Phenolic compounds from Nicotiana tabacum inflorescence: optimized extraction, chemical composition, evaluation of antioxidant activity integrating network pharmacology and molecular docking analysis

Phenolic compounds from Nicotiana tabacum inflorescence: optimized extraction, chemical composition, evaluation of antioxidant activity integrating network pharmacology and molecular docking analysis | 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 Research Article Phenolic compounds from Nicotiana tabacum inflorescence: optimized extraction, chemical composition, evaluation of antioxidant activity integrating network pharmacology and molecular docking analysis Li-feng Jin, Zong-yu Hu, Wei-guan Li, Zhao-peng Luo, Yue Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8626691/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Tobacco ( Nicotiana tabacum ) inflorescence, an agricultural by-product that remains underutilized, is an excellent source of bioactive components but has received limited research interest. This study developed an ultrasound-assisted extraction (UAE) process to optimize the recovery of phenolic compounds (PCs) from this material. The phenolic profiles and antioxidant capacities were systematically evaluated across five cultivars (YY87, ZY100, K326, HD, and CB1). Optimal UAE conditions were determined as follows: ultrasound power of 335 W, liquid-solid ratio of 51 mL/g, ethanol concentration of 45%, and extraction time of 31 min, achieving a PCs yield of 24.33 ± 0.54 mg GAE/g DW. The model's reliability was confirmed by the close match between the predicted and experimental values. Considerable variations in PCs content, phenolic component, and antioxidant activities were found among cultivars, with K326 and HD showing the highest levels. Rutin, chlorogenic acid, and neochlorogenic acid were strongly associated with ABTS, DPPH, and hydroxyl radical scavenging activities, as well as reducing power. Network pharmacology analysis revealed that nine PCs target 71 oxidative stress-associated genes. Protein-protein interaction (PPI) network analysis identified AKT1 and TNF as central hub targets. Molecular docking confirmed stable binding interactions between these key PCs and the targets, with binding energies ≤ -7.0 kcal/mol. These findings provide a comprehensive experimental basis for the utilization of tobacco inflorescence PCs as a prospective source of antioxidants. Nicotiana tabacum inflorescences ultrasound-assisted extraction UHPLC-MS/MS antioxidant activity network pharmacology molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Oxidative stress is a pathological condition causing from the disequilibrium between the overproduction of reactive oxygen species (ROS), and the insufficient capacity of the antioxidant defense system, and can inflict substantial cellular damage via mechanisms such as lipid peroxidation, protein modification, and DNA strand breaks (Zhu et al., 2023 ). The major chronic diseases, encompassing cardiovascular disorders, diabetes, neurodegenerative conditions, and various cancers, is significantly underpinned by this imbalance (Cheung and Vousden, 2022 ; Jomova et al., 2023 ). Therefore, counteracting oxidative stress represents a pivotal therapeutic strategy. Plant-derived phenolic compounds (PCs) serve as potent exogenous antioxidants, capable of directly scavenging free radicals, chelating pro-oxidant metals, and modulating redox-sensitive signaling pathways (Rathod et al., 2023 ). Consequently, PCs exhibiting antioxidant potential have become a major focus of nutraceutical and pharmaceutical research. Elucidating the protective mechanisms of plant phenolics against oxidative stress is increasingly recognized as a multifaceted challenge that transcends traditional single-target paradigms, necessitating the adoption of more advanced and holistic approaches (Nogales et al., 2022 ). Network pharmacology is now widely employed as a methodology that combines systems biology, bioinformatics, and polypharmacology for unraveling the "multi-compound/multi-target/multi-pathway" of natural products (Patel et al., 2024 ). This integrative way facilitates the recognition of core targets and key pathways by constructing comprehensive compound-target-pathway networks, thereby providing a systematic overview of the interactions and biological effects of complex natural extracts. As a key computational tool, molecular docking may predict atomic level interactions between bioactive compounds (PCs, flavonoids, polysaccharides, etc.) and their protein targets, providing important support for network pharmacology. By predicting and validating binding affinity and stability, molecular docking not only may confirm the feasibility of interactions identified through network analysis, but also offer detailed insights into structure-activity relationships (Pinzi and Rastelli, 2019 ). The combination of macroscopic network analysis with microscopic molecular docking may enable a slightly broader understanding of the mechanisms underlying the biological activities of plant PCs. The synergistic application of network pharmacology and molecular docking has proven highly effective in deciphering the mechanisms of complex natural extracts, providing a robust foundation for developing novel therapeutic strategies (Zeng et al., 2025 ). Thus, this integrated methodology may enhance the efficiency of target identification and validation while facilitating the rational design of multi-target therapeutics, a promising approach for addressing the complexity of chronic diseases. As a varied group of secondary metabolites, PCs have been detected in different plant organs, including flowers, leaves, fruits, roots, etc. (Bekavac et al., 2025 ; Gil-Martín et al., 2022 ). These PCs serve vital functions in plant growth and development, including UV protection and defense against pathogens, while also exhibit diverse bioactivities, like antioxidant, anti-inflammatory, and antibacterial properties (Shahidi and Ambigaipalan, 2015 ). Owing to these health-promoting effects, the efficient recovery of PCs is highly desirable, driving the need for advanced extraction techniques. Ultrasound-assisted extraction (UAE) has become a prominent way to extract PCs owing to its benefits over traditional methods, like faster kinetics, higher efficiency, lower solvent use, and reduced energy consumption (Amoriello et al., 2025 ; Ivanović et al., 2021 ). The effectiveness of UAE primarily stems from acoustic cavitation, where collapsing microbubbles produce strong shear forces, localized heating, and high pressure. This phenomenon disrupts plant cell structures, thereby accelerating the release of intracellular PCs, and further elevating mass transfer (Avdović et al., 2025 ). To achieve optimal performance, key parameters including solvent composition, liquid-solid ratio (LSR), ultrasound power, and extraction time must be carefully optimized, as they greatly affect the yield and quality of the extracted PCs. Response Surface Methodology (RSM), is commonly applied for the efficient optimization of these parameters (Liao et al., 2022 ). RSM may enable the assessment of the interactions among multiple parameters, and helps identify optimal conditions for UAE, thereby improving the efficiency and efficacy during the extraction process of plant bioactive compounds. Despite the well-documented benefits and widespread application of UAE in phenolic extraction from various plant materials, its use for recovering phenolics from underutilized agricultural waste remains relatively underexplored. Tobacco ( Nicotiana tabacum L.) is both a crucial traditional crop and an exemplary model plant for biological research. Throughout its lifecycle, from field harvesting to final processing, approximately 30% of the total biomass is rendered waste (An et al., 2025 ; Banožić et al., 2021 ; Manthos and Tsigkou, 2025 ). Tobacco inflorescence, a byproduct of the agricultural practice of "topping" (which involves removal of the floral axis and lateral buds to redirect nutrients toward leaf development, enhancing leaf quality and yield), represents a largely underutilized source of bioactive compounds (Shi et al., 2025 ). This practice results in vast quantities of waste, the disposal of which poses an environmental burden and represents a missed opportunity for resource utilization (An et al., 2025 ; Banožić et al., 2019 ). Moreover, tobacco inflorescences and leaves were used as an abundant source of bioactive ingredients, including PCs and alkaloids, which possess significant antioxidant and anti-inflammatory activity (Zhang et al., 2012 ; Manthos and Tsigkou, 2025 ), holding considerable potential for applications in cosmeceuticals, nutraceuticals, and pharmaceuticals. However, comprehensive studies integrating optimized extraction techniques, detailed phenolic profiling, and mechanistic insights into their health benefits are conspicuously lacking. Herein, a UAE protocol of PCs extraction from tobacco inflorescence was firstly established and optimized using RSM. The resulting PCs extracts from five cultivars (YY87, ZY100, K326, HD, CB1) were then comprehensively evaluated for extraction yield, antioxidant activity, and phenolic composition. Further, the core antioxidant mechanisms of the main PCs were analyzed through network pharmacology and molecular docking. These studies contribute to establishing a scientific foundation for utilizing tobacco inflorescence and new perspectives on its bioactive components. 2. Materials and methods 2.1. Materials and chemicals Tobacco (Nicotiana tabacum) cultivars used in this study included ‘Yunyan 87’ (YY87), ‘Zhongyan 100’ (ZY100), ‘K326’, ‘Honghuadajinyuan’ (HD), and ‘Cuibi 1’ (CB1) (Fig. 1 ). The inflorescences from these cultivars were collected from an experimental field in Xiangcheng County, Xuchang City, China (33°57′49″N, 113°27′43″E). After harvesting, the plant materials were subjected to dry at 65°C, and comminuted into a fine particulate matter utilizing an electric grinding apparatus (Model FW-80; Taisite, Tianjin, China) and fractionated using a 40-mesh sieve. These samples were stored at room temperature for further analysis. 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) were procured from BASF Bioscience Co., Ltd. (Hefei, China). Gallic acid, vitamin C, Folin–Ciocalteu reagent, and Trolox were acquired from Macklin Biochemical Co., Ltd. (Shanghai, China). Rutin, chlorogenic acid, cryptochlorogenic acid, neochlorogenic acid, caffeic acid, scopoletin, quercetin, kaempferol, and ferulic acid (purity ≥ 98%) were supplied by Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China). 2.2. Experimental design of PCs extraction 2.2.1. Single-factor tests The influences of ethanol content, LSR, extraction time, ultrasound power, and extraction temperature on the total phenolic content (TPC) was tested and analyzed (Table S1 ). Briefly, a 1.0 g aliquot of a blended powder comprising five tobacco cultivars in equal proportions in conical flasks was subjected to UAE via an ultrasonic apparatus. The extraction process adhered to the conditions outlined in Table S1 . The extracts were centrifuged at 8000 rpm for 5 min. Following this procedure, the resultant supernatant was obtained for subsequent using. 2.2.2. Box-Behnken design (BBD) experiments According to the single-factor test results, a BBD model was carried out using four key factors: ethanol concentration (A), LSR (B), extraction time (C), and ultrasound power (D). The response variables were the PCs yield from tobacco inflorescence, with factor levels provided in Table S2. All experiments were conducted in triplicate. The model's adequacy and goodness were evaluated by ANOVA. Its predictive performance was evaluated utilizing the coefficient of determination (R 2 ) and adjusted R 2 (adjR 2 ). A series of validation experiments was then performed to authenticate the robustness of the statistical methodology and delineate the optimum parameters for the extraction of tobacco inflorescence PCs. 2.2.3 Model validation Using the BBD results, the factor levels were optimized to achieve the maximum predicted TPC. The resulting optimaal parameters were then applied to experimentally validate the model's effectiveness. The experimental and predicted yields were compared and assessed the accuracy and reliability of the obtained model. 2.3 Determination of total phenolic content (TPC) TPC analysis was performed in accordance with the Folin-Ciocalteu method (Xu et al., 2022 ). Initially, 400 µL sample was combined with 400 µL Folin-Ciocalteu reagent, 1.2 mL of 7.5% Na₂CO₃ solution, and 2 mL H 2 O. Following a 60 min incubation in the dark, the absorbance was determined at 765 nm. Ultimately, the TPC was quantified by comparison with a gallic acid calibration curve and expressed as mg gallic acid equivalents (GAE)/g dry weight (DW). 2.4 Preparation of PCs extracts PCs were extracted from five tobacco inflorescences (YY87, ZY100, K326, HD, CB1) following the optimized protocol. The resulting extraction solutions were subjected to filtration and subsequently concentrated via rotary evaporation (RE-52A, Yarong Instrument Co., Ltd., Shanghai, China). These extracts were dried under vacuum at 50°C, and the dried samples were stored at 4°C for using. 2.5. UHPLC-MS/MS analysis For sample pretreatment, an appropriate amount of sample was weighed, added with 50% methanol, and extracted by ultrasonication for 30 min. These mixtures were centrifuged by 12000 rpm for 10min, and the supernatants were obtained and separated using a Shimadzu Nexera X2 LC-30AD UHPLC system (Shimadzu, Kyoto, Japan). The mobile phases included 0.1% formic acid in aqueous solution (A) and 0.1% formic acid in acetonitrile (B). Chromatographic separation of these samples was obtained using a Waters ACQUITY UPLC BEH C18 column (1.7 µm, 2.1 mm × 100 mm) at 40°C. The autosampler was operated at 4°C to preserve sample integrity. The gradient elution program was as following: 5–70% solvent B over 0–17.5 min, 70–90% solvent B over 17.5–18.5 min 90% solvent B from 18.5–20 minutes, 90–5% solvent B over 20–20.5 min, and 5% solvent B from 20.5–25 min. The flow rate was controlled at 300 µL/min, and the volume of injection was 1 µL. Mass spectrometric analysis was conducted using a 5500 QTRAP instrument (AB SCIEX) in negative electrospray ionization (ESI) mode. The parameters of ESI were configured as follows: ion spray voltage of -4500 V, source temperature of 550°C, ion source gas 1 (GS1) of 55 units, ion source gas 2 (GS2) of 55 units, and curtain gas (CUR), 35 units. Quantify of nine PCs was accomplished according to standard curves, with detection in MRM mode. The specific MRM transitions and calibration data are detailed in Table S3. 2.6. Fourier-transform infrared (FTIR) Analysis Dried PCs extract powders (1 mg) from the five varieties (YY87, ZY100, K326, HD, and CB1) were individually mixed with KBr (200 mg) and pressed into pellets. FTIR spectra were recorded on a Thermo Nicolet iS5 spectrometer over the wavenumber range of 4000–400 cm − 1 . The acquisition parameters included: 32 scans per spectrum at a 4 cm − 1 resolution, with a mirror velocity of 0.4747 cm/s and an aperture setting of 100.00. 2.7. Assay of Antioxidant Activity 2.7.1. ABTS radical scavenging activity ABTS radical scavenging activity was assessed using previously reported method (Zhang et al., 2024 ). Briefly, ABTS of 7.0 mM and potassium persulfate of 2.45 mM (1:1, v/v) were mixed and then stewed 16 h in the dark. The mixture was diluted up to an OD734 nm of 0.700 ± 0.02, representing the working solvent. Subsequently, the reaction mixtures included 200 µL PCs extracts and 3 mL ABTS working solvent. Following a 6-min incubation in the dark, OD734 values were recorded. Data was analyzed though the following formula: Scavenging activity (%) = (1 − A/A 0 ) × 100, where A 0 represents OD734 values of blank control, and A expressed the OD734 values of PCs extracts. Vitamin C (Vc) and Trolox were set as positive controls. 2.7.2. DPPH radical scavenging activity DPPH radical scavenging activity was determined using Wang et al. method (2023). Briefly, the reaction mixtures included PCs extracts of 100 µL, DPPH solution of 2 mL (0.06 mM) and H 2 O of 900 µL. Following vortexing, these mixtures were subjected to a 30- min incubation period in the dark, and the values of OD517 were recorded. The scavenging rate was analyzed though the following formula: (1 − A/A 0 ) × 100, where A 0 represented OD517 values of blank control, and A represented OD517 values of PCs extracts. Vc and Trolox were set as positive controls. 2.7.3. Hydroxyl (·OH) radical scavenging activity Hydroxyl radical scavenging activities were measured based on the method of Ma et al. ( 2024 ). In brief, the reaction mixture contained equal volumes of PC extract, 3 mM FeSO 4 , 6 mM salicylic acid, and 3 mM H 2 O 2 ., and then were kept in the dark for 15 min to incubate. The values of OD510 were recorded. The scavenging rate (%) analyzed though the following formula: (1 – A/A 0 ) × 100%, where A 0 represents OD517 values of blank control, and A represents the OD517 values of PCs extracts. Vc and Trolox were set as positive controls. 2.7.4. Reducing power (RP) assay Reducing power was assessed based on the method of Song et al. ( 2024 ). The reaction mixture, comprising 500 µL of PCs extracts, 500 µL of phosphate-buffered saline (PBS; 0.2 M, pH 6.6), and 500 µL of 1% potassium ferricyanide, was incubated for 20 min at 50°C. The reaction was subsequently terminated by the addition of 500 µL of 10% trichloroacetic acid, followed by centrifugation of the mixture. 1.5 mL of the resultant supernatant was combined with 1.5 mL of 0.02% ferric chloride solution. After a 10-min incubation in the dark, the OD700 values were recorded. Vc and Trolox were set as positive controls. 2.8. Network pharmacology analysis The canonical SMILES of rutin, chlorogenic acid, scopoletin, quercetin, neochlorogenic acid, caffeic acid, cryptochlorogenic acid, ferulic acid, and kaempferol were retrieved from https://pubchem.ncbi.nlm.nih.gov . Their potential targets were predicted using the SwissTargetPrediction database ( http://www.swisstargetprediction.ch/ ). Targets related to "oxidative stress" were collected from GeneCards ( http://www.genecards.org ). The intersection between the compound targets and the disease targets was identified and visualized as a Venn diagram using the Bioinformatics.com.cn platform ( http://www.bioinformatics.com.cn/ ). A compound-target-disease network was then constructed with Cytoscape 3.9.1. Furthermore, the overlapping targets were used to generate a protein-protein interaction (PPI) network via the STRING database ( https://string-db.org/ ) (Szklarczyk et al., 2021 ), which was subsequently imported into Cytoscape for topological analysis based on Betweenness Centrality (BC), Closeness Centrality (CC), and Degree Centrality (DC) (Shannon et al., 2003 ). Finally, functional enrichment analysis for Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed on the intersecting targets using the DAVID database ( https://david.ncifcrf.gov/ ), with a p -value < 0.05 considered statistically significant. 2.9. Molecular docking Molecular docking simulations were conducted utilizing AutoDock Vina to evaluate the binding affinities of the components to key target proteins. The 2D structures of 9 PCs were retrieved in SDF format from https://pubchem.ncbi.nlm.nih.gov , and imported into ChemBio3D for energy minimization. The minimized structures were then processed in AutoDockTools v1.5.7 to add hydrogen atoms, compute Gasteiger charges, assign atomic charges, and define rotatable bonds before saving them in PDBQT format. The key target proteins, including tumor necrosis factor (TNF) and protein kinase B (AKT1), were searched from Protein Data Bank (PDB) ( https://www.rcsb.org/ ), with priority given to human derived structures co crystallized with ligands exhibiting high structural similarity to the compounds being docked and selecting complexes with higher resolution. These protein structures were imported into PyMOL to remove any existing ligands and water. Subsequently, the simulated molecules were loaded into AutoDockTools v1.5.7 for adding hydrogen atoms, computing and assigning charges, and designating atom types (Trott and Olson, 2010 ), followed by saving in PDBQT format. For the docking simulations, the grid box was centered on the co-crystallized ligand of each protein to ensure accurate targeting of the binding site. In cases where the co-crystallized ligand was unavailable, the grid box was centered on the reported key amino acid residues involved in ligand binding. The grid box was arranged to span the whole binding site while retaining the default parameters for other settings to maintain consistency and accuracy in the docking process. Finally, the interaction modes of the compounds with the target proteins were characterized using PyMOL visualization software. 2.10. Statistical analysis Data were collected in triplicate, with statistical significance set at a p-value < 0.05. The analysis was performed using SPSS 27.0 (IBM® Corporation, San Jose, CA, USA) with one-way ANOVA followed by Tukey’s test. Graphical representations were generated using Origin 2024 (OriginLab Corporation, Northampton, MA, USA). UHPLC-MS/MS data were calculated in Analyst 1.6.3 software (SCIEX, Framingham, MA, USA) to obtain chromatographic peak areas and retention times. Identification of PCs were carried out based on the retention times and peak profiles of reference standards. 3. Results and discussion 3.1. Single-factor experiment 3.1.1. Effects of ethanol concentration on TPC Ethanol and water are extensively employed as solvents for PCs extraction. Generally, ethanol may enhance the solubility of PCs, while H 2 O may possibly enhance their desorption from the sample matrix. The ratio of these solvents significantly influences the extraction yield of plant PCs (Metrouh-Amir et al., 2015 ). As shown in Fig. 2 A, TPC showed firstly increase and then decrease with rising ethanol contents. The maximum TPC value, 21.08 ± 0.63 mg/g, was reached at 40% ethanol concentration. However, a decline in yield was recorded when the ethanol level exceeded 60%, a trend consistent with findings reported for PCs extraction from Limonium sinuatum flowers (Xu et al., 2017 ). This phenomenon might due to the fact that lower ethanol concentrations may disturb cell membranes, and the high proportion of water in the solvent mixture facilitates the swelling of plant material, enhancing solution release. Furthermore, increasing ethanol levels may change the polarity of the extraction solution, which in turn may affect the dissolving capacity of PCs, thereby influencing extraction efficiency. These results are consistent with the reported superiority of ethanol-water mixtures over pure solvents in extracting PCs (Drăghici-Popa et al., 2025 ). Consequently, a 40% ethanol concentration was determined as one of the optimal parameters for next extraction. 3.1.2. Effects of LSR on TPC LSR may influence the extraction yield of bioactive components and process economy (Liao et al., 2022 ). As exhibited in Fig. 2 B, TPC increased with LSR and reached a maximum of 21.10 ± 0.81 mg GAE/g DW at 40 mL/g. This improvement is likely due to enhanced solute-solvent contact and more efficient cavitation effects under UAE conditions (Ma et al., 2025 ). Beyond this point, the further rising LSR resulted in a decline in PCs yield, reaching 17.28 ± 0.81 mg GAE/g DW at 90 mL/g. The decrease may result from excessive dilution, which reduces cavitation intensity and compromises extraction kinetics. Thus, a LSR of 40 mL/g was set as one of the optimal parameters. 3.1.3. Effects of ultrasound power on TPC Ultrasound power can impact the PCs yield from plant materials though altering the vibration amplitude of the extraction solvent (Huo et al., 2025 ). As shown in Fig. 2 C, the TPC gradually increased from 16.59 ± 0.27 mg GAE/g DW to 22.54 ± 0.41 mg GAE/g DW up to ultrasound power of 300 W. However, the extraction yield began to decline when the ultrasound power exceeded 350 W. Within a specific range, higher ultrasound power intensifies ultrasonic effects, leading to more cavitation bubbles and stronger implosive shear forces. These forces facilitate the disruption of cell structures, accelerate mass transfer, and enhance these PCs release (Yusoff et al., 2022 ). Conversely, excessive ultrasound power may cause overheating and excessive mechanical stress, potentially degrading the target compounds and thus reducing extraction efficiency (Kuvendziev et al., 2024 ). Additionally, under high ultrasound power, cavitation bubbles may coalesce into larger ones, which attenuates the implosion effect (Hu et al., 2024 ). These phenomena may due to the destruction of structural integrity of PCs, and further diminishing their bioactivity. Thus, 300 W of ultrasound power was determined as one of the optimal parameters to maximize extraction efficiency. 3.1.4. Effects of extraction time on TPC Extraction time is a pivotal parameter among extraction parameters, significantly influencing both the recovery of PCs from plant materials and the economic viability during the extraction process (Zheng et al., 2022 ). As exhibited in Fig. 2 D, TPC gradually raised with the rising time from 5 to 25 min, reaching its peak with 22.21 ± 0.79 mg GAE/g DW. This trend reflects the gradual release of PCs into the solvent, aided by ultrasonic cavitation that disrupts cell walls and enhances mass transfer (Sharma and Dash, 2022 ). Beyond 25 min, the yield stabilized briefly before declining slightly with further prolongation. The reduction may result from a diminished concentration gradient across cell membranes and potential degradation of PCs under prolonged ultrasonic conditions involving elevated temperature and reactive gases (Wang et al., 2023 ). Thus, extraction time of 25 min was set as one of the optimal parameters for next studies. 3.1.5. Effects of extraction temperature on TPC Extraction temperature may influence the extraction process by altering the thermodynamic driving forces for mass transfer (Palma et al., 2021 ). As illustrated in Fig. 2 E, the PCs yield showed a gradual raise with the rising extraction temperature up to 30°C, reaching a maximum of 22.41 ± 0.46 mg/g. This improvement might be due to the increasing mass transfer and compound solubility at higher temperatures, thereby boosting extraction yield (Chia et al., 2020 ). However, higher temperature, especially when it exceeds 50°C, resulted in the decline in PCs yield (Fig. 2 E). This decrease may due to the degradation of heat-sensitive PCs at higher extraction temperatures (Nipornram et al., 2018 ). Furthermore, higher temperatures are associated with increased energy consumption and operational costs. Therefore, considering both yield and process economics, 30°C was set as one of the optimal extraction parameters. 3.2. Optimizing the extraction by RSM Since extraction yield of PCs is affected by multiple extraction parameters and their interactions, it is essential to systematically evaluate these variables to improve efficiency (Gil-Martín et al., 2022 ). As observed in Fig. 2 E, the effect of extraction temperature was less pronounced compared to the other parameters. Therefore, a BBD model was established using four extraction parameters of ethanol concentration (20–60%), LSR (20–60 mL/g), ultrasonic power (200–400 W), and extraction time (10–40 min). Meanwhile, TPC was set as the response values. The variable levels were detailed in Table S4, obtaining 29 experimental groups. To optimize the yield of PCs, a second-order polynomial model was developed to elucidate the corrections between the independent variables and the corresponding response values (Eq. 1). Y TPC = 23.91 + 1.00A + 1.21B + 0.87C + 0.43D − 0.70AD + 0.92CD − 2.78A 2 − 0.93B 2 − 1.86C 2 − 2.08D 2 , (Eq. 1), where Y TPC denoted the predicted TPC, A, B, C, and D represented ethanol concentration, LSR, ultrasound power, and extraction time, respectively. As exhibited in Table 1 , the effects of experimental parameters on TPC followed the order: LSR > ethanol concentration > extraction time > ultrasound power. All factors showed p -values below 0.05, confirming their significant impacts on TPC. Additionally, Eq. 1 and Table 1 revealed that the interaction effects between ethanol concentration (A) and ultrasound power (D), and between extraction time (C) and ultrasound power (D), are significant ( p < 0.05). Table 1 Analysis of variance (ANOVA) for RSM and regression modeling of BBD. Source Sum of Squares df Mean Square F- value p -value Model 124.06 14 8.86 23.21 < 0.0001 a A-Ethanol concentration 12 1 12 31.43 < 0.0001 a B-Liquid-solid ratio 17.47 1 17.47 45.77 < 0.0001 a C-Extraction time 8.99 1 8.99 23.54 0.0003 a D-Ultrasound power 2.17 1 2.17 5.69 0.0318 a AB 0.277 1 0.277 0.7256 0.4087 b AC 1.73 1 1.73 4.53 0.0514 b AD 1.97 1 1.97 5.16 0.0394 a BC 0.2588 1 0.2588 0.678 0.4241 b BD 1.2 1 1.2 3.15 0.0977 b CD 3.39 1 3.39 8.89 0.0099 a A² 50.26 1 50.26 131.63 < 0.0001 a B² 5.56 1 5.56 14.57 0.0019 a C² 22.39 1 22.39 58.65 < 0.0001 a D² 28.05 1 28.05 73.47 < 0.0001 a Residual 5.35 14 0.3818 Lack of Fit 3.01 10 0.301 0.5155 0.8197 b Pure Error 2.34 4 0.5838 Cor Total 129.41 28 Std. Dev. 0.6179 Mean 20.74 C.V. % 2.98 Adeq Precision 13.9055 R 2 0.9587 Adjusted R 2 0.9174 Pred R 2 0.8378 YTPC = 23.91 + 1.00A + 1.21B + 0.87C + 0.43D − 0.70AD + 0.92CD − 2.78A 2 − 0.93B 2 − 1.86C 2 − 2.08D 2 Note C.V.% coefficient of variation. df = degrees of freedom. a Significant at p < 0.05. b Not significant. The model exhibited strong predictive ability, as showed by a higher similarity of predicted and experimental yields (R 2 = 0.9587) (Ferreira et al., 2007 ). ANOVA further validated the model, showing good consistency with the experimental results. The adjusted determination coefficient (AdjR 2 = 0.9174) indicated that the model may express 91.74% of the response variability, demonstrating an excellent fit. The highly significant p-values of all models ( p 0.05) collectively attest to the model’s remarkable accuracy and reliability in predicting the TPC yield from tobacco inflorescences. The statistical analysis further underscores that the model terms may exert a substantial influence on the response variable, and capture the relationships between the independent variables and the response values. These findings collectively bolster the model’s reliability and robustness in forecasting outcomes based on the specified variables. Moreover, statistical analysis also revealed that linear terms (A, B, C, D) and quadratic terms (AD, CD, A 2 , B 2 , C 2 , D 2 ) had significant effects on TPC ( p < 0.05). This indicated that not only the individual factors but also their interactions and squared terms played crucial roles in determining the TPC. The significant linear terms suggested that changes in these factors directly influenced the TPC, while the significant quadratic terms highlighted the optimal conditions for maximizing TPC may lie within specific ranges of these variables. The 3D response surfaces vividly depicted the interactions among parameters and their profound impacts on extraction yield of PCs. A more pronounced curvature of the surface was indicative of more substantial interactive effects between the variables (Sun et al., 2023 ). Correspondingly, the contour plots, which serve as 2D projections of the response surfaces, mirrored the interplay between pairs of factors. The elliptical contour shape suggested that there was a significant interaction between the variables (Xu et al., 2022 ). As exhibited in Fig. 3 , the TPC initially showed a positive correlation with each of the four parameters, steadily increasing before peaking and subsequently declining. This trend aligned with the observations from the single-factor experiments (Fig. 2 ). Overall, these findings confirmed the model's capacity to accurately represent the complex interactions among variables and predict outcomes. This validation underscored the model's value as a robust tool for optimizing extraction parameters, and maximizing the TPC yield from tobacco inflorescences. 3.3. Validation and scale-up experiments of PCs yield The best extraction conditions were pinpointed by means of 3D response surface analysis and the regression model application, namely: ethanol concentration 44.7%, LSR 55.4 mL/g, extraction time 31.1 min, and ultrasound power 333.4 W. Under these meticulously determined parameters, the model predicted TPC of 24.81 mg GAE/g DW for tobacco inflorescence. For practical application, the conditions were slightly adjusted to more readily achievable values: ethanol concentration of 45%, LSR of 55 mL/g, extraction time of 31 min, and ultrasound power of 335 W. Under these adjusted settings, the experimentally obtained TPC value was 24.33 ± 0.54 mg GAE/g DW, which exhibited excellent concordance with the predicted value. The RSD of 2.22% further underscored the high predictive accuracy of the regression model for PCs extraction from tobacco inflorescences. Under these optimized conditions, the TPC values for different tobacco cultivars were as follows: YY87 (24.50 ± 0.49 mg GAE/g DW), ZY100 (21.27 ± 0.42 mg GAE/g DW), K326 (28.15 ± 1.96 mg GAE/g DW), HD (28.73 ± 0.66 mg GAE/g DW), and CB1 (17.68 ± 0.88 mg GAE/g DW), as depicted in Fig. 4 A. The significant differences observed among these values highlighted the cultivar-dependent of TPC in tobacco inflorescences, indicating that the TPC can vary substantially depending on the specific cultivar being examined. To evaluate the extraction performance under larger-volume conditions and provide valuable insights for potential industrial application, a scale-up extraction was conducted at a 500 mL volume based on the established regression model and the optimized experimental parameters. Under these optimized parameters, TPC achieved 23.81 ± 0.39 mg GAE/g DW in the scale-up experiment, which is in outstanding accordance with both the predicted result and the small-scale trial data. As illustrated in Fig. 4 A, the TPC values for YY87, ZY100, K326, HD, and CB1 obtained in the scale-up experiment were also in accordance with those from small-scale tests. These results collectively suggested the robustness and reliability of this model in guiding the optimal extraction processes for tobacco inflorescences PCs, paving the way for efficient and scalable industrial applications. 3.4. Composition analysis of PCs Antioxidant capacity is commonly closely associated with the content and profile of PCs, with notable variations observed among different cultivars (Zhao et al., 2014 ). As detailed in Table 2 , nine main PCs were identified and quantified (Table 2 ). The identification was confirmed according to retention times, mass spectral fragmentation patterns, and collision energies (Table S3). Overall, the PCs were found in descending order of abundance: rutin > chlorogenic acid > cryptochlorogenic acid, neochlorogenic acid > caffeic acid, scopoletin > quercetin > kaempferol and ferulic acid. Rutin emerged as the most abundant PC in the tobacco inflorescences, a finding that aligns with previous reports on tobacco (Banožić et al., 2021 , 2019 ). The content of rutin across the five cultivars ranged from 64676.69 ± 1793.84 to 85572.57 ± 3480.74 µg/g, while ferulic acid content varied between 4.18 ± 0.13 and 7.90 ± 0.15 µg/g. Specifically, cultivar K326 displayed the highest levels of rutin (85572.57 ± 3480.74 µg/g) and neochlorogenic acid (1239.59 ± 18.07 µg/g). Cultivar HD contained the highest concentrations of chlorogenic acid (7715.19 ± 230.51 µg/g), cryptochlorogenic acid (1368.54 ± 37.30 µg/g), caffeic acid (311.62 ± 6.34 µg/g), and ferulic acid (7.90 ± 0.15 µg/g). Conversely, cultivar YY87 accumulated the highest amounts of scopoletin (273.90 ± 31.63 µg/g) and kaempferol (9.16 ± 0.11 µg/g). These results clearly demonstrated significant quantitative differences in individual PCs among five cultivars, indicating that the phenolic composition profile is strongly cultivar-dependent. This significant variation likely originated from inherent genetic differences among these cultivars, which influenced the regulation of biosynthetic pathways responsible for PCs production (Chen et al., 2023 ). Understanding such compositional variations is crucial for elucidating the potential antioxidant capacity and other intrinsic biological activities unique to inflorescences of different tobacco cultivars. Such insights can inform the selection and utilization of specific cultivars for targeted PCs applications, leveraging their distinct phenolic profiles to maximize desired health benefits and functional properties. Table 2 Identification and quantification of major phenolic compounds in the extracts of tobacco inflorescences. No. name YY87 (µg/g) ZY100 (µg/g) K326 (µg/g) HD (µg/g) CB1 (µg/g) 1 Rutin 73674.59 ± 1063.58b 68290.83 ± 194.44c 85572.57 ± 3480.74a 74375.34 ± 2485.83b 64676.69 ± 1793.84d 2 Chlorogenic acid 3008.79 ± 83.47d 4365.17 ± 53.22c 5623.43 ± 171.06b 7715.19 ± 230.51a 5718.22 ± 43.83b 3 Cryptochlorogenic acid 1061.16 ± 91.27bcd 988.08 ± 10.47d 1006.45 ± 2.63c 1368.54 ± 37.30a 1131.29 ± 23.71b 4 Neochlorogenic acid 934.50 ± 30.03cd 901.25 ± 14.36d 1239.59 ± 18.07a 1074.57 ± 29.58b 987.77 ± 25.59c 5 Caffeic acid 169.35 ± 18.51c 75.98 ± 0.55e 243.89 ± 1.99b 311.62 ± 6.34a 110.85 ± 4.09d 6 Scopoletin 273.90 ± 31.63a 76.70 ± 0.66e 166.77 ± 1.84b 108.87 ± 2.47d 138.28 ± 2.48c 7 Quercetin 46.71 ± 3.70a 48.67 ± 2.35a 35.33 ± 3.13b 47.70 ± 1.52a 45.26 ± 2.00a 8 Kaempferol 9.16 ± 0.11a 4.02 ± 0.05e 6.87 ± 0.13b 4.72 ± 0.07d 4.92 ± 0.14c 9 Ferulic acid 4.80 ± 0.39c 4.18 ± 0.13d 5.61 ± 0.06b 7.90 ± 0.15a 4.74 ± 0.09c a-e, means data in the same row with different letters are significantly different ( p < 0.05). 3.5. FTIR analysis FTIR spectroscopy a powerful and widely utilized analytical technique for the organic functional groups of PCs and bioactive constituents and for revealing their key structural features (Grasel et al., 2016 ). It may effectively detect characteristic stretching and bending vibration bands corresponding to hydroxyl and carboxyl groups (Patle et al., 2020 ). FTIR spectra of PCs extracts from five tobacco cultivars (YY87, ZY100, K326, HD, CB1) were presented in Fig. 4 B. Despite subtle spectral variations, five PCs extracts exhibited seven similar characteristic absorption peaks, indicating a comparable phenolic composition across the different cultivars. The band of around 3380 cm − 1 may be assigned to O–H stretching vibrations, thereby verifying the existence of phenolic hydroxyl groups. The peak of 2930–2935 cm − 1 is indicative of C–H stretching vibrations. Bands detected in the 1610–1619 cm − 1 and 1387–1405 cm − 1 can be attributed to asymmetric and symmetric stretching vibrations of carboxylate groups, respectively. The spectral region below 1100 cm − 1 points to the presence of aromatic C–H bending vibrations and C–O vibrations (Iftikhar et al., 2020 ). These finding suggested that the fundamental chemical structures of the phenolic compounds are largely conserved among five cultivars. The spectral similarities suggested the presence of common functional groups among the cultivars. Minor peak shifts, such as the band at 1405 cm − 1 in CB1 compared to 1387–1390 cm − 1 in the other cultivars, may reflect variations in the carboxylate group environment or differences in hydrogen bonding patterns (Fig. 4 B). Overall, these results not only exhibited the existence of key functional components in PCs from tobacco inflorescence, but also provided insights into the subtle structural differences that may influence the biological activities of these compounds. 3.6. In vitro antioxidant activity Earlier reports have highlighted the remarkable radical scavenging ability of PCs and their crucial role in ROS scavenging (Eghbaliferiz and Iranshahi, 2016 ; Yusoff et al., 2022 ). To evaluate antioxidant activity of plant-derived bioactive components, ABTS, DPPH, and hydroxyl radical scavenging capacities, as well as RP, which are widely applied in assessing various plant extracts (Wang et al., 2023 ; Zhang et al., 2024 ). As depicted in Fig. 5 (A-H), the present results consistently demonstrated a significant concentration-dependent increase in antioxidant activity for all PCs extracts. ABTS radical scavenging assay revealed the following order of efficacy: K326, HD > YY87 > CB1 > ZY100 (Fig. 5 A). This ranking was further quantified by IC 50 values (Fig. 5 B), where ZY100 exhibited the highest IC 50 (0.88 ± 0.02 mg/mL), indicating the weakest activity, while K326 (0.51 ± 0.01 mg/mL) and HD (0.51 ± 0.03 mg/mL) showed the lowest IC 50 values, signifying the strongest scavenging ability. The DPPH radical scavenging assay exhibited a similar trend across the five PCs extracts (Fig. 5 C). Consistent with the ABTS results, ZY100 displayed the highest DPPH IC 50 value (1.89 ± 0.05 mg/mL), whereas K326 (1.12 ± 0.04 mg/mL) and HD (1.11 ± 0.03 mg/mL) demonstrated the strongest activity with the lowest IC 50 values (Fig. 5 D). In contrast, the ·OH radical scavenging activity yielded different results (Fig. 5 E). While all extracts demonstrated measurable ·OH scavenging capacity, their overall effectiveness was notably lower compared to their performance those of in the ABTS and DPPH assays. Furthermore, the activity ranking differed: HD > K326 > YY87 > ZY100 > CB1. Quantification confirmed CB1 as the least active extract against ·OH radicals (highest IC 50 = 5.18 ± 0.07 mg/mL), with HD being the most effective (lowest IC 50 = 2.90 ± 0.11 mg/mL) (Fig. 5 F). This distinct pattern and reduced efficacy towards ·OH radicals likely reflected differences in the specific phenolic profiles (composition and concentrations) among these five cultivars, influencing their interaction with this particular radical species. The RP values showed a trend congruent with the ABTS and DPPH results. The concentration required to achieve an absorbance of 1.0 (EC 1.0 ) was highest for ZY100 (4.06 ± 0.12 mg/mL) and lowest for K326 (2.53 ± 0.07 mg/mL) and HD (2.55 ± 0.06 mg/mL) (Fig. 5 G), confirming their superior reducing capacity (Fig. 5 H). Collectively, these results demonstrated that PCs extracts from inflorescences of five tobacco cultivars possess significant antioxidant capacity. A significant positive correlation was recorded between PCs extract concentration and activity across multiple assays, coupled with the distinct performance rankings of the cultivars, particularly the consistently high activity of K326 and HD extracts in ABTS, DPPH, and RP assays. This strongly supported the association of this antioxidant activity with their phenolic constituents. The observed variations in activity, especially the different efficacy against ·OH radicals, are likely attributable to differences in the specific composition and concentration of PCs present in inflorescences of five tobacco cultivars. 3.7. Correlation analysis and principal component analysis (PCA) Antioxidant capacity of PCs is intricately influenced by their structural features, concentrations, and interactive effects (Olszowy, 2019 ; Shahidi and Ambigaipalan, 2015 ). To elucidate the relationships among TPC, individual PCs, and antioxidant activities in tobacco inflorescence PCs extracts, Pearson correlation analysis was conducted (Fig. 6 A). Among the nine PCs identified, TPC exhibited strong positive correlations ( p < 0.01) with rutin, neochlorogenic acid, caffeic acid, chlorogenic acid, and ferulic acid (r = 0.668–0.928), as well as with antioxidant capacities (r = 0.740–0.976). The four antioxidant measures were also mutually positively correlated (r = 0.700–0.968, p < 0.01). Notably, ABTS, DPPH, and RP activities shared similar correlation patterns with individual PCs, showing positive associations with rutin, ferulic acid, caffeic acid, neochlorogenic acid, and f chlorogenic acid (r = 0.528–0.957, p < 0.05). However, a negative correlation with quercetin was observed (r = − 0.525 to − 0.533, p < 0.05). In contrast, ·OH scavenging activity was positively correlated with chlorogenic acid, ferulic acid, caffeic acid, and cryptochlorogenic acid (r = 0.560–0.865, p < 0.05). As shown in Fig. 6 B, PCA further visualized the relationships among samples based on TPC, PCs profiles and antioxidant activities. The first two principal components explained 82.8% of the total variance (PC1 of 59.7%, and PC2 of 23.1%). PCs extracts from inflorescences of K326 and HD were closely associated with higher levels of TPC, antioxidant activities, and most PCs, consistent with their high phenolic contents as determined by UHPLC-MS/MS. The distinct correlation patterns observed for different antioxidant assays further emphasize the importance of considering the specific types and concentrations of PCs when evaluating their bioactivity. Moreover, rutin, chlorogenic acid, and neochlorogenic acid were identified as key antioxidants in PCs extracts of tobacco inflorescence, which will help to underscore the significant role of these compounds in contributing to the overall antioxidant capacity derived from PCs extracts of tobacco inflorescence, and offer valuable insights for future research. 3.8 Network pharmacology analysis PCs primarily exert antioxidant effects through two mechanisms: direct free radical scavenging or by modulating key proteins in cellular signaling pathways. This dual action inhibits ROS generation and regulating antioxidant defense systems (Kan et al., 2023 ). In this study, network pharmacology analysis showed that 460 potential targets are predicted for nine main PCs of tobacco inflorescence, which were consolidated to 186 unique targets after deduplication. In parallel, 1,025 oxidative stress-related disease targets (Score > 10) were retrieved from GeneCards. Intersection analysis revealed 71 shared targets (Fig. 7 A), suggesting an important role in adjusting the antioxidant effects of PCs extract. The compound-target interaction network demonstrated complex multi-component, multi-target relationships (Fig. 7 B), consistent with the synergistic mechanism characteristic of botanical formulations (Hong et al., 2021 ). PPI network analysis and topological screening (with criteria of BC ≥ 0.0119, CC ≥ 0.583, DC ≥ 18.73) identified 17 hub targets (Fig. 7 E). Among these, AKT1 and TNF exhibited the highest centrality, alongside PTGS2, STAT3, EGFR, and ESR1 (Fig. 7 F). These findings highlighted their pivotal roles as regulators of oxidative stress. AKT1, a serine/threonine kinase, orchestrates cellular responses to oxidative damage by activating the NRF2 pathway to enhance antioxidant enzyme expression and suppressing NLRP3 inflammasome-mediated inflammation (Linton et al., 2019 ; Wang et al., 2022 ). TNF, a master inflammatory cytokine, modulates redox balance through NF-κB signaling and ROS generation pathways (Blaser et al., 2016 ). Their central positions in the PPI network underscore their suitability as core targets for subsequent molecular docking validation. Functional enrichment analysis further elucidated the mechanistic framework. GO analysis annotated 595 terms (381 biological processes, 53 cellular components, 141 molecular functions), with top entries highlighting regulation of oxidative stress response and phosphorylation (Fig. 7 C). KEGG analysis found 150 significant pathways (p < 0.05), accompanying the 20 most enriched pathways (Fig. 7 D), revealing multi-pathway synergy. PI3K-Akt signaling pathway was the most enriched one, which governs cell survival and antioxidant responses under stress (Wikan et al., 2021 ). Other notable pathways included AGE-RAGE, insulin resistance, and fluid shear stress and atherosclerosis pathways, which converge on diabetic complications and atherosclerosis. The pathways of EGFR tyrosine kinase inhibitor resistance and endocrine resistance, which are linked to carcinogenesis associated with oxidative stress (Hong et al., 2021 ; Kan et al., 2023 ). Notably, enrichment of PI3K-Akt signaling pathway, which is coordinately regulated by AKT1 and TNF, provides a molecular basis for the extract's ability to attenuate oxidative damage through interconnected survival and inflammatory pathways (Fruman et al., 2017 ; Wikan et al., 2021 ). Collectively, these results delineated a framework of multi-compound, multi-target, multi-pathway, in which the phenolic constituents of tobacco inflorescence PCs synergistically modulate oxidative stress via core targets AKT1 and TNF, primarily through PI3K-Akt signaling, ROS-related carcinogenesis pathways, and inflammatory cascades. This systematic analysis established a pharmacological foundation for further mechanistic exploration, with AKT1 and TNF prioritized as critical nodes for experimental validation given their network centrality and established roles in oxidative stress regulation. 3.9 Molecular docking analysis Interactions between nine key PCs from tobacco inflorescence and the hub targets AKT1 and TNF were validated using molecular docking analysis, which were identified via network pharmacology as central regulators of oxidative stress. The analysis revealed that lower docking scores indicate stronger binding affinity (Fig. 8 A). The binding energy heatmap confirmed stable binding of each compound to both targets, with most exhibiting binding energies ≤ − 5.0 kcal/mol, suggesting good affinity. Notably, binding energies of ≤ − 7.0 kcal/mol denoted higher affinity (Noshad et al., 2022 ). Rutin showed the strongest binding to TNF (–7.7 kcal/mol), interacting with residues PHE-144, LYS-65, LEU-142, PRO-139, ASP-140, GLN-67 (Fig. 8 B). It also exhibited robust binding to AKT1 (–7.4 kcal/mol), engaging residues TRP-80, LEU-78, and ASP-32 (Fig. 8 C). Other compounds including kaempferol, neochlorogenic acid, cryptochlorogenic acid, quercetin, and chlorogenic acid, displayed favorable binding energies ranging from − 7.1 to − 6.0 kcal/mol to both targets. These interactions involved critical residues such as GLU-17/LYS-14 (AKT1-kaempferol) and ARG-32 (TNF-kaempferol) (Fig. 8 A and Fig. S1 ). The engagement of functional residues, such as TRP-80/ASP-32 in AKT1’s PH domain and PHE-144/LEU-142 near TNF’s receptor-binding site, suggested potential modulation of target activity. These findings are consistent with established phenolic-protein interaction models (Jin and Wei, 2024 ). They supported the notion that AKT1 and TNF are primary mediators of the antioxidant effects of tobacco inflorescence PCs. The high affinity of rutin, chlorogenic acid, and neochlorogenic acid aligns with their prior identification as key antioxidant markers ( p < 0.05), which are related with the results from ABTS, DPPH, ·OH and RP antioxidant assays (Fig. 5 ). Their structural complementarity to AKT1 and TNF provided a mechanistic basis for antioxidant activity. Critically, their abundance of these compounds in the most active extracts, for example K326 (highest rutin and neochlorogenic acid) and HD (highest chlorogenic acid, cryptochlorogenic acid, caffeic acid, ferulic acid), further underscored their functional significance. AKT1, a master regulator of the PI3K-Akt pathway, orchestrates antioxidant responses via NRF2 activation, which enhances the expression of antioxidant molecules, like HO-1/NQO1, and by suppressing NLRP3 inflammasome-mediated inflammation (Fruman et al., 2017 ). TNF modulates redox balance through NF-κB and ROS pathways (Wikan et al., 2021 ). The concerted binding of these PCs to both targets suggested a synergistic attenuation of oxidative damage via PI3K-Akt (survival) and TNF-NF-κB (anti-inflammatory) pathways. Overall, these results reinforce the framework of multi-compound, multi-target, highlighting the synergistic interactions of these PCs. Given their network centrality, established roles in oxidative stress, and validated binding affinity, AKT1 and TNF are proposed as priority candidates for experimental validation. Future studies should focus on confirming their contribution to the antioxidant efficacy of tobacco inflorescence PCs through cellular assays, western blotting, and other relevant experimental techniques. 4. Conclusions The present study employed a UAE protocol optimized through RSM for the efficient extraction of PCs from tobacco inflorescence. Marked differences were observed in the extraction yield, antioxidant capacity, and phenolic profiles among inflorescence PCs extracts from five tobacco cultivars (YY87, ZY100, K326, HD, and CB1) under the optimized conditions. Quantitative and correlation analyses revealed that rutin, chlorogenic acid, and neochlorogenic acid are key antioxidants, exhibiting considerable variation across cultivars. These PCs are strongly correlated with ABTS, DPPH, hydroxyl radical (·OH) scavenging capacities, and RP values. Network pharmacology analysis predicted that nine PCs target 71 oxidative stress-associated genes, with AKT1 and TNF identified as hub targets through topological analysis. These targets mainly play a role in regulating oxidative stress via the PI3K-Akt, ROS-mediated carcinogenesis, and inflammatory signaling pathway. Molecular docking simulations confirmed highly stable binding between these nine phenolics and the hub targets, with binding energies ≤ − 7.0 kcal/mol. Collectively, the findings established a solid scientific basis for leveraging tobacco inflorescence as a promising source of PCs. Moreover, these results also suggested that these PCs have the potential to be used as natural antioxidants in pharmaceuticals, and nutraceuticals, aimed at preventing and managing oxidative stress-related diseases. While in vitro activity data have been successfully obtained, the most significant insight of this study lies in elucidating the complex interactions among these PCs. Future research will employ in vivo and in vitro models to delineate the antioxidant mechanisms of tobacco inflorescence PCs and their function in regulating the oxidative stress-disease axis. Declarations Author contributions Li-feng Jin: Methodology, Data curation, Software, Writing-original draft, Writing – review & editing. Zong-yu Hu: Data curation, Formal analysis, Writing-original draft, Writing – review & editing. Wei-guan Li: Formal analysis, Writing – review & editing. Zhao-peng Luo: Writing – review & editing, Data curation, Investigation. Yue Yang : Writing – review & editing, Formal analysis. Shao-jun Fan : Formal analysis, Writing – review & editing. Shun Gao: Formal analysis, and writing—review and editing. Xue-fen Wang : Conceptualization, Supervision, Writing – review & editing, Writing – original draft. Feng Li: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing, Writing – original draft. Funding This research was supported by the Tobacco Key Project 10202102033, 110202101042 (No. JY-19), 2022530000241007, 110202202028 and KJXM-2024–1. Availability of data and materials The data and materials presented in this study are available upon request from the corresponding author. Ethics approval and consent to participate Not applicable. Consent for publication All authors listed have read the complete manuscript and have approved submission of the paper. Appendix A. Supplementary data Supplementary material: Supplementary Figures and Tables Competing interests The authors declare no competing interests. References Amoriello, T., Mellara, F., Ciorba, R., Ceccarelli, D., Amoriello, M., Taddei, F., Ciccoritti, R., 2025. Phenols extraction from sorghum byproducts: upcycling strategies and food applications. Antioxidants 14, 668. https://doi.org/10.3390/antiox14060668 An, Q., Sun, J., Yang, J., Yuetikuer, A., Zhang, S., Leng, L., Zhan, H., 2025. 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Prod. 39, 162–169. https://doi.org/10.1016/j.indcrop.2012.02.029 Zhao, H., Zhang, H., Yang, S., 2014. Phenolic compounds and its antioxidant activities in ethanolic extracts from seven cultivars of Chinese jujube. Food Sci. Hum. Well. 3, 183–190. https://doi.org/10.1016/j.fshw.2014.12.005 Zheng, B., Yuan, Y., Xiang, J., Jin, W., Johnson, J.B., Li, Z., Wang, C., Luo, D., 2022. Green extraction of phenolic compounds from foxtail millet bran by ultrasonic-assisted deep eutectic solvent extraction: Optimization, comparison and bioactivities. LWT 154, 112740. https://doi.org/10.1016/j.lwt.2021.112740 Zhu, L., Luo, M., Zhang, Y., Fang, F., Li, M., An, F., Zhao, D., Zhang, J., 2023. Free radical as a double-edged sword in disease: deriving strategic opportunities for nanotherapeutics. Coord. Chem. Rev. 475, 214875. https://doi.org/10.1016/j.ccr.2022.214875 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx GraphicalAbstract.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 19 Jan, 2026 First submitted to journal 17 Jan, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8626691","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582730596,"identity":"e7db4f27-8dbc-49f8-81bf-6c171831f789","order_by":0,"name":"Li-feng Jin","email":"","orcid":"","institution":"Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Li-feng","middleName":"","lastName":"Jin","suffix":""},{"id":582730598,"identity":"c0a841c6-fad8-4280-bb20-ce773afece9b","order_by":1,"name":"Zong-yu Hu","email":"","orcid":"","institution":"China Tobacco Jiangsu Industrial Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zong-yu","middleName":"","lastName":"Hu","suffix":""},{"id":582730600,"identity":"ebdc019f-76ce-4268-90d9-23fdacdd9a70","order_by":2,"name":"Wei-guan Li","email":"","orcid":"","institution":"Henan Tobacco Company of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Wei-guan","middleName":"","lastName":"Li","suffix":""},{"id":582730601,"identity":"ff0cac65-122e-4c90-9752-9da3df7083e7","order_by":3,"name":"Zhao-peng Luo","email":"","orcid":"","institution":"Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Zhao-peng","middleName":"","lastName":"Luo","suffix":""},{"id":582730609,"identity":"046d382e-477a-4f84-ab8e-2b00eeda16a5","order_by":4,"name":"Yue Yang","email":"","orcid":"","institution":"China Tobacco Jiangsu Industrial Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yang","suffix":""},{"id":582730610,"identity":"71a8061a-f48c-416e-bd74-f19b5d3ecfc3","order_by":5,"name":"Shao-jun Fan","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shao-jun","middleName":"","lastName":"Fan","suffix":""},{"id":582730612,"identity":"75086772-6426-487d-a565-b9e615b08a38","order_by":6,"name":"Shun Gao","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shun","middleName":"","lastName":"Gao","suffix":""},{"id":582730613,"identity":"195bc24b-6e53-4736-b61c-4d06fffe1d8e","order_by":7,"name":"Xue-fen Wang","email":"","orcid":"","institution":"Henan Tobacco Company of CNTC","correspondingAuthor":false,"prefix":"","firstName":"Xue-fen","middleName":"","lastName":"Wang","suffix":""},{"id":582730614,"identity":"1de42ea7-1fab-45d7-8143-39d43d8f85d8","order_by":8,"name":"Feng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYBACPmbGhw+ANA/xWtiYmY0NGBJI0sLAbCYB1EICYGNnZqv8+eOeDAP74aMbGGruEOUwthsSCcU8DDxpaTcYjj0jRgv/sRsGCQk8DBI8ZjcYGw4TZ0tBAslaGA6QqoVZsiEtgYcN5JeEY0Ro4ec/zPjxh02CPT/74WM3PtQQoQVhHYhIIEHDKBgFo2AUjAI8AAANhCrrCCRd3AAAAABJRU5ErkJggg==","orcid":"","institution":"Zhengzhou Tobacco Research Institute of CNTC","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-17 14:38:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8626691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8626691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101500086,"identity":"a70e64c7-bace-4ba7-8634-78a93724f084","added_by":"auto","created_at":"2026-01-30 13:14:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4885531,"visible":true,"origin":"","legend":"\u003cp\u003eInflorescences and dried powder of five tobacco cultivars.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/6bf74aa37f932206e59243c8.jpeg"},{"id":101500046,"identity":"2f1e090c-c18b-401b-ab4e-079a3e152a60","added_by":"auto","created_at":"2026-01-30 13:13:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5193046,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of ethanol concentration (A), liquid-solid ratio (B), ultrasound power (C), extraction time (D), and extraction temperature (E) on the yield of total phenolic compounds from tobacco inflorescences. Values are expressed as means ± SD (\u003cem\u003en\u003c/em\u003e = 3). Different lowercase letters (a–e) indicate statistically significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/2a9e5adf628b3d90b4a729df.jpeg"},{"id":101500058,"identity":"16404ba8-7e74-4ee0-809a-cb40a4b89e52","added_by":"auto","created_at":"2026-01-30 13:13:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2549612,"visible":true,"origin":"","legend":"\u003cp\u003eResponse surface and contour plots showing the interactions between various factors on the TPC yield:\u003cstrong\u003e (\u003c/strong\u003eA) Ethanol concentration and liquid-solid ratio, (B) ethanol concentration and extraction time, (C) ethanol concentration and ultrasound power, (D) liquid-solid ratio and extraction time, (E) liquid-solid ratio and ultrasound power, and (F) extraction time and ultrasound power. Data are presented as mean ± SEM (\u003cem\u003en\u003c/em\u003e = 3).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/92485ef4d55d4d4b3988a236.jpeg"},{"id":101500073,"identity":"d142b987-250e-4cc8-ac90-86b7a260753e","added_by":"auto","created_at":"2026-01-30 13:14:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1818002,"visible":true,"origin":"","legend":"\u003cp\u003eCompositional analysis of tobacco inflorescences from five cultivars. (A) TPC yield under optimal conditions. (B) FTIR analysis of extracts.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/aa76441684da8eeaf9cccb77.jpeg"},{"id":101500041,"identity":"d21c5b42-a926-4011-aca6-a2ee69e01c7f","added_by":"auto","created_at":"2026-01-30 13:13:58","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2099956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e antioxidant activity of extracts from five cultivars of tobacco inflorescences. (A) ABTS radical scavenging activity. (B) IC\u003csub\u003e50\u003c/sub\u003e values for ABTS radical scavenging. (C) DPPH radical scavenging activity. (D) IC\u003csub\u003e50\u003c/sub\u003e values for DPPH radical scavenging. (E) ·OH (hydroxyl) radical scavenging activity. (F) IC\u003csub\u003e50\u003c/sub\u003e values for ·OH radical scavenging. (G) Reducing power. (H) Effective concentration for 1.0 absorbance (EC\u003csub\u003e1.0\u003c/sub\u003e) in the reducing power assay. Data are presented as mean ± SD (\u003cem\u003en\u003c/em\u003e = 3). Different lowercase letters above bars indicate statistically significant differences (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) among cultivars. IC\u003csub\u003e50\u003c/sub\u003e, half maximal inhibitory concentration.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/dbd849dc77e8b02fae913a51.jpeg"},{"id":101500075,"identity":"59b94307-06dd-42c5-9ac1-c6bb47c07dd9","added_by":"auto","created_at":"2026-01-30 13:14:06","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":946381,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of the correlation among TPC, \u003cem\u003ein vitro\u003c/em\u003e antioxidant activity, and individual phenolic compounds. (A) Correlation heat map. (B) PCA Plot.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/5d15f9684380253b1827f099.jpeg"},{"id":101500050,"identity":"dc377a36-f58b-4cc1-b560-7ec8eb282455","added_by":"auto","created_at":"2026-01-30 13:13:58","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3484259,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork pharmacology analysis of the antioxidant capacity of phenolic compounds in tobacco inflorescence extracts. Venn diagram of intersecting targets between phenolic compounds and oxidative stress-related diseases (A). Compound-target interaction network (B). GO enrichment analysis (C). KEGG enrichment analysis bubble plot (D). PPI network analysis (E) and core target subnetwork after screening (F).\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/313318943fe5693d22123e74.jpeg"},{"id":101500072,"identity":"44ef4663-5f35-4edc-9be5-cff0acac7efb","added_by":"auto","created_at":"2026-01-30 13:14:05","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2817892,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking validation of phenolic compounds with core targets. (A) Molecular docking binding energy heatmap (Red indicates lower binding energy). (B) Molecular docking of rutin, quercetin, and chlorogenic acid with TNF. (C) Molecular docking of rutin, kaempferol, and neochlorogenic acid with AKT1.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/0b5c0a5e0c28690abc89ee3c.jpeg"},{"id":101500095,"identity":"23bb8ff1-3faa-44e6-9e24-6e0dab2f5908","added_by":"auto","created_at":"2026-01-30 13:14:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25246959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/ded24954-6957-4711-84dc-0ae99d11dbe3.pdf"},{"id":101500033,"identity":"ad680733-6786-4084-9355-2a26ea51ae29","added_by":"auto","created_at":"2026-01-30 13:13:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3706826,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/558e41e2391175c7b3b4eac0.docx"},{"id":101500059,"identity":"97ae6a04-7d8f-40ac-8906-f80fa3905646","added_by":"auto","created_at":"2026-01-30 13:14:00","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":435913,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8626691/v1/83a8c5c206e68b798547ce44.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phenolic compounds from Nicotiana tabacum inflorescence: optimized extraction, chemical composition, evaluation of antioxidant activity integrating network pharmacology and molecular docking analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOxidative stress is a pathological condition causing from the disequilibrium between the overproduction of reactive oxygen species (ROS), and the insufficient capacity of the antioxidant defense system, and can inflict substantial cellular damage \u003cem\u003evia\u003c/em\u003e mechanisms such as lipid peroxidation, protein modification, and DNA strand breaks (Zhu et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The major chronic diseases, encompassing cardiovascular disorders, diabetes, neurodegenerative conditions, and various cancers, is significantly underpinned by this imbalance (Cheung and Vousden, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jomova et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, counteracting oxidative stress represents a pivotal therapeutic strategy. Plant-derived phenolic compounds (PCs) serve as potent exogenous antioxidants, capable of directly scavenging free radicals, chelating pro-oxidant metals, and modulating redox-sensitive signaling pathways (Rathod et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, PCs exhibiting antioxidant potential have become a major focus of nutraceutical and pharmaceutical research. Elucidating the protective mechanisms of plant phenolics against oxidative stress is increasingly recognized as a multifaceted challenge that transcends traditional single-target paradigms, necessitating the adoption of more advanced and holistic approaches (Nogales et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNetwork pharmacology is now widely employed as a methodology that combines systems biology, bioinformatics, and polypharmacology for unraveling the \"multi-compound/multi-target/multi-pathway\" of natural products (Patel et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This integrative way facilitates the recognition of core targets and key pathways by constructing comprehensive compound-target-pathway networks, thereby providing a systematic overview of the interactions and biological effects of complex natural extracts. As a key computational tool, molecular docking may predict atomic level interactions between bioactive compounds (PCs, flavonoids, polysaccharides, etc.) and their protein targets, providing important support for network pharmacology. By predicting and validating binding affinity and stability, molecular docking not only may confirm the feasibility of interactions identified through network analysis, but also offer detailed insights into structure-activity relationships (Pinzi and Rastelli, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The combination of macroscopic network analysis with microscopic molecular docking may enable a slightly broader understanding of the mechanisms underlying the biological activities of plant PCs. The synergistic application of network pharmacology and molecular docking has proven highly effective in deciphering the mechanisms of complex natural extracts, providing a robust foundation for developing novel therapeutic strategies (Zeng et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, this integrated methodology may enhance the efficiency of target identification and validation while facilitating the rational design of multi-target therapeutics, a promising approach for addressing the complexity of chronic diseases.\u003c/p\u003e \u003cp\u003eAs a varied group of secondary metabolites, PCs have been detected in different plant organs, including flowers, leaves, fruits, roots, etc. (Bekavac et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gil-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These PCs serve vital functions in plant growth and development, including UV protection and defense against pathogens, while also exhibit diverse bioactivities, like antioxidant, anti-inflammatory, and antibacterial properties (Shahidi and Ambigaipalan, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Owing to these health-promoting effects, the efficient recovery of PCs is highly desirable, driving the need for advanced extraction techniques. Ultrasound-assisted extraction (UAE) has become a prominent way to extract PCs owing to its benefits over traditional methods, like faster kinetics, higher efficiency, lower solvent use, and reduced energy consumption (Amoriello et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ivanović et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The effectiveness of UAE primarily stems from acoustic cavitation, where collapsing microbubbles produce strong shear forces, localized heating, and high pressure. This phenomenon disrupts plant cell structures, thereby accelerating the release of intracellular PCs, and further elevating mass transfer (Avdović et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To achieve optimal performance, key parameters including solvent composition, liquid-solid ratio (LSR), ultrasound power, and extraction time must be carefully optimized, as they greatly affect the yield and quality of the extracted PCs. Response Surface Methodology (RSM), is commonly applied for the efficient optimization of these parameters (Liao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). RSM may enable the assessment of the interactions among multiple parameters, and helps identify optimal conditions for UAE, thereby improving the efficiency and efficacy during the extraction process of plant bioactive compounds. Despite the well-documented benefits and widespread application of UAE in phenolic extraction from various plant materials, its use for recovering phenolics from underutilized agricultural waste remains relatively underexplored.\u003c/p\u003e \u003cp\u003eTobacco (\u003cem\u003eNicotiana tabacum\u003c/em\u003e L.) is both a crucial traditional crop and an exemplary model plant for biological research. Throughout its lifecycle, from field harvesting to final processing, approximately 30% of the total biomass is rendered waste (An et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Banožić et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Manthos and Tsigkou, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Tobacco inflorescence, a byproduct of the agricultural practice of \"topping\" (which involves removal of the floral axis and lateral buds to redirect nutrients toward leaf development, enhancing leaf quality and yield), represents a largely underutilized source of bioactive compounds (Shi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This practice results in vast quantities of waste, the disposal of which poses an environmental burden and represents a missed opportunity for resource utilization (An et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Banožić et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, tobacco inflorescences and leaves were used as an abundant source of bioactive ingredients, including PCs and alkaloids, which possess significant antioxidant and anti-inflammatory activity (Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Manthos and Tsigkou, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), holding considerable potential for applications in cosmeceuticals, nutraceuticals, and pharmaceuticals. However, comprehensive studies integrating optimized extraction techniques, detailed phenolic profiling, and mechanistic insights into their health benefits are conspicuously lacking. Herein, a UAE protocol of PCs extraction from tobacco inflorescence was firstly established and optimized using RSM. The resulting PCs extracts from five cultivars (YY87, ZY100, K326, HD, CB1) were then comprehensively evaluated for extraction yield, antioxidant activity, and phenolic composition. Further, the core antioxidant mechanisms of the main PCs were analyzed through network pharmacology and molecular docking. These studies contribute to establishing a scientific foundation for utilizing tobacco inflorescence and new perspectives on its bioactive components.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials and chemicals\u003c/h2\u003e \u003cp\u003eTobacco (Nicotiana tabacum) cultivars used in this study included \u0026lsquo;Yunyan 87\u0026rsquo; (YY87), \u0026lsquo;Zhongyan 100\u0026rsquo; (ZY100), \u0026lsquo;K326\u0026rsquo;, \u0026lsquo;Honghuadajinyuan\u0026rsquo; (HD), and \u0026lsquo;Cuibi 1\u0026rsquo; (CB1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inflorescences from these cultivars were collected from an experimental field in Xiangcheng County, Xuchang City, China (33\u0026deg;57\u0026prime;49\u0026Prime;N, 113\u0026deg;27\u0026prime;43\u0026Prime;E). After harvesting, the plant materials were subjected to dry at 65\u0026deg;C, and comminuted into a fine particulate matter utilizing an electric grinding apparatus (Model FW-80; Taisite, Tianjin, China) and fractionated using a 40-mesh sieve. These samples were stored at room temperature for further analysis. 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2\u0026prime;-Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) were procured from BASF Bioscience Co., Ltd. (Hefei, China). Gallic acid, vitamin C, Folin\u0026ndash;Ciocalteu reagent, and Trolox were acquired from Macklin Biochemical Co., Ltd. (Shanghai, China). Rutin, chlorogenic acid, cryptochlorogenic acid, neochlorogenic acid, caffeic acid, scopoletin, quercetin, kaempferol, and ferulic acid (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%) were supplied by Shanghai Yuanye Bio-Technology Co., Ltd. (Shanghai, China).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental design of PCs extraction\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Single-factor tests\u003c/h2\u003e \u003cp\u003eThe influences of ethanol content, LSR, extraction time, ultrasound power, and extraction temperature on the total phenolic content (TPC) was tested and analyzed (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Briefly, a 1.0 g aliquot of a blended powder comprising five tobacco cultivars in equal proportions in conical flasks was subjected to UAE \u003cem\u003evia\u003c/em\u003e an ultrasonic apparatus. The extraction process adhered to the conditions outlined in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The extracts were centrifuged at 8000 rpm for 5 min. Following this procedure, the resultant supernatant was obtained for subsequent using.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Box-Behnken design (BBD) experiments\u003c/h2\u003e \u003cp\u003eAccording to the single-factor test results, a BBD model was carried out using four key factors: ethanol concentration (A), LSR (B), extraction time (C), and ultrasound power (D). The response variables were the PCs yield from tobacco inflorescence, with factor levels provided in Table S2. All experiments were conducted in triplicate. The model's adequacy and goodness were evaluated by ANOVA. Its predictive performance was evaluated utilizing the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) and adjusted R\u003csup\u003e2\u003c/sup\u003e (adjR\u003csup\u003e2\u003c/sup\u003e). A series of validation experiments was then performed to authenticate the robustness of the statistical methodology and delineate the optimum parameters for the extraction of tobacco inflorescence PCs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Model validation\u003c/h2\u003e \u003cp\u003eUsing the BBD results, the factor levels were optimized to achieve the maximum predicted TPC. The resulting optimaal parameters were then applied to experimentally validate the model's effectiveness. The experimental and predicted yields were compared and assessed the accuracy and reliability of the obtained model.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determination of total phenolic content (TPC)\u003c/h2\u003e \u003cp\u003eTPC analysis was performed in accordance with the Folin-Ciocalteu method (Xu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Initially, 400 \u0026micro;L sample was combined with 400 \u0026micro;L Folin-Ciocalteu reagent, 1.2 mL of 7.5% Na₂CO₃ solution, and 2 mL H\u003csub\u003e2\u003c/sub\u003eO. Following a 60 min incubation in the dark, the absorbance was determined at 765 nm. Ultimately, the TPC was quantified by comparison with a gallic acid calibration curve and expressed as mg gallic acid equivalents (GAE)/g dry weight (DW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Preparation of PCs extracts\u003c/h2\u003e \u003cp\u003ePCs were extracted from five tobacco inflorescences (YY87, ZY100, K326, HD, CB1) following the optimized protocol. The resulting extraction solutions were subjected to filtration and subsequently concentrated \u003cem\u003evia\u003c/em\u003e rotary evaporation (RE-52A, Yarong Instrument Co., Ltd., Shanghai, China). These extracts were dried under vacuum at 50\u0026deg;C, and the dried samples were stored at 4\u0026deg;C for using.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5. UHPLC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eFor sample pretreatment, an appropriate amount of sample was weighed, added with 50% methanol, and extracted by ultrasonication for 30 min. These mixtures were centrifuged by 12000 rpm for 10min, and the supernatants were obtained and separated using a Shimadzu Nexera X2 LC-30AD UHPLC system (Shimadzu, Kyoto, Japan). The mobile phases included 0.1% formic acid in aqueous solution (A) and 0.1% formic acid in acetonitrile (B). Chromatographic separation of these samples was obtained using a Waters ACQUITY UPLC BEH C18 column (1.7 \u0026micro;m, 2.1 mm \u0026times; 100 mm) at 40\u0026deg;C. The autosampler was operated at 4\u0026deg;C to preserve sample integrity. The gradient elution program was as following: 5\u0026ndash;70% solvent B over 0\u0026ndash;17.5 min, 70\u0026ndash;90% solvent B over 17.5\u0026ndash;18.5 min 90% solvent B from 18.5\u0026ndash;20 minutes, 90\u0026ndash;5% solvent B over 20\u0026ndash;20.5 min, and 5% solvent B from 20.5\u0026ndash;25 min. The flow rate was controlled at 300 \u0026micro;L/min, and the volume of injection was 1 \u0026micro;L.\u003c/p\u003e \u003cp\u003eMass spectrometric analysis was conducted using a 5500 QTRAP instrument (AB SCIEX) in negative electrospray ionization (ESI) mode. The parameters of ESI were configured as follows: ion spray voltage of -4500 V, source temperature of 550\u0026deg;C, ion source gas 1 (GS1) of 55 units, ion source gas 2 (GS2) of 55 units, and curtain gas (CUR), 35 units. Quantify of nine PCs was accomplished according to standard curves, with detection in MRM mode. The specific MRM transitions and calibration data are detailed in Table S3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Fourier-transform infrared (FTIR) Analysis\u003c/h2\u003e \u003cp\u003eDried PCs extract powders (1 mg) from the five varieties (YY87, ZY100, K326, HD, and CB1) were individually mixed with KBr (200 mg) and pressed into pellets. FTIR spectra were recorded on a Thermo Nicolet iS5 spectrometer over the wavenumber range of 4000\u0026ndash;400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The acquisition parameters included: 32 scans per spectrum at a 4 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e resolution, with a mirror velocity of 0.4747 cm/s and an aperture setting of 100.00.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Assay of Antioxidant Activity\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.7.1. ABTS radical scavenging activity\u003c/h2\u003e \u003cp\u003eABTS radical scavenging activity was assessed using previously reported method (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Briefly, ABTS of 7.0 mM and potassium persulfate of 2.45 mM (1:1, v/v) were mixed and then stewed 16 h in the dark. The mixture was diluted up to an OD734 nm of 0.700\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02, representing the working solvent. Subsequently, the reaction mixtures included 200 \u0026micro;L PCs extracts and 3 mL ABTS working solvent. Following a 6-min incubation in the dark, OD734 values were recorded. Data was analyzed though the following formula: Scavenging activity (%) = (1\u0026thinsp;\u0026minus;\u0026thinsp;A/A\u003csub\u003e0\u003c/sub\u003e) \u0026times; 100, where A\u003csub\u003e0\u003c/sub\u003e represents OD734 values of blank control, and A expressed the OD734 values of PCs extracts. Vitamin C (Vc) and Trolox were set as positive controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.7.2. DPPH radical scavenging activity\u003c/h2\u003e \u003cp\u003eDPPH radical scavenging activity was determined using Wang et al. method (2023). Briefly, the reaction mixtures included PCs extracts of 100 \u0026micro;L, DPPH solution of 2 mL (0.06 mM) and H\u003csub\u003e2\u003c/sub\u003eO of 900 \u0026micro;L. Following vortexing, these mixtures were subjected to a 30- min incubation period in the dark, and the values of OD517 were recorded. The scavenging rate was analyzed though the following formula: (1\u0026thinsp;\u0026minus;\u0026thinsp;A/A\u003csub\u003e0\u003c/sub\u003e) \u0026times; 100, where A\u003csub\u003e0\u003c/sub\u003e represented OD517 values of blank control, and A represented OD517 values of PCs extracts. Vc and Trolox were set as positive controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.7.3. Hydroxyl (\u0026middot;OH) radical scavenging activity\u003c/h2\u003e \u003cp\u003eHydroxyl radical scavenging activities were measured based on the method of Ma et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In brief, the reaction mixture contained equal volumes of PC extract, 3 mM FeSO\u003csub\u003e4\u003c/sub\u003e, 6 mM salicylic acid, and 3 mM H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e., and then were kept in the dark for 15 min to incubate. The values of OD510 were recorded. The scavenging rate (%) analyzed though the following formula: (1 \u0026ndash; A/A\u003csub\u003e0\u003c/sub\u003e) \u0026times; 100%, where A\u003csub\u003e0\u003c/sub\u003e represents OD517 values of blank control, and A represents the OD517 values of PCs extracts. Vc and Trolox were set as positive controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.7.4. Reducing power (RP) assay\u003c/h2\u003e \u003cp\u003eReducing power was assessed based on the method of Song et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The reaction mixture, comprising 500 \u0026micro;L of PCs extracts, 500 \u0026micro;L of phosphate-buffered saline (PBS; 0.2 M, pH 6.6), and 500 \u0026micro;L of 1% potassium ferricyanide, was incubated for 20 min at 50\u0026deg;C. The reaction was subsequently terminated by the addition of 500 \u0026micro;L of 10% trichloroacetic acid, followed by centrifugation of the mixture. 1.5 mL of the resultant supernatant was combined with 1.5 mL of 0.02% ferric chloride solution. After a 10-min incubation in the dark, the OD700 values were recorded. Vc and Trolox were set as positive controls.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Network pharmacology analysis\u003c/h2\u003e \u003cp\u003eThe canonical SMILES of rutin, chlorogenic acid, scopoletin, quercetin, neochlorogenic acid, caffeic acid, cryptochlorogenic acid, ferulic acid, and kaempferol were retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Their potential targets were predicted using the SwissTargetPrediction database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Targets related to \"oxidative stress\" were collected from GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genecards.org\u003c/span\u003e\u003cspan address=\"http://www.genecards.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The intersection between the compound targets and the disease targets was identified and visualized as a Venn diagram using the Bioinformatics.com.cn platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.com.cn/\u003c/span\u003e\u003cspan address=\"http://www.bioinformatics.com.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A compound-target-disease network was then constructed with Cytoscape 3.9.1. Furthermore, the overlapping targets were used to generate a protein-protein interaction (PPI) network via the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Szklarczyk et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which was subsequently imported into Cytoscape for topological analysis based on Betweenness Centrality (BC), Closeness Centrality (CC), and Degree Centrality (DC) (Shannon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Finally, functional enrichment analysis for Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed on the intersecting targets using the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Molecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking simulations were conducted utilizing AutoDock Vina to evaluate the binding affinities of the components to key target proteins. The 2D structures of 9 PCs were retrieved in SDF format from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, and imported into ChemBio3D for energy minimization. The minimized structures were then processed in AutoDockTools v1.5.7 to add hydrogen atoms, compute Gasteiger charges, assign atomic charges, and define rotatable bonds before saving them in PDBQT format. The key target proteins, including tumor necrosis factor (TNF) and protein kinase B (AKT1), were searched from Protein Data Bank (PDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with priority given to human derived structures co crystallized with ligands exhibiting high structural similarity to the compounds being docked and selecting complexes with higher resolution. These protein structures were imported into PyMOL to remove any existing ligands and water. Subsequently, the simulated molecules were loaded into AutoDockTools v1.5.7 for adding hydrogen atoms, computing and assigning charges, and designating atom types (Trott and Olson, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), followed by saving in PDBQT format. For the docking simulations, the grid box was centered on the co-crystallized ligand of each protein to ensure accurate targeting of the binding site. In cases where the co-crystallized ligand was unavailable, the grid box was centered on the reported key amino acid residues involved in ligand binding. The grid box was arranged to span the whole binding site while retaining the default parameters for other settings to maintain consistency and accuracy in the docking process. Finally, the interaction modes of the compounds with the target proteins were characterized using PyMOL visualization software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Statistical analysis\u003c/h2\u003e \u003cp\u003eData were collected in triplicate, with statistical significance set at a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The analysis was performed using SPSS 27.0 (IBM\u0026reg; Corporation, San Jose, CA, USA) with one-way ANOVA followed by Tukey\u0026rsquo;s test. Graphical representations were generated using Origin 2024 (OriginLab Corporation, Northampton, MA, USA). UHPLC-MS/MS data were calculated in Analyst 1.6.3 software (SCIEX, Framingham, MA, USA) to obtain chromatographic peak areas and retention times. Identification of PCs were carried out based on the retention times and peak profiles of reference standards.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Single-factor experiment\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Effects of ethanol concentration on TPC\u003c/h2\u003e \u003cp\u003eEthanol and water are extensively employed as solvents for PCs extraction. Generally, ethanol may enhance the solubility of PCs, while H\u003csub\u003e2\u003c/sub\u003eO may possibly enhance their desorption from the sample matrix. The ratio of these solvents significantly influences the extraction yield of plant PCs (Metrouh-Amir et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, TPC showed firstly increase and then decrease with rising ethanol contents. The maximum TPC value, 21.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63 mg/g, was reached at 40% ethanol concentration. However, a decline in yield was recorded when the ethanol level exceeded 60%, a trend consistent with findings reported for PCs extraction from \u003cem\u003eLimonium sinuatum\u003c/em\u003e flowers (Xu et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This phenomenon might due to the fact that lower ethanol concentrations may disturb cell membranes, and the high proportion of water in the solvent mixture facilitates the swelling of plant material, enhancing solution release. Furthermore, increasing ethanol levels may change the polarity of the extraction solution, which in turn may affect the dissolving capacity of PCs, thereby influencing extraction efficiency. These results are consistent with the reported superiority of ethanol-water mixtures over pure solvents in extracting PCs (Drăghici-Popa et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, a 40% ethanol concentration was determined as one of the optimal parameters for next extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Effects of LSR on TPC\u003c/h2\u003e \u003cp\u003eLSR may influence the extraction yield of bioactive components and process economy (Liao et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, TPC increased with LSR and reached a maximum of 21.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mg GAE/g DW at 40 mL/g. This improvement is likely due to enhanced solute-solvent contact and more efficient cavitation effects under UAE conditions (Ma et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Beyond this point, the further rising LSR resulted in a decline in PCs yield, reaching 17.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 mg GAE/g DW at 90 mL/g. The decrease may result from excessive dilution, which reduces cavitation intensity and compromises extraction kinetics. Thus, a LSR of 40 mL/g was set as one of the optimal parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Effects of ultrasound power on TPC\u003c/h2\u003e \u003cp\u003eUltrasound power can impact the PCs yield from plant materials though altering the vibration amplitude of the extraction solvent (Huo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, the TPC gradually increased from 16.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27 mg GAE/g DW to 22.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41 mg GAE/g DW up to ultrasound power of 300 W. However, the extraction yield began to decline when the ultrasound power exceeded 350 W. Within a specific range, higher ultrasound power intensifies ultrasonic effects, leading to more cavitation bubbles and stronger implosive shear forces. These forces facilitate the disruption of cell structures, accelerate mass transfer, and enhance these PCs release (Yusoff et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Conversely, excessive ultrasound power may cause overheating and excessive mechanical stress, potentially degrading the target compounds and thus reducing extraction efficiency (Kuvendziev et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, under high ultrasound power, cavitation bubbles may coalesce into larger ones, which attenuates the implosion effect (Hu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These phenomena may due to the destruction of structural integrity of PCs, and further diminishing their bioactivity. Thus, 300 W of ultrasound power was determined as one of the optimal parameters to maximize extraction efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Effects of extraction time on TPC\u003c/h2\u003e \u003cp\u003eExtraction time is a pivotal parameter among extraction parameters, significantly influencing both the recovery of PCs from plant materials and the economic viability during the extraction process (Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, TPC gradually raised with the rising time from 5 to 25 min, reaching its peak with 22.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 mg GAE/g DW. This trend reflects the gradual release of PCs into the solvent, aided by ultrasonic cavitation that disrupts cell walls and enhances mass transfer (Sharma and Dash, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Beyond 25 min, the yield stabilized briefly before declining slightly with further prolongation. The reduction may result from a diminished concentration gradient across cell membranes and potential degradation of PCs under prolonged ultrasonic conditions involving elevated temperature and reactive gases (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, extraction time of 25 min was set as one of the optimal parameters for next studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5. Effects of extraction temperature on TPC\u003c/h2\u003e \u003cp\u003eExtraction temperature may influence the extraction process by altering the thermodynamic driving forces for mass transfer (Palma et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, the PCs yield showed a gradual raise with the rising extraction temperature up to 30\u0026deg;C, reaching a maximum of 22.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46 mg/g. This improvement might be due to the increasing mass transfer and compound solubility at higher temperatures, thereby boosting extraction yield (Chia et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, higher temperature, especially when it exceeds 50\u0026deg;C, resulted in the decline in PCs yield (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). This decrease may due to the degradation of heat-sensitive PCs at higher extraction temperatures (Nipornram et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, higher temperatures are associated with increased energy consumption and operational costs. Therefore, considering both yield and process economics, 30\u0026deg;C was set as one of the optimal extraction parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Optimizing the extraction by RSM\u003c/h2\u003e \u003cp\u003eSince extraction yield of PCs is affected by multiple extraction parameters and their interactions, it is essential to systematically evaluate these variables to improve efficiency (Gil-Mart\u0026iacute;n et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, the effect of extraction temperature was less pronounced compared to the other parameters. Therefore, a BBD model was established using four extraction parameters of ethanol concentration (20\u0026ndash;60%), LSR (20\u0026ndash;60 mL/g), ultrasonic power (200\u0026ndash;400 W), and extraction time (10\u0026ndash;40 min). Meanwhile, TPC was set as the response values. The variable levels were detailed in Table S4, obtaining 29 experimental groups. To optimize the yield of PCs, a second-order polynomial model was developed to elucidate the corrections between the independent variables and the corresponding response values (Eq.\u0026nbsp;1). Y\u003csub\u003eTPC\u003c/sub\u003e = 23.91\u0026thinsp;+\u0026thinsp;1.00A\u0026thinsp;+\u0026thinsp;1.21B\u0026thinsp;+\u0026thinsp;0.87C\u0026thinsp;+\u0026thinsp;0.43D\u0026thinsp;\u0026minus;\u0026thinsp;0.70AD\u0026thinsp;+\u0026thinsp;0.92CD\u0026thinsp;\u0026minus;\u0026thinsp;2.78A\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;0.93B\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1.86C\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2.08D\u003csup\u003e2\u003c/sup\u003e, (Eq.\u0026nbsp;1), where Y\u003csub\u003eTPC\u003c/sub\u003e denoted the predicted TPC, A, B, C, and D represented ethanol concentration, LSR, ultrasound power, and extraction time, respectively. As exhibited in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the effects of experimental parameters on TPC followed the order: LSR\u0026thinsp;\u0026gt;\u0026thinsp;ethanol concentration\u0026thinsp;\u0026gt;\u0026thinsp;extraction time\u0026thinsp;\u0026gt;\u0026thinsp;ultrasound power. All factors showed \u003cem\u003ep\u003c/em\u003e-values below 0.05, confirming their significant impacts on TPC. Additionally, Eq.\u0026nbsp;1 and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed that the interaction effects between ethanol concentration (A) and ultrasound power (D), and between extraction time (C) and ultrasound power (D), are significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eAnalysis of variance (ANOVA) for RSM and regression modeling of BBD.\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Squares\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\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF- value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-Ethanol concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Liquid-solid ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Extraction time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Ultrasound power\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0318\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4087\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0514\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0394 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4241\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0977\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0099\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0019\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3818\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8197\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5838\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCor Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC.V. %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdeq Precision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.9055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePred R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eYTPC\u0026thinsp;=\u0026thinsp;23.91\u0026thinsp;+\u0026thinsp;1.00A\u0026thinsp;+\u0026thinsp;1.21B\u0026thinsp;+\u0026thinsp;0.87C\u0026thinsp;+\u0026thinsp;0.43D\u0026thinsp;\u0026minus;\u0026thinsp;0.70AD\u0026thinsp;+\u0026thinsp;0.92CD\u0026thinsp;\u0026minus;\u0026thinsp;2.78A\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;0.93B\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;1.86C\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026minus;\u0026thinsp;2.08D\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eC.V.% coefficient of variation. df\u0026thinsp;=\u0026thinsp;degrees of freedom. a Significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. b Not significant.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe model exhibited strong predictive ability, as showed by a higher similarity of predicted and experimental yields (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9587) (Ferreira et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). ANOVA further validated the model, showing good consistency with the experimental results. The adjusted determination coefficient (AdjR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9174) indicated that the model may express 91.74% of the response variability, demonstrating an excellent fit. The highly significant p-values of all models (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the non-significant lack of fit (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) collectively attest to the model\u0026rsquo;s remarkable accuracy and reliability in predicting the TPC yield from tobacco inflorescences. The statistical analysis further underscores that the model terms may exert a substantial influence on the response variable, and capture the relationships between the independent variables and the response values. These findings collectively bolster the model\u0026rsquo;s reliability and robustness in forecasting outcomes based on the specified variables. Moreover, statistical analysis also revealed that linear terms (A, B, C, D) and quadratic terms (AD, CD, A\u003csup\u003e2\u003c/sup\u003e, B\u003csup\u003e2\u003c/sup\u003e, C\u003csup\u003e2\u003c/sup\u003e, D\u003csup\u003e2\u003c/sup\u003e) had significant effects on TPC (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This indicated that not only the individual factors but also their interactions and squared terms played crucial roles in determining the TPC. The significant linear terms suggested that changes in these factors directly influenced the TPC, while the significant quadratic terms highlighted the optimal conditions for maximizing TPC may lie within specific ranges of these variables.\u003c/p\u003e \u003cp\u003eThe 3D response surfaces vividly depicted the interactions among parameters and their profound impacts on extraction yield of PCs. A more pronounced curvature of the surface was indicative of more substantial interactive effects between the variables (Sun et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Correspondingly, the contour plots, which serve as 2D projections of the response surfaces, mirrored the interplay between pairs of factors. The elliptical contour shape suggested that there was a significant interaction between the variables (Xu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the TPC initially showed a positive correlation with each of the four parameters, steadily increasing before peaking and subsequently declining. This trend aligned with the observations from the single-factor experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, these findings confirmed the model's capacity to accurately represent the complex interactions among variables and predict outcomes. This validation underscored the model's value as a robust tool for optimizing extraction parameters, and maximizing the TPC yield from tobacco inflorescences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Validation and scale-up experiments of PCs yield\u003c/h2\u003e \u003cp\u003eThe best extraction conditions were pinpointed by means of 3D response surface analysis and the regression model application, namely: ethanol concentration 44.7%, LSR 55.4 mL/g, extraction time 31.1 min, and ultrasound power 333.4 W. Under these meticulously determined parameters, the model predicted TPC of 24.81 mg GAE/g DW for tobacco inflorescence. For practical application, the conditions were slightly adjusted to more readily achievable values: ethanol concentration of 45%, LSR of 55 mL/g, extraction time of 31 min, and ultrasound power of 335 W. Under these adjusted settings, the experimentally obtained TPC value was 24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 mg GAE/g DW, which exhibited excellent concordance with the predicted value. The RSD of 2.22% further underscored the high predictive accuracy of the regression model for PCs extraction from tobacco inflorescences. Under these optimized conditions, the TPC values for different tobacco cultivars were as follows: YY87 (24.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 mg GAE/g DW), ZY100 (21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42 mg GAE/g DW), K326 (28.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 mg GAE/g DW), HD (28.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66 mg GAE/g DW), and CB1 (17.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 mg GAE/g DW), as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The significant differences observed among these values highlighted the cultivar-dependent of TPC in tobacco inflorescences, indicating that the TPC can vary substantially depending on the specific cultivar being examined.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the extraction performance under larger-volume conditions and provide valuable insights for potential industrial application, a scale-up extraction was conducted at a 500 mL volume based on the established regression model and the optimized experimental parameters. Under these optimized parameters, TPC achieved 23.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 mg GAE/g DW in the scale-up experiment, which is in outstanding accordance with both the predicted result and the small-scale trial data. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the TPC values for YY87, ZY100, K326, HD, and CB1 obtained in the scale-up experiment were also in accordance with those from small-scale tests. These results collectively suggested the robustness and reliability of this model in guiding the optimal extraction processes for tobacco inflorescences PCs, paving the way for efficient and scalable industrial applications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Composition analysis of PCs\u003c/h2\u003e \u003cp\u003eAntioxidant capacity is commonly closely associated with the content and profile of PCs, with notable variations observed among different cultivars (Zhao et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, nine main PCs were identified and quantified (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The identification was confirmed according to retention times, mass spectral fragmentation patterns, and collision energies (Table S3). Overall, the PCs were found in descending order of abundance: rutin\u0026thinsp;\u0026gt;\u0026thinsp;chlorogenic acid\u0026thinsp;\u0026gt;\u0026thinsp;cryptochlorogenic acid, neochlorogenic acid\u0026thinsp;\u0026gt;\u0026thinsp;caffeic acid, scopoletin\u0026thinsp;\u0026gt;\u0026thinsp;quercetin\u0026thinsp;\u0026gt;\u0026thinsp;kaempferol and ferulic acid. Rutin emerged as the most abundant PC in the tobacco inflorescences, a finding that aligns with previous reports on tobacco (Banožić et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The content of rutin across the five cultivars ranged from 64676.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1793.84 to 85572.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3480.74 \u0026micro;g/g, while ferulic acid content varied between 4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 and 7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 \u0026micro;g/g. Specifically, cultivar K326 displayed the highest levels of rutin (85572.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3480.74 \u0026micro;g/g) and neochlorogenic acid (1239.59\u0026thinsp;\u0026plusmn;\u0026thinsp;18.07 \u0026micro;g/g). Cultivar HD contained the highest concentrations of chlorogenic acid (7715.19\u0026thinsp;\u0026plusmn;\u0026thinsp;230.51 \u0026micro;g/g), cryptochlorogenic acid (1368.54\u0026thinsp;\u0026plusmn;\u0026thinsp;37.30 \u0026micro;g/g), caffeic acid (311.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34 \u0026micro;g/g), and ferulic acid (7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15 \u0026micro;g/g). Conversely, cultivar YY87 accumulated the highest amounts of scopoletin (273.90\u0026thinsp;\u0026plusmn;\u0026thinsp;31.63 \u0026micro;g/g) and kaempferol (9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 \u0026micro;g/g). These results clearly demonstrated significant quantitative differences in individual PCs among five cultivars, indicating that the phenolic composition profile is strongly cultivar-dependent. This significant variation likely originated from inherent genetic differences among these cultivars, which influenced the regulation of biosynthetic pathways responsible for PCs production (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Understanding such compositional variations is crucial for elucidating the potential antioxidant capacity and other intrinsic biological activities unique to inflorescences of different tobacco cultivars. Such insights can inform the selection and utilization of specific cultivars for targeted PCs applications, leveraging their distinct phenolic profiles to maximize desired health benefits and functional properties.\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\u003eIdentification and quantification of major phenolic compounds in the extracts of tobacco inflorescences.\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\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ename\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYY87 (\u0026micro;g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZY100 (\u0026micro;g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK326 (\u0026micro;g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHD (\u0026micro;g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCB1 (\u0026micro;g/g)\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\u003eRutin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73674.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1063.58b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68290.83\u0026thinsp;\u0026plusmn;\u0026thinsp;194.44c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85572.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3480.74a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74375.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2485.83b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64676.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1793.84d\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\u003eChlorogenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3008.79\u0026thinsp;\u0026plusmn;\u0026thinsp;83.47d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4365.17\u0026thinsp;\u0026plusmn;\u0026thinsp;53.22c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5623.43\u0026thinsp;\u0026plusmn;\u0026thinsp;171.06b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7715.19\u0026thinsp;\u0026plusmn;\u0026thinsp;230.51a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5718.22\u0026thinsp;\u0026plusmn;\u0026thinsp;43.83b\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\u003eCryptochlorogenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1061.16\u0026thinsp;\u0026plusmn;\u0026thinsp;91.27bcd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.47d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1006.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1368.54\u0026thinsp;\u0026plusmn;\u0026thinsp;37.30a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1131.29\u0026thinsp;\u0026plusmn;\u0026thinsp;23.71b\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\u003eNeochlorogenic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e934.50\u0026thinsp;\u0026plusmn;\u0026thinsp;30.03cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e901.25\u0026thinsp;\u0026plusmn;\u0026thinsp;14.36d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1239.59\u0026thinsp;\u0026plusmn;\u0026thinsp;18.07a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1074.57\u0026thinsp;\u0026plusmn;\u0026thinsp;29.58b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e987.77\u0026thinsp;\u0026plusmn;\u0026thinsp;25.59c\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\u003eCaffeic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169.35\u0026thinsp;\u0026plusmn;\u0026thinsp;18.51c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e243.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e311.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09d\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\u003eScopoletin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e273.90\u0026thinsp;\u0026plusmn;\u0026thinsp;31.63a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e138.28\u0026thinsp;\u0026plusmn;\u0026thinsp;2.48c\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\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.35a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.52a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00a\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\u003eKaempferol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14c\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\u003eFerulic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09c\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\u003ea-e, means data in the same row with different letters are significantly different (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.5. FTIR analysis\u003c/h2\u003e \u003cp\u003eFTIR spectroscopy a powerful and widely utilized analytical technique for the organic functional groups of PCs and bioactive constituents and for revealing their key structural features (Grasel et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It may effectively detect characteristic stretching and bending vibration bands corresponding to hydroxyl and carboxyl groups (Patle et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). FTIR spectra of PCs extracts from five tobacco cultivars (YY87, ZY100, K326, HD, CB1) were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. Despite subtle spectral variations, five PCs extracts exhibited seven similar characteristic absorption peaks, indicating a comparable phenolic composition across the different cultivars. The band of around 3380 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e may be assigned to O\u0026ndash;H stretching vibrations, thereby verifying the existence of phenolic hydroxyl groups. The peak of 2930\u0026ndash;2935 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is indicative of C\u0026ndash;H stretching vibrations. Bands detected in the 1610\u0026ndash;1619 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1387\u0026ndash;1405 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e can be attributed to asymmetric and symmetric stretching vibrations of carboxylate groups, respectively. The spectral region below 1100 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e points to the presence of aromatic C\u0026ndash;H bending vibrations and C\u0026ndash;O vibrations (Iftikhar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These finding suggested that the fundamental chemical structures of the phenolic compounds are largely conserved among five cultivars. The spectral similarities suggested the presence of common functional groups among the cultivars. Minor peak shifts, such as the band at 1405 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in CB1 compared to 1387\u0026ndash;1390 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the other cultivars, may reflect variations in the carboxylate group environment or differences in hydrogen bonding patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Overall, these results not only exhibited the existence of key functional components in PCs from tobacco inflorescence, but also provided insights into the subtle structural differences that may influence the biological activities of these compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.6. In vitro antioxidant activity\u003c/h2\u003e \u003cp\u003eEarlier reports have highlighted the remarkable radical scavenging ability of PCs and their crucial role in ROS scavenging (Eghbaliferiz and Iranshahi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yusoff et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To evaluate antioxidant activity of plant-derived bioactive components, ABTS, DPPH, and hydroxyl radical scavenging capacities, as well as RP, which are widely applied in assessing various plant extracts (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (A-H), the present results consistently demonstrated a significant concentration-dependent increase in antioxidant activity for all PCs extracts. ABTS radical scavenging assay revealed the following order of efficacy: K326, HD\u0026thinsp;\u0026gt;\u0026thinsp;YY87\u0026thinsp;\u0026gt;\u0026thinsp;CB1\u0026thinsp;\u0026gt;\u0026thinsp;ZY100 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). This ranking was further quantified by IC\u003csub\u003e50\u003c/sub\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), where ZY100 exhibited the highest IC\u003csub\u003e50\u003c/sub\u003e (0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 mg/mL), indicating the weakest activity, while K326 (0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg/mL) and HD (0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg/mL) showed the lowest IC\u003csub\u003e50\u003c/sub\u003e values, signifying the strongest scavenging ability. The DPPH radical scavenging assay exhibited a similar trend across the five PCs extracts (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Consistent with the ABTS results, ZY100 displayed the highest DPPH IC\u003csub\u003e50\u003c/sub\u003e value (1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 mg/mL), whereas K326 (1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg/mL) and HD (1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg/mL) demonstrated the strongest activity with the lowest IC\u003csub\u003e50\u003c/sub\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In contrast, the \u0026middot;OH radical scavenging activity yielded different results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). While all extracts demonstrated measurable \u0026middot;OH scavenging capacity, their overall effectiveness was notably lower compared to their performance those of in the ABTS and DPPH assays. Furthermore, the activity ranking differed: HD\u0026thinsp;\u0026gt;\u0026thinsp;K326\u0026thinsp;\u0026gt;\u0026thinsp;YY87\u0026thinsp;\u0026gt;\u0026thinsp;ZY100\u0026thinsp;\u0026gt;\u0026thinsp;CB1. Quantification confirmed CB1 as the least active extract against \u0026middot;OH radicals (highest IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 mg/mL), with HD being the most effective (lowest IC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11 mg/mL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). This distinct pattern and reduced efficacy towards \u0026middot;OH radicals likely reflected differences in the specific phenolic profiles (composition and concentrations) among these five cultivars, influencing their interaction with this particular radical species. The RP values showed a trend congruent with the ABTS and DPPH results. The concentration required to achieve an absorbance of 1.0 (EC\u003csub\u003e1.0\u003c/sub\u003e) was highest for ZY100 (4.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 mg/mL) and lowest for K326 (2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 mg/mL) and HD (2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 mg/mL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), confirming their superior reducing capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH). Collectively, these results demonstrated that PCs extracts from inflorescences of five tobacco cultivars possess significant antioxidant capacity. A significant positive correlation was recorded between PCs extract concentration and activity across multiple assays, coupled with the distinct performance rankings of the cultivars, particularly the consistently high activity of K326 and HD extracts in ABTS, DPPH, and RP assays. This strongly supported the association of this antioxidant activity with their phenolic constituents. The observed variations in activity, especially the different efficacy against \u0026middot;OH radicals, are likely attributable to differences in the specific composition and concentration of PCs present in inflorescences of five tobacco cultivars.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Correlation analysis and principal component analysis (PCA)\u003c/h2\u003e \u003cp\u003eAntioxidant capacity of PCs is intricately influenced by their structural features, concentrations, and interactive effects (Olszowy, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shahidi and Ambigaipalan, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To elucidate the relationships among TPC, individual PCs, and antioxidant activities in tobacco inflorescence PCs extracts, Pearson correlation analysis was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among the nine PCs identified, TPC exhibited strong positive correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with rutin, neochlorogenic acid, caffeic acid, chlorogenic acid, and ferulic acid (r\u0026thinsp;=\u0026thinsp;0.668\u0026ndash;0.928), as well as with antioxidant capacities (r\u0026thinsp;=\u0026thinsp;0.740\u0026ndash;0.976). The four antioxidant measures were also mutually positively correlated (r\u0026thinsp;=\u0026thinsp;0.700\u0026ndash;0.968, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, ABTS, DPPH, and RP activities shared similar correlation patterns with individual PCs, showing positive associations with rutin, ferulic acid, caffeic acid, neochlorogenic acid, and f chlorogenic acid (r\u0026thinsp;=\u0026thinsp;0.528\u0026ndash;0.957, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, a negative correlation with quercetin was observed (r = \u0026minus;\u0026thinsp;0.525 to \u0026minus;\u0026thinsp;0.533, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, \u0026middot;OH scavenging activity was positively correlated with chlorogenic acid, ferulic acid, caffeic acid, and cryptochlorogenic acid (r\u0026thinsp;=\u0026thinsp;0.560\u0026ndash;0.865, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, PCA further visualized the relationships among samples based on TPC, PCs profiles and antioxidant activities. The first two principal components explained 82.8% of the total variance (PC1 of 59.7%, and PC2 of 23.1%). PCs extracts from inflorescences of K326 and HD were closely associated with higher levels of TPC, antioxidant activities, and most PCs, consistent with their high phenolic contents as determined by UHPLC-MS/MS. The distinct correlation patterns observed for different antioxidant assays further emphasize the importance of considering the specific types and concentrations of PCs when evaluating their bioactivity. Moreover, rutin, chlorogenic acid, and neochlorogenic acid were identified as key antioxidants in PCs extracts of tobacco inflorescence, which will help to underscore the significant role of these compounds in contributing to the overall antioxidant capacity derived from PCs extracts of tobacco inflorescence, and offer valuable insights for future research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Network pharmacology analysis\u003c/h2\u003e \u003cp\u003ePCs primarily exert antioxidant effects through two mechanisms: direct free radical scavenging or by modulating key proteins in cellular signaling pathways. This dual action inhibits ROS generation and regulating antioxidant defense systems (Kan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, network pharmacology analysis showed that 460 potential targets are predicted for nine main PCs of tobacco inflorescence, which were consolidated to 186 unique targets after deduplication. In parallel, 1,025 oxidative stress-related disease targets (Score\u0026thinsp;\u0026gt;\u0026thinsp;10) were retrieved from GeneCards. Intersection analysis revealed 71 shared targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), suggesting an important role in adjusting the antioxidant effects of PCs extract. The compound-target interaction network demonstrated complex multi-component, multi-target relationships (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), consistent with the synergistic mechanism characteristic of botanical formulations (Hong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). PPI network analysis and topological screening (with criteria of BC\u0026thinsp;\u0026ge;\u0026thinsp;0.0119, CC\u0026thinsp;\u0026ge;\u0026thinsp;0.583, DC\u0026thinsp;\u0026ge;\u0026thinsp;18.73) identified 17 hub targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Among these, AKT1 and TNF exhibited the highest centrality, alongside PTGS2, STAT3, EGFR, and ESR1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). These findings highlighted their pivotal roles as regulators of oxidative stress. AKT1, a serine/threonine kinase, orchestrates cellular responses to oxidative damage by activating the NRF2 pathway to enhance antioxidant enzyme expression and suppressing NLRP3 inflammasome-mediated inflammation (Linton et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). TNF, a master inflammatory cytokine, modulates redox balance through NF-κB signaling and ROS generation pathways (Blaser et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Their central positions in the PPI network underscore their suitability as core targets for subsequent molecular docking validation. Functional enrichment analysis further elucidated the mechanistic framework. GO analysis annotated 595 terms (381 biological processes, 53 cellular components, 141 molecular functions), with top entries highlighting regulation of oxidative stress response and phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). KEGG analysis found 150 significant pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), accompanying the 20 most enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), revealing multi-pathway synergy. PI3K-Akt signaling pathway was the most enriched one, which governs cell survival and antioxidant responses under stress (Wikan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other notable pathways included AGE-RAGE, insulin resistance, and fluid shear stress and atherosclerosis pathways, which converge on diabetic complications and atherosclerosis. The pathways of EGFR tyrosine kinase inhibitor resistance and endocrine resistance, which are linked to carcinogenesis associated with oxidative stress (Hong et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kan et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Notably, enrichment of PI3K-Akt signaling pathway, which is coordinately regulated by AKT1 and TNF, provides a molecular basis for the extract's ability to attenuate oxidative damage through interconnected survival and inflammatory pathways (Fruman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wikan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Collectively, these results delineated a framework of multi-compound, multi-target, multi-pathway, in which the phenolic constituents of tobacco inflorescence PCs synergistically modulate oxidative stress \u003cem\u003evia\u003c/em\u003e core targets AKT1 and TNF, primarily through PI3K-Akt signaling, ROS-related carcinogenesis pathways, and inflammatory cascades. This systematic analysis established a pharmacological foundation for further mechanistic exploration, with AKT1 and TNF prioritized as critical nodes for experimental validation given their network centrality and established roles in oxidative stress regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Molecular docking analysis\u003c/h2\u003e \u003cp\u003eInteractions between nine key PCs from tobacco inflorescence and the hub targets AKT1 and TNF were validated using molecular docking analysis, which were identified \u003cem\u003evia\u003c/em\u003e network pharmacology as central regulators of oxidative stress. The analysis revealed that lower docking scores indicate stronger binding affinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The binding energy heatmap confirmed stable binding of each compound to both targets, with most exhibiting binding energies \u0026le; \u0026minus;\u0026thinsp;5.0 kcal/mol, suggesting good affinity. Notably, binding energies of \u0026le; \u0026minus;\u0026thinsp;7.0 kcal/mol denoted higher affinity (Noshad et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rutin showed the strongest binding to TNF (\u0026ndash;7.7 kcal/mol), interacting with residues PHE-144, LYS-65, LEU-142, PRO-139, ASP-140, GLN-67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). It also exhibited robust binding to AKT1 (\u0026ndash;7.4 kcal/mol), engaging residues TRP-80, LEU-78, and ASP-32 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Other compounds including kaempferol, neochlorogenic acid, cryptochlorogenic acid, quercetin, and chlorogenic acid, displayed favorable binding energies ranging from \u0026minus;\u0026thinsp;7.1 to \u0026minus;\u0026thinsp;6.0 kcal/mol to both targets. These interactions involved critical residues such as GLU-17/LYS-14 (AKT1-kaempferol) and ARG-32 (TNF-kaempferol) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The engagement of functional residues, such as TRP-80/ASP-32 in AKT1\u0026rsquo;s PH domain and PHE-144/LEU-142 near TNF\u0026rsquo;s receptor-binding site, suggested potential modulation of target activity. These findings are consistent with established phenolic-protein interaction models (Jin and Wei, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They supported the notion that AKT1 and TNF are primary mediators of the antioxidant effects of tobacco inflorescence PCs. The high affinity of rutin, chlorogenic acid, and neochlorogenic acid aligns with their prior identification as key antioxidant markers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which are related with the results from ABTS, DPPH, \u0026middot;OH and RP antioxidant assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Their structural complementarity to AKT1 and TNF provided a mechanistic basis for antioxidant activity. Critically, their abundance of these compounds in the most active extracts, for example K326 (highest rutin and neochlorogenic acid) and HD (highest chlorogenic acid, cryptochlorogenic acid, caffeic acid, ferulic acid), further underscored their functional significance. AKT1, a master regulator of the PI3K-Akt pathway, orchestrates antioxidant responses \u003cem\u003evia\u003c/em\u003e NRF2 activation, which enhances the expression of antioxidant molecules, like HO-1/NQO1, and by suppressing NLRP3 inflammasome-mediated inflammation (Fruman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). TNF modulates redox balance through NF-κB and ROS pathways (Wikan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The concerted binding of these PCs to both targets suggested a synergistic attenuation of oxidative damage \u003cem\u003evia\u003c/em\u003e PI3K-Akt (survival) and TNF-NF-κB (anti-inflammatory) pathways. Overall, these results reinforce the framework of multi-compound, multi-target, highlighting the synergistic interactions of these PCs. Given their network centrality, established roles in oxidative stress, and validated binding affinity, AKT1 and TNF are proposed as priority candidates for experimental validation. Future studies should focus on confirming their contribution to the antioxidant efficacy of tobacco inflorescence PCs through cellular assays, western blotting, and other relevant experimental techniques.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe present study employed a UAE protocol optimized through RSM for the efficient extraction of PCs from tobacco inflorescence. Marked differences were observed in the extraction yield, antioxidant capacity, and phenolic profiles among inflorescence PCs extracts from five tobacco cultivars (YY87, ZY100, K326, HD, and CB1) under the optimized conditions. Quantitative and correlation analyses revealed that rutin, chlorogenic acid, and neochlorogenic acid are key antioxidants, exhibiting considerable variation across cultivars. These PCs are strongly correlated with ABTS, DPPH, hydroxyl radical (\u0026middot;OH) scavenging capacities, and RP values. Network pharmacology analysis predicted that nine PCs target 71 oxidative stress-associated genes, with AKT1 and TNF identified as hub targets through topological analysis. These targets mainly play a role in regulating oxidative stress \u003cem\u003evia\u003c/em\u003e the PI3K-Akt, ROS-mediated carcinogenesis, and inflammatory signaling pathway. Molecular docking simulations confirmed highly stable binding between these nine phenolics and the hub targets, with binding energies \u0026le; \u0026minus;\u0026thinsp;7.0 kcal/mol. Collectively, the findings established a solid scientific basis for leveraging tobacco inflorescence as a promising source of PCs. Moreover, these results also suggested that these PCs have the potential to be used as natural antioxidants in pharmaceuticals, and nutraceuticals, aimed at preventing and managing oxidative stress-related diseases. While \u003cem\u003ein vitro\u003c/em\u003e activity data have been successfully obtained, the most significant insight of this study lies in elucidating the complex interactions among these PCs. Future research will employ \u003cem\u003ein vivo\u003c/em\u003e and \u003cem\u003ein vitro\u003c/em\u003e models to delineate the antioxidant mechanisms of tobacco inflorescence PCs and their function in regulating the oxidative stress-disease axis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLi-feng Jin:\u003c/strong\u003e Methodology, Data curation, Software, Writing-original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eZong-yu Hu:\u003c/strong\u003e Data curation, Formal analysis, Writing-original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eWei-guan Li:\u003c/strong\u003e Formal analysis, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eZhao-peng Luo:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Data curation, Investigation. \u003cstrong\u003eYue Yang\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Formal analysis. \u003cstrong\u003eShao-jun Fan\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Formal analysis, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eShun Gao:\u003c/strong\u003e Formal analysis, and writing\u0026mdash;review and editing.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eXue-fen Wang\u003c/strong\u003e: Conceptualization, Supervision, Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft. \u003cstrong\u003eFeng Li:\u003c/strong\u003e Conceptualization, Funding acquisition, Project administration, Supervision, Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Tobacco Key Project 10202102033, 110202101042 (No. JY-19), 2022530000241007, 110202202028 and KJXM-2024\u0026ndash;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials presented in this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors listed have read the complete manuscript and have approved submission of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix A. Supplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary material: Supplementary Figures and Tables\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmoriello, T., Mellara, F., Ciorba, R., Ceccarelli, D., Amoriello, M., Taddei, F., Ciccoritti, R., 2025. Phenols extraction from sorghum byproducts: upcycling strategies and food applications. Antioxidants 14, 668. https://doi.org/10.3390/antiox14060668\u003c/li\u003e\n\u003cli\u003eAn, Q., Sun, J., Yang, J., Yuetikuer, A., Zhang, S., Leng, L., Zhan, H., 2025. Thermochemical valorization of tobacco wastes into biofuels and carbon materials: A comprehensive review. Chem. Eng. 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Rev. 475, 214875. https://doi.org/10.1016/j.ccr.2022.214875\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":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Nicotiana tabacum inflorescences, ultrasound-assisted extraction, UHPLC-MS/MS, antioxidant activity, network pharmacology, molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-8626691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8626691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTobacco (\u003cem\u003eNicotiana tabacum\u003c/em\u003e) inflorescence, an agricultural by-product that remains underutilized, is an excellent source of bioactive components but has received limited research interest. This study developed an ultrasound-assisted extraction (UAE) process to optimize the recovery of phenolic compounds (PCs) from this material. The phenolic profiles and antioxidant capacities were systematically evaluated across five cultivars (YY87, ZY100, K326, HD, and CB1). Optimal UAE conditions were determined as follows: ultrasound power of 335 W, liquid-solid ratio of 51 mL/g, ethanol concentration of 45%, and extraction time of 31 min, achieving a PCs yield of 24.33 ± 0.54 mg GAE/g DW. The model's reliability was confirmed by the close match between the predicted and experimental values. Considerable variations in PCs content, phenolic component, and antioxidant activities were found among cultivars, with K326 and HD showing the highest levels. Rutin, chlorogenic acid, and neochlorogenic acid were strongly associated with ABTS, DPPH, and hydroxyl radical scavenging activities, as well as reducing power. Network pharmacology analysis revealed that nine PCs target 71 oxidative stress-associated genes. Protein-protein interaction (PPI) network analysis identified AKT1 and TNF as central hub targets. Molecular docking confirmed stable binding interactions between these key PCs and the targets, with binding energies ≤ -7.0 kcal/mol. These findings provide a comprehensive experimental basis for the utilization of tobacco inflorescence PCs as a prospective source of antioxidants.\u003c/p\u003e","manuscriptTitle":"Phenolic compounds from Nicotiana tabacum inflorescence: optimized extraction, chemical composition, evaluation of antioxidant activity integrating network pharmacology and molecular docking analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 13:12:40","doi":"10.21203/rs.3.rs-8626691/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-17T07:14:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T06:02:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T16:36:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T21:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220996157034112085100649134865934513466","date":"2026-01-30T00:50:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37754039297944018392506230056140678142","date":"2026-01-29T17:09:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126236740002806014981313301914138619047","date":"2026-01-29T16:45:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T16:30:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T17:22:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T17:21:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chemical and Biological Technologies in Agriculture","date":"2026-01-17T14:21:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4a999214-e7b2-41f0-99bb-bd94a806e0cb","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T06:57:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 13:12:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8626691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8626691","identity":"rs-8626691","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}