and injection port temperatures were set at 250°C. The column temperature was initially held at 50°C for 1 min, then increased to 250°C at a rate of 10°C/min, and maintained at 250°C for an additional minute. The sample components were ionized in electron impact mode at 70 eV, and mass spectra were recorded at 40 to 300 m/z. Each EO sample was injected in a volume of 1 µL in split mode, and the compounds were identified by comparing their retention times and mass spectra with reference libraries, including the National Institute of Standards database. LabSolutions software (version 2.5, Shimadzu, Kyoto, Japan) was used for data processing [ 13 – 14 ]. 3. Antioxidant activity 3.1. DPPH radical scavenging assay The free radical scavenging activity of the EOs and their various combinations, generated using an experimental design approach, was assessed using 2,2-diphenyl 1-picrylhydrazyl (DPPH). Five concentrations of each EO were prepared by mixing the three plants at the following levels: 0.240 mg/ml, 0.500 mg/ml, 1 mg/ml, 1.5 mg/ml, and 2 mg/ml, starting from a stock solution of 1 mg/mL. A total of 4 mg DPPH was solubilized in 100 ml methanol and left to stir for 30 min. Subsequently, 0.5 ml of the EO solution was added to 2.5 ml of the DPPH solution. This mixture was shaken vigorously for 30 s and incubated at room temperature in the dark for 30 min. Absorbances was measured at 517 nm, and ascorbic acid used as a positive control [ 15 – 16 ]. Radical Scavenging Activity (%) = ((A 0 − A 1 )/A 0 ) × 100 The measurements were taken for the absorbance in the absence of the sample (A 0 ) and the absorbance in the presence of the sample (A 1 ). Ascorbic acid was employed as the reference compound. The IC 50 value, representing the concentration of the EO that causes 50% inhibition of Antioxidant activity, was determined graphically by plotting the inhibition percentage against the logarithm of EO concentration. 3.2. Total antioxidant capacity The total antioxidant capacity (TAC) of the EOs studied was assessed by the phosphomolybdenum method [ 17 ]. Five concentrations were prepared from dilute solutions of each EO (25 µl of EO + 1.5 µl of methanol) mixed with 1 ml of reagent solution (0.6 M sulfuric acid H 2 SO 4 , 28 mM sodium phosphate Na 3 PO 4 , and 4 mM ammonium molybdate (NH4)6Mo7O24. The tubes were sealed and incubated at 95°C for 90 min. After cooling, the absorbance of the solutions was measured at 695 nm. The antioxidant capacity was expressed as ascorbic acid equivalents using a standard curve made with ascorbic acid. A blank solution, which contained all the reagents except the test sample, was also included. The experiments were conducted three times [ 18 ]. 4. Antimicrobial activity 4.1. Strain selection The in vitro antibacterial activity was evaluated against two Gram-negative bacteria, Escherichia coli (ATCC 49) and Pseudomonas aeruginosa (ATCC 189), and two fungal strains, Candida albicans and Penicillium digitatum , from the microbial biotechnology laboratory at Faculty of Science, Mohammed First University [ 19 ]. 4.2. MIC determination The experimental protocol used in this study to determine the minimum inhibitory concentration (MIC) of EOs followed the 96-well microplate method. Mueller-Hinton culture medium containing 0.15% agar was chosen because of the insolubility of EOs in water. MIC was assessed over a concentration range from 8 to 0.0015%. Microplates were incubated at specific temperatures (37°C for 24 h for bacteria and 25°C for 48 h for fungi). Resazurin was used to monitor growth, with visual observations of color change. Gentamicin and cycloheximide were positive controls for bacteria and fungi, respectively. Results were obtained by performing the triplicate test to ensure the observations' reproducibility [ 20 ]. 4.3. MBC and MFC determination To determine the minimum bactericidal concentration (MBC) and minimum fungicidal concentration (MFC), 3 µL samples from wells showing no observable microbial growth were transferred to Mueller-Hinton agar (MHA) for bacteria and yeast extract glucose (YEG) culture medium for fungi. The samples were then incubated at 37°C for 24 h for MBC determination and 25°C for 48 to 72 h for MFC determination. After the incubation period, minimum concentrations of EOs were established, such as those of EOs that caused no observable microbial growth or inhibited microbial growth were established [ 21 ]. 5. Mixture design methodology In this study, the ternary EO formulation was optimized using an augmented simplex-centroid design. This mixture design is suitable for studying systems where the proportions of the components add up to a constant. Besides the vertices and centroids of a standard simplex-centroid design, this augmented design includes axial points [ 22 ], enabling more robust modelling of non-linear relationships and interactions between the EOs. Including axial points improves curvature detection in the response surface, facilitating a more thorough understanding of the relationship between mixture composition and anti-diabetic activity. 6. Mixture design components Table 1 demonstrates that each of the three components can range from 0 to 1, with the constraint that their proportions sum to unity. This represents an unconstrained mixture design [ 23 ]. Table 1 Identification of formulation components. Component Coded variable Level − Level + S. aromaticum X1 0 1 Z. officinale X2 0 1 C. odorata X3 0 1 Sum of proportions 1 7. Experiment matrix The augmented simplex-centroid design matrix, comprising 12 experimental trials, was constructed to efficiently explore the mixture space (Fig. 2 ). The design includes 10 unique points: the vertices (pure components), edge midpoints (binary 1:1 mixtures), the overall centroid (1:1:1 ternary mixture), and axial points. Three replicates at the centroid enable the estimation of pure error variance, increasing the reliability of the statistical analysis and the accuracy of predictive models [ 11 ]. 8. Selected mathematical model A special cubic model was chosen to describe the relationship between EO proportions and antioxidant and antimicrobial activity [ 24 ]. This third-order polynomial model is well-suited for mixture designs, as it accounts for linear effects, binary interactions, and potentially significant ternary interactions. The following equation gives the general form of the model. The general form of the model is given by: $$\:Y={{{\alpha\:}}_{1}\text{X}}_{1}\:+\:{{{\alpha\:}}_{2}\text{X}}_{2}\:+\:{{{\alpha\:}}_{3}\text{X}}_{3}\:+\:{{\alpha\:}}_{12}{\text{X}}_{1}{\text{X}}_{2}\:+\:{{\alpha\:}}_{13}{\text{X}}_{1}{\text{X}}_{3}+\:{{\alpha\:}}_{23}{\text{X}}_{2}{\text{X}}_{3}+{{\alpha\:}}_{123}{\text{X}}_{1}{\text{X}}_{2}{\text{X}}_{3}+{\upvarepsilon\:}$$ where, Y represents the measured response, quantified by IC 50 and TAC, expressed in µg/ml and µM AAE/mg, respectively, and MIC values for E. coli , P. aeruginosa , C. albicans , and P. digitatum , expressed in µg/ml. The coefficients α₁, α₂ and α₃ represent the individual effects of each EO. the binary interaction coefficients α₁₂, α₁₃ and α₂₃ indicate synergistic or antagonistic effects between EO pairs, while the ternary interaction coefficient α₁₂₃ represents the combined effect of all three EOs. The term ε denotes the residual error. 9. Statistical analysis Model significance was assessed using analysis of variance (ANOVA) via the F-test (MS R /MS r ), comparing the mean square due to regression (MS R ) to the residual mean square (MS r ). Lack of fit was evaluated using the ratio of the mean square lack of fit (MS LOF ) to the mean square pure error (M SPE ) (F LOF/PE ), with larger values indicating a poorly fitting model. The coefficient of determination (R²) was used to assess the goodness of fit. The significance of the estimated coefficients was determined using Student's t-test. All statistical tests were performed at α = 0.05. JMP software (V.16) and Expert Design (V.12) were used for experimental design, statistical analysis, and graphical representation. Results are presented as mean ± standard deviation (SD). 10. Optimization tools Optimal EOs formulations were identified using contour and surface plots of the response surface. The desirability function was then employed to determine the optimal factor settings, balancing potentially competing objectives. Desirability values range from 0 (undesirable response) to 1 (optimal response). This approach facilitated the precise quantification of the optimal formulation within the design space [ 10 ]. 11. Molecular docking The docking method is equipped with tools that facilitate the study of interactions between a supposedly rigid active site and a ligand molecule, making it a viable strategy for drug discovery [ 25 – 27 ]. Molecular docking studies were carried out to assess the interactions of GC-MS-identified compounds based on the main peaks, concentrating on antioxidant and antibacterial activities [ 28 ]. The most identified molecules in the mixture, α-zingiberen, cinnamal, and o-cresol, were downloaded from the PubChem database. The biomolecules (ID codes: 4LEP, 6KZV, and 2CDU) were acquired in Protein Data Bank (PDB) format [ 29 ]. The ligands were prepared using various software packages, including Discovery Studio and ChemOffice [ 30 ]. The ligand structure was docked into the active site of the chosen enzyme using AutoDockTools [ 31 ]. Finally, the interactions between the residues of the enzyme's active site and the ligand molecules were analyzed using Discovery Studio Visualizer, which allows interactions to be examined in 2D [ 25 ]. Results and Discussion 1. Phytochemical composition The chromatographic profile of each was established using GC-MS analysis. The chromatograms obtained were compared with a library of reference spectra, enabling the identification and quantification of the compounds present in each sample (Fig. 3 and Table 2 ). So, the analysis of the chromatograms revealed significant differences in the chemical composition of the three samples studied: S. aromaticum, Z. officinale , and C. odorata . Table 2 provides a detailed list of the compounds identified, including alcohols, terpenes, esters, and ketones and their relative concentrations. In S. aromaticum EO, cinnamal was found to be the majority compound, accounting for 53.23% of the total composition. This high concentration probably gives the oil its dominant organoleptic characteristics. Eucalyptol (16.37%) and camphene (9.74%) also contribute to freshness and aroma. β-linalool, although present in lesser quantities (1.45%), may contribute to the oil's floral nuances. Z. officinale EO has a more balanced composition. α-zingiberene, a compound typical of ginger oil, predominates at 19.33%. Eucalyptol (16.37%), β-citral (12.93%), and β-cymene (7.16%) are also significant components, probably contributing significantly to this oil's complex aromatic profile. In C. odorata EO, o-cresol is the majority compound (14.09%). Germacrene D (11.16%) and β-linalool (10.47%) are also present in significant concentrations. α-Farnesene, although less abundant (5.52%), may contribute to the oil's subtle notes. In the study conducted by Ahamad, GC-MS analysis of clove EO made it possible to identify 37 chemical components, representing approximately 99.49% of the total oil. The main components were eugenol (59.87%), caryophyllene (23.58%), α-selinene (4.67%), α-terpinyl acetate (4.12%) and humulene (3.74%) [ 32 ]. Kiki also confirmed the majority presence of eugenol in this EO, representing 76.78% of the total composition [ 33 ]. Allali Aimada's study focused on the chemical composition of the EO of Z. officinale (EOZ). The main constituents identified were α-zingiberene (23.85%), geranial (14.16%) and (E,E)-α-farnesene (9.98%) [ 34 ]. Meher Nahid, on the other hand, used GC-MS to isolate and identify 81 components in EO from leaves, where the main constituents were sabinene (14.99%), L-pinene (14.32%), caryophyllene oxide (13.85%), caryophyllene (9%) and another caryophyllene isomer (9.47%) [ 35 ]. In the case of Kediri's accession, studied by Kurniawati, the dominant compounds of the EO of Cananga flowers were linalool, caryophyllene, β-copaene and benzyl benzoate [ 36 ]. Finally, Manzano detected volatile compounds in samples using GC-MS, with benzyl acetate (21.78–27.82%), benzyl benzoate (12.30-18.07%), linalool (9.45–14.39%), and germacrene D as the main constituents (9,17 − 14,46%) [ 37 ]. These results highlight the diversity and complexity of the chemical profiles of the EOs studied. The differences observed in the composition and relative concentrations of the compounds could explain the organoleptic and potentially biological properties specific to each oil. The predominance of cinnamal in S. aromaticum suggests a central role for this compound in the oil's properties. Similarly, the notable presence of α-zingiberene in Z. officinalis and o-cresol in C. odorata could be decisive for their respective characteristics. Table 2 Chemical composition of Syzygium aromaticum , Zingiber officinale and Cananga odorata using GC-MS. N Compound R. T S. aromaticum Z. officinalis C. odorata 1 alpha.-Pinene 5.199 1.31 2.8 0.65 2 Camphene 5.461 - 9.74 0.24 3 alpha.-Thujene 6.375 0.8 1.22 0.27 4 (+)-2-Carene 6.575 2.1 - - 5 beta.-Cymene 6.724 3.96 - 7.16 6 (+)-Sabinene 6.809 12.62 - 3.45 7 Eucalyptol 6.832 - 16.37 5.73 8 beta.-Linalool 7.978 2.49 1.45 10.47 9 o-Cresol 9.191 - 0.88 14.09 10 (+)-(E)-Limonene oxide 9.261 - 0.85 - 11 4-Terpineol 9.319 1.66 0.88 - 12 alpha.-Terpineol 9.549 1.25 1.31 - 13 alpha.-Citral 10.228 - 7.3 0.54 14 beta.-Citronellal 10.508 - 0.64 0.3 15 beta.-Citral 10.672 - 12.93 - 16 Cinnamal 10.81 53.23 2.49 0.35 22 Eugenol 12.051 2.2 - 0.76 24 Geraniol acetate 12.23 - - 4.15 25 Copaene 12.297 - - 1.33 26 (-)-.beta.-Elemene 12.503 - - 0.55 27 Caryophyllene 12.969 13.45 - 5.58 28 Cinnamyl acetate 13.224 1.56 - 3.54 29 alpha.-Caryophyllene 13.447 2.26 - 1.98 30 gamma.-Muurolene 13.704 - 4.67 1.46 31 Germacrene D 13.808 - 1.11 11.16 32 alpha.-Zingiberene 13.872 - 19.33 - 33 alpha.-Farnesene 13.962 - 5.32 5.52 34 alpha.-Amorphene 14.01 - 5.32 1.26 35 Isoledene 14.231 - - 0.67 36 alpha.-Cubebene 14.305 - - 2.88 37 Cubenol 14.699 - 0.62 - 38 Nerolidol 2 14.736 - 0.67 - 39 Cedrene 15.172 - 0.51 - 40 Caryophyllene oxide 15.208 1.11 0.77 0.49 41 alpha.-Cadinol 15.916 - 0.84 1.22 42 (-)-.delta.-Cadinol 16.083 - 1.17 1.59 43 Caryophyllene oxide 16.418 - 0.81 0.62 55 cis-Farnesol 16.756 - - 5.82 45 2Farnesyl acetate 17.375 - - 2.97 46 alpha.-Thiocyanatotoluene 17.958 - - 3.2 2. Single antioxidant effect of EOs This study evaluated the antioxidant activity of EO extracts of S. aromaticum, Z. officinalis , and C. odorata , as well as their mixtures, using the DPPH radical scavenging method. The percentage of inhibition of S. aromaticum , Z. officinalis , C. odorata , and ascorbic acid at varying concentrations is shown in Fig. 4 . The results show that all substances exhibit a dose-dependent inhibition pattern, with distinct differences in their inhibitory abilities. At lower concentrations (0.25 µg/mL), S. aromaticum has the highest inhibition, close to 100%, indicating a robust antioxidant or inhibitory potential, higher than that of the other samples; Z. officinalis and C. odorata exhibit intermediate inhibition activities, displaying consistent and stable inhibition levels throughout the whole concentration range; and ascorbic acid, used as a standard, has the lowest percentage of inhibition, capping at approximately 60%, which is significantly lower than natural substances, especially S. aromaticum . The dose-dependent pattern highlights the importance of concentration in efficiency and the potency of S. aromaticum , likely due to its high phenolic component content, even at low concentrations. However, at greater doses, Z. officinale and C. odorata have a limited inhibitory potential, indicating antagonistic interactions or saturation effects. These findings suggest that natural compounds, especially those of S. aromaticum, may be potent substitutes or supplements to synthetic inhibitors like ascorbic acid in applications needing antioxidant or antibacterial activity. The IC 50 values of ascorbic acid, Z. officinale , C. odorata , and S. aromaticum are displayed in the bar chart, indicating their respective inhibitory powers (Fig. 5 ). The ascorbic acid (0.021 mg/ml) and the other natural chemicals ( C. odorata , 0.096 mg/ml; Z. officinale , 0.163 mg/ml) are both outperformed by S. aromaticum , which has the lowest IC 50 (0.013 mg/ml), indicating outstanding activity. This aligns with its high inhibition rates at low doses, likely due to bioactive compounds such as eugenol. While C. odorata and Z. officinale could need larger dosages or combinations to increase their efficacy, these findings demonstrate the potential of S. aromaticum as a robust natural inhibitor for pharmacological, culinary, or cosmetic applications. 2.1. Total antioxidant activity Figure 6 illustrates the results obtained when evaluating the antioxidant activity of the extracts studied using the phosphomolybdenum test. This spectrophotometric method, based on reducing phosphomolybdenum (VI) to phosphomolybdenum (V), enables overall quantification of the samples' antioxidant capacity. The measured absorbance intensity is directly proportional to the extract's reducing capacity and antioxidant potential. Higher values indicate more antioxidant inhibition. The graph displays the antioxidant activity of Z. officinalis , S. aromaticum , C. odorata , and ascorbic acid as determined by the phosphomolybdenum test. Compared to the synthetic standard and other natural extracts, Z. officinalis exhibits the highest antioxidant activity, with a value of 525.2000. With an intermediate value of 326.0900, ascorbic acid is less effective than Z. officinalis fields but more effective than S. aromaticum (139.6800) and C. odorata (128.5400). These findings demonstrate Z. officinale 's strong antioxidant potential, making it a viable option for applications needing potent natural antioxidants. On the other hand, S. aromaticum and C. odorata , which have fewer activities, might be considered in some situations. Z. officinale 's advantage over ascorbic acid. It also provides an indication of the potential value of natural extracts as potent alternatives to artificial antioxidants, improving their potential applicability in the food, cosmetic, and pharmaceutical industries. The results obtained using the phosphomolybdenum test are in corroboration and complement previous observations relative to the antioxidant activity of these extracts. The better performance of ginger extract in this test confirms its remarkable reducing capacity and high potential to scavenge free radicals, as pointed out by other evaluation methods. The preeminence of ginger in this test can be explained by its composition rich in phenolic compounds and gingerols, molecules well known for their great antioxidant power [ 38 ]. These compounds can readily surrender electrons or hydrogen atoms, thereby promoting the reduction of phosphomolybdenum (VI) to phosphomolybdenum (V). Although not negligible, the more moderate antioxidant activity of ylang-ylang (X3) suggests a lower concentration of reducing compounds in this extract. This observation could be explained by the predominance of terpene compounds in ylang-ylang EO, which, while having antioxidant properties, are generally less potent than phenolic compounds [ 39 ]. It should be noted that the phosphomolybdenum assay measures total antioxidant capacity, including both hydrophilic and lipophilic compounds. This makes it a useful complement to other methods of determining antioxidant activity and provides a fuller picture of extracts for their antioxidant potential. This suggests that different chemical compositions are very important for the antioxidant activities of an EO. The difference that has been shown between different extracts could form the basis of the formulation of antioxidants depending on specific applications in terms of their potency and mechanisms of action. 2.2. Single antibacterial effect of EOs The MIC values of the three EOs against the four microbial strains reveal distinct patterns of antimicrobial activity (Table 3 ). Against E. coli , S. aromaticum and Z. officinalis showed moderate activity with MICs of 1.5 and 1 µg/ml, respectively, while C. odorata exhibited slightly weaker activity (2 µg/ml). For P. aeruginosa , S. aromaticum demonstrated good activity (MIC 1 µg/ml), Z. officinalis showed better activity (2 µg/ml), and C. odorata had similar efficacy to its action against E. coli (2 µg/ml). In the case of C. albicans , Z. officinalis exhibited the strongest activity (0.5 µg/ml), suggesting potential antifungal properties, followed by C. odorata (1 µg/ml) and S. aromaticum (2 µg/ml). Finally, S. aromaticum demonstrated significantly stronger activity against P. digitatum (0.125 µg/ml) than the other two oils, which had MICs of 1 µg/ml. Table 3 Antimicrobial activity S. aromaticum , Z. officinale and C. odorata essential oils. Strain MIC/MBC (µg/ml) S. aromaticum Z. officinale C. odorata E. coli 1.5/2 1/2 2/4 P. aeruginosa 1/2 2/4 2/4 C. albicans 2/4 0.5/1 1/2 P. digitatum 0.125/1 1/2 1/2 Analyzing the MBC values for each strain in relation to the three EOs provides insights into their bactericidal and fungicidal efficacy. For E. coli , the lowest MBC is observed for S. aromaticum and Z. officinale (2 µg/ml), while C. odorata exhibits the highest MBC (4 µg/ml). In the case of P. aeruginosa , S. aromaticum demonstrates superior activity with an MBC of 2 µg/ml, whereas both Z. officinale and C. odorata require higher concentrations (4 µg/ml), reflecting lower effectiveness. For C. albicans , Z. officinale is again the most effective, achieving an MBC of 1 µg/ml, followed by C. odorata with an MBC of 2 µg/ml. In comparison, S. aromaticum shows less efficacy with an MBC of 4 µg/ml. Lastly, for P. digitatum , S. aromaticum stands out with an exceptionally low MBC of 1 µg/ml, highlighting its strong fungicidal activity, whereas Z. officinale and C. odorata exhibit similar, less potent effects with an MBC of 2 µg/ml each. The data highlight the potential use of these EOs in antimicrobial applications, with S. aromaticum as the most promising candidate for further development. 2.3. EOs formulation by mixture design A simplex-centroid mixture design was implemented to explore combinations of the EOs under investigation. Table 4 presents the experimental results for each EO combination, including the observed responses for DPPH-IC 50 , TAC, and MIC against E. coli , P. aeruginosa , C. albicans , and P. digitatum . Experiments were randomized to minimize bias, and each reported value represents the mean of three replicates. Table 4 Experimental results for essential oil combinations: DPPH-IC 50 , TAC, and MIC against E. coli , P. aeruginosa , C. albicans and P. digitatum . N° a S. aromaticum Z. officinale C. odorata DPPH-IC 50 TAC MIC E. coli MIC P. aeruginosa MIC C. albicans MIC P. digitatum 1 1 0 0 21. 98 525. 2 1. 5 1 2 1. 5 2 0 1 0 171. 5 139. 68 1 2 0. 5 1 3 0 0 1 104. 01 128. 54 2 2 1 1 4 0. 5 0. 5 0 18. 67 275. 07 1 1 0. 5 0. 125 5 0. 5 0 0. 5 44. 29 156. 83 1 0. 125 1 0. 5 6 0 0. 5 0. 5 21. 82 265. 85 2 2 1 1 7 0. 333 0. 333 0. 333 66. 47 217. 01 2 1 0. 5 1 8 0. 333 0. 333 0. 333 40. 45 185. 06 2 1 0. 5 1 9 0. 333 0. 333 0. 333 61. 6 210. 76 2 1 0. 5 1 10 0. 667 0. 167 0. 167 47. 34 282. 59 1. 5 0. 5 1 1 11 0. 167 0. 667 0. 167 66. 37 177. 33 1. 5 1. 5 0. 5 1 12 0. 167 0. 167 0. 667 53. 1 197 2 1 0. 5 1 a Experiments were carried out after randomization. 2.4. Statistical analysis and model validation ANOVA was performed to assess the relevance and goodness of fit of the proposed models for all studied responses. Table 5 Variance analysis for the fitted models by the augmented simplex-centroid design. DPPH-IC 50 Model DF SS MS F p-value Regression 6 19225. 775 3204. 3 23. 6946 0. 0016 Residual 5 676. 167 135. 23 Total 11 19901. 942 R² 0.96 R² adj 0.92 TAC Regression 6 121033. 92 20172. 3 41. 7022 0. 0004 Residual 5 2418. 62 483. 7 Total 11 123452. 54 R² 0.98 R² adj 0.95 MIC E. coli Regression 6 2. 0456818 0. 340947 101. 3626 < 0. 0001 Residual 5 0. 0168182 0. 003364 Total 11 2. 0625 R² 0.99 R² adj 0.98 MIC P. aeruginosa Regression 6 3. 8356866 0. 639281 59. 594 0. 0002 Residual 5 0. 0536364 0. 010727 Total 11 3. 8893229 R² 0.98 R² adj 0.96 MIC C. albicans Regression 6 2. 1618939 0. 360316 26. 7802 0. 0012 Residual 5 0. 0672727 0. 013455 Total 11 2. 2291667 R² 0.96 R² adj 0.93 MIC P. digitatum Regression 6 1. 1595502 0. 193258 22. 8585 0. 0017 Residual 5 0. 0422727 0. 008455 Total 11 1. 2018229 R² 0.96 R² adj 0.92 DF: degrees of freedom; SS: sum of squares; MS: mean square; F: Calculated Fisher value; R²: Coefficient of determination; Adj: Adjusted; *: statistically significant at 95% confidence level. ANOVA results, presented in Table 5 , indicate that the regression models for all five responses were statistically significant (p < 0.05). The six models' calculated F-ratios (MSR/MSr) exceeded the critical F-value (F 0.05,6,5 = 4.95). The coefficients of determination (R²) were 0.96, 0.98, 0.99, 0.98, 0.96, and 0.96 for DPPH-IC 50 , TAC, and MIC against E. coli , P. aeruginosa , C. albicans , and P. digitatum , respectively. The close agreement between R² and adjusted R² values (difference < 0.2) further supports the model's goodness of fit. The graphical representation of observed versus predicted values (Fig. 7 ) corroborates these statistical results. The linear arrangement of points along the regression line for both responses visually confirms the quality of fit and robust predictive capacity of the models. These consistent statistical results confirm the validity and predictive accuracy of the mathematical models developed to describe the inhibitory activity of EO mixtures against free radicals and microbial strains. 2.5. EOs' effects and fitted model The effects of the studied factors were analyzed using Student's t-tests, and the resulting t-statistics and p-values are summarized in Table 6 . This table provides an overview of the significance of each term in the models. Table 6 Estimated regression coefficients of the special cubic models Response α₁ α 2 α 3 α₁ 2 α₁ 3 α 23 α₁ 23 S. aromaticum Z. officinale C. odorata S. aromaticum × Z. officinale S. aromaticum × C. odorata Z. officinale × C. odorata S. aromaticum × Z. officinale × C. odorata DPPH IC50 Estimate 18.345909 161.50591 94.203182 -307.5364 -69.70182 -471.3418 1395.18 P-value 0.1633 < 0.0001* 0.0004* 0.0029* 0.2726 0.0004* 0.0062* TAC Estimate 521.29922 132.20831 138.19467 -252.2249 -668.6522 531.32596 -487.5768 P-value < 0.0001* 0.0016* 0.0013* 0.0649 0.0015* 0.0042* 0.4402 MIC E.coli Estimate 1.5118182 0.9663636 2.0118182 -1.043636 -2.952727 1.9563636 19.44 P-value < 0.0001* < 0.0001* < 0.0001* 0.014* 0.0001* 0.001* < 0.0001* MIC P. aeruginosa Estimate 0.9963636 2.0077273 1.9509091 -1.991818 -5.605455 -0.082727 4.23 P-value 0.0002* < 0.0001* < 0.0001* 0.0108* 0.0001* 0.876 0.1833 MIC C. albicans Estimate 2.0236364 0.5236364 0.9327273 -2.905455 -2.087273 0.9127273 -6.12 P-value < 0.0001* 0.0055* 0.0004* 0.0036* 0.014* 0.1666 0.1027 MIC P.digitatum Estimate 1.5263636 1.0263636 0.9922727 -4.394545 -2.962727 0.0372727 18.27 P-value < 0.0001* < 0.0001* 0.0001* 0.0002* 0.0012* 0.9368 0.0007* *Statistically significant at 95% confidence level. Table 6 reveals several statistically significant terms. Specifically, the linear terms of Z. officinale (α2), and C. odorata (α3) are significant contributors to the DPPH-IC 50 response. Moreover, the binary interactions between S. aromaticum and Z. officinale (α12) and between Z. officinale and C. odorata (α23) also exhibit strong significance, with p-values of 0.0029 and 0.0004, respectively. This implies that these pairs of EOs interact synergistically to influence the DPPH-IC 50 response. The ternary interaction between all three EOs (α123) is likewise significant, as evidenced by its p-value of 0.0062, highlighting the complex interplay among the oils in determining antioxidant activity. The following equation can express the mathematical model representing this relationship: $$\:{Y}_{DPPH-IC50}=\:{\text{161,5}\text{X}}_{2}\:+\:{\text{94,2}\text{X}}_{3}\:-\text{307,53}{\text{X}}_{1}{\text{X}}_{2}\:-\text{471,34}{\text{X}}_{2}{\text{X}}_{3}+\text{1395,18}{\text{X}}_{1}{\text{X}}_{2}{\text{X}}_{3}+{\upvarepsilon\:}$$ The Total Antioxidant Capacity (TAC) response is significantly influenced by the linear contributions of S. aromaticum (P < 0.0001), Z. officinale (P = 0.0016), and C. odorata (P = 0.0013), indicating that these EOs individually play a crucial role in enhancing TAC. Among the binary interactions, S. aromaticum × C. odorata (P = 0.0015) and Z. officinale × C. odorata (P = 0.0042) are statistically significant, suggesting notable synergistic or antagonistic effects between these pairs. On the other hand, the interaction of S. aromaticum and Z. officinale (P = 0.0649) is insignificant thus demonstrating very limited combined influence. All EOs also exhibited an insignificantly ternary interaction, P = 0.4402, showing that the three together do not affect TAC that much. This is the relationship describing TAC and significant factors: $$\:{Y}_{TAC}={\text{521,29}\text{X}}_{1}\:+\:{\text{132,2}\text{X}}_{2}\:+\:{\text{138,19}\text{X}}_{3}\:-\text{668,65}{\text{X}}_{1}{\text{X}}_{3}\text{531,32}{\text{X}}_{2}{\text{X}}_{3}{\upvarepsilon\:}$$ Linear and all interaction terms of the model significantly affected MIC against E. coli . The linear terms of S. aromaticum (P < 0.0001), Z. officinale (P < 0.0001), and C. odorata (P < 0.0001) were highly significant to show the individual contribution to reducing MIC. Among the binary interactions, S. aromaticum × Z. officinale (P = 0.014), S. aromaticum × C. odorata (P = 0.0001), and Z. officinale × C. odorata P = 0.001) are statistically significant, reflecting notable synergistic or antagonistic effects between these pairs of oils. Besides, the ternary interaction due to the three EOs is significant (P < 0.0001), further evidencing their complicated interaction in determining MIC. The analysis thus brings out the contribution of oils concerning both individual and combined vital roles. In this context, a considerable contribution was also given by the ternary interaction, thus providing evidence that all EOs interact together and result in increased antibacterial activities. The relationship between MIC with significant factors can be explained as given in equation: $$\:Y={\text{1,51}\text{X}}_{1}\:+\:{\text{0,96}\text{X}}_{2}\:+\:{\text{2,01}\text{X}}_{3}\:-\text{1,04}{\text{X}}_{1}{\text{X}}_{2}\:-\text{2,95}{\text{X}}_{1}{\text{X}}_{3}\text{1,95}{\text{X}}_{2}{\text{X}}_{3}\text{19,44}{\text{X}}_{1}{\text{X}}_{2}{\text{X}}_{3}+{\upvarepsilon\:}$$ The MIC against P. aeruginosa is significantly influenced by the individual effects of the EOs and certain interactions. S. aromaticum , Z. officinale , and C. odorata all individually increase the MIC, as indicated by their significant positive linear coefficients (p < 0.05). Moreover, combining S. aromaticum with either Z. officinale (p = 0.0108) or C. odorata (p = 0.0001) results in significant synergistic interactions, reducing the expected MIC based on their individual effects. While the interaction between Z. officinale and C. odorata is not significant, the three-way interaction is also not statistically significant (p = 0.1833). The model for predicting MIC against P. aeruginosa is: $$\:Y={0.99\text{X}}_{1}\:+\:{2\text{X}}_{2}\:+\:{1.95\text{X}}_{3}\:\:-1.99{\text{X}}_{1}{\text{X}}_{2}\:-5.6{\text{X}}_{1}{\text{X}}_{3}+{\upvarepsilon\:}$$ The individual EOs and specific interactions significantly affect the MIC against C. albicans . S. aromaticum , Z. officinale , and C. odorata all exhibit significant positive linear effects (p < 0.05), indicating that increasing their individual proportions increases the MIC. Further, significant binary interactions are observed when S. aromaticum is combined with either Z. officinale (p = 0.0036) or C. odorata (p = 0.014. The interaction between Z. officinale and C. odorata is not significant. Notably, the three-way interaction among all oils is significant (p = 0.01027). The model for predicting MIC against C. albicans is: $$\:Y={2.02\text{X}}_{1}\:+\:{0.52\text{X}}_{2}\:+\:{0.93\text{X}}_{3}\:-2.9{\text{X}}_{1}{\text{X}}_{2}\:-2.08{\text{X}}_{1}{\text{X}}_{3}+{\upvarepsilon\:}$$ The MIC for P. digitatum is influenced by individual EO components. S. aromaticum , Z. officinale , and C. odorata increase the MIC, as evidenced by their significant positive linear coefficients (p < 0.0001). Synergistic effects are observed; combining S. aromaticum with either Z. officinale (p = 0.0002) or C. odorata (p = 0.0012) results in a significantly lower MIC than expected from their individual effects. The interaction between Z. officinale and C. odorata is not significant. Interestingly, the three-way interaction among all oils is highly significant (p = 0.0007) and has a large positive coefficient. This positive coefficient suggests an antagonistic effect, increasing the MIC when all three EOs are combined, counteracting the synergistic binary interactions. The model for predicting MIC against P. digitatum is: $$\:Y={1.52\text{X}}_{1}\:+\:{1.02\text{X}}_{2}\:+\:{0.99\text{X}}_{3}\:-4.39{\text{X}}_{1}{\text{X}}_{2}\:-2.96{\text{X}}_{1}{\text{X}}_{3}18.27{\text{X}}_{1}{\text{X}}_{2}{\text{X}}_{3}+{\upvarepsilon\:}$$ 2.6. EOs mixture optimization and desirability study The Optimization of the mixture design aims to identify the EO formulation that simultaneously maximizes antioxidant activity (minimizes DPPH-IC 50 and TAC) and antimicrobial activity (minimizes MIC). The Optimization of all responses was achieved using 2D contour plots and desirability profiles, as illustrated in Figs. 8 and 9 . 2.7. EO mixture antioxidant activity The 2D clearly shows that two distinct zones offer the potential to accomplish a DPPH-IC 50 value of around 10 µg/ml. This optimal zone is located along the binary axis of S. aromaticum and Z. officinale (Fig. 8 A). These findings suggest that the interaction between these EOs is critical in determining the antioxidant capacity, with certain combinations proving more effective than others. The desirability function, depicted in Fig. 9 A, further refined the optimization process by identifying the precise mixture that maximizes the DPPH-IC 50 response. The desirability profile confirms that an optimal mixture consisting of 73% S. aromaticum and 27% Z. officinale produces an IC 50 value of 4.38 µg/ml with a desirability score of 99%. This high desirability score indicates that this mixture is nearly ideal for achieving the best possible antioxidant activity. The high desirability also suggests that this combination of oils has potential applications in formulating products where antioxidant efficacy is crucial, such as in developing natural preservatives or therapeutic agents. Concerning the response TAC, the 2D contour plots (Fig. 8 B) highlight the optimal region for maximizing this response. According to the correspondent graphs, a TAC value exceeding 450 µM AAE/mg can be achieved with a mixture of S. aromaticum . This suggests that this EO is the most potent in terms of antioxidant activity when used on its own. To further refine the optimization process and determine the precise conditions that yield the maximum TAC response, the desirability function was employed, as shown in Fig. 9 B. The desirability profile confirms the findings from the 2D and 3D iso-response plots. It indicates that S. aromaticum alone can achieve a TAC value of 525 µM AAE/mg, with a desirability score of 99%. This high desirability score suggests that S. aromaticum is the most effective EO for maximizing the TAC response and could be considered for applications requiring high antioxidant capacity, such as in food preservation or cosmetic formulations. 2.8. EO mixture antibacterial activity The two bacterial strains E. coli and P. aerogenosa show important sensibility against EOs formulations, Thus, The analysis of the response surface contours (Fig. 8 C) revealed that binary combinations of Syzygium aromaticum and Zingiber officinale EOs exhibited significant antimicrobial activity against E. coli , with a MIC of approximately 1 µg/mL. Optimization through desirability function analysis predicted optimal inhibition (99% desirability) at an MIC of 0.9 µg/mL, achieved with a binary mixture ratio of 23:77 (v/v) S. aromaticum : Z. officinale (Fig. 9 C). Similarly, the response surface analysis (Fig. 8 D) demonstrated enhanced antimicrobial efficacy against P. aeruginosa when combining S. aromaticum and Cymbopogon odorata EOs, yielding an approximate MIC of 0.1 µg/mL. The optimal formulation, predicted with 99% desirability, achieved an MIC of 0.031 µg/mL using a 58:42 (v/v) ratio of S. aromaticum : C. odorata (Fig. 9 D). Concerning antifungal activities, Response surface analysis (Fig. 8 E) demonstrated that binary combinations of S. aromaticum and Z. officinale EOs exhibited potent antifungal activity against C. albicans , with a MIC of approximately 0.4 µg/mL. Optimization through desirability function analysis predicted optimal inhibition (99% desirability) at a MIC of 0.35 µg/mL, achieved with a binary mixture ratio of 25:75 (v/v) S. aromaticum : Z. officinale (Fig. 9 E). In the same way, the contour plot analysis (Fig. 8 F) further revealed significant antifungal efficacy against P. digitatum , with a MIC under 0.25 µg/mL using the same EO combination. The optimal formulation, predicted with 99% desirability, achieved an MIC of 0.16 µg/mL using a 44:56 (v/v) ratio of S. aromaticum : Z. officinale (Fig. 9 F). 3. Synergy between EOs Significant synergistic interactions between most components in the three-component EO blend are revealed from S. aromaticum , Z. officinalis , and C. odorata . When compared to their individual effects, interactions between cinnamal (found in S. aromaticum ), alpha.-Zingiberene (found in Z. officinale ), and o-Cresol (found in C. odorata ) considerably increase antioxidant and antibacterial activities, according to predictive modeling utilizing mixture designs. From an antioxidant perspective, o-Cresol and alpha.-Zingiberene enhance this effect with their radical-stabilizing and antioxidant-regenerating qualities. At the same time, cinnamal, a significant phenolic component, functions as a potent free-radical scavenger because of its aromatic ring and hydroxyl group. In complex systems, these chemicals work together to decrease lipid oxidation and extend the duration of the antioxidant effect. On the antimicrobial front, complementary mechanisms are seen: o-Cresol and alpha.-Zingiberene strengthen the impact of cinnamal by preventing bacterial growth through other intracellular targets, particularly the disruption of vital metabolic pathways, while cinnamal breaks down bacterial membranes, causing intracellular ions and macromolecules to leak out. It is feasible to block a variety of microorganisms, including resistant bacteria, at doses significantly lower than those needed for each component alone because of this synergistic combination. The ratios of the three EOs were adjusted to improve these effects using predictive modeling techniques employed in this investigation. The results, for instance, indicate that certain ratios of nearly 60% S. aromaticum , 16% Z. officinal e, and 16% C. odorata offer the best effectiveness. Furthermore, this method lowers the possible danger of toxicity connected with high concentrations of each component and the overall amount needed. These findings demonstrate the importance of mixture modeling and design in creating natural formulations with excellent performance and economic viability. Additionally, they present encouraging opportunities for use in the food industry of natural antioxidants in perishable goods, the pharmaceutical industry with natural preservatives or therapeutic agents, and the cosmetics industry (active components in anti-aging or protective products). 4. Molecular docking 4.1. Antibacterial activity Drug design is based on molecular docking, a powerful technique for understanding how protein receptors interact with ligands. This technique provides crucial information about binding affinities, interaction mechanisms, and the potential for optimizing drug efficacy and specificity. The main compounds identified by GC-MS, α-zingiberene and o-cresol, have antibacterial activity. The compound α-zingiberene affects the C. albicans receptor (PDB ID: 4LEP), and the compound o-cresol affects the E. coli receptor (PDB ID: 6KZV). We performed molecular docking of these two compounds, which revealed that all exhibited high negative binding energy values of -6.12 kcal/mol for the α-zingiberen-4LEP complex and also for the o-cresol-6KZV complex, a value of -5.13 kcal/mol. The results of the molecular docking of the α-zingiberen-4LEP complex are shown in Fig. 10 . Figure 10 shows the results of the molecular docking study showed the α-zingiberene compound exhibited one Pi-Sigma bonding interaction with the protein residues Trp-224, with a distance greater than 3.73 Å, and also seven alkyl bonds with the protein residues Tyr-21, Arg-171, Pro-29, Tyr-226, Pro-26, Val-119 and Tyr-23, with a distance greater than 5.23, 3.78, 3.87, 4.56, 4.42, 35.45, and 4.79 Å, respectively. The results suggest that α-zingiberene is a potential inhibitor of the C. albicans receptor and could be used as an antibacterial agent. Similarly, the antibacterial activity of the various extracts from our plant shows that the main compounds identified by GC-MS affect the E. coli receptor. The results of molecular docking of the o-cresol-6KZV complex are shown in Fig. 11 . The molecular docking results, revealing that the o-cresol compound forms a hydrogen bond with the protein residue Val-71 at a distance of more than 1.71 Å, as well as a Pi-Sigma bond with Val-43 at a distance greater than 3.89 Å. Additionally, the molecular docking results for the o-cresol compound indicate an Alkyl and Pi-alkyl bond with Val-167 and Ala-47 at a distance of 4.87 and 3.80 Å, respectively. These findings suggest that o-cresol may serve as potential inhibitors of the E. coli receptor, highlighting their potential as antibacterial agents. 4.2. Antioxidant activity The oxidative activity of the various extracts from our plant shows that the main compound identified by GC-MS, cinnamal, has an antioxidant effect (PDB ID: 2CDU). We performed molecular docking of the most identified compound, which revealed that all had negative binding energy values of -5.98 kcal/mol. The results of the molecular docking of cinnamal-2CDU are shown in Fig. 12 . The results of molecular docking are presented in Fig. 12 , showing that the cinnamal compound forms two hydrogen bonds with the protein residues Ile-160 and Gly-161 at distances of 2.32 Å and 2.23 Å, respectively, and one alkyl bond with Pro-117 at distances of 5.35 Å and a Pi-Sigma bond with the protein residue Cys-242 at distances of 3.62 Å, as well as one Pi-Pi-stacked bond with Phe-245 at distances of 4.22 Å. This suggests that cinnamal may act as a potential inhibitor of the oxidation receptor, indicating its potential as an antioxidant agent. After a thorough analysis of the molecular docking of the three main compounds identified in the EOs, it was observed that these compounds interact and correlate with the binding sites of various selected proteins. As a result, it can be concluded that these compounds possess significant antioxidant and antibacterial activities. Conclusion This study characterized the chemical composition of EOs extracted from S. aromaticum, Z. officinale , and C. odorata and evaluated their antioxidant and antimicrobial properties, both individually and in combination, using a well-defined mixing design. The results highlight the richness of bioactive compounds in these EOs, conferring significant activity against tested microbial species and remarkable antioxidant potential. Analysis of the EO blends revealed beneficial synergies, reinforcing the overall effectiveness of the formulations. This research supports the valorization of Comorian natural resources and contributes to sustainable development strategies by offering natural alternatives for combating infections and oxidative stress. Additionally, molecular docking analysis confirmed that key EO compounds interact with selected protein binding sites, reinforcing their potential antioxidant and antibacterial activities. These results encourage future research into the mechanisms of action of these EOs and their broader applications in health, pharmaceuticals, and the cosmetics industry. Declarations Author Contributions: Conceptualization, D.K.N.E.; methodology, E.H.L.; software, S.L. and B.H.; validation, R.S. and M.F.; formal analysis, M.ER and M.T.; investigation, M.I.Y.; resources, A.Z; data curation, A.S.; writing—original draft preparation, B.H. M.M.A; writing—review and editing, A.B, F.Z, A.E, A.S, M.D, K.A., R.S., and L.R.; visualization F.Z, M.D., and K.A.; supervision, L.M., and L.R; All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the Researchers Supporting Project number (RSPD2025R628), King Saud University, Riyadh, Saudi Arabia . This work was financially supported by the MHESRI/DHESR-Morocco and the MIT-Hungary (Conventions 2023, no. 1 & 2). Acknowledgments: The authors extend their appreciation to the Researchers Supporting Project number (RSPD2025R628), King Saud University, Riyadh, Saudi Arabia for supporting this research. The authors gratefully acknowledge the support from the MHESRI/DHESR-Morocco and MIT-Hungary (Conventions 2023, no. 1 & 2). Institutional Review Board Statement: Not applicable. Data Availability Statement: The data presented in this study are available upon request from the corresponding authors. 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Baroroh , et al. ,(2023) Molecular interaction analysis and visualization of protein-ligand docking using Biovia Discovery Studio Visualizer . 2(1), 22-30. G. M. Morris, R. Huey, and A. J. Olson,(2008) Using autodock for ligand‐receptor docking . 24(1), 8.14. 1-8.14. 40. J. Ahamad,(2023) Characterization of Essential Oil Composition of Syzygium aromaticum Linn.(Clove) by GC-MS and Evaluation of its Antioxidant Activity . 7(1), 1-5. M. J. Kiki,(2023) In vitro antiviral potential, antioxidant, and chemical composition of clove (Syzygium aromaticum) essential oil . 28(6), 2421. A. Aimad , et al. ,(2024) Phytochemical analysis and pharmacological activities of essential oils extracted from Zingiber officinale (Roscoe) used in mediterranean diet: in vitro and in silico studies . 27(1), 1180-1199. M. Nahid and M. N. I. Bhuiyan,(2024) Gc-ms analysis of leaf and rhizome essential oil of zingiber purpureum roxb. From bangladesh . 53(1), 153-158. A. Kurniawati and M. Naimah. Dynamics of flower production and flower oil components from two accessions of Cananga (Cananga odorata) in dry season. in IOP Conference Series: Earth and Environmental Science. 2024. P. Manzano , et al. ,(2024) Effect of industrial steam distillation conditions on volatiles and antioxidant capacity of Cananga odorata essential oils . 1-11. Q.-Q. Mao , et al. ,(2019) Bioactive compounds and bioactivities of ginger (Zingiber officinale Roscoe) . 8(6), 185. H. A. Dahlmann, A. J. McKinney, M. P. Santos, and L. O. Davis,(2016) Organocatalyzed intramolecular carbonyl-ene reactions . 21(6), 713. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviews received at journal 20 May, 2025 Reviewers agreed at journal 10 May, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 18 Mar, 2025 Editor invited by journal 17 Mar, 2025 Submission checks completed at journal 15 Mar, 2025 First submitted to journal 08 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6184540","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430819315,"identity":"7153d174-17ff-464c-aeaf-a10f55730e00","order_by":0,"name":"Djanah-Karene Nacer-Eddine","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Djanah-Karene","middleName":"","lastName":"Nacer-Eddine","suffix":""},{"id":430819319,"identity":"fa86425a-b4bc-4e63-b09d-3aeaf6d04c52","order_by":1,"name":"EL Hassania Loukili","email":"","orcid":"","institution":"Euromed University of Fes","correspondingAuthor":false,"prefix":"","firstName":"EL","middleName":"Hassania","lastName":"Loukili","suffix":""},{"id":430819323,"identity":"67951138-0170-4b3f-a115-bbcc71f00520","order_by":2,"name":"Sara Lebrazi","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Lebrazi","suffix":""},{"id":430819324,"identity":"dce67c5b-d53f-49e1-b906-4068e447be97","order_by":3,"name":"Rachid Sabbahi","email":"","orcid":"","institution":"Université Ibn Zohr","correspondingAuthor":false,"prefix":"","firstName":"Rachid","middleName":"","lastName":"Sabbahi","suffix":""},{"id":430819325,"identity":"733377bd-7b31-4e48-a7a8-ba8072c5b71d","order_by":4,"name":"Mouhcine Fadil","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Mouhcine","middleName":"","lastName":"Fadil","suffix":""},{"id":430819326,"identity":"15ad44d9-b026-477e-8f55-587f3ffa2f51","order_by":5,"name":"Mohammed Er-rajy","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Er-rajy","suffix":""},{"id":430819327,"identity":"b68b6ad0-8046-48ad-adbc-7b986b35cc8a","order_by":6,"name":"Mohamed Taibi","email":"","orcid":"","institution":"Mohamed I University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Taibi","suffix":""},{"id":430819328,"identity":"4a6e526d-4f54-40ac-8b6f-ae4588ae09e9","order_by":7,"name":"Meryem Idrissi Yahyaoui","email":"","orcid":"","institution":"Mohamed I University","correspondingAuthor":false,"prefix":"","firstName":"Meryem","middleName":"Idrissi","lastName":"Yahyaoui","suffix":""},{"id":430819329,"identity":"4bdeec1b-3793-495c-96e7-bfc3b8030ea6","order_by":8,"name":"Abdellah Azougay","email":"","orcid":"","institution":"Mohamed I University","correspondingAuthor":false,"prefix":"","firstName":"Abdellah","middleName":"","lastName":"Azougay","suffix":""},{"id":430819330,"identity":"2790caf6-17ce-44b2-abdb-03ed38e75755","order_by":9,"name":"Abdeslam Asehraou","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Abdeslam","middleName":"","lastName":"Asehraou","suffix":""},{"id":430819331,"identity":"be316fc8-d6c2-46c5-8473-ede5a16a6d13","order_by":10,"name":"Larbi Rhazi1","email":"","orcid":"","institution":"Artois University","correspondingAuthor":false,"prefix":"","firstName":"Larbi","middleName":"","lastName":"Rhazi1","suffix":""},{"id":430819334,"identity":"60c15bfd-7a87-4a6f-ba39-36c3d27ed6ac","order_by":11,"name":"Mohammed M. Alanazi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACxgMgko29DcJrIEYPRAvPMVK1MEikEalFd9rhBwd+7rCz55N8lvyZh8FGdsMB9ocf8Gkxu51mcLD3TDIzm3TaMWkehjTjDQd4jCXwa0kwOMDbxszGJp3exszDcDgRqIWBgJb0Dwf/ttXzsEkebwY67D9QC/vjH/i15Bgc5m07LMEmwXYA6LADQC0MZgRsySk4LNt23ICNJy1Nco5BsvHMwzxmFgQctvHh27Zqe/n2Y8Yf3lTYyfYdb398A58WNGAAxMwkqB8Fo2AUjIJRgB0AABJ5ScCHnj74AAAAAElFTkSuQmCC","orcid":"","institution":"King Saud University","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"M.","lastName":"Alanazi","suffix":""},{"id":430819335,"identity":"f023ad10-520d-4047-8459-a5a0e28c098b","order_by":12,"name":"Belkheir Hammouti","email":"","orcid":"","institution":"Euromed University of Fes","correspondingAuthor":false,"prefix":"","firstName":"Belkheir","middleName":"","lastName":"Hammouti","suffix":""},{"id":430819336,"identity":"d4cac47e-6c91-40c4-8aa4-abb8db7e99be","order_by":13,"name":"Khalil Azzaoui","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Khalil","middleName":"","lastName":"Azzaoui","suffix":""},{"id":430819337,"identity":"cabf4d55-cc28-4c7e-9467-346058bda7e5","order_by":14,"name":"Mohammed Lachkar","email":"","orcid":"","institution":"Sidi Mohamed Ben Abdellah University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Lachkar","suffix":""}],"badges":[],"createdAt":"2025-03-08 14:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6184540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6184540/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-30345-2","type":"published","date":"2026-01-07T15:59:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78879038,"identity":"577c22fe-f26e-499e-baf6-b36ee3350701","added_by":"auto","created_at":"2025-03-20 08:03:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":430384,"visible":true,"origin":"","legend":"\u003cp\u003eCollection sites of \u003cem\u003eSyzygium aromaticum\u003c/em\u003e, \u003cem\u003eCananga odorata\u003c/em\u003e, \u003cem\u003eand Zingiber officinale\u003c/em\u003e in the Comoros Islands.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/9e0f2402cf6bda324d011b20.png"},{"id":78879037,"identity":"d07c54f1-b729-4343-beba-246992210a9a","added_by":"auto","created_at":"2025-03-20 08:03:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72914,"visible":true,"origin":"","legend":"\u003cp\u003eAn overview of augmented-simplex-centroid design for three-component mixture.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/b6cd5a93ca216c43010a9c12.png"},{"id":78879042,"identity":"408c2329-ed65-437a-b1fc-8c66e3ff30cf","added_by":"auto","created_at":"2025-03-20 08:03:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95688,"visible":true,"origin":"","legend":"\u003cp\u003eChromatographic profiles of the essential oils analyzed using GC-MS.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/707bcb477a33fc8ec277e8f3.png"},{"id":78879734,"identity":"ef2fb6c9-dfa5-4d5d-a210-54f051cd9ab3","added_by":"auto","created_at":"2025-03-20 08:11:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84060,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage inhibition as a function of different concentrations.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/a4a6e5c86714ec7a8f55c068.png"},{"id":78879736,"identity":"931d1a56-504a-4936-80a2-892af2354923","added_by":"auto","created_at":"2025-03-20 08:11:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":121108,"visible":true,"origin":"","legend":"\u003cp\u003eIC\u003csub\u003e50\u003c/sub\u003e values for the essential oils studied using the DPPH test.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/e331a8dbe8d1805044ada46d.png"},{"id":78879039,"identity":"8543c8a3-307f-407a-b2ab-a7de2d7d1039","added_by":"auto","created_at":"2025-03-20 08:03:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":20503,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of total antioxidant activity of the essentials oils using the phosphomolybdenum test\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/1c199a5ebe13379ac75b3f45.png"},{"id":78879823,"identity":"ef964e46-2558-4742-9e3f-3dba15d49d71","added_by":"auto","created_at":"2025-03-20 08:19:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":330623,"visible":true,"origin":"","legend":"\u003cp\u003eObserved versus predicted values for DPPH-IC\u003csub\u003e50\u003c/sub\u003e (A), TAC (B), and minimum inhibitory concentration (MIC) against \u003cem\u003eE. coli\u003c/em\u003e (C), \u003cem\u003eP. aeruginosa \u003c/em\u003e(D), \u003cem\u003eC. albicans \u003c/em\u003eand \u003cem\u003eP. digitatum \u003c/em\u003e(F).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/a68c0d05e4c134e35b73556d.png"},{"id":78879050,"identity":"89d4f1b8-dbd8-481e-b3bb-27c8e5355320","added_by":"auto","created_at":"2025-03-20 08:03:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":235438,"visible":true,"origin":"","legend":"\u003cp\u003eobtained contour plots of the optimal compromise zone led to the best DPPH-IC\u003csub\u003e50\u003c/sub\u003e (A), TAC(B), MIC\u003csub\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/sub\u003e (C), MIC\u003csub\u003e\u003cem\u003eP. aeruginosa \u003c/em\u003e\u003c/sub\u003e(D), MIC\u003csub\u003e\u003cem\u003eC. albicans\u003c/em\u003e\u003c/sub\u003e, and MIC\u003csub\u003e\u003cem\u003eP. digitatum \u003c/em\u003e\u003c/sub\u003e(F).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/b755fff64744f6151df4f843.png"},{"id":78879743,"identity":"ad3bd87c-c74c-48f0-a2b1-8c7b1075f520","added_by":"auto","created_at":"2025-03-20 08:11:17","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":293405,"visible":true,"origin":"","legend":"\u003cp\u003eDesirability plots of the optimal value DPPH-IC\u003csub\u003e50\u003c/sub\u003e (A), TAC(B), MIC\u003csub\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/sub\u003e (C), MIC\u003csub\u003e\u003cem\u003eP. aeruginosa \u003c/em\u003e\u003c/sub\u003e(D), MIC\u003csub\u003e\u003cem\u003eC. albicans\u003c/em\u003e\u003c/sub\u003e, and MIC\u003csub\u003e\u003cem\u003eP. digitatum \u003c/em\u003e\u003c/sub\u003e(F).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/80906f6fc51e9c313ccc895d.png"},{"id":78879046,"identity":"4dc750c9-d545-4833-9a0f-23801baea666","added_by":"auto","created_at":"2025-03-20 08:03:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":280921,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of α-zingiberene with the 4LEP target protein.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/bff3fd10ce2803d85c286bc1.png"},{"id":78879744,"identity":"1e7ea1d8-98aa-4a5b-9754-7eaaa53a1dfc","added_by":"auto","created_at":"2025-03-20 08:11:18","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":345325,"visible":true,"origin":"","legend":"\u003cp\u003eOutcomes of molecular docking of o-cresol with the target protein 6KZV.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/dc223f5b5a29fec5cbcf8904.png"},{"id":78879072,"identity":"3a58cce5-0a2d-47b9-9c39-7be9857d6585","added_by":"auto","created_at":"2025-03-20 08:03:18","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":371956,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking of cinnamal with the target protein 2CDU.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/36f96502ff644e30af8f6629.png"},{"id":100070940,"identity":"e18a3138-6708-41b4-bd9d-bd494d365aed","added_by":"auto","created_at":"2026-01-12 16:18:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4533457,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6184540/v1/1a77f244-816a-463c-920d-db64c1ff7aa0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Antioxidant and Antimicrobial Activity through mixture design predictive modelling of an Essential Oil Blend from Syzygium aromaticum, Zingiber officinale, and Cananga odorata of Comoros Islands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent decades, interest in plant-derived products has surged, driven by increasing concerns over synthetic chemical compounds and the demand for sustainable therapeutic alternatives [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Medicinal and aromatic plants have emerged as promising sources of bioactive compounds [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], attracting significant attention from researchers and practitioners due to their effectiveness and relatively straightforward extraction processes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Comoros Islands harbor a rich biodiversity of medicinal plants, including three particularly significant species: \u003cem\u003eSyzygium aromaticum\u003c/em\u003e (L.) Merr. \u0026amp; L.M.Perry (clove), \u003cem\u003eCananga odorata\u003c/em\u003e (Lam.) Hook. f. \u0026amp; Thomson (ylang-ylang), and \u003cem\u003eZingiber officinale\u003c/em\u003e Roscoe (ginger). \u003cem\u003eC. odorata\u003c/em\u003e is distinguished by its high content of sesquiterpene alcohols and esters, contributing to its therapeutic properties [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. \u003cem\u003eS. aromaticum\u003c/em\u003e contains significant concentrations of eugenol, a compound widely recognized for its antiseptic and analgesic properties [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies indicate that \u003cem\u003eZ. officinale\u003c/em\u003e, rich in zingiberene and gingerol, exhibits notable anti-inflammatory and stimulant effects [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite their traditional use in Comorian medicine, comprehensive scientific research on their bioactive properties, particularly in combination, remains limited. Moreover, the valorization of extraction by-products, such as ginger residues, is an unexplored research area aligned with sustainability goals [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOxidation and bacterial resistance are significant problems in many scientific and industrial sectors. Oxidation, which involves the loss of electrons, can degrade industrial materials, food products, and pharmaceutical compounds, deteriorating their physicochemical characteristics and decreasing efficacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, bacterial resistance to antimicrobial agents has become a global public health concern, making infection control more difficult and endangering the effectiveness of current treatments. Innovative formulations usually include active ingredients with synergistic properties to combat oxidation and bacterial resistance simultaneously, improving stability and enhancing antimicrobial potency.\u003c/p\u003e \u003cp\u003eMixture design methodologies are invaluable for evaluating interactions between components to achieve desired effects, whether antagonistic, synergistic, or additive. These designs help researchers determine optimal ratios where the combined impact of substances surpasses the sum of their individual effects [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This approach is frequently employed in food science, agrochemicals, and pharmaceutical formulation industries, where precise ingredient balance is necessary to maximize effectiveness while minimizing costs and adverse effects. For more reliable and efficient outcomes, mixture designs aid in streamlining formulation procedures and maximizing experimental trials.\u003c/p\u003e \u003cp\u003eThe present study uses a mixture design approach to phytochemically characterize essential oils (EOs) and evaluate their antioxidant and antimicrobial properties, both individually and in combination. In addition, it examines their antimicrobial activities at different concentrations using a structured mixing scheme. This research enhances the value of Comorian natural resources while addressing contemporary sustainable development challenges. Through mixture design methodology, this work enhanced the synergistic antibacterial and antioxidant properties of \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e EOs. Molecular interaction analyses of the primary compounds in these EOs demonstrate their strong binding affinity to active sites of key target proteins. This suggests that these EOs have strong antibacterial and antioxidant properties, which supports their possible use in medicinal and pharmaceutical formulations.\u003c/p\u003e \u003cp\u003eThis integrated approach advances our understanding of these plants' biological properties and explores innovative ways to utilise extraction by-products, promoting a more sustainable use of natural resources. Furthermore, this study addresses the growing need for evidence-based validation of traditional medicinal plants while potentially identifying new applications in pharmaceutical and environmental sectors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Plant material and EO extraction\u003c/h2\u003e \u003cp\u003eThe plants \u003cem\u003eSyzygium aromaticum\u003c/em\u003e (L.) ( Merr. \u0026amp; L.M.Perry), \u003cem\u003eCananga odorata\u003c/em\u003e (Lam.) (Hook. f. \u0026amp; Thomson) and \u003cem\u003eZingiber officinale\u003c/em\u003e were collected in the Comoros Islands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Professor Mohamed ADDI, a researcher and expert botanist at the Laboratory for Improvement of Agricultural Production, Biotechnology, and Environment (LAPABE), Faculty of Sciences, Mohammed Premier University, Oujda, Morocco, taxonomically identified the plants. Voucher specimens (CLP-013, CLP-014, and CLP-015) were placed at the Faculty of Sciences' herbarium in Oujda and assigned to \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e, respectively.\u003c/p\u003e \u003cp\u003e This study complied with current guidelines and regulations concerning the collection and use of plant resources. Plant samples were dried under continuous ventilation in a dark place at room temperature (25\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C). EOs were extracted via hydrodistillation of 100 g dried plant material for 180 min using a Clevenger-type apparatus [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Chemical composition of EOs by GC-MS\u003c/h3\u003e\n\u003cp\u003eUsing a Shimadzu GC system (Kyoto, Japan) with a BPX25 capillary column (30 m, 0.25 mm I.D., and 0.25 \u0026micro;m film thickness), containing 5% diphenyl and 95% dimethylpolysiloxane, and connected to a QP2010 mass spectrometer, the phytochemical profile of the EOs was determined. Helium (99.99%) was used as the carrier gas at a flow rate of 1.69 L/min. The interface, ion source, and injection port temperatures were set at 250\u0026deg;C. The column temperature was initially held at 50\u0026deg;C for 1 min, then increased to 250\u0026deg;C at a rate of 10\u0026deg;C/min, and maintained at 250\u0026deg;C for an additional minute. The sample components were ionized in electron impact mode at 70 eV, and mass spectra were recorded at 40 to 300 m/z. Each EO sample was injected in a volume of 1 \u0026micro;L in split mode, and the compounds were identified by comparing their retention times and mass spectra with reference libraries, including the National Institute of Standards database. LabSolutions software (version 2.5, Shimadzu, Kyoto, Japan) was used for data processing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003e3. Antioxidant activity\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. DPPH radical scavenging assay\u003c/h2\u003e \u003cp\u003eThe free radical scavenging activity of the EOs and their various combinations, generated using an experimental design approach, was assessed using 2,2-diphenyl 1-picrylhydrazyl (DPPH). Five concentrations of each EO were prepared by mixing the three plants at the following levels: 0.240 mg/ml, 0.500 mg/ml, 1 mg/ml, 1.5 mg/ml, and 2 mg/ml, starting from a stock solution of 1 mg/mL. A total of 4 mg DPPH was solubilized in 100 ml methanol and left to stir for 30 min. Subsequently, 0.5 ml of the EO solution was added to 2.5 ml of the DPPH solution. This mixture was shaken vigorously for 30 s and incubated at room temperature in the dark for 30 min. Absorbances was measured at 517 nm, and ascorbic acid used as a positive control [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRadical Scavenging Activity (%) = ((A\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;A\u003csub\u003e1\u003c/sub\u003e)/A\u003csub\u003e0\u003c/sub\u003e) \u0026times; 100\u003c/p\u003e \u003cp\u003eThe measurements were taken for the absorbance in the absence of the sample (A\u003csub\u003e0\u003c/sub\u003e) and the absorbance in the presence of the sample (A\u003csub\u003e1\u003c/sub\u003e). Ascorbic acid was employed as the reference compound. The IC\u003csub\u003e50\u003c/sub\u003e value, representing the concentration of the EO that causes 50% inhibition of Antioxidant activity, was determined graphically by plotting the inhibition percentage against the logarithm of EO concentration.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2. Total antioxidant capacity\u003c/h3\u003e\n\u003cp\u003eThe total antioxidant capacity (TAC) of the EOs studied was assessed by the phosphomolybdenum method [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Five concentrations were prepared from dilute solutions of each EO (25 \u0026micro;l of EO\u0026thinsp;+\u0026thinsp;1.5 \u0026micro;l of methanol) mixed with 1 ml of reagent solution (0.6 M sulfuric acid H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, 28 mM sodium phosphate Na\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, and 4 mM ammonium molybdate (NH4)6Mo7O24. The tubes were sealed and incubated at 95\u0026deg;C for 90 min. After cooling, the absorbance of the solutions was measured at 695 nm. The antioxidant capacity was expressed as ascorbic acid equivalents using a standard curve made with ascorbic acid. A blank solution, which contained all the reagents except the test sample, was also included. The experiments were conducted three times [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4. Antimicrobial activity\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e4.1. Strain selection\u003c/h2\u003e \u003cp\u003eThe in vitro antibacterial activity was evaluated against two Gram-negative bacteria, \u003cem\u003eEscherichia coli\u003c/em\u003e (ATCC 49) and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e (ATCC 189), and two fungal strains, \u003cem\u003eCandida albicans\u003c/em\u003e and \u003cem\u003ePenicillium digitatum\u003c/em\u003e, from the microbial biotechnology laboratory at Faculty of Science, Mohammed First University [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e4.2. MIC determination\u003c/h3\u003e\n\u003cp\u003eThe experimental protocol used in this study to determine the minimum inhibitory concentration (MIC) of EOs followed the 96-well microplate method. Mueller-Hinton culture medium containing 0.15% agar was chosen because of the insolubility of EOs in water. MIC was assessed over a concentration range from 8 to 0.0015%. Microplates were incubated at specific temperatures (37\u0026deg;C for 24 h for bacteria and 25\u0026deg;C for 48 h for fungi). Resazurin was used to monitor growth, with visual observations of color change. Gentamicin and cycloheximide were positive controls for bacteria and fungi, respectively. Results were obtained by performing the triplicate test to ensure the observations' reproducibility [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. MBC and MFC determination\u003c/h2\u003e \u003cp\u003eTo determine the minimum bactericidal concentration (MBC) and minimum fungicidal concentration (MFC), 3 \u0026micro;L samples from wells showing no observable microbial growth were transferred to Mueller-Hinton agar (MHA) for bacteria and yeast extract glucose (YEG) culture medium for fungi. The samples were then incubated at 37\u0026deg;C for 24 h for MBC determination and 25\u0026deg;C for 48 to 72 h for MFC determination. After the incubation period, minimum concentrations of EOs were established, such as those of EOs that caused no observable microbial growth or inhibited microbial growth were established [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5. Mixture design methodology\u003c/h2\u003e \u003cp\u003eIn this study, the ternary EO formulation was optimized using an augmented simplex-centroid design. This mixture design is suitable for studying systems where the proportions of the components add up to a constant. Besides the vertices and centroids of a standard simplex-centroid design, this augmented design includes axial points [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], enabling more robust modelling of non-linear relationships and interactions between the EOs. Including axial points improves curvature detection in the response surface, facilitating a more thorough understanding of the relationship between mixture composition and anti-diabetic activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e6. Mixture design components\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e demonstrates that each of the three components can range from 0 to 1, with the constraint that their proportions sum to unity. This represents an unconstrained mixture design [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentification of formulation components.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoded variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel \u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel \u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSum of proportions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e7. Experiment matrix\u003c/h2\u003e \u003cp\u003eThe augmented simplex-centroid design matrix, comprising 12 experimental trials, was constructed to efficiently explore the mixture space (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The design includes 10 unique points: the vertices (pure components), edge midpoints (binary 1:1 mixtures), the overall centroid (1:1:1 ternary mixture), and axial points. Three replicates at the centroid enable the estimation of pure error variance, increasing the reliability of the statistical analysis and the accuracy of predictive models [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e8. Selected mathematical model\u003c/h2\u003e \u003cp\u003eA special cubic model was chosen to describe the relationship between EO proportions and antioxidant and antimicrobial activity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This third-order polynomial model is well-suited for mixture designs, as it accounts for linear effects, binary interactions, and potentially significant ternary interactions. The following equation gives the general form of the model. The general form of the model is given by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Y={{{\\alpha\\:}}_{1}\\text{X}}_{1}\\:+\\:{{{\\alpha\\:}}_{2}\\text{X}}_{2}\\:+\\:{{{\\alpha\\:}}_{3}\\text{X}}_{3}\\:+\\:{{\\alpha\\:}}_{12}{\\text{X}}_{1}{\\text{X}}_{2}\\:+\\:{{\\alpha\\:}}_{13}{\\text{X}}_{1}{\\text{X}}_{3}+\\:{{\\alpha\\:}}_{23}{\\text{X}}_{2}{\\text{X}}_{3}+{{\\alpha\\:}}_{123}{\\text{X}}_{1}{\\text{X}}_{2}{\\text{X}}_{3}+{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere, Y represents the measured response, quantified by IC\u003csub\u003e50\u003c/sub\u003e and TAC, expressed in \u0026micro;g/ml and \u0026micro;M AAE/mg, respectively, and MIC values for \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eC. albicans\u003c/em\u003e, and \u003cem\u003eP. digitatum\u003c/em\u003e, expressed in \u0026micro;g/ml. The coefficients α₁, α₂ and α₃ represent the individual effects of each EO. the binary interaction coefficients α₁₂, α₁₃ and α₂₃ indicate synergistic or antagonistic effects between EO pairs, while the ternary interaction coefficient α₁₂₃ represents the combined effect of all three EOs. The term ε denotes the residual error.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e9. Statistical analysis\u003c/h2\u003e \u003cp\u003eModel significance was assessed using analysis of variance (ANOVA) via the F-test (MS\u003csub\u003eR\u003c/sub\u003e/MS\u003csub\u003er\u003c/sub\u003e), comparing the mean square due to regression (MS\u003csub\u003eR\u003c/sub\u003e) to the residual mean square (MS\u003csub\u003er\u003c/sub\u003e). Lack of fit was evaluated using the ratio of the mean square lack of fit (MS\u003csub\u003eLOF\u003c/sub\u003e) to the mean square pure error (M\u003csub\u003eSPE\u003c/sub\u003e) (F\u003csub\u003eLOF/PE\u003c/sub\u003e), with larger values indicating a poorly fitting model. The coefficient of determination (R\u0026sup2;) was used to assess the goodness of fit. The significance of the estimated coefficients was determined using Student's t-test. All statistical tests were performed at α\u0026thinsp;=\u0026thinsp;0.05. JMP software (V.16) and Expert Design (V.12) were used for experimental design, statistical analysis, and graphical representation. Results are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e10. Optimization tools\u003c/h2\u003e \u003cp\u003eOptimal EOs formulations were identified using contour and surface plots of the response surface. The desirability function was then employed to determine the optimal factor settings, balancing potentially competing objectives. Desirability values range from 0 (undesirable response) to 1 (optimal response). This approach facilitated the precise quantification of the optimal formulation within the design space [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e11. Molecular docking\u003c/h2\u003e \u003cp\u003eThe docking method is equipped with tools that facilitate the study of interactions between a supposedly rigid active site and a ligand molecule, making it a viable strategy for drug discovery [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Molecular docking studies were carried out to assess the interactions of GC-MS-identified compounds based on the main peaks, concentrating on antioxidant and antibacterial activities [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most identified molecules in the mixture, α-zingiberen, cinnamal, and o-cresol, were downloaded from the PubChem database. The biomolecules (ID codes: 4LEP, 6KZV, and 2CDU) were acquired in Protein Data Bank (PDB) format [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The ligands were prepared using various software packages, including Discovery Studio and ChemOffice [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe ligand structure was docked into the active site of the chosen enzyme using AutoDockTools [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Finally, the interactions between the residues of the enzyme's active site and the ligand molecules were analyzed using Discovery Studio Visualizer, which allows interactions to be examined in 2D [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e1. Phytochemical composition\u003c/h2\u003e \u003cp\u003eThe chromatographic profile of each was established using GC-MS analysis. The chromatograms obtained were compared with a library of reference spectra, enabling the identification and quantification of the compounds present in each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). So, the analysis of the chromatograms revealed significant differences in the chemical composition of the three samples studied: \u003cem\u003eS. aromaticum, Z. officinale\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a detailed list of the compounds identified, including alcohols, terpenes, esters, and ketones and their relative concentrations.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eS. aromaticum\u003c/em\u003e EO, cinnamal was found to be the majority compound, accounting for 53.23% of the total composition. This high concentration probably gives the oil its dominant organoleptic characteristics. Eucalyptol (16.37%) and camphene (9.74%) also contribute to freshness and aroma. β-linalool, although present in lesser quantities (1.45%), may contribute to the oil's floral nuances. \u003cem\u003eZ. officinale\u003c/em\u003e EO has a more balanced composition. α-zingiberene, a compound typical of ginger oil, predominates at 19.33%. Eucalyptol (16.37%), β-citral (12.93%), and β-cymene (7.16%) are also significant components, probably contributing significantly to this oil's complex aromatic profile. In \u003cem\u003eC. odorata\u003c/em\u003e EO, o-cresol is the majority compound (14.09%). Germacrene D (11.16%) and β-linalool (10.47%) are also present in significant concentrations. α-Farnesene, although less abundant (5.52%), may contribute to the oil's subtle notes.\u003c/p\u003e \u003cp\u003eIn the study conducted by Ahamad, GC-MS analysis of clove EO made it possible to identify 37 chemical components, representing approximately 99.49% of the total oil. The main components were eugenol (59.87%), caryophyllene (23.58%), α-selinene (4.67%), α-terpinyl acetate (4.12%) and humulene (3.74%) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Kiki also confirmed the majority presence of eugenol in this EO, representing 76.78% of the total composition [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Allali Aimada's study focused on the chemical composition of the EO of \u003cem\u003eZ. officinale\u003c/em\u003e (EOZ). The main constituents identified were α-zingiberene (23.85%), geranial (14.16%) and (E,E)-α-farnesene (9.98%) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Meher Nahid, on the other hand, used GC-MS to isolate and identify 81 components in EO from leaves, where the main constituents were sabinene (14.99%), L-pinene (14.32%), caryophyllene oxide (13.85%), caryophyllene (9%) and another caryophyllene isomer (9.47%) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In the case of Kediri's accession, studied by Kurniawati, the dominant compounds of the EO of \u003cem\u003eCananga\u003c/em\u003e flowers were linalool, caryophyllene, β-copaene and benzyl benzoate [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Finally, Manzano detected volatile compounds in samples using GC-MS, with benzyl acetate (21.78\u0026ndash;27.82%), benzyl benzoate (12.30-18.07%), linalool (9.45\u0026ndash;14.39%), and germacrene D as the main constituents (9,17\u0026thinsp;\u0026minus;\u0026thinsp;14,46%) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese results highlight the diversity and complexity of the chemical profiles of the EOs studied. The differences observed in the composition and relative concentrations of the compounds could explain the organoleptic and potentially biological properties specific to each oil. The predominance of cinnamal in \u003cem\u003eS. aromaticum\u003c/em\u003e suggests a central role for this compound in the oil's properties. Similarly, the notable presence of α-zingiberene in \u003cem\u003eZ. officinalis\u003c/em\u003e and o-cresol in \u003cem\u003eC. odorata\u003c/em\u003e could be decisive for their respective characteristics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical composition of \u003cem\u003eSyzygium aromaticum\u003c/em\u003e, \u003cem\u003eZingiber officinale\u003c/em\u003e and \u003cem\u003eCananga odorata\u003c/em\u003e using GC-MS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR. T\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eZ. officinalis\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Pinene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamphene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Thujene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)-2-Carene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeta.-Cymene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)-Sabinene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEucalyptol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeta.-Linalool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eo-Cresol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(+)-(E)-Limonene oxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4-Terpineol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Terpineol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Citral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeta.-Citronellal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebeta.-Citral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCinnamal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEugenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeraniol acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopaene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)-.beta.-Elemene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaryophyllene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCinnamyl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Caryophyllene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egamma.-Muurolene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGermacrene D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Zingiberene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Farnesene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Amorphene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsoledene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Cubebene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCubenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNerolidol 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCedrene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaryophyllene oxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Cadinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)-.delta.-Cadinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaryophyllene oxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecis-Farnesol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2Farnesyl acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealpha.-Thiocyanatotoluene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2. Single antioxidant effect of EOs\u003c/h2\u003e \u003cp\u003eThis study evaluated the antioxidant activity of EO extracts of \u003cem\u003eS. aromaticum, Z. officinalis\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e, as well as their mixtures, using the DPPH radical scavenging method. The percentage of inhibition of \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinalis\u003c/em\u003e, \u003cem\u003eC. odorata\u003c/em\u003e, and ascorbic acid at varying concentrations is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results show that all substances exhibit a dose-dependent inhibition pattern, with distinct differences in their inhibitory abilities. At lower concentrations (0.25 \u0026micro;g/mL), \u003cem\u003eS. aromaticum\u003c/em\u003e has the highest inhibition, close to 100%, indicating a robust antioxidant or inhibitory potential, higher than that of the other samples; \u003cem\u003eZ. officinalis\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e exhibit intermediate inhibition activities, displaying consistent and stable inhibition levels throughout the whole concentration range; and ascorbic acid, used as a standard, has the lowest percentage of inhibition, capping at approximately 60%, which is significantly lower than natural substances, especially \u003cem\u003eS. aromaticum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe dose-dependent pattern highlights the importance of concentration in efficiency and the potency of \u003cem\u003eS. aromaticum\u003c/em\u003e, likely due to its high phenolic component content, even at low concentrations. However, at greater doses, \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e have a limited inhibitory potential, indicating antagonistic interactions or saturation effects. These findings suggest that natural compounds, especially those of S. aromaticum, may be potent substitutes or supplements to synthetic inhibitors like ascorbic acid in applications needing antioxidant or antibacterial activity.\u003c/p\u003e \u003cp\u003eThe IC\u003csub\u003e50\u003c/sub\u003e values of ascorbic acid, \u003cem\u003eZ. officinale\u003c/em\u003e, \u003cem\u003eC. odorata\u003c/em\u003e, and \u003cem\u003eS. aromaticum\u003c/em\u003e are displayed in the bar chart, indicating their respective inhibitory powers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The ascorbic acid (0.021 mg/ml) and the other natural chemicals (\u003cem\u003eC. odorata\u003c/em\u003e, 0.096 mg/ml; \u003cem\u003eZ. officinale\u003c/em\u003e, 0.163 mg/ml) are both outperformed by \u003cem\u003eS. aromaticum\u003c/em\u003e, which has the lowest IC\u003csub\u003e50\u003c/sub\u003e (0.013 mg/ml), indicating outstanding activity. This aligns with its high inhibition rates at low doses, likely due to bioactive compounds such as eugenol. While \u003cem\u003eC. odorata\u003c/em\u003e and \u003cem\u003eZ. officinale\u003c/em\u003e could need larger dosages or combinations to increase their efficacy, these findings demonstrate the potential of \u003cem\u003eS. aromaticum\u003c/em\u003e as a robust natural inhibitor for pharmacological, culinary, or cosmetic applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Total antioxidant activity\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the results obtained when evaluating the antioxidant activity of the extracts studied using the phosphomolybdenum test. This spectrophotometric method, based on reducing phosphomolybdenum (VI) to phosphomolybdenum (V), enables overall quantification of the samples' antioxidant capacity. The measured absorbance intensity is directly proportional to the extract's reducing capacity and antioxidant potential. Higher values indicate more antioxidant inhibition. The graph displays the antioxidant activity of \u003cem\u003eZ. officinalis\u003c/em\u003e, \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eC. odorata\u003c/em\u003e, and ascorbic acid as determined by the phosphomolybdenum test. Compared to the synthetic standard and other natural extracts, \u003cem\u003eZ. officinalis\u003c/em\u003e exhibits the highest antioxidant activity, with a value of 525.2000. With an intermediate value of 326.0900, ascorbic acid is less effective than \u003cem\u003eZ. officinalis\u003c/em\u003e fields but more effective than \u003cem\u003eS. aromaticum\u003c/em\u003e (139.6800) and \u003cem\u003eC. odorata\u003c/em\u003e (128.5400). These findings demonstrate \u003cem\u003eZ. officinale\u003c/em\u003e's strong antioxidant potential, making it a viable option for applications needing potent natural antioxidants. On the other hand, \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e, which have fewer activities, might be considered in some situations. \u003cem\u003eZ. officinale\u003c/em\u003e's advantage over ascorbic acid. It also provides an indication of the potential value of natural extracts as potent alternatives to artificial antioxidants, improving their potential applicability in the food, cosmetic, and pharmaceutical industries.\u003c/p\u003e \u003cp\u003eThe results obtained using the phosphomolybdenum test are in corroboration and complement previous observations relative to the antioxidant activity of these extracts. The better performance of ginger extract in this test confirms its remarkable reducing capacity and high potential to scavenge free radicals, as pointed out by other evaluation methods. The preeminence of ginger in this test can be explained by its composition rich in phenolic compounds and gingerols, molecules well known for their great antioxidant power [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These compounds can readily surrender electrons or hydrogen atoms, thereby promoting the reduction of phosphomolybdenum (VI) to phosphomolybdenum (V). Although not negligible, the more moderate antioxidant activity of ylang-ylang (X3) suggests a lower concentration of reducing compounds in this extract. This observation could be explained by the predominance of terpene compounds in ylang-ylang EO, which, while having antioxidant properties, are generally less potent than phenolic compounds [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt should be noted that the phosphomolybdenum assay measures total antioxidant capacity, including both hydrophilic and lipophilic compounds. This makes it a useful complement to other methods of determining antioxidant activity and provides a fuller picture of extracts for their antioxidant potential. This suggests that different chemical compositions are very important for the antioxidant activities of an EO. The difference that has been shown between different extracts could form the basis of the formulation of antioxidants depending on specific applications in terms of their potency and mechanisms of action.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e2.2. Single antibacterial effect of EOs\u003c/h2\u003e \u003cp\u003eThe MIC values of the three EOs against the four microbial strains reveal distinct patterns of antimicrobial activity (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Against \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinalis\u003c/em\u003e showed moderate activity with MICs of 1.5 and 1 \u0026micro;g/ml, respectively, while \u003cem\u003eC. odorata\u003c/em\u003e exhibited slightly weaker activity (2 \u0026micro;g/ml). For \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eS. aromaticum\u003c/em\u003e demonstrated good activity (MIC 1 \u0026micro;g/ml), \u003cem\u003eZ. officinalis\u003c/em\u003e showed better activity (2 \u0026micro;g/ml), and \u003cem\u003eC. odorata\u003c/em\u003e had similar efficacy to its action against \u003cem\u003eE. coli\u003c/em\u003e (2 \u0026micro;g/ml). In the case of \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eZ. officinalis\u003c/em\u003e exhibited the strongest activity (0.5 \u0026micro;g/ml), suggesting potential antifungal properties, followed by \u003cem\u003eC. odorata\u003c/em\u003e (1 \u0026micro;g/ml) and \u003cem\u003eS. aromaticum\u003c/em\u003e (2 \u0026micro;g/ml). Finally, \u003cem\u003eS. aromaticum\u003c/em\u003e demonstrated significantly stronger activity against \u003cem\u003eP. digitatum\u003c/em\u003e (0.125 \u0026micro;g/ml) than the other two oils, which had MICs of 1 \u0026micro;g/ml.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAntimicrobial activity \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e essential oils.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStrain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMIC/MBC (\u0026micro;g/ml)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eC. albicans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP. digitatum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalyzing the MBC values for each strain in relation to the three EOs provides insights into their bactericidal and fungicidal efficacy. For \u003cem\u003eE. coli\u003c/em\u003e, the lowest MBC is observed for \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinale\u003c/em\u003e (2 \u0026micro;g/ml), while \u003cem\u003eC. odorata\u003c/em\u003e exhibits the highest MBC (4 \u0026micro;g/ml). In the case of \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eS. aromaticum\u003c/em\u003e demonstrates superior activity with an MBC of 2 \u0026micro;g/ml, whereas both \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e require higher concentrations (4 \u0026micro;g/ml), reflecting lower effectiveness. For \u003cem\u003eC. albicans\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e is again the most effective, achieving an MBC of 1 \u0026micro;g/ml, followed by \u003cem\u003eC. odorata\u003c/em\u003e with an MBC of 2 \u0026micro;g/ml. In comparison, \u003cem\u003eS. aromaticum\u003c/em\u003e shows less efficacy with an MBC of 4 \u0026micro;g/ml. Lastly, for \u003cem\u003eP. digitatum\u003c/em\u003e, \u003cem\u003eS. aromaticum\u003c/em\u003e stands out with an exceptionally low MBC of 1 \u0026micro;g/ml, highlighting its strong fungicidal activity, whereas \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e exhibit similar, less potent effects with an MBC of 2 \u0026micro;g/ml each. The data highlight the potential use of these EOs in antimicrobial applications, with \u003cem\u003eS. aromaticum\u003c/em\u003e as the most promising candidate for further development.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e2.3. EOs formulation by mixture design\u003c/h2\u003e \u003cp\u003eA simplex-centroid mixture design was implemented to explore combinations of the EOs under investigation. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the experimental results for each EO combination, including the observed responses for DPPH-IC\u003csub\u003e50\u003c/sub\u003e, TAC, and MIC against \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eC. albicans\u003c/em\u003e, and \u003cem\u003eP. digitatum\u003c/em\u003e. Experiments were randomized to minimize bias, and each reported value represents the mean of three replicates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental results for essential oil combinations: DPPH-IC\u003csub\u003e50\u003c/sub\u003e, TAC, and MIC against \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eC. albicans\u003c/em\u003e and \u003cem\u003eP. digitatum\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u0026deg; \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDPPH-IC\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMIC \u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMIC \u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMIC \u003cem\u003eC. albicans\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMIC \u003cem\u003eP. digitatum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21. 98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e525. 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1. 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139. 68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e104. 01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128. 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18. 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e275. 07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0. 125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44. 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156. 83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0. 125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21. 82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e265. 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66. 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e217. 01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40. 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e185. 06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61. 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e210. 76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47. 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e282. 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66. 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e177. 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0. 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0. 167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53. 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e \u003cem\u003ea\u003c/em\u003e \u003c/sup\u003e \u003cem\u003eExperiments were carried out after randomization.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e2.4. Statistical analysis and model validation\u003c/h2\u003e \u003cp\u003eANOVA was performed to assess the relevance and goodness of fit of the proposed models for all studied responses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariance analysis for the fitted models by the augmented simplex-centroid design.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDPPH-IC\u003csub\u003e50\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19225. 775\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3204. 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23. 6946\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 0016\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e676. 167\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135. 23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19901. 942\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121033. 92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20172. 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41. 7022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2418. 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e483. 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123452. 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMIC\u003csub\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2. 0456818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 340947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101. 3626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0. 0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 0168182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 003364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2. 0625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMIC\u003csub\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. 8356866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 639281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59. 594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 0536364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 010727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. 8893229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMIC\u003csub\u003e\u003cem\u003eC. albicans\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2. 1618939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 360316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26. 7802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 0012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 0672727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 013455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2. 2291667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMIC\u003csub\u003e\u003cem\u003eP. digitatum\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. 1595502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 193258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22. 8585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0. 0017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0. 0422727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0. 008455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1. 2018229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026sup2;\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDF: degrees of freedom; SS: sum of squares; MS: mean square; F: Calculated Fisher value; R\u0026sup2;: Coefficient of determination; Adj: Adjusted; *: statistically significant at 95% confidence level.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eANOVA results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, indicate that the regression models for all five responses were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The six models' calculated F-ratios (MSR/MSr) exceeded the critical F-value (F\u003csub\u003e0.05,6,5\u003c/sub\u003e= 4.95). The coefficients of determination (R\u0026sup2;) were 0.96, 0.98, 0.99, 0.98, 0.96, and 0.96 for DPPH-IC\u003csub\u003e50\u003c/sub\u003e, TAC, and MIC against \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eP. aeruginosa\u003c/em\u003e, \u003cem\u003eC. albicans\u003c/em\u003e, and \u003cem\u003eP. digitatum\u003c/em\u003e, respectively. The close agreement between R\u0026sup2; and adjusted R\u0026sup2; values (difference\u0026thinsp;\u0026lt;\u0026thinsp;0.2) further supports the model's goodness of fit. The graphical representation of observed versus predicted values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) corroborates these statistical results. The linear arrangement of points along the regression line for both responses visually confirms the quality of fit and robust predictive capacity of the models. These consistent statistical results confirm the validity and predictive accuracy of the mathematical models developed to describe the inhibitory activity of EO mixtures against free radicals and microbial strains.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e2.5. EOs' effects and fitted model\u003c/h2\u003e \u003cp\u003eThe effects of the studied factors were analyzed using Student's t-tests, and the resulting t-statistics and p-values are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. This table provides an overview of the significance of each term in the models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated regression coefficients of the special cubic models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα₁\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eα₁\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eα₁\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eα\u003csub\u003e23\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eα₁\u003csub\u003e23\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eS. aromaticum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003cp\u003e\u003cem\u003eZ. officinale\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u0026times;\u003c/p\u003e \u003cp\u003e\u003cem\u003eC. odorata\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eDPPH\u003c/em\u003e\u003csub\u003e\u003cem\u003eIC50\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.345909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.50591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.203182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-307.5364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-69.70182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-471.3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1395.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0029*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0062*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTAC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e521.29922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.20831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138.19467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-252.2249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-668.6522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e531.32596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-487.5768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0016*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0042*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.4402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMIC\u003c/em\u003e\u003csub\u003e\u003cem\u003eE.coli\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5118182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9663636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0118182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.043636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.952727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.9563636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMIC\u003c/em\u003e\u003csub\u003e\u003cem\u003eP. aeruginosa\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9963636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0077273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9509091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.991818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.605455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.082727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0108*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMIC\u003c/em\u003e\u003csub\u003e\u003cem\u003eC. albicans\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0236364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5236364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9327273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.905455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.087273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9127273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0055*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0036*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.1027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eMIC\u003c/em\u003e\u003csub\u003e\u003cem\u003eP.digitatum\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5263636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0263636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9922727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.394545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.962727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0372727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*Statistically significant at 95% confidence level.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reveals several statistically significant terms. Specifically, the linear terms of \u003cem\u003eZ. officinale\u003c/em\u003e (α2), and \u003cem\u003eC. odorata\u003c/em\u003e (α3) are significant contributors to the DPPH-IC\u003csub\u003e50\u003c/sub\u003e response. Moreover, the binary interactions between \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinale\u003c/em\u003e (α12) and between \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e (α23) also exhibit strong significance, with p-values of 0.0029 and 0.0004, respectively. This implies that these pairs of EOs interact synergistically to influence the DPPH-IC\u003csub\u003e50\u003c/sub\u003e response. The ternary interaction between all three EOs (α123) is likewise significant, as evidenced by its p-value of 0.0062, highlighting the complex interplay among the oils in determining antioxidant activity. The following equation can express the mathematical model representing this relationship:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{DPPH-IC50}=\\:{\\text{161,5}\\text{X}}_{2}\\:+\\:{\\text{94,2}\\text{X}}_{3}\\:-\\text{307,53}{\\text{X}}_{1}{\\text{X}}_{2}\\:-\\text{471,34}{\\text{X}}_{2}{\\text{X}}_{3}+\\text{1395,18}{\\text{X}}_{1}{\\text{X}}_{2}{\\text{X}}_{3}+{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Total Antioxidant Capacity (TAC) response is significantly influenced by the linear contributions of \u003cem\u003eS. aromaticum\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), \u003cem\u003eZ. officinale\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.0016), and \u003cem\u003eC. odorata\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.0013), indicating that these EOs individually play a crucial role in enhancing TAC. Among the binary interactions, \u003cem\u003eS. aromaticum\u003c/em\u003e \u0026times; \u003cem\u003eC. odorata\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.0015) and \u003cem\u003eZ. officinale\u003c/em\u003e \u0026times; \u003cem\u003eC. odorata\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.0042) are statistically significant, suggesting notable synergistic or antagonistic effects between these pairs. On the other hand, the interaction of \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinale\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.0649) is insignificant thus demonstrating very limited combined influence. All EOs also exhibited an insignificantly ternary interaction, P\u0026thinsp;=\u0026thinsp;0.4402, showing that the three together do not affect TAC that much. This is the relationship describing TAC and significant factors:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{TAC}={\\text{521,29}\\text{X}}_{1}\\:+\\:{\\text{132,2}\\text{X}}_{2}\\:+\\:{\\text{138,19}\\text{X}}_{3}\\:-\\text{668,65}{\\text{X}}_{1}{\\text{X}}_{3}\\text{531,32}{\\text{X}}_{2}{\\text{X}}_{3}{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLinear and all interaction terms of the model significantly affected MIC against \u003cem\u003eE. coli\u003c/em\u003e. The linear terms of \u003cem\u003eS. aromaticum\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), \u003cem\u003eZ. officinale\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and \u003cem\u003eC. odorata\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were highly significant to show the individual contribution to reducing MIC. Among the binary interactions, \u003cem\u003eS. aromaticum\u003c/em\u003e \u0026times; \u003cem\u003eZ. officinale\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.014), \u003cem\u003eS. aromaticum\u003c/em\u003e \u0026times; \u003cem\u003eC. odorata\u003c/em\u003e (P\u0026thinsp;=\u0026thinsp;0.0001), and \u003cem\u003eZ. officinale\u003c/em\u003e \u0026times; \u003cem\u003eC. odorata\u003c/em\u003e P\u0026thinsp;=\u0026thinsp;0.001) are statistically significant, reflecting notable synergistic or antagonistic effects between these pairs of oils. Besides, the ternary interaction due to the three EOs is significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), further evidencing their complicated interaction in determining MIC. The analysis thus brings out the contribution of oils concerning both individual and combined vital roles. In this context, a considerable contribution was also given by the ternary interaction, thus providing evidence that all EOs interact together and result in increased antibacterial activities. The relationship between MIC with significant factors can be explained as given in equation:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Y={\\text{1,51}\\text{X}}_{1}\\:+\\:{\\text{0,96}\\text{X}}_{2}\\:+\\:{\\text{2,01}\\text{X}}_{3}\\:-\\text{1,04}{\\text{X}}_{1}{\\text{X}}_{2}\\:-\\text{2,95}{\\text{X}}_{1}{\\text{X}}_{3}\\text{1,95}{\\text{X}}_{2}{\\text{X}}_{3}\\text{19,44}{\\text{X}}_{1}{\\text{X}}_{2}{\\text{X}}_{3}+{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe MIC against \u003cem\u003eP. aeruginosa\u003c/em\u003e is significantly influenced by the individual effects of the EOs and certain interactions. \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e all individually increase the MIC, as indicated by their significant positive linear coefficients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, combining \u003cem\u003eS. aromaticum\u003c/em\u003e with either \u003cem\u003eZ. officinale\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0108) or \u003cem\u003eC. odorata\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0001) results in significant synergistic interactions, reducing the expected MIC based on their individual effects. While the interaction between \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e is not significant, the three-way interaction is also not statistically significant (p\u0026thinsp;=\u0026thinsp;0.1833). The model for predicting MIC against \u003cem\u003eP. aeruginosa\u003c/em\u003e is:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:Y={0.99\\text{X}}_{1}\\:+\\:{2\\text{X}}_{2}\\:+\\:{1.95\\text{X}}_{3}\\:\\:-1.99{\\text{X}}_{1}{\\text{X}}_{2}\\:-5.6{\\text{X}}_{1}{\\text{X}}_{3}+{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe individual EOs and specific interactions significantly affect the MIC against \u003cem\u003eC. albicans\u003c/em\u003e. \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e all exhibit significant positive linear effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that increasing their individual proportions increases the MIC. Further, significant binary interactions are observed when \u003cem\u003eS. aromaticum\u003c/em\u003e is combined with either \u003cem\u003eZ. officinale\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0036) or \u003cem\u003eC. odorata\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.014. The interaction between \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e is not significant. Notably, the three-way interaction among all oils is significant (p\u0026thinsp;=\u0026thinsp;0.01027). The model for predicting MIC against \u003cem\u003eC. albicans\u003c/em\u003e is:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:Y={2.02\\text{X}}_{1}\\:+\\:{0.52\\text{X}}_{2}\\:+\\:{0.93\\text{X}}_{3}\\:-2.9{\\text{X}}_{1}{\\text{X}}_{2}\\:-2.08{\\text{X}}_{1}{\\text{X}}_{3}+{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe MIC for \u003cem\u003eP. digitatum\u003c/em\u003e is influenced by individual EO components. \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinale\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e increase the MIC, as evidenced by their significant positive linear coefficients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Synergistic effects are observed; combining \u003cem\u003eS. aromaticum\u003c/em\u003e with either \u003cem\u003eZ. officinale\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0002) or \u003cem\u003eC. odorata\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.0012) results in a significantly lower MIC than expected from their individual effects. The interaction between \u003cem\u003eZ. officinale\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e is not significant. Interestingly, the three-way interaction among all oils is highly significant (p\u0026thinsp;=\u0026thinsp;0.0007) and has a large positive coefficient. This positive coefficient suggests an antagonistic effect, increasing the MIC when all three EOs are combined, counteracting the synergistic binary interactions. The model for predicting MIC against \u003cem\u003eP. digitatum\u003c/em\u003e is:\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:Y={1.52\\text{X}}_{1}\\:+\\:{1.02\\text{X}}_{2}\\:+\\:{0.99\\text{X}}_{3}\\:-4.39{\\text{X}}_{1}{\\text{X}}_{2}\\:-2.96{\\text{X}}_{1}{\\text{X}}_{3}18.27{\\text{X}}_{1}{\\text{X}}_{2}{\\text{X}}_{3}+{\\upvarepsilon\\:}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e2.6. EOs mixture optimization and desirability study\u003c/h2\u003e \u003cp\u003eThe Optimization of the mixture design aims to identify the EO formulation that simultaneously maximizes antioxidant activity (minimizes DPPH-IC\u003csub\u003e50\u003c/sub\u003e and TAC) and antimicrobial activity (minimizes MIC). The Optimization of all responses was achieved using 2D contour plots and desirability profiles, as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e2.7. EO mixture antioxidant activity\u003c/h2\u003e \u003cp\u003eThe 2D clearly shows that two distinct zones offer the potential to accomplish a DPPH-IC\u003csub\u003e50\u003c/sub\u003e value of around 10 \u0026micro;g/ml. This optimal zone is located along the binary axis of \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinale\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). These findings suggest that the interaction between these EOs is critical in determining the antioxidant capacity, with certain combinations proving more effective than others. The desirability function, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA, further refined the optimization process by identifying the precise mixture that maximizes the DPPH-IC\u003csub\u003e50\u003c/sub\u003e response. The desirability profile confirms that an optimal mixture consisting of 73% \u003cem\u003eS. aromaticum\u003c/em\u003e and 27% \u003cem\u003eZ. officinale\u003c/em\u003e produces an IC\u003csub\u003e50\u003c/sub\u003e value of 4.38 \u0026micro;g/ml with a desirability score of 99%. This high desirability score indicates that this mixture is nearly ideal for achieving the best possible antioxidant activity. The high desirability also suggests that this combination of oils has potential applications in formulating products where antioxidant efficacy is crucial, such as in developing natural preservatives or therapeutic agents.\u003c/p\u003e \u003cp\u003eConcerning the response TAC, the 2D contour plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB) highlight the optimal region for maximizing this response. According to the correspondent graphs, a TAC value exceeding 450 \u0026micro;M AAE/mg can be achieved with a mixture of \u003cem\u003eS. aromaticum\u003c/em\u003e. This suggests that this EO is the most potent in terms of antioxidant activity when used on its own. To further refine the optimization process and determine the precise conditions that yield the maximum TAC response, the desirability function was employed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB. The desirability profile confirms the findings from the 2D and 3D iso-response plots. It indicates that \u003cem\u003eS. aromaticum\u003c/em\u003e alone can achieve a TAC value of 525 \u0026micro;M AAE/mg, with a desirability score of 99%. This high desirability score suggests that \u003cem\u003eS. aromaticum\u003c/em\u003e is the most effective EO for maximizing the TAC response and could be considered for applications requiring high antioxidant capacity, such as in food preservation or cosmetic formulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e2.8. EO mixture antibacterial activity\u003c/h2\u003e \u003cp\u003eThe two bacterial strains \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eP. aerogenosa\u003c/em\u003e show important sensibility against EOs formulations, Thus, The analysis of the response surface contours (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC) revealed that binary combinations of \u003cem\u003eSyzygium aromaticum\u003c/em\u003e and \u003cem\u003eZingiber officinale\u003c/em\u003e EOs exhibited significant antimicrobial activity against \u003cem\u003eE. coli\u003c/em\u003e, with a MIC of approximately 1 \u0026micro;g/mL. Optimization through desirability function analysis predicted optimal inhibition (99% desirability) at an MIC of 0.9 \u0026micro;g/mL, achieved with a binary mixture ratio of 23:77 (v/v) \u003cem\u003eS. aromaticum\u003c/em\u003e:\u003cem\u003eZ. officinale\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Similarly, the response surface analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD) demonstrated enhanced antimicrobial efficacy against \u003cem\u003eP. aeruginosa\u003c/em\u003e when combining \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eCymbopogon odorata\u003c/em\u003e EOs, yielding an approximate MIC of 0.1 \u0026micro;g/mL. The optimal formulation, predicted with 99% desirability, achieved an MIC of 0.031 \u0026micro;g/mL using a 58:42 (v/v) ratio of \u003cem\u003eS. aromaticum\u003c/em\u003e:\u003cem\u003eC. odorata\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eConcerning antifungal activities, Response surface analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) demonstrated that binary combinations of \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinale\u003c/em\u003e EOs exhibited potent antifungal activity against \u003cem\u003eC. albicans\u003c/em\u003e, with a MIC of approximately 0.4 \u0026micro;g/mL. Optimization through desirability function analysis predicted optimal inhibition (99% desirability) at a MIC of 0.35 \u0026micro;g/mL, achieved with a binary mixture ratio of 25:75 (v/v) \u003cem\u003eS. aromaticum\u003c/em\u003e:\u003cem\u003eZ. officinale\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). In the same way, the contour plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF) further revealed significant antifungal efficacy against \u003cem\u003eP. digitatum\u003c/em\u003e, with a MIC under 0.25 \u0026micro;g/mL using the same EO combination. The optimal formulation, predicted with 99% desirability, achieved an MIC of 0.16 \u0026micro;g/mL using a 44:56 (v/v) ratio of \u003cem\u003eS. aromaticum\u003c/em\u003e:\u003cem\u003eZ. officinale\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. Synergy between EOs\u003c/h3\u003e\n\u003cp\u003eSignificant synergistic interactions between most components in the three-component EO blend are revealed from \u003cem\u003eS. aromaticum\u003c/em\u003e, \u003cem\u003eZ. officinalis\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e. When compared to their individual effects, interactions between cinnamal (found in \u003cem\u003eS. aromaticum\u003c/em\u003e), alpha.-Zingiberene (found in \u003cem\u003eZ. officinale\u003c/em\u003e), and o-Cresol (found in \u003cem\u003eC. odorata\u003c/em\u003e) considerably increase antioxidant and antibacterial activities, according to predictive modeling utilizing mixture designs. From an antioxidant perspective, o-Cresol and alpha.-Zingiberene enhance this effect with their radical-stabilizing and antioxidant-regenerating qualities. At the same time, cinnamal, a significant phenolic component, functions as a potent free-radical scavenger because of its aromatic ring and hydroxyl group. In complex systems, these chemicals work together to decrease lipid oxidation and extend the duration of the antioxidant effect. On the antimicrobial front, complementary mechanisms are seen: o-Cresol and alpha.-Zingiberene strengthen the impact of cinnamal by preventing bacterial growth through other intracellular targets, particularly the disruption of vital metabolic pathways, while cinnamal breaks down bacterial membranes, causing intracellular ions and macromolecules to leak out. It is feasible to block a variety of microorganisms, including resistant bacteria, at doses significantly lower than those needed for each component alone because of this synergistic combination. The ratios of the three EOs were adjusted to improve these effects using predictive modeling techniques employed in this investigation. The results, for instance, indicate that certain ratios of nearly 60% \u003cem\u003eS. aromaticum\u003c/em\u003e, 16% \u003cem\u003eZ. officinal\u003c/em\u003ee, and 16% \u003cem\u003eC. odorata\u003c/em\u003e offer the best effectiveness. Furthermore, this method lowers the possible danger of toxicity connected with high concentrations of each component and the overall amount needed.\u003c/p\u003e \u003cp\u003eThese findings demonstrate the importance of mixture modeling and design in creating natural formulations with excellent performance and economic viability. Additionally, they present encouraging opportunities for use in the food industry of natural antioxidants in perishable goods, the pharmaceutical industry with natural preservatives or therapeutic agents, and the cosmetics industry (active components in anti-aging or protective products).\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e4. Molecular docking\u003c/h2\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e4.1. Antibacterial activity\u003c/h2\u003e \u003cp\u003eDrug design is based on molecular docking, a powerful technique for understanding how protein receptors interact with ligands. This technique provides crucial information about binding affinities, interaction mechanisms, and the potential for optimizing drug efficacy and specificity.\u003c/p\u003e \u003cp\u003eThe main compounds identified by GC-MS, α-zingiberene and o-cresol, have antibacterial activity. The compound α-zingiberene affects the \u003cem\u003eC. albicans\u003c/em\u003e receptor (PDB ID: 4LEP), and the compound o-cresol affects the \u003cem\u003eE. coli\u003c/em\u003e receptor (PDB ID: 6KZV). We performed molecular docking of these two compounds, which revealed that all exhibited high negative binding energy values of -6.12 kcal/mol for the α-zingiberen-4LEP complex and also for the o-cresol-6KZV complex, a value of -5.13 kcal/mol.\u003c/p\u003e \u003cp\u003eThe results of the molecular docking of the α-zingiberen-4LEP complex are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the results of the molecular docking study showed the α-zingiberene compound exhibited one Pi-Sigma bonding interaction with the protein residues Trp-224, with a distance greater than 3.73 \u0026Aring;, and also seven alkyl bonds with the protein residues Tyr-21, Arg-171, Pro-29, Tyr-226, Pro-26, Val-119 and Tyr-23, with a distance greater than 5.23, 3.78, 3.87, 4.56, 4.42, 35.45, and 4.79 \u0026Aring;, respectively. The results suggest that α-zingiberene is a potential inhibitor of the \u003cem\u003eC. albicans\u003c/em\u003e receptor and could be used as an antibacterial agent.\u003c/p\u003e \u003cp\u003eSimilarly, the antibacterial activity of the various extracts from our plant shows that the main compounds identified by GC-MS affect the \u003cem\u003eE. coli\u003c/em\u003e receptor. The results of molecular docking of the o-cresol-6KZV complex are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The molecular docking results, revealing that the o-cresol compound forms a hydrogen bond with the protein residue Val-71 at a distance of more than 1.71 \u0026Aring;, as well as a Pi-Sigma bond with Val-43 at a distance greater than 3.89 \u0026Aring;. Additionally, the molecular docking results for the o-cresol compound indicate an Alkyl and Pi-alkyl bond with Val-167 and Ala-47 at a distance of 4.87 and 3.80 \u0026Aring;, respectively. These findings suggest that o-cresol may serve as potential inhibitors of the \u003cem\u003eE. coli\u003c/em\u003e receptor, highlighting their potential as antibacterial agents.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section4\"\u003e \u003ch2\u003e4.2. Antioxidant activity\u003c/h2\u003e \u003cp\u003eThe oxidative activity of the various extracts from our plant shows that the main compound identified by GC-MS, cinnamal, has an antioxidant effect (PDB ID: 2CDU). We performed molecular docking of the most identified compound, which revealed that all had negative binding energy values of -5.98 kcal/mol. The results of the molecular docking of cinnamal-2CDU are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results of molecular docking are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, showing that the cinnamal compound forms two hydrogen bonds with the protein residues Ile-160 and Gly-161 at distances of 2.32 \u0026Aring; and 2.23 \u0026Aring;, respectively, and one alkyl bond with Pro-117 at distances of 5.35 \u0026Aring; and a Pi-Sigma bond with the protein residue Cys-242 at distances of 3.62 \u0026Aring;, as well as one Pi-Pi-stacked bond with Phe-245 at distances of 4.22 \u0026Aring;. This suggests that cinnamal may act as a potential inhibitor of the oxidation receptor, indicating its potential as an antioxidant agent.\u003c/p\u003e \u003cp\u003eAfter a thorough analysis of the molecular docking of the three main compounds identified in the EOs, it was observed that these compounds interact and correlate with the binding sites of various selected proteins. As a result, it can be concluded that these compounds possess significant antioxidant and antibacterial activities.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study characterized the chemical composition of EOs extracted from \u003cem\u003eS. aromaticum, Z. officinale\u003c/em\u003e, and \u003cem\u003eC. odorata\u003c/em\u003e and evaluated their antioxidant and antimicrobial properties, both individually and in combination, using a well-defined mixing design. The results highlight the richness of bioactive compounds in these EOs, conferring significant activity against tested microbial species and remarkable antioxidant potential. Analysis of the EO blends revealed beneficial synergies, reinforcing the overall effectiveness of the formulations. This research supports the valorization of Comorian natural resources and contributes to sustainable development strategies by offering natural alternatives for combating infections and oxidative stress. Additionally, molecular docking analysis confirmed that key EO compounds interact with selected protein binding sites, reinforcing their potential antioxidant and antibacterial activities. These results encourage future research into the mechanisms of action of these EOs and their broader applications in health, pharmaceuticals, and the cosmetics industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, D.K.N.E.; methodology, E.H.L.; software, S.L. and B.H.; validation, R.S. and M.F.; formal analysis, M.ER and M.T.; investigation, M.I.Y.; resources, A.Z; data curation, A.S.; writing\u0026mdash;original draft preparation, B.H. M.M.A; writing\u0026mdash;review and editing, A.B, F.Z, A.E, A.S, M.D, K.A., R.S., and L.R.; visualization F.Z, M.D., and K.A.; supervision, L.M., and L.R; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by the Researchers Supporting Project number (RSPD2025R628), King Saud University, Riyadh, Saudi Arabia\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThis work was financially supported by the MHESRI/DHESR-Morocco and the MIT-Hungary (Conventions 2023,\u0026nbsp;no.\u0026nbsp;1\u0026nbsp;\u0026amp;\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors extend their appreciation to the Researchers Supporting Project number (RSPD2025R628), King Saud University, Riyadh, Saudi Arabia for supporting this research.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge the support from the MHESRI/DHESR-Morocco and MIT-Hungary (Conventions 2023, no. 1 \u0026amp; 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe data presented in this study are available upon request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eA. 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From bangladesh\u003cem\u003e.\u003c/em\u003e 53(1), 153-158.\u003c/li\u003e\n\u003cli\u003eA. Kurniawati and M. Naimah. Dynamics of flower production and flower oil components from two accessions of Cananga (Cananga odorata) in dry season.\u003cem\u003e \u003c/em\u003ein IOP Conference Series: Earth and Environmental Science. 2024.\u003c/li\u003e\n\u003cli\u003eP. Manzano\u003cem\u003e, et al.\u003c/em\u003e,(2024) Effect of industrial steam distillation conditions on volatiles and antioxidant capacity of Cananga odorata essential oils\u003cem\u003e.\u003c/em\u003e 1-11.\u003c/li\u003e\n\u003cli\u003eQ.-Q. Mao\u003cem\u003e, et al.\u003c/em\u003e,(2019) Bioactive compounds and bioactivities of ginger (Zingiber officinale Roscoe)\u003cem\u003e.\u003c/em\u003e 8(6), 185.\u003c/li\u003e\n\u003cli\u003eH. A. Dahlmann, A. J. McKinney, M. P. Santos, and L. O. Davis,(2016) Organocatalyzed intramolecular carbonyl-ene reactions\u003cem\u003e.\u003c/em\u003e 21(6), 713.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Comoros Islands, Syzygium aromaticum, Zingiber officinale, Cananga odorata, essential oil, mixture design, antioxidant, antimicrobial, synergy","lastPublishedDoi":"10.21203/rs.3.rs-6184540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6184540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMedicinal and aromatic plants from the Comoros Islands are a potential source of unexploited bioactive compounds. This study investigates the chemical composition and biological properties of essential oils (EOs) from three emblematic Comorian plants:\u003cem\u003e Syzygium aromaticum \u003c/em\u003e(L.) Merr. \u0026amp; L.M.Perry (clove),\u003cem\u003e Cananga odorata \u003c/em\u003e(Lam.) Hook. f. \u0026amp; Thomson (ylang-ylang), and\u003cem\u003e Zingiber officinale \u003c/em\u003eRoscoe (ginger). EOs were characterized using gas chromatography coupled with mass spectrometry (GC-MS). Antioxidant activities were evaluated using the DPPH and molybdenum-reducing power methods, while antimicrobial properties were determined by the microdilution method against various pathogenic strains. A mixture design was applied to optimize EO combinations, identifying significant synergies in their biological activities. GC-MS analysis revealed that the major compounds in \u003cem\u003eS. aromaticum\u003c/em\u003e EO were cinnamal (53.23%), eucalyptol (16.37%), and camphene (9.74%), \u003cem\u003eZ. officinale\u003c/em\u003e EO was characterized by α-zingiberene (19.33%), eucalyptol (16.37%), β-citral (12.93%), and β-cymene (7.16%), while \u003cem\u003eC. odorata\u003c/em\u003e EO contained primarily o-cresol (14.09%), germacrene D (11.16%), and β-linalool (10.47%). Mixture design optimization revealed that the combination of \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eZ. officinale \u003c/em\u003eEOs significantly enhanced antioxidant activity (DPPH assay). Additionally, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eCandida albicans,\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa \u003c/em\u003eexhibited the highest susceptibility to a binary mixture of \u003cem\u003eS. aromaticum\u003c/em\u003e and \u003cem\u003eC. odorata\u003c/em\u003e EOs. Moreover, \u003cem\u003eS. aromaticum\u003c/em\u003e EO alone demonstrated the highest total antioxidant activity in the phosphomolybdenum assay. Furthermore, molecular docking analysis of the three main compounds in the EOs revealed strong interactions with the binding sites of various selected proteins, confirming their potential antioxidant and antibacterial properties. This research contributes to the valorization of Comorian natural resources and opens new perspectives for their exploitation in pharmaceutical and environmental sectors.\u003c/p\u003e","manuscriptTitle":"Enhanced Antioxidant and Antimicrobial Activity through mixture design predictive modelling of an Essential Oil Blend from Syzygium aromaticum, Zingiber officinale, and Cananga odorata of Comoros Islands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 08:03:12","doi":"10.21203/rs.3.rs-6184540/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T06:19:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T17:34:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63099234557615670256845265941413713815","date":"2025-07-10T08:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203051885146389050298138073765732194947","date":"2025-07-07T18:48:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-02T04:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167813359450375794452272023274323543912","date":"2025-06-23T07:26:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T18:17:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242879235457334181081587083686360347414","date":"2025-05-10T08:32:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-18T05:53:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-18T05:50:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-17T18:17:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-15T15:07:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-08T14:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88588b4b-1dd2-441a-8f93-fe9ca3cad6f1","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45884881,"name":"Biological sciences/Plant sciences"},{"id":45884882,"name":"Physical sciences/Chemistry"}],"tags":[],"updatedAt":"2026-01-12T16:16:41+00:00","versionOfRecord":{"articleIdentity":"rs-6184540","link":"https://doi.org/10.1038/s41598-025-30345-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-07 15:59:17","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-03-20 08:03:12","video":"","vorDoi":"10.1038/s41598-025-30345-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-30345-2","workflowStages":[]},"version":"v1","identity":"rs-6184540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6184540","identity":"rs-6184540","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}