Valorization of Fruit Pomaces for Glycosidic Enzymes Production via Solid State Fermentation

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Hafez, Abeer E. Mahmoud, Hadeer A. Mahmoud, Amira T. Mohammed, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7602826/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Agro-industrial waste represents an efficient and affordable substrate for microbial enzyme production; as an underutilized agricultural and food processing by-product, usage of Agro-industrial waste as a substrate for enzyme production fits within the framework of the circular bioeconomy by supporting waste valorization and environmental sustainability. This research evaluated the effective valorization of fruit-based agro-wastes (pomegranate, mango, orange, and grape pomace) for microbial production of glycosidic enzymes (amylase, xylanase, pectinase) using 14 microbial strains under solid-state fermentation conditions. Results For the 14 strains studied, Candida guilliermondii NRRL Y-2075 resulted in the highest reported amylase activity (4344.67 U/gds) when using pomegranate pomace. A response surface methodology was used with a central composite design model to optimize key parameters affecting amylase production for solid state fermentation: pH, inoculum size, incubation temperature and time. The optimum pH, size of inoculum, incubation temperature and incubation time were 5.6, 12.2%, 30.7°C and 24 hours, respectively, which resulted in a validated amylase activity of 4838.23 U/gds. The one-factor-at-a-time optimization revealed that the addition of external carbon, nitrogen, amino acids, or metal ions reduced C. guilliermondii amylase production given the richness of pomegranate pomace as a fermentation medium. Conclusion This study shows that fruit pomaces, especially pomegranate, can be used as a sole medium for glycosidic enzyme production, providing both efficiency and sustainability, emphasizing the relevance to industrial bioprocesses. Glycosidases Agro-industrial Wastes Solid State Fermentation Pomegranate Peels Candida guilliermondii Amylase RSM. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Agro-industrial activities generate over 2 billion tons of waste annually, primarily from agricultural production and food processing industries [ 1 ]. Improper disposal of this organic waste through landfilling or incineration contributes to serious environmental problems, including greenhouse gas emissions, unpleasant odors, and contamination of water and soil [ 1 ]. Recently, agro-industrial waste, specifically by-products from fruit processing, has been suggested as a potential source of raw materials, rather than as an environmental burden. These wastes contain large amounts of bioactive compounds, which serve as an excellent substrate for microbial growth with a potential of high-value metabolites production, including enzymes, pigments, and single-cell proteins. Furthermore, many industries, like pharmaceutical, textile, and food industries, show an increasing reliance on microbial metabolites. Agro-industrial waste valorization allows not only the incorporation of zero waste and circular economy principles but also advances Sustainable Development Goals 12 and 13 [ 2 , 3 ]. Solid-state fermentation (SSF) is one of the novel and effective eco-technological ways of converting agro-industrial waste into products of value [ 4 ]. Per the definition, SSF is defined as microbial cultivation on solid material with little to no free water. Not only is SSF a unique method of continuously producing value-added products, but it is also one of the most energy-efficient and environmentally sustainable ways of recovering value from agro-industrial waste by utilizing an organic substrate [ 5 , 6 ]. Glycoside hydrolases (GHs) are enzymes that hydrolyze glycosidic bonds in carbohydrates to soluble sugars [ 7 ]. They are commonly referred to as glycosidases, and occur in almost all living organisms, where they have various biological roles [ 8 ]. GHs have numerous industrial applications, including biofuel production and the paper industry, where they hydrolyze starch coatings to enhance paper smoothness, improving writing quality [ 9 ]. Among GHs, amylases are widely utilized across various industries. Within the detergent industry, amylases are used to remove starchy stains, while in the textile industry, they are used to desize fabrics. They are also very important in the food industry, as they are essential enzymes in baking, brewing, and starch liquefaction. Amylases also have diagnostic and therapeutic applications in the clinical and pharmaceutical sectors [ 10 , 11 ]. However, there is limited research exploring the comparative potential of diverse fruit wastes under SSF conditions for glycosidic enzyme production. Additionally, the optimization of this process using Response Surface Methodology (RSM), a statistical technique ideal for maximizing enzyme production by adjusting multiple fermentation parameters, remains underexplored. Thus, this study aims to valorize agro-industrial fruit wastes, namely grape pomace, mango, orange, and pomegranate peels, as cost-effective substrates for glycosidic enzymes production under SSF. Particular attention was given to optimizing critical process parameters using RSM, aiming to maximize enzyme productivity and contribute to sustainable bioprocess development within the context of a circular bioeconomy. Materials and methods Fruit pomace Fruit pomaces, including pomegranate ( Punica granatum ) peels, mango ( Mangifera indica ) peels, orange ( Citrus sinensis ) peels and grape ( Vitis vinifera ) pomace, were generously collected from juice extraction shops and food processing factories located in Cairo, Egypt, during their respective harvesting seasons. Pomaces were collected fresh, washed with tap water, sliced, minced in a mixer and stored at − 20℃ until used. Microorganisms Bacterial strains were obtained from the Molecular Genetics Department, Biotechnology Research Institute, National Research Centre. One gram of various soil samples collected from different locations in Egypt were transported to the microbial genetics laboratory and transferred into fresh 100 mL salt medium [(g/L): glucose, 10; NaNO₃, 0.5; K₂HPO₄, 1.0; MgSO₄·7H₂O, 0.5; KCl, 0.5; FeSO₄·7H₂O, 0.001]. The cultures were incubated at 37°C for 48 hours and the bacterial strains were identified biochemically and morphologically according to Holt, Krieg et al. [ 12 ]. Molecular identification was subsequently performed by 16S rDNA gene sequencing [ 13 ] (Table 1 ). Yeast strains were purchased from the Agricultural Research Service, Peoria, Illinois, USA (Table 2 ). Table 1 Bacterial strain used during the study Strain Number Strain name Source Accession number 1 Bacillus cereus Soil LC315566 2 B. subtilis Soil LC315565 3 B. licheniformis Soil LC315920 4 B. thuringiensis Soil LC438914 5 B. amyloliquefaciens Soil PV569636 6 B. proteolyticus Soil PV569637 7 B. velezensis Soil PV569638 8 B. siamensis Soil PV569639 9 B. atrophaeus Soil PV569640 10 B. amyloliquefaciens plantarum Soil PV569641 Table 2 Yeast strains used during the study Strain number Strain name 1 Kluyveromyces marxianus NRRL Y-7571 2 Kluyveromyces marxianus NRRL Y-8281 3 Candida bambicola NRRL Y-17069 4 Candida guilliermondii NRRL Y-2075 Medium composition and growth condition Bacterial strains were adapted according to the method described by the American Public Health Association [ 16 ], while yeast strains were adapted following the procedure outlined by Wickerham [ 17 ]. Screening of different microorganisms for glycosidases production The ability of various microorganisms to utilize various agro-industrial wastes, including grape pomace and mango, orange, and pomegranate peels, to produce glycosidic enzymes, namely amylase, xylanase, and pectinase under SSF was screened. The microorganism exhibiting the highest enzyme activity was selected for further optimization of fermentation parameters. Solid state fermentation For SSF, suspension aliquots of 1 ml (approximately 1.5 ×10 8 CFU/ml, corresponding to a 0.5 McFarland standard) were inoculated into 250 ml Erlenmeyer flasks containing 10 g of sterilized fruit pomace (autoclaved at 121℃ for 20 minutes at 15 psi). Samples were incubated statically at 35°C for 48 hours [ 18 ]. Enzyme extraction Crude enzyme was extracted from the fermented fruit pomaces by adding distilled water at a 1:10 (w/v) ratio and shaking (150 rpm) at 25°C for 60 minutes. The resulting slurry was then filtered through a double-layered muslin cloth by manual squeezing, and the filtrate was collected and used as the crude enzyme extract [ 19 ]. Enzyme assay Glycosidases activities were determined spectrophotometrically by measuring reducing sugars released according to Nelson [ 20 ] and Somogyi [ 21 ] at 540 nm. One unit of enzyme activity was defined as the amount of enzyme that releases µmol equivalents of reducing sugars (maltose for amylase, xylose for xylanase, and galacturonic acid for pectinase) in 1 minute under assay conditions. Enzyme activities are expressed as unit per gram dry substrate (U/gds). U / ml = \(\:\frac{\text{O}.\:\text{D}\:\text{o}\text{f}\:\text{t}\text{e}\text{s}\text{t}}{\text{O}.\:\text{D}\:\text{o}\text{f}\:\text{s}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}}\) × \(\:\frac{\text{C}\text{o}\text{n}\text{c}.\:\text{o}\text{f}\:\text{s}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}}{\text{M}\text{o}\text{l}\text{e}\text{c}\text{u}\text{l}\text{a}\text{r}\:\text{w}\text{e}\text{i}\text{g}\text{h}\text{t}\:\text{o}\text{f}\:\text{s}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}}\) × \(\:\frac{1}{\text{m}\text{l}\:\text{o}\text{f}\:\text{e}\text{n}\text{z}\text{y}\text{m}\text{e}}\) Optimization of fermentation process parameters Various physico-chemical and nutritional parameters influencing enzyme production during SSF were optimized. Optimization was performed using response surface methodology with a central composite design model for four quantitative factors, while qualitative factors were subsequently optimized using the one-factor-at-a-time (OFAT) approach. Response surface methodology based on central composite design optimization The optimization of four independent variables, namely pH (A), inoculum size (B), incubation temperature (C), and incubation time (D), was carried out using a CCD model constructed with Design-Expert software (version 11.1.2.0, Stat-Ease Inc., USA). The experimental design was based on five levels for each factor, where the coded values of -1, 0, and + 1 represent the low, middle, and high levels, respectively. The CCD model consisted of 8 axial points, 10 center points, and two replicates of the factorial points, resulting in a total of 50 experimental runs. The design parameters and their corresponding coded and actual values are summarized in Table 3 . Table 3 Experimental factors and their corresponding coded and actual values used in the CCD model Factor Name Units Min. Max. Coded Low (-1) Coded High (+ 1) Mean (0) Std. Dev. A pH - 3.00 12.00 -1 = 5.61 + 1 = 9.39 7.50 1.78 B Inoculum size % 5.00 30.00 -1 = 12.24 + 1 = 22.76 17.50 4.94 C Temp. °C 25.00 45.00 -1 = 30.80 + 1 = 39.20 35.00 3.95 D Time Hour 7. 46 64.54 -1 = 24.00 + 1 = 48.00 36.00 11.28 Validation of response surface methodology optimum conditions The optimized conditions obtained from the RSM model were validated by conducting independent experiments in triplicate under the predicted optimal parameters. Amylase activity (U/gds) was measured as the response variable. The percentage error between the predicted and experimentally obtained enzyme activities was calculated to assess the model’s accuracy. Statistical description of RSM model Data were analyzed using Design-Expert software (Stat-Ease Inc., Minneapolis, MN, USA). Model adequacy was evaluated by analysis of variance (ANOVA). The significance of model terms was assessed, and the model fit was determined using R², adjusted R², predicted R², and lack-of-fit tests. Three-dimensional response surface plots were generated to visualize interaction effects between the independent variables. One-factor-at-a-time optimization The influence of various qualitative factors was evaluated using the one-factor-at-a-time (OFAT) approach. The effects of different carbon sources, nitrogen sources, amino acids and metal ions were tested under optimized CCD conditions to further enhance enzyme production. Effect of different carbon sources To examine the effect of additional carbon sources on enzyme production, the fermentation medium was supplemented individually with glucose, xylose, fructose, mannose, sucrose, lactose, sorbitol, mannitol, and soluble starch at a 1% (w/w) concentration. Effect of different nitrogen sources To study the effect of supplementation of additional nitrogen sources on enzyme production, some organic nitrogen sources (malt extract, peptone, urea, yeast extract, and casein) and inorganic nitrogen sources (ammonium sulfate and ammonium nitrate) were added solely to the fermentation medium at 1% (w/w) concentration on an equivalent nitrogen basis. Effect of different amino acids The effect of adding different amino acids to the fermentation medium was studied. Arginine, asparagine, glycine, histidine, methionine, leucine, tyrosine, and tryptophan were added solely to the fermentation medium at a concentration of 1% (w/w). Effect of various metal ions Various metal chlorides, namely calcium, barium, cobalt, copper, ferric, ferrous, magnesium, potassium, sodium, and zinc were separately added to the fermentation medium at a concentration of 1% (w/w) to investigate their effect on enzyme production. Statistical analysis All data are expressed as mean ± standard deviation (SD) based on three independent batches. Statistical analyses were performed using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA). A one-way ANOVA, followed by Tukey’s HSD post hoc test, was conducted to determine significant differences among treatment means. Differences were considered statistically significant at p < 0.05. Results and discussion The global demand for industrial enzymes has increased significantly over recent decades. The market value of industrial enzymes grew from approximately 0.31 billion USD in 1960 to 6 billion USD in 2020, and it is projected to surpass 9 billion USD by 2027 [ 22 ]. Microbial enzymes are favored for their thermal and pH stability, ease of cultivation, and multifunctional applications, making them highly suitable for diverse industrial applications [ 23 ]. Therefore, our study focused on producing various glycoside hydrolases using different microorganisms under SSF. Screening and Identification of the Most Potent Glycosidase-Producing Microorganism The screening of 14 microorganisms for glycosidases production (amylase, pectinase, and xylanase) using different fruit pomaces as substrates revealed significant variations in enzymatic activity, highlighting the influence of microbial species and substrate composition on enzyme yield [ 24 ]. This study provides valuable insights into the potential of agro-industrial byproducts as sustainable substrates for microbial enzyme production. As shown in Figs. 1 , 2 , 3 , and 4 , across all tested agro-industrial wastes (pomegranate, mango, grape, and orange pomaces), the unfermented substrates recorded no detectable amylase activity. Among the tested microbes, C. guilliermondii exhibited the highest amylase activity (4344.67 U/gds) on pomegranate pomace, which was significantly higher than all other microbes within the same pomace and clearly distinct from the unfermented control ( p < 0.05). This could be due to its metabolic adaptability to the polyphenol-rich nature of pomegranate pomace [ 25 ]. According to Figs. 1 , 2 , 3 , and 4 , and unlike amylase, all the unfermented pomaces in the study—pomegranate, mango, grape, and orange—showed measurable pectinase activity, possibly due to residual endogenous enzymes [ 26 ]. Among the tested microorganisms, C. guilliermondii again demonstrated superior performance, achieving the highest pectinase activity (4021.57 U/gds) on orange pomace. This value was significantly higher than that observed for other microbes and the unfermented control ( p < 0.05), confirming the broad enzymatic potential of the strain. Unfermented pomegranate and orange pomaces exhibited no detectable xylanase activity, whereas mango and grape pomaces did, likely due to intrinsic xylanase enzymes. As illustrated in Figs. 1 , 2 , 3 , and 4 , Kluyveromyces marxianus NRRL Y-8281 exhibited the highest xylanase activity among all tested microorganisms when cultivated on mango pomace, showing a statistically significant difference ( p < 0.05). Based on the screening results, C. guilliermondii was selected for further optimization of amylase production due to its highest enzymatic activity on pomegranate pomace. Optimization of Amylase Production by C. guilliermondii NRRL Y-2075 Optimization of amylase activity using RSM Several parameters, including microorganism, substrate selection, pH, temperature, inoculum size, and humidity can greatly affect the SSF process. Optimizing these factors is important for increasing enzyme yield and making the process more cost-effective [ 27 ]. In this study, a CCD model was constructed to optimize four key parameters: pH, inoculum size, incubation temperature, and incubation time. The model was used to evaluate the individual and interactive effects of these variables on enzyme production and to determine their optimal conditions, as shown in Table 3 . Table 4 shows close proximity between actual and predicted amylase yields, indicating the model’s reliability and experimental stability. The ANOVA results from Table 5 show that the quadratic model was highly significant ( F -value = 243517.84, p < 0.0001) and that all main effects (pH, inoculum size, temperature, and time) and their interactions significantly influence amylase production. The small residual mean square as well as the lack of fit not being significant ( F -value = 1.10, p = 0.4025) confirms the adequacy of the model and implies that the probability of such a large F -value arising from random noise is extremely low [ 28 ]. The results shown in Table 6 indicate that there is a strong foundation for the model with R², adjusted R², and predicted R² equal to 1.0000, indicating there was an excellent fit and reliability to predict amylase production with high accuracy. Similar high model accuracy (R² = 1) has also been reported in endoglucanase optimization using RSM by [ 24 ], indicating that such results are achievable under well-controlled experimental conditions. Table 4 CCD results of C. guilliermondii amylase Run Order Experimental factors Actual Value U/gds Predicted Value U/gds A: pH B: Inoculum size (%) C: Temperature (°C) D: Time (hour) 1 7.5 17.5 35 36 3669.17 3671.31 2 5.60798 22.7556 30.7955 24 4672.16 4672.97 3 5.60798 22.7556 39.2045 24 3943.69 3941.77 4 7.5 17.5 35 36 3671.25 3671.31 5 5.60798 12. 2444 39.2045 48 3299.22 3299.71 6 7.5 17.5 35 36 3671.23 3671.31 7 7.5 17.5 35 36 3668.23 3671.31 8 9.39202 12. 2444 39. 2045 24 3769.21 3769.55 9 7.5 17.5 35 36 3667.23 3671.31 10 7.5 17.5 35 36 3674.66 3671.31 11 9.39202 22. 7556 30.7955 24 2991.65 2989.75 12 7.5 17.5 35 36 3670.69 3671.31 13 9.39202 12.2444 30.7955 24 3527.97 3531.53 14 9.39202 22.7556 39.2045 24 3280.90 3283.61 15 5.60798 22.7556 30.7955 24 4673.56 4672.97 16 7.5 17.5 35 36 3674.26 3671.31 17 9.39202 22.7556 30.7955 48 2885.01 2887.18 18 7.5 17.5 35 7.45 4178.77 4178.31 19 7.5 17.5 35 36 3671.00 3671.31 20 5. 60798 12. 2444 39.2045 24 4049.01 4049.80 21 12 17.5 35 36 3090.32 3088.86 22 9. 39202 22.7556 30.7955 24 2990.10 2989.75 23 5. 60798 22.7556 30.7955 48 3585.78 3586.50 24 5. 60798 22.7556 30.7955 48 3589.23 3586.50 25 9. 39202 22.7556 39.2045 24 3281.78 3283.61 26 9. 39202 12.2444 39.2045 24 3770.13 3769.55 27 7.5 17.5 35 64.54 3164.01 3164.32 28 9. 392 22.755 30.7955 48 2886.24 2887.18 29 7.5 17.5 25 36 3574.17 3574.94 30 5.60798 22.7556 39.2045 24 3942.11 3941.77 31 5.60798 12.2444 30.7955 48 3433.60 3431.50 32 9.39202 12.2444 30.7955 48 3110.94 3110.10 33 9.39202 12.2444 30.7955 24 3533.59 3531.53 34 5.60798 12.2444 30.7955 48 3430.55 3431.50 Table 4 CCD results of C. guilliermondii amylase (cont.) 35 5.60798 12.2444 39. 2045 48 3300.20 3299.71 36 5.60798 12.2444 30. 7955 24 4835.05 4836.83 37 7.5 17.5 35 36 3675.02 3671.31 38 9.39202 22.7556 39.2045 48 3838.14 3836.29 39 9.39202 12.2444 39.2045 48 4001.32 4003.37 40 7.5 30 35 36 3476.15 3474.53 41 7.5 17.5 45 36 3768.12 3767.68 42 9.39202 12.2444 30.7955 48 3109.24 3110.10 43 9.39202 12.2444 39.2045 48 4002.25 4003.37 44 5.60798 12.2444 39.2045 24 4050.31 4049.80 45 3 17.5 35 36 4251.95 4253.77 46 7.5 5 35 36 3872.00 3868.10 47 5.60798 22.7556 39.2045 48 3510.10 3510.55 48 9.39202 22.7556 39.2045 48 3838. 10 3836.29 49 5.60798 22.7556 39.2045 48 3509.10 3510.55 50 5.60798 12.2444 30.7955 24 4837.20 4836.83 Table 5 Analysis of variance (ANOVA) for the response surface quadratic model for amylase production Source Sum of Squares Df Mean Square F-value p-value Model 1. 032E + 07 10 1. 032E + 06 2. 435E + 05 < 0.0001 significant A-pH 2. 598E + 06 1 2. 598E + 06 6. 127E + 05 < 0.0001 B-Inoculum size 2. 965E + 05 1 2. 965E + 05 69930.79 < 0.0001 C-Temperature 71109.17 1 71109.17 16771.50 < 0.0001 D-Time 1. 968E + 06 1 1. 968E + 06 4. 642E + 05 < 0.0001 AB 2. 856E + 05 1 2. 856E + 05 67369.68 < 0.0001 AC 2. 101E + 06 1 2. 101E + 06 4. 956E + 05 < 0.0001 AD 1. 936E + 06 1 1. 936E + 06 4. 566E + 05 < 0.0001 BC 6235.09 1 6235.09 1470.58 < 0.0001 BD 2. 033E + 05 1 2. 033E + 05 47960.44 < 0.0001 CD 8. 587E + 05 1 8. 587E + 05 2. 025E + 05 < 0.0001 Residual 165.36 39 4. 24 Lack of Fit 63.07 14 4. 51 1. 10 0.4025 not significant Pure Error 102.28 25 4. 09 Cor Total 1. 033E + 07 49 Table 6 Fit statistics of the response surface quadratic model for amylase production Std. Dev. 2.06 R² 1. 0000 Mean 3671.31 Adjusted R² 1. 0000 C. V. % 0.0561 Predicted R² 1. 0000 Adequate Precision 2018. 6891 The predicted R² of 1.0000 is in reasonable agreement with the adjusted R² of 1.0000; i.e., the difference is less than 0.2. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 2018.689 indicates an adequate signal. This model can be used to navigate the design space. Final equation in terms of actual factors The experimental data were fitted to a second-order polynomial equation to model the effects of the studied variables on amylase activity. The resulting regression equation, in terms of actual factors, is Amylase activity = 20628.33 − 1480.68 × pH − 12.09 × Inoculum size − 359.91 × Temperature − 234.77 × Time − 9.50 × pH × Inoculum size + 32.21 × pH × Temperature + 10.83 × pH × Time + 0.63 × Inoculum size × Temperature + 1.26 × Inoculum size × Time + 3.24 × Temperature × Time The effect of the interaction of various fermentation process parameters on the amylase production (Z axis) was studied by plotting three-dimensional response surface curves against any two independent variables while keeping the other independent variable at their (0) levels. Therefore, six response surfaces were obtained by considering all the possible combinations The 3D response surface plots as demonstrated in Fig. 5 illustrate the interactive effects of the independent variables on amylase activity under SSF. These visualizations were derived from the CCD model and provide insight into the relationship between variable pairs and their combined influence on enzyme production. According to Fig. 5 plots A and D show that prolonged incubation time combined with either higher inoculum size or optimal temperature resulted in enhanced enzyme activity. Similarly, plots B and F suggest that enzyme production was sensitive to changes in pH when paired with inoculum size or time, with a peak observed at moderate levels. Overall, these plots confirm the importance of fine-tuning multiple factors simultaneously rather than optimizing each independently, as significant interactions between variables contribute to maximizing enzymatic output. These findings support the model's validity and are consistent with previous studies that emphasize the effectiveness of RSM in optimizing enzyme production parameters. As shown in Fig. 6 , the optimal fermentation conditions for amylase production by C. guilliermondii were pH 5.6, an incubation temperature of 30.7°C, and an inoculum size of 12.2% (v/w), with peak activity observed after 24 hours. These findings are consistent with those of Yalcın and Corbaci [ 29 ], who reported optimal amylase production by Saccharomycopsis fibuligera at pH 5.5 and 30°C on amylase activity medium (AAM), as well as Aggarwal and Mondal [ 30 ] who reported 30°C as the optimum temperature for C. guilliermondii growth. Likewise, the 24-hour incubation period aligns with the observations of Wanderley, Torres et al. [ 31 ] for Cryptococcus flavus amylase production on starch-containing medium. The inoculum size also closely matches that reported by Mrudula and Kokila [ 32 ], who found 15% (v/w) to be optimal for amylase activity by Bacillus cereus under solid-state fermentation using wheat bran. Additionally, Acourene and Ammouche [ 33 ] reported comparable optimum conditions for C. guilliermondii amylase production from date waste syrup at 30°C and a pH of 6.0. These findings highlight the rapid enzyme secretion capabilities of certain yeast strains under slightly acidic and mesophilic conditions. Validation of response surface methodology optimum conditions Validation of the optimized conditions for amylase production confirmed the reliability of the RSM model. The predicted mean amylase activity (4836.83 U/gds) closely matched the experimentally observed value (4838.23 U/gds), with a negligible standard deviation (2.0591), indicating high model accuracy. The narrow 95% prediction interval (4832.09–4841.58 U/gds) further supports the model’s precision, as the observed value falls well within this range. These results align with previous studies demonstrating that RSM effectively optimizes enzyme production parameters while minimizing variability [ 34 ]. The close agreement between predicted and experimental values suggests that the model is robust and suitable for scaling up amylase production under the derived optimal conditions (Table 7 ). Table 7 Experimental validation of RSM-optimized conditions for amylase production Response Predicted Mean Observed Std Dev n SE Pred 95% PI low 95% PI high Amylase activity 4836.83 4838.23 2.0591 1 2.3476 4832.09 4841.58 One-factor-at-a-time optimization Different carbon and nitrogen sources, amino acids and metal ions were added separately to the fermentation medium to study their influence on C. guilliermondii amylase production. The influence of different additives on amylase enzyme production was found to be statistically significant ( p < 0.05) using one-way ANOVA/Tukey HSD post hoc tests. According to Fig. 7A, the addition of carbon sources to pomegranate pomace did not result in a significant increase in amylase production compared to the no-additive control. However, monosaccharides and disaccharides showed slightly higher amylase activity than polysaccharides and sugar alcohols. Interestingly, the presence of monosaccharides and disaccharides reduced C. guilliermondii amylase production, which may be attributed to catabolite repression, as C. guilliermondii preferentially assimilates easily metabolised sugars such as glucose, sucrose and galactose over the more complex sugars present in pomegranate pomace, thereby inhibiting the expression of amylase-encoding genes and reducing the amylase production [ 35 , 36 ]. Similarly, the addition of polysaccharides, including starch, also led to reduced enzyme activity. This could be due to the inability of C. guilliermondii to utilize starch effectively [ 30 ]. This is partially aligned with the findings of Simair, Qureshi et al. [ 37 ] who reported supplementation of the medium with agro-industrial waste, specifically molasses and date syrup, to have higher amylase production than starch addition, but contrary to Saad, Othman et al. [ 38 ], who reported enhanced amylase production by B. licheniformis cultivated in a modified starch broth medium. Similarly, the addition of sugar alcohols significantly suppressed amylase production in this study, which contradicts the findings of Saha, Maity et al. [ 39 ], who reported an increased amylase yield by B. amyloliquefaciens when cultivated on wheat bran under SSF upon the addition of inositol and mannitol. Optimal conditions for maximum enzyme production can vary considerably depending on the specific microbial strain [ 40 ]. The type and concentration of nitrogen sources play a crucial role in regulating enzyme synthesis by influencing both nitrogen assimilation and metabolic activity [ 38 ]. As shown in Fig. 7B, the addition of external nitrogen sources generally resulted in a reduction in amylase production. However, urea caused the least reduction, yielding 2944.77 U/gds, compared to the highest activity observed in the no-additive control (4838.23 U/gds). This result aligns with the findings of Singh, Mishra et al. [ 41 ] who reported that the addition of various organic and inorganic nitrogen sources led to a decrease in α-amylase production by Bacillus cereus MTCC 1305 under SSF using wheat bran as a substrate. Amino compounds are considered stimulators of amylase synthesis and excretion rather than primary nitrogen or carbon sources [ 42 ]. However, in the present study, the addition of amino acids to pomegranate pomace resulted in a significant decline in amylase production by C. guilliermondii . As illustrated in Fig. 7C, leucine supplementation caused a moderate decrease in enzyme yield, while arginine exhibited a strong inhibitory effect. These findings partially align with those of Chaurasia, Chaurasia et al. [ 43 ], who reported that asparagine supplementation enhanced Rhizopus oryzae amylase production using Fernando's broth medium, whereas the addition of isoleucine, phenylalanine, and lysine inhibited its synthesis. The addition of metal ions to the fermentation medium negatively affected amylase production by C. guilliermondii . As presented in Fig. 7D, Fe⁺³ caused the least inhibition with 2044.23 U/gds, compared to the no additive control, while Na⁺ caused the greatest suppression (935.7 U/gds), which may be attributed to the fact that NaCl exerts osmotic pressure on C. guilliermondii cells [ 44 ]. These results contradict previous studies. For instance, Abo-Kamer, Abd-El-salam et al. [ 40 ] reported that Ca⁺² and Mg⁺² enhanced amylase production in Bacillus cereus A1-5 using synthetic starch medium under submerged fermentation, whereas Ba⁺² had an inhibitory effect. Similarly, Rehman, Saeed et al. [ 45 ] found that Ca + 2 and Na + enhanced amylase production in Bacillus cereus AS2 using Luria basal broth medium, while Mg + 2 , Zn + 2 , Hg + 2 , Cu + 2 and Fe + 3 were inhibitory. The reduced amylase production by C. guilliermondii upon the addition of external carbon, nitrogen sources, amino acids, and metal ions highlights the inherent nutritional adequacy of pomegranate pomace. With a composition of 4.9% protein, 17.7% carbohydrates, and a variety of essential minerals including calcium, potassium, phosphorus, sodium, iron, copper, manganese, and zinc the pomace alone appears sufficient to support microbial growth and enzyme synthesis [ 25 ]. The introduction of external additives may have disrupted this natural nutrient balance, resulting in inhibitory rather than beneficial effects on enzyme production. Conclusion This study indicates that agro-industrial fruit wastes, especially pomegranate pomace, are promising, consistent and sustainable substrates for microbial enzyme production under solid-state fermentation. C. guilliermondii NRRL Y-2075 was the most effective strain for amylase production with a maximum activity of 4344.67 U/gds. Using RSM, we determined optimum fermentation conditions and experimentally validated these results with a maximum amylase production at 4838.23 U/gds. The small difference between predicted and observed values indicates that the model is reliable. External supplementation of various additives (carbon, nitrogen sources, amino acids or metal ions) to the fermentation medium indicated an enzyme activity reduction, further establishing the nutritional value of simply using pomegranate pomace. This study provides valuable information toward developing cost-effective, environmentally friendly options for enzyme production while also promoting the possibilities of exploiting agro-waste as a substrate for sustainable industrial biotechnology. Abbreviations SSF Solid State Fermentation RSM Response Surface Methodology GHs Glycoside hydrolases U/gds Units per gram dry substrate CCD Central Composite Design OFAT One Factor At a Time ANOVA Analysis of Variance SD Standard Deviation Df Degrees of Freedom Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests Funding Open access funding was provided by the Science, Technology & Innovation Funding Authority (STDF) in cooperation with the Egyptian Knowledge Bank (EKB). Author Contribution Z. H. H., A.A.E. M., A. T. M., H. A. M., and M. K. I. contributed equally to this work. 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Optimization of physicochemical parameters for maximum amylase production by indigenously isolated Bacillus cereus AS2 strain. Pak J Pharm Sci. 2019;32:889–94. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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12:03:53","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":388,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/ea45ef71803b69d678c0a9ca.png"},{"id":94319000,"identity":"a1020520-edcb-455c-9046-096485fb4656","added_by":"auto","created_at":"2025-10-27 12:04:11","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171683,"visible":true,"origin":"","legend":"","description":"","filename":"22c666fe528e4bb38def2eb1d7a36bb01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/3991f0cec4e706f8c5031258.xml"},{"id":94319146,"identity":"1fd2fc70-0d91-4c80-87f5-6bfe3c310a18","added_by":"auto","created_at":"2025-10-27 12:04:19","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":182407,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/71752c1d5cb7d386c38fbbb0.html"},{"id":94489036,"identity":"03f280c6-3790-4de5-9db9-ccea5e039817","added_by":"auto","created_at":"2025-10-27 16:56:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAmylase, pectinase, and xylanase activities of different microbes cultivated on grape waste under solid-state fermentation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e· Unfermented fruit pomace: non-cultivated autoclaved fruit pomace.\u003c/p\u003e\n\u003cp\u003e· Data are presented as mean ± standard deviation from three independent batches.\u003c/p\u003e\n\u003cp\u003e· Differences between groups were analyzed using one-way ANOVA/Tukey HSD post hoc tests.\u003c/p\u003e\n\u003cp\u003e· Means bearing different letters superscripts within the same enzyme are significantly different from each other, at a significance level of \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/9061c4c4c5885f3c762738d5.png"},{"id":94319312,"identity":"f8721d05-5486-4a58-a535-2a3795bbe99d","added_by":"auto","created_at":"2025-10-27 12:04:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77868,"visible":true,"origin":"","legend":"\u003cp\u003eAmylase, pectinase, and xylanase activities of different microbes cultivated on mango waste under solid-state fermentation.\u003c/p\u003e\n\u003cp\u003e· Unfermented fruit pomace: non-cultivated autoclaved fruit pomace.\u003c/p\u003e\n\u003cp\u003e· Data are presented as mean ± standard deviation from three independent batches.\u003c/p\u003e\n\u003cp\u003e· Differences between groups were analyzed using one-way ANOVA/Tukey HSD post hoc tests.\u003c/p\u003e\n\u003cp\u003e· Means bearing different letters superscripts within the same enzyme are significantly different from each other, at a significance level of \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/60a058d94b6361d1520a7fa7.png"},{"id":94318624,"identity":"9f29a923-9573-4a0e-a372-177f79ee0724","added_by":"auto","created_at":"2025-10-27 12:03:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77011,"visible":true,"origin":"","legend":"\u003cp\u003eAmylase, pectinase, and xylanase activities of different microbes cultivated on orange waste under solid-state fermentation.\u003c/p\u003e\n\u003cp\u003e· Unfermented fruit pomace: non-cultivated autoclaved fruit pomace.\u003c/p\u003e\n\u003cp\u003e· Data are presented as mean ± standard deviation from three independent batches.\u003c/p\u003e\n\u003cp\u003e· Differences between groups were analyzed using one-way ANOVA/Tukey HSD post hoc tests.\u003c/p\u003e\n\u003cp\u003e· Means bearing different letters superscripts within the same enzyme are significantly different from each other, at a significance level of \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/b625966b048cc26dbbf1a5d2.png"},{"id":94319238,"identity":"522bbd44-e167-49fd-98ba-4e16ff369f3d","added_by":"auto","created_at":"2025-10-27 12:04:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74774,"visible":true,"origin":"","legend":"\u003cp\u003eAmylase, pectinase, and xylanase activities of different microbes cultivated on pomegranate waste under solid-state fermentation.\u003c/p\u003e\n\u003cp\u003e· Unfermented fruit pomace: non-cultivated autoclaved fruit pomace.\u003c/p\u003e\n\u003cp\u003e· Data are presented as mean ± standard deviation from three independent batches.\u003c/p\u003e\n\u003cp\u003e· Differences between groups were analyzed using one-way ANOVA/Tukey HSD post hoc tests.\u003c/p\u003e\n\u003cp\u003eMeans bearing different letters superscripts within the same enzyme are significantly different from each other, at a significance level of \u003cem\u003ep \u0026lt; 0.05\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/090f19ac31ff7729b3c046a2.png"},{"id":94319236,"identity":"f079e7a0-9d3e-48bc-bf45-c5c19f3578f1","added_by":"auto","created_at":"2025-10-27 12:04:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":758864,"visible":true,"origin":"","legend":"\u003cp\u003e3D response surface plots illustrating the interaction effects of four independent variables on amylase production by \u003cem\u003eC. guilliermondii\u003c/em\u003e NRRL Y-2075. The plots show the effects of (A) inoculum size (%) and incubation time (h), (B) pH and inoculum size (%), (C) pH and temperature (°C), (D) temperature (°C) and incubation time (h), (E) inoculum size (%) and temperature (°C), and (F) pH and incubation time (h) on amylase activity, while the other two factors are held constant at their zero (0) level.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/fba1d55578039abd673f789f.png"},{"id":99309209,"identity":"7c24007a-f464-4731-a8a1-44822836923b","added_by":"auto","created_at":"2025-12-31 16:09:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2722631,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7602826/v1/d02e5421-f7e9-4534-96fb-978f7c8ce6a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Valorization of Fruit Pomaces for Glycosidic Enzymes Production via Solid State Fermentation","fulltext":[{"header":"Background","content":"\u003cp\u003eAgro-industrial activities generate over 2\u0026nbsp;billion tons of waste annually, primarily from agricultural production and food processing industries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Improper disposal of this organic waste through landfilling or incineration contributes to serious environmental problems, including greenhouse gas emissions, unpleasant odors, and contamination of water and soil [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecently, agro-industrial waste, specifically by-products from fruit processing, has been suggested as a potential source of raw materials, rather than as an environmental burden. These wastes contain large amounts of bioactive compounds, which serve as an excellent substrate for microbial growth with a potential of high-value metabolites production, including enzymes, pigments, and single-cell proteins. Furthermore, many industries, like pharmaceutical, textile, and food industries, show an increasing reliance on microbial metabolites. Agro-industrial waste valorization allows not only the incorporation of zero waste and circular economy principles but also advances Sustainable Development Goals 12 and 13 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSolid-state fermentation (SSF) is one of the novel and effective eco-technological ways of converting agro-industrial waste into products of value [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Per the definition, SSF is defined as microbial cultivation on solid material with little to no free water. Not only is SSF a unique method of continuously producing value-added products, but it is also one of the most energy-efficient and environmentally sustainable ways of recovering value from agro-industrial waste by utilizing an organic substrate [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGlycoside hydrolases (GHs) are enzymes that hydrolyze glycosidic bonds in carbohydrates to soluble sugars [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. They are commonly referred to as glycosidases, and occur in almost all living organisms, where they have various biological roles [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. GHs have numerous industrial applications, including biofuel production and the paper industry, where they hydrolyze starch coatings to enhance paper smoothness, improving writing quality [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAmong GHs, amylases are widely utilized across various industries. Within the detergent industry, amylases are used to remove starchy stains, while in the textile industry, they are used to desize fabrics. They are also very important in the food industry, as they are essential enzymes in baking, brewing, and starch liquefaction. Amylases also have diagnostic and therapeutic applications in the clinical and pharmaceutical sectors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, there is limited research exploring the comparative potential of diverse fruit wastes under SSF conditions for glycosidic enzyme production. Additionally, the optimization of this process using Response Surface Methodology (RSM), a statistical technique ideal for maximizing enzyme production by adjusting multiple fermentation parameters, remains underexplored.\u003c/p\u003e\u003cp\u003eThus, this study aims to valorize agro-industrial fruit wastes, namely grape pomace, mango, orange, and pomegranate peels, as cost-effective substrates for glycosidic enzymes production under SSF. Particular attention was given to optimizing critical process parameters using RSM, aiming to maximize enzyme productivity and contribute to sustainable bioprocess development within the context of a circular bioeconomy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFruit pomace\u003c/h2\u003e\u003cp\u003eFruit pomaces, including pomegranate (\u003cem\u003ePunica granatum\u003c/em\u003e) peels, mango (\u003cem\u003eMangifera indica\u003c/em\u003e) peels, orange (\u003cem\u003eCitrus sinensis\u003c/em\u003e) peels and grape (\u003cem\u003eVitis vinifera\u003c/em\u003e) pomace, were generously collected from juice extraction shops and food processing factories located in Cairo, Egypt, during their respective harvesting seasons. Pomaces were collected fresh, washed with tap water, sliced, minced in a mixer and stored at \u0026minus;\u0026thinsp;20℃ until used.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMicroorganisms\u003c/h3\u003e\n\u003cp\u003eBacterial strains were obtained from the Molecular Genetics Department, Biotechnology Research Institute, National Research Centre. One gram of various soil samples collected from different locations in Egypt were transported to the microbial genetics laboratory and transferred into fresh 100 mL salt medium [(g/L): glucose, 10; NaNO₃, 0.5; K₂HPO₄, 1.0; MgSO₄\u0026middot;7H₂O, 0.5; KCl, 0.5; FeSO₄\u0026middot;7H₂O, 0.001]. The cultures were incubated at 37\u0026deg;C for 48 hours and the bacterial strains were identified biochemically and morphologically according to Holt, Krieg et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Molecular identification was subsequently performed by 16S rDNA gene sequencing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Yeast strains were purchased from the Agricultural Research Service, Peoria, Illinois, USA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBacterial strain used during the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrain Number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrain name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAccession number\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\u003e\u003cem\u003eBacillus cereus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC315566\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\u003e\u003cem\u003eB. subtilis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC315565\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\u003e\u003cem\u003eB. licheniformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC315920\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eB. thuringiensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC438914\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\u003e\u003cem\u003eB. amyloliquefaciens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePV569636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eB. proteolyticus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePV569637\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\u003e\u003cem\u003eB. velezensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePV569638\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\u003e\u003cem\u003eB. siamensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePV569639\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\u003e\u003cem\u003eB. atrophaeus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePV569640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eB. amyloliquefaciens plantarum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePV569641\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\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\u003eYeast strains used during the study\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStrain number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrain name\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\u003e\u003cem\u003eKluyveromyces marxianus\u003c/em\u003e NRRL Y-7571\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\u003e\u003cem\u003eKluyveromyces marxianus\u003c/em\u003e NRRL Y-8281\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\u003e\u003cem\u003eCandida bambicola\u003c/em\u003e NRRL Y-17069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCandida guilliermondii\u003c/em\u003e NRRL Y-2075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMedium composition and growth condition\u003c/h3\u003e\n\u003cp\u003eBacterial strains were adapted according to the method described by the American Public Health Association [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while yeast strains were adapted following the procedure outlined by Wickerham [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eScreening of different microorganisms for glycosidases production\u003c/h3\u003e\n\u003cp\u003eThe ability of various microorganisms to utilize various agro-industrial wastes, including grape pomace and mango, orange, and pomegranate peels, to produce glycosidic enzymes, namely amylase, xylanase, and pectinase under SSF was screened. The microorganism exhibiting the highest enzyme activity was selected for further optimization of fermentation parameters.\u003c/p\u003e\n\u003ch3\u003eSolid state fermentation\u003c/h3\u003e\n\u003cp\u003eFor SSF, suspension aliquots of 1 ml (approximately 1.5 \u0026times;10\u003csup\u003e8\u003c/sup\u003e CFU/ml, corresponding to a 0.5 McFarland standard) were inoculated into 250 ml Erlenmeyer flasks containing 10 g of sterilized fruit pomace (autoclaved at 121℃ for 20 minutes at 15 psi). Samples were incubated statically at 35\u0026deg;C for 48 hours [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEnzyme extraction\u003c/h2\u003e\u003cp\u003eCrude enzyme was extracted from the fermented fruit pomaces by adding distilled water at a 1:10 (w/v) ratio and shaking (150 rpm) at 25\u0026deg;C for 60 minutes. The resulting slurry was then filtered through a double-layered muslin cloth by manual squeezing, and the filtrate was collected and used as the crude enzyme extract [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnzyme assay\u003c/h3\u003e\n\u003cp\u003eGlycosidases activities were determined spectrophotometrically by measuring reducing sugars released according to Nelson [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Somogyi [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] at 540 nm. One unit of enzyme activity was defined as the amount of enzyme that releases \u0026micro;mol equivalents of reducing sugars (maltose for amylase, xylose for xylanase, and galacturonic acid for pectinase) in 1 minute under assay conditions. Enzyme activities are expressed as unit per gram dry substrate (U/gds).\u003c/p\u003e\u003cp\u003eU / ml = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{O}.\\:\\text{D}\\:\\text{o}\\text{f}\\:\\text{t}\\text{e}\\text{s}\\text{t}}{\\text{O}.\\:\\text{D}\\:\\text{o}\\text{f}\\:\\text{s}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e \u0026times; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{C}\\text{o}\\text{n}\\text{c}.\\:\\text{o}\\text{f}\\:\\text{s}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}}{\\text{M}\\text{o}\\text{l}\\text{e}\\text{c}\\text{u}\\text{l}\\text{a}\\text{r}\\:\\text{w}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}\\:\\text{o}\\text{f}\\:\\text{s}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e \u0026times;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\text{m}\\text{l}\\:\\text{o}\\text{f}\\:\\text{e}\\text{n}\\text{z}\\text{y}\\text{m}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eOptimization of fermentation process parameters\u003c/h3\u003e\n\u003cp\u003eVarious physico-chemical and nutritional parameters influencing enzyme production during SSF were optimized. Optimization was performed using response surface methodology with a central composite design model for four quantitative factors, while qualitative factors were subsequently optimized using the one-factor-at-a-time (OFAT) approach.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eResponse surface methodology based on central composite design optimization\u003c/h2\u003e\u003cp\u003eThe optimization of four independent variables, namely pH (A), inoculum size (B), incubation temperature (C), and incubation time (D), was carried out using a CCD model constructed with Design-Expert software (version 11.1.2.0, Stat-Ease Inc., USA). The experimental design was based on five levels for each factor, where the coded values of -1, 0, and +\u0026thinsp;1 represent the low, middle, and high levels, respectively. The CCD model consisted of 8 axial points, 10 center points, and two replicates of the factorial points, resulting in a total of 50 experimental runs. The design parameters and their corresponding coded and actual values are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExperimental factors and their corresponding coded and actual values used in the CCD model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\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\u003eUnits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoded Low\u003c/p\u003e\u003cp\u003e (-1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoded High\u003c/p\u003e\u003cp\u003e(+\u0026thinsp;1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e(0)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eStd. Dev.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1\u0026thinsp;=\u0026thinsp;5.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;1\u0026thinsp;=\u0026thinsp;9.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInoculum size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1\u0026thinsp;=\u0026thinsp;12.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;1\u0026thinsp;=\u0026thinsp;22.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e17.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemp.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026deg;C\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1\u0026thinsp;=\u0026thinsp;30.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;1\u0026thinsp;=\u0026thinsp;39.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e35.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHour\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7. 46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e64.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1\u0026thinsp;=\u0026thinsp;24.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e+\u0026thinsp;1\u0026thinsp;=\u0026thinsp;48.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e36.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e11.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eValidation of response surface methodology optimum conditions\u003c/h2\u003e\u003cp\u003eThe optimized conditions obtained from the RSM model were validated by conducting independent experiments in triplicate under the predicted optimal parameters. Amylase activity (U/gds) was measured as the response variable. The percentage error between the predicted and experimentally obtained enzyme activities was calculated to assess the model\u0026rsquo;s accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eStatistical description of RSM model\u003c/h2\u003e\u003cp\u003eData were analyzed using Design-Expert software (Stat-Ease Inc., Minneapolis, MN, USA). Model adequacy was evaluated by analysis of variance (ANOVA). The significance of model terms was assessed, and the model fit was determined using R\u0026sup2;, adjusted R\u0026sup2;, predicted R\u0026sup2;, and lack-of-fit tests. Three-dimensional response surface plots were generated to visualize interaction effects between the independent variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eOne-factor-at-a-time optimization\u003c/h2\u003e\u003cp\u003eThe influence of various qualitative factors was evaluated using the one-factor-at-a-time (OFAT) approach. The effects of different carbon sources, nitrogen sources, amino acids and metal ions were tested under optimized CCD conditions to further enhance enzyme production.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEffect of different carbon sources\u003c/h2\u003e\u003cp\u003eTo examine the effect of additional carbon sources on enzyme production, the fermentation medium was supplemented individually with glucose, xylose, fructose, mannose, sucrose, lactose, sorbitol, mannitol, and soluble starch at a 1% (w/w) concentration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eEffect of different nitrogen sources\u003c/h2\u003e\u003cp\u003eTo study the effect of supplementation of additional nitrogen sources on enzyme production, some organic nitrogen sources (malt extract, peptone, urea, yeast extract, and casein) and inorganic nitrogen sources (ammonium sulfate and ammonium nitrate) were added solely to the fermentation medium at 1% (w/w) concentration on an equivalent nitrogen basis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEffect of different amino acids\u003c/h2\u003e\u003cp\u003eThe effect of adding different amino acids to the fermentation medium was studied. Arginine, asparagine, glycine, histidine, methionine, leucine, tyrosine, and tryptophan were added solely to the fermentation medium at a concentration of 1% (w/w).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eEffect of various metal ions\u003c/h2\u003e\u003cp\u003eVarious metal chlorides, namely calcium, barium, cobalt, copper, ferric, ferrous, magnesium, potassium, sodium, and zinc were separately added to the fermentation medium at a concentration of 1% (w/w) to investigate their effect on enzyme production.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) based on three independent batches. Statistical analyses were performed using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA). A one-way ANOVA, followed by Tukey\u0026rsquo;s HSD post hoc test, was conducted to determine significant differences among treatment means. Differences were considered statistically significant at \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThe global demand for industrial enzymes has increased significantly over recent decades. The market value of industrial enzymes grew from approximately 0.31\u0026nbsp;billion USD in 1960 to 6\u0026nbsp;billion USD in 2020, and it is projected to surpass 9\u0026nbsp;billion USD by 2027 [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Microbial enzymes are favored for their thermal and pH stability, ease of cultivation, and multifunctional applications, making them highly suitable for diverse industrial applications [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, our study focused on producing various glycoside hydrolases using different microorganisms under SSF.\u003c/p\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003eScreening and Identification of the Most Potent Glycosidase-Producing Microorganism\u003c/h2\u003e\n\u003cp\u003eThe screening of 14 microorganisms for glycosidases production (amylase, pectinase, and xylanase) using different fruit pomaces as substrates revealed significant variations in enzymatic activity, highlighting the influence of microbial species and substrate composition on enzyme yield [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. This study provides valuable insights into the potential of agro-industrial byproducts as sustainable substrates for microbial enzyme production.\u003c/p\u003e\n\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, across all tested agro-industrial wastes (pomegranate, mango, grape, and orange pomaces), the unfermented substrates recorded no detectable amylase activity. Among the tested microbes, \u003cem\u003eC. guilliermondii\u003c/em\u003e exhibited the highest amylase activity (4344.67 U/gds) on pomegranate pomace, which was significantly higher than all other microbes within the same pomace and clearly distinct from the unfermented control (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This could be due to its metabolic adaptability to the polyphenol-rich nature of pomegranate pomace [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eAccording to Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, and unlike amylase, all the unfermented pomaces in the study\u0026mdash;pomegranate, mango, grape, and orange\u0026mdash;showed measurable pectinase activity, possibly due to residual endogenous enzymes [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Among the tested microorganisms, \u003cem\u003eC. guilliermondii\u003c/em\u003e again demonstrated superior performance, achieving the highest pectinase activity (4021.57 U/gds) on orange pomace. This value was significantly higher than that observed for other microbes and the unfermented control (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), confirming the broad enzymatic potential of the strain.\u003c/p\u003e\n\u003cp\u003eUnfermented pomegranate and orange pomaces exhibited no detectable xylanase activity, whereas mango and grape pomaces did, likely due to intrinsic xylanase enzymes. As illustrated in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cem\u003eKluyveromyces marxianus\u003c/em\u003e NRRL Y-8281 exhibited the highest xylanase activity among all tested microorganisms when cultivated on mango pomace, showing a statistically significant difference (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Based on the screening results, \u003cem\u003eC. guilliermondii\u003c/em\u003e was selected for further optimization of amylase production due to its highest enzymatic activity on pomegranate pomace.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimization of Amylase Production by\u003c/strong\u003e \u003cstrong\u003eC. guilliermondii\u003c/strong\u003e \u003cstrong\u003eNRRL Y-2075\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003ch2\u003eOptimization of amylase activity using RSM\u003c/h2\u003e\n\u003cp\u003eSeveral parameters, including microorganism, substrate selection, pH, temperature, inoculum size, and humidity can greatly affect the SSF process. Optimizing these factors is important for increasing enzyme yield and making the process more cost-effective [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this study, a CCD model was constructed to optimize four key parameters: pH, inoculum size, incubation temperature, and incubation time. The model was used to evaluate the individual and interactive effects of these variables on enzyme production and to determine their optimal conditions, as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows close proximity between actual and predicted amylase yields, indicating the model\u0026rsquo;s reliability and experimental stability. The ANOVA results from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e show that the quadratic model was highly significant (\u003cem\u003eF\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;243517.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and that all main effects (pH, inoculum size, temperature, and time) and their interactions significantly influence amylase production. The small residual mean square as well as the lack of fit not being significant (\u003cem\u003eF\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4025) confirms the adequacy of the model and implies that the probability of such a large \u003cem\u003eF\u003c/em\u003e-value arising from random noise is extremely low [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The results shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e indicate that there is a strong foundation for the model with R\u0026sup2;, adjusted R\u0026sup2;, and predicted R\u0026sup2; equal to 1.0000, indicating there was an excellent fit and reliability to predict amylase production with high accuracy. Similar high model accuracy (R\u0026sup2; = 1) has also been reported in endoglucanase optimization using RSM by [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], indicating that such results are achievable under well-controlled experimental conditions.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCCD results of \u003cem\u003eC. guilliermondii\u003c/em\u003e amylase\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eRun Order\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eExperimental factors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eActual Value\u003c/p\u003e\n\u003cp\u003eU/gds\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePredicted Value\u003c/p\u003e\n\u003cp\u003eU/gds\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eA: pH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eB: Inoculum size (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eC: Temperature (\u0026deg;C)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eD: Time (hour)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3669.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4672.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4672.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3943.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3941.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12. 2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3299.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3299.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3668.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12. 2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39. 2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3769.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3769.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3667.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3674.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22. 7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2991.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2989.75\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3670.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3527.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3531.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3280.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3283.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4673.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4672.97\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3674.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2885.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2887.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4178.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4178.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5. 60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12. 2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4049.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4049.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3090.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3088.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9. 39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2990.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2989.75\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5. 60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3585.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3586.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5. 60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3589.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3586.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9. 39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3281.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3283.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9. 39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3770.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3769.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3164.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3164.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9. 392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.755\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2886.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2887.18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3574.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3574.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3942.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3941.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3433.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3431.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3110.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3110.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3533.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3531.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3430.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3431.50\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCCD results of \u003cem\u003eC. guilliermondii\u003c/em\u003e amylase (cont.)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e39. 2045\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3300.20\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e3299.71\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30. 7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4835.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4836.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3675.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3838.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3836.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4001.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4003.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3476.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3474.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3768.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3767.68\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3109.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3110.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4002.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4003.37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4050.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4049.80\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4251.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4253.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3872.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3868.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3510.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3510.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.39202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3838. 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3836.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.7556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2045\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3509.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3510.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.60798\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.2444\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.7955\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4837.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4836.83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAnalysis of variance (ANOVA) for the response surface quadratic model for amylase production\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSource\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSum of Squares\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean Square\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 032E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 032E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 435E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003esignificant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eA-pH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 598E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 598E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6. 127E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eB-Inoculum size\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 965E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 965E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69930.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC-Temperature\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71109.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71109.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16771.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eD-Time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 968E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 968E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4. 642E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 856E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 856E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67369.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 101E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 101E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4. 956E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 936E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 936E\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4. 566E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6235.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6235.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1470.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 033E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 033E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47960.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8. 587E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8. 587E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2. 025E\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResidual\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4. 24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLack of Fit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4. 51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4025\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003enot significant\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePure Error\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4. 09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCor Total\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 033E\u0026thinsp;+\u0026thinsp;07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eFit statistics of the response surface quadratic model for amylase production\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd. Dev.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2.06\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR\u0026sup2;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e1. 0000\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3671.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eC. V. %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0561\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePredicted R\u0026sup2;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1. 0000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdequate Precision\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2018. 6891\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe predicted R\u0026sup2; of 1.0000 is in reasonable agreement with the adjusted R\u0026sup2; of 1.0000; i.e., the difference is less than 0.2. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 2018.689 indicates an adequate signal. This model can be used to navigate the design space.\u003c/p\u003e\n\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n\u003ch2\u003eFinal equation in terms of actual factors\u003c/h2\u003e\n\u003cp\u003eThe experimental data were fitted to a second-order polynomial equation to model the effects of the studied variables on amylase activity. The resulting regression equation, in terms of actual factors, is\u003c/p\u003e\n\u003cp\u003eAmylase activity\u0026thinsp;=\u0026thinsp;20628.33\u0026thinsp;\u0026minus;\u0026thinsp;1480.68 \u0026times; pH\u0026thinsp;\u0026minus;\u0026thinsp;12.09 \u0026times; Inoculum size\u0026thinsp;\u0026minus;\u0026thinsp;359.91 \u0026times; Temperature\u0026thinsp;\u0026minus;\u0026thinsp;234.77 \u0026times; Time\u0026thinsp;\u0026minus;\u0026thinsp;9.50 \u0026times; pH \u0026times; Inoculum size\u0026thinsp;+\u0026thinsp;32.21 \u0026times; pH \u0026times; Temperature\u0026thinsp;+\u0026thinsp;10.83 \u0026times; pH \u0026times; Time\u0026thinsp;+\u0026thinsp;0.63 \u0026times; Inoculum size \u0026times; Temperature\u0026thinsp;+\u0026thinsp;1.26 \u0026times; Inoculum size \u0026times; Time\u0026thinsp;+\u0026thinsp;3.24 \u0026times; Temperature \u0026times; Time\u003c/p\u003e\n\u003cp\u003eThe effect of the interaction of various fermentation process parameters on the amylase production (Z axis) was studied by plotting three-dimensional response surface curves against any two independent variables while keeping the other independent variable at their (0) levels. Therefore, six response surfaces were obtained by considering all the possible combinations\u003c/p\u003e\n\u003cp\u003eThe 3D response surface plots as demonstrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrate the interactive effects of the independent variables on amylase activity under SSF. These visualizations were derived from the CCD model and provide insight into the relationship between variable pairs and their combined influence on enzyme production.\u003c/p\u003e\n\u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e plots A and D show that prolonged incubation time combined with either higher inoculum size or optimal temperature resulted in enhanced enzyme activity. Similarly, plots B and F suggest that enzyme production was sensitive to changes in pH when paired with inoculum size or time, with a peak observed at moderate levels.\u003c/p\u003e\n\u003cp\u003eOverall, these plots confirm the importance of fine-tuning multiple factors simultaneously rather than optimizing each independently, as significant interactions between variables contribute to maximizing enzymatic output. These findings support the model's validity and are consistent with previous studies that emphasize the effectiveness of RSM in optimizing enzyme production parameters.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, the optimal fermentation conditions for amylase production by \u003cem\u003eC. guilliermondii\u003c/em\u003e were pH 5.6, an incubation temperature of 30.7\u0026deg;C, and an inoculum size of 12.2% (v/w), with peak activity observed after 24 hours. These findings are consistent with those of Yalcın and Corbaci [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], who reported optimal amylase production by \u003cem\u003eSaccharomycopsis fibuligera\u003c/em\u003e at pH 5.5 and 30\u0026deg;C on amylase activity medium (AAM), as well as Aggarwal and Mondal [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] who reported 30\u0026deg;C as the optimum temperature for \u003cem\u003eC. guilliermondii\u003c/em\u003e growth. Likewise, the 24-hour incubation period aligns with the observations of Wanderley, Torres et al. [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e] for \u003cem\u003eCryptococcus flavus\u003c/em\u003e amylase production on starch-containing medium. The inoculum size also closely matches that reported by Mrudula and Kokila [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], who found 15% (v/w) to be optimal for amylase activity by \u003cem\u003eBacillus cereus\u003c/em\u003e under solid-state fermentation using wheat bran. Additionally, Acourene and Ammouche [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] reported comparable optimum conditions for \u003cem\u003eC. guilliermondii\u003c/em\u003e amylase production from date waste syrup at 30\u0026deg;C and a pH of 6.0. These findings highlight the rapid enzyme secretion capabilities of certain yeast strains under slightly acidic and mesophilic conditions.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n\u003ch2\u003eValidation of response surface methodology optimum conditions\u003c/h2\u003e\n\u003cp\u003eValidation of the optimized conditions for amylase production confirmed the reliability of the RSM model. The predicted mean amylase activity (4836.83 U/gds) closely matched the experimentally observed value (4838.23 U/gds), with a negligible standard deviation (2.0591), indicating high model accuracy. The narrow 95% prediction interval (4832.09\u0026ndash;4841.58 U/gds) further supports the model\u0026rsquo;s precision, as the observed value falls well within this range. These results align with previous studies demonstrating that RSM effectively optimizes enzyme production parameters while minimizing variability [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The close agreement between predicted and experimental values suggests that the model is robust and suitable for scaling up amylase production under the derived optimal conditions (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eExperimental validation of RSM-optimized conditions for amylase production\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eResponse\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePredicted Mean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eObserved\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStd Dev\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003en\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE Pred\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% PI low\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% PI high\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAmylase activity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4836.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4838.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0591\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3476\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4832.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4841.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n\u003ch2\u003eOne-factor-at-a-time optimization\u003c/h2\u003e\n\u003cp\u003eDifferent carbon and nitrogen sources, amino acids and metal ions were added separately to the fermentation medium to study their influence on \u003cem\u003eC. guilliermondii\u003c/em\u003e amylase production. The influence of different additives on amylase enzyme production was found to be statistically significant (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) using one-way ANOVA/Tukey HSD post hoc tests. According to Fig.\u0026nbsp;7A, the addition of carbon sources to pomegranate pomace did not result in a significant increase in amylase production compared to the no-additive control. However, monosaccharides and disaccharides showed slightly higher amylase activity than polysaccharides and sugar alcohols.\u003c/p\u003e\n\u003cp\u003eInterestingly, the presence of monosaccharides and disaccharides reduced \u003cem\u003eC. guilliermondii\u003c/em\u003e amylase production, which may be attributed to catabolite repression, as \u003cem\u003eC. guilliermondii\u003c/em\u003e preferentially assimilates easily metabolised sugars such as glucose, sucrose and galactose over the more complex sugars present in pomegranate pomace, thereby inhibiting the expression of amylase-encoding genes and reducing the amylase production [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similarly, the addition of polysaccharides, including starch, also led to reduced enzyme activity. This could be due to the inability of \u003cem\u003eC. guilliermondii\u003c/em\u003e to utilize starch effectively [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is partially aligned with the findings of Simair, Qureshi et al. [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] who reported supplementation of the medium with agro-industrial waste, specifically molasses and date syrup, to have higher amylase production than starch addition, but contrary to Saad, Othman et al. [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], who reported enhanced amylase production by \u003cem\u003eB. licheniformis\u003c/em\u003e cultivated in a modified starch broth medium.\u003c/p\u003e\n\u003cp\u003eSimilarly, the addition of sugar alcohols significantly suppressed amylase production in this study, which contradicts the findings of Saha, Maity et al. [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], who reported an increased amylase yield by \u003cem\u003eB. amyloliquefaciens\u003c/em\u003e when cultivated on wheat bran under SSF upon the addition of inositol and mannitol.\u003c/p\u003e\n\u003cp\u003eOptimal conditions for maximum enzyme production can vary considerably depending on the specific microbial strain [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The type and concentration of nitrogen sources play a crucial role in regulating enzyme synthesis by influencing both nitrogen assimilation and metabolic activity [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. As shown in Fig.\u0026nbsp;7B, the addition of external nitrogen sources generally resulted in a reduction in amylase production. However, urea caused the least reduction, yielding 2944.77 U/gds, compared to the highest activity observed in the no-additive control (4838.23 U/gds). This result aligns with the findings of Singh, Mishra et al. [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] who reported that the addition of various organic and inorganic nitrogen sources led to a decrease in \u0026alpha;-amylase production by \u003cem\u003eBacillus cereus\u003c/em\u003e MTCC 1305 under SSF using wheat bran as a substrate.\u003c/p\u003e\n\u003cp\u003eAmino compounds are considered stimulators of amylase synthesis and excretion rather than primary nitrogen or carbon sources [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, in the present study, the addition of amino acids to pomegranate pomace resulted in a significant decline in amylase production by \u003cem\u003eC. guilliermondii\u003c/em\u003e. As illustrated in Fig.\u0026nbsp;7C, leucine supplementation caused a moderate decrease in enzyme yield, while arginine exhibited a strong inhibitory effect. These findings partially align with those of Chaurasia, Chaurasia et al. [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e], who reported that asparagine supplementation enhanced \u003cem\u003eRhizopus oryzae\u003c/em\u003e amylase production using Fernando's broth medium, whereas the addition of isoleucine, phenylalanine, and lysine inhibited its synthesis.\u003c/p\u003e\n\u003cp\u003eThe addition of metal ions to the fermentation medium negatively affected amylase production by \u003cem\u003eC. guilliermondii\u003c/em\u003e. As presented in Fig.\u0026nbsp;7D, Fe⁺\u0026sup3; caused the least inhibition with 2044.23 U/gds, compared to the no additive control, while Na⁺ caused the greatest suppression (935.7 U/gds), which may be attributed to the fact that NaCl exerts osmotic pressure on \u003cem\u003eC. guilliermondii\u003c/em\u003e cells [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. These results contradict previous studies. For instance, Abo-Kamer, Abd-El-salam et al. [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] reported that Ca⁺\u0026sup2; and Mg⁺\u0026sup2; enhanced amylase production in \u003cem\u003eBacillus cereus\u003c/em\u003e A1-5 using synthetic starch medium under submerged fermentation, whereas Ba⁺\u0026sup2; had an inhibitory effect. Similarly, Rehman, Saeed et al. [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e] found that Ca\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e and Na\u003csup\u003e+\u003c/sup\u003e enhanced amylase production in \u003cem\u003eBacillus cereus\u003c/em\u003e AS2 using Luria basal broth medium, while Mg\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e, Zn\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e, Hg\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e, Cu\u003csup\u003e+\u0026thinsp;2\u003c/sup\u003e and Fe\u003csup\u003e+\u0026thinsp;3\u003c/sup\u003e were inhibitory. The reduced amylase production by \u003cem\u003eC. guilliermondii\u003c/em\u003e upon the addition of external carbon, nitrogen sources, amino acids, and metal ions highlights the inherent nutritional adequacy of pomegranate pomace. With a composition of 4.9% protein, 17.7% carbohydrates, and a variety of essential minerals including calcium, potassium, phosphorus, sodium, iron, copper, manganese, and zinc the pomace alone appears sufficient to support microbial growth and enzyme synthesis [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The introduction of external additives may have disrupted this natural nutrient balance, resulting in inhibitory rather than beneficial effects on enzyme production.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study indicates that agro-industrial fruit wastes, especially pomegranate pomace, are promising, consistent and sustainable substrates for microbial enzyme production under solid-state fermentation. \u003cem\u003eC. guilliermondii\u003c/em\u003e NRRL Y-2075 was the most effective strain for amylase production with a maximum activity of 4344.67 U/gds. Using RSM, we determined optimum fermentation conditions and experimentally validated these results with a maximum amylase production at 4838.23 U/gds. The small difference between predicted and observed values indicates that the model is reliable. External supplementation of various additives (carbon, nitrogen sources, amino acids or metal ions) to the fermentation medium indicated an enzyme activity reduction, further establishing the nutritional value of simply using pomegranate pomace. This study provides valuable information toward developing cost-effective, environmentally friendly options for enzyme production while also promoting the possibilities of exploiting agro-waste as a substrate for sustainable industrial biotechnology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\n\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSSF\u003c/div\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eSolid State Fermentation\u003c/div\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eRSM\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eResponse Surface Methodology\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eGHs\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eGlycoside hydrolases\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eU/gds\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eUnits per gram dry substrate\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eCCD\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eCentral Composite Design\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eOFAT\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eOne Factor At a Time\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eANOVA\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eAnalysis of Variance\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eSD\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eStandard Deviation\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cdiv class=\"SimplePara\"\u003eDf\u003c/div\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cdiv class=\"SimplePara\"\u003eDegrees of Freedom\u003c/div\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003cbr/\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eOpen access funding was provided by the Science, Technology \u0026amp; Innovation Funding Authority (STDF) in cooperation with the Egyptian Knowledge Bank (EKB).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ. H. H., A.A.E. M., A. T. M., H. A. M., and M. K. I. contributed equally to this work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the Egyptian Academy of Scientific Research and Technology for funding this work and the Biochemistry Department, Biotechnology Research Institute, National Research Centre for providing the facilities required to conduct this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKhan R, Anwar F, Ghazali FM, Mahyudin NA. Valorization of waste: Innovative techniques for extracting bioactive compounds from fruit and vegetable peels - A comprehensive review. 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Antimicrob Agents Chemother. 2018;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/aac.02528\u0026thinsp;\u0026ndash;\u0026thinsp;02517\u003c/span\u003e\u003cspan address=\"10.1128/aac.02528\u0026thinsp;\u0026ndash;\u0026thinsp;02517\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRehman A, Saeed A, Asad W, Kiran T, Naz D, Aijaz S. Optimization of physicochemical parameters for maximum amylase production by indigenously isolated Bacillus cereus AS2 strain. Pak J Pharm Sci. 2019;32:889\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glycosidases, Agro-industrial Wastes, Solid State Fermentation, Pomegranate Peels, Candida guilliermondii, Amylase, RSM.","lastPublishedDoi":"10.21203/rs.3.rs-7602826/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7602826/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAgro-industrial waste represents an efficient and affordable substrate for microbial enzyme production; as an underutilized agricultural and food processing by-product, usage of Agro-industrial waste as a substrate for enzyme production fits within the framework of the circular bioeconomy by supporting waste valorization and environmental sustainability. This research evaluated the effective valorization of fruit-based agro-wastes (pomegranate, mango, orange, and grape pomace) for microbial production of glycosidic enzymes (amylase, xylanase, pectinase) using 14 microbial strains under solid-state fermentation conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFor the 14 strains studied, \u003cem\u003eCandida guilliermondii\u003c/em\u003e NRRL Y-2075 resulted in the highest reported amylase activity (4344.67 U/gds) when using pomegranate pomace. A response surface methodology was used with a central composite design model to optimize key parameters affecting amylase production for solid state fermentation: pH, inoculum size, incubation temperature and time. The optimum pH, size of inoculum, incubation temperature and incubation time were 5.6, 12.2%, 30.7\u0026deg;C and 24 hours, respectively, which resulted in a validated amylase activity of 4838.23 U/gds. The one-factor-at-a-time optimization revealed that the addition of external carbon, nitrogen, amino acids, or metal ions reduced \u003cem\u003eC. guilliermondii\u003c/em\u003e amylase production given the richness of pomegranate pomace as a fermentation medium.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study shows that fruit pomaces, especially pomegranate, can be used as a sole medium for glycosidic enzyme production, providing both efficiency and sustainability, emphasizing the relevance to industrial bioprocesses.\u003c/p\u003e","manuscriptTitle":"Valorization of Fruit Pomaces for Glycosidic Enzymes Production via Solid State Fermentation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 10:55:58","doi":"10.21203/rs.3.rs-7602826/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b534bf9-c378-4536-ac80-d4de44a50788","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-23T11:25:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-24 10:55:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7602826","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7602826","identity":"rs-7602826","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}