{"paper_id":"2931bf0c-bc59-45f8-aeaa-477635e6c6fb","body_text":"Optimization of fermentation conditions and enzymatic properties of extracellular lipase produced by Serratia marcescens derived from food waste | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimization of fermentation conditions and enzymatic properties of extracellular lipase produced by Serratia marcescens derived from food waste XiaoDong Tian, ChuYun Huang, Shen Wang, QiHui Meng, Yuxin Chang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7922278/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in World Journal of Microbiology and Biotechnology → Version 1 posted 11 You are reading this latest preprint version Abstract A strain designated C41802, capable of high lipase production, was isolated from a food waste environment. Based on morphological characteristics, physiological and biochemical tests, and phylogenetic analysis, the strain was identified as Serratia marcescens . To optimize lipase production by C41802, fermentation conditions were refined using an Artificial Neural Network (ANN) combined with a Genetic Algorithm (GA), establishing a GA-ANN model. The enzymatic properties of the extracellular crude lipase were then characterized under these optimal conditions. Results demonstrated that the ANN-GA model surpassed the Response Surface Methodology with Central Composite Design (RSM-CCD) in predicting optimal fermentation conditions and maximum enzyme yield, with a relative error of merely 0.67% for the ANN-GA model compared to 2.54% for RSM-CCD. Optimal fermentation conditions, as determined by the GA-ANN model, included: olive oil at 10 g/L, glucose at 8 g/L, tryptone at 3.1 g/L, K₂HPO₄ at 2 g/L, an inoculum size of 3.5%, cultivation time of 26.2 hours, temperature at 30°C, and initial pH at 5, achieving a lipase yield of 613.39 U/mL, which is 6.45 times higher than before optimization. Under these optimized conditions, the extracellular crude lipase produced by S. marcescens C41802 exhibited an optimal temperature of 70°C and pH of 7. After incubation at 50°C for 12 hours, the enzyme retained 52.56% of its relative activity, indicating substantial potential for industrial applications. lipase enzyme production optimization artificial neural network genetic algorithm enzymatic properties Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Food waste primarily consists of edible food discarded or spoiled due to over-preparation, improper storage, and inefficient operations in households or the catering industry (Yahia & Mourad, 2020 ). It typically comprises carbohydrates, proteins, and fats, with its specific composition varying based on the mixture of different food types (Lelicińska-Serafin et al., 2020). The global generation of food waste is substantial, with estimates suggesting that approximately 1.3 billion metric tons are produced annually (Huang et al., 2022 ). According to authoritative estimates, global annual food loss and waste amount to roughly 1.3 to 1.4 billion metric tons, imposing significant resource pressures on the environment. This massive volume incurs remarkable economic and social costs: direct financial losses from food waste alone are estimated at around 1 trillion USD annually (Marimuthu et al., 2024 ). Among the various components of food waste, oils are of particular concern due to their difficulty in natural degradation and potential environmental risks, presenting new challenges for subsequent processing and resource utilization. Food waste contains a significant amount of lipids, which are difficult to degrade naturally (Lin et al., 2014 ). The unsaturated fatty acids in these lipids undergo auto-oxidation, producing peroxides that further decompose into a complex mixture of volatile aldehydes, ketones, and organic acids, resulting in unpleasant odors (Frankel E N, 2005; Liu et al., 2023 ). This not only poses environmental hazards but also constitutes a potential threat to public safety (Kumar et al., 2025 ). Improper handling can lead to the accumulation and transformation of lipids, causing severe harm to ecosystems. Therefore, developing effective methods for the harmless treatment of lipids in food waste has become an urgent priority.Currently, common methods for lipid treatment include anaerobic fermentation, physicochemical separation, wet hydrothermal processing, and microbial degradation. Anaerobic fermentation is relatively complex and time-consuming, generating substantial amounts of wastewater during the process (He et al., 2024 ). Physicochemical separation methods such as flotation, adsorption, and magnetic adsorption often require the addition of flocculants or adsorbents, leading to high operational costs and potential secondary pollution (Iskander et al., 2024; Khalidi-Idrissi et al., 2025 ). Wet hydrothermal processing relies on high temperature and pressure to transfer solid-phase lipids to the liquid phase but is energy-intensive, incomplete in extraction, and involves a cumbersome process (Munir et al., 2018 ).In contrast, microbial methods utilizing lipases to degrade waste oils offer significant advantages: they are cost-effective, involve mild conditions, are environmentally friendly, and enable the resource utilization of oils (Vishnoi et al., 2020 ). Lipases, as biological catalysts capable of hydrolyzing oils, show great potential in food waste management (Zhao et al., 2024 ; Bhatia et al., 2025 ). Their products, glycerol and fatty acids, can be further converted into biofuels or other high-value chemicals. Within the broader family of microbial lipases, most enzymes utilized in biotechnological applications and organic chemistry are derived from natural bacteria, fungi, and recombinant strains. The efficacy of microbial lipases is highly dependent on factors such as temperature, pH, and substrate specificity, which serve as critical parameters in industrial production and find wide application in food processing, fine chemicals, biodiesel synthesis, and other fields (Treichel et al., 2010 ; Chandra et al., 2010). However, the performance of natural microorganisms in enzyme production and lipid degradation is often suboptimal, necessitating additional optimization measures to enhance enzyme activity in lipase-producing strains.One popular approach for optimization involves the use of Genetic Algorithm-Artificial Neural Network (GA-ANN) methods. This technique plays a crucial role in increasing lipase yield and improving lipid degradation efficiency, serving as an effective strategy to boost microbial lipid degradation capabilities. For instance, Lau et al. ( 2023 ) successfully optimized the lipase production conditions for Burkholderia cenocepacia using GA-ANN, achieving a lipase activity of 225 U/mL, representing a 1.6-fold increase compared to pre-optimization levels. In another study, Benhoula-M et al employed Response Surface Methodology to investigate bacterial lipase production from olive oil wastewater, achieving a maximum lipase activity of 8.82 U/mL. These studies highlight that precise control and optimization of cultivation conditions can significantly enhance the lipase production capacity of specific microbial strains. In this study, a bacterium capable of producing extracellular lipase was isolated from food waste. The fermentation conditions were precisely optimized using the GA-ANN method, and the optimized process parameters were evaluated through experimental results, demonstrating a significant enhancement in lipase activity. Additionally, some physicochemical properties of the crude extracellular lipase were characterized to deepen our understanding of its characteristics.The optimization results provide a basis for establishing suitable fermentation production parameters and offer valuable references for determining optimal kinetics and economic parameters in subsequent scale-up studies of this bioprocess. Furthermore, the strain identified in this study serves as an excellent biological resource for lipase production and contributes to the recycling and utilization of resources.This research not only enhances the efficiency of lipase production but also paves the way for broader applications in industrial biotechnology, particularly in the context of sustainable resource management. 2. Materials and Methods 2.1. Isolation and identification of lipase-producing bacteria In this study, food waste samples and food waste wastewater were collected from the Food Waste Bin at the cafeteria of Guangxi University for Nationalities in Xixiangtang District, Nanning City, Guangxi Zhuang Autonomous Region (coordinates: 22°50′26″N, 108°11′23″E). After collection, the samples were stored in sterile bags. In the laboratory, the samples were serially diluted to 10⁻⁷ using sterile water and spread on enrichment medium plates. The plates were incubated at 36°C for 48 hours, and colonies with distinct morphologies were selected for two rounds of purification on single plates. Single colonies were then inoculated onto tributyrin agar (TBA) medium and incubated at 36°C for 12 hours. Lipase production capability was assessed by observing the clear zones around the colonies. The biochemical characteristics of the strains were determined according to Bergey's Manual of Determinative Bacteriology (Bergey & Holt, 1994), followed by Gram staining. Colonies were cultured on Luria Bertani (LB) medium for 24 hours to observe colony morphology. Genomic DNA was extracted using a DNA extraction kit (Sangon Biotech, Shanghai Co., Ltd.), and the 16S rRNA gene sequence was amplified using universal primers 27F/1492R. The PCR products were sequenced, and the 16S rRNA gene sequences were compared using NCBI BLASTn. Sequences with high similarity were downloaded for phylogenetic analysis. Phylogenetic trees were constructed using MEGA 12.0. 2.2. Determination of extracellular lipase Extracellular lipase activity was measured using the p-nitrophenylpalmitate (pNPP) assay, based on the method described by Winkler et al. with slight modifications. The following steps were used: 30 mg of pNPP was dissolved in 10 mL of isopropanol and then mixed with 90 mL of 0.05 M phosphate buffer (pH 8.0, containing 207 mg of sodium deoxycholate and 100 mg of gum arabic) to prepare the substrate solution. 2.4 mL of freshly prepared substrate solution, preheated at 37°C, was added to 0.1 mL of cell-free supernatant and mixed. The mixture was incubated at 37°C for 15 minutes and then cooled on ice for 5 minutes to terminate the reaction. Enzyme activity was calculated by measuring the OD₄₁₀ and comparing it with a no-enzyme control. One unit (U) of enzyme activity was defined as the activity required to enzymatically release 1 µmol of p-nitrophenol from the substrate per minute. 2.3. Optimization of culture medium and fermentation conditions for lipase production In this study, a basal fermentation medium (olive oil emulsion 5 g/L, peptone 10 g/L, K₂HPO₄ 1 g/L, KH₂PO₄ 0.5 g/L) was used as the initial reference system. Preliminary single-factor experiments were conducted to optimize inoculum size (1%, 3%, 5%, 7%, 9%) and cultivation time (12 h, 24 h, 36 h, 48 h, 60 h), thereby establishing the basic operational parameters for subsequent optimization. Based on the determined inoculum size and cultivation time, further single-factor experiments were systematically performed to evaluate the effects of different inducers (olive oil, peanut oil, sesame oil, rapeseed oil, and soybean oil; all oils were pre-emulsified), carbon sources (glucose, maltose, sucrose, lactose, soluble starch), nitrogen sources (ammonium sulfate, ammonium chloride, ammonium dihydrogen phosphate, sodium nitrate, beef extract, yeast extract), and inorganic salts (ferrous sulfate, magnesium sulfate, potassium dihydrogen phosphate, dipotassium hydrogen phosphate, copper sulfate) on extracellular lipase production, in order to identify the optimal medium formulation. Finally, based on this optimized formulation, the initial pH (4, 5, 6, 7, 8, 9) and incubation temperature (26°C, 29°C, 32°C, 35°C, 38°C) were further refined, leading to the establishment of fermentation conditions most favorable for extracellular lipase production. 2.4. Plackett-Burman design and response surface optimization Based on the results of single-factor experiments, a Plackett–Burman (PB) design was performed using Design-Expert 12.0 (Stat-Ease, Inc., Minneapolis, USA) (see Supplementary Table S1 ). Eight variables—glucose (A), beef extract (B), olive oil (C), KH₂PO₄ (D), pH (E), temperature (F), cultivation time (G), and inoculum size (H)—were coded at two levels (− 1, + 1) to identify the most significant fa ctors influencing lipase production. The three variables with the greatest impact were subsequently subjected to response surface methodology (RSM) optimization. A five-level coded design was employed (see Supplementary Table S2), generating a total of 23 experimental runs (including axial and replicated center points). The data were fitted to a second-order polynomial model to evaluate main effects, interaction effects, and quadratic terms. 2.5. Artificial neural network The data obtained from the response surface experiments were normalized and subsequently used for artificial neural network (ANN) training in MATLAB R2023a (The MathWorks, Inc., Natick, MA, USA). A feed-forward neural network based on error backpropagation was constructed, and the Levenberg–Marquardt (LM) algorithm was employed for optimization. Beef extract concentration (g/L), inoculum size (%), and cultivation time (h) were used as input variables, while lipase activity (U/mL) served as the output variable to establish the predictive ANN model. The ANN was trained using the RSM CCD dataset, which was randomly divided into 70% for training, 15% for validation, and 15% for testing to perform BP learning and generalization assessment. Signals generated by the hidden layer were propagated to the output layer, where the predicted responses were compared with the experimentally measured values for the given input dataset. The mean squared error (MSE) across all datasets was calculated to evaluate model performance. The backpropagation (BP) training algorithm was applied to minimize the error function by adjusting weights and biases (Nasab et al., 2019 ). 2.6.Genetic algorithm After establishing and validating the ANN model, a genetic algorithm (GA) was employed to optimize the multi-parameter space of the fermentation process. Each chromosome encoded three decision variables—nitrogen source (beef extract concentration), cultivation time, and inoculum size—all continuously coded within the ranges defined by the RSM CCD design. The trained GA–ANN model was used as the fitness evaluator: for each candidate solution (chromosome), forward prediction was performed, and the predicted lipase activity was assigned as the individual’s fitness value. Using the MATLAB Genetic Algorithm Toolbox, the population size, crossover rate, and mutation rate were specified, and the initial population was generated. During iterative generations, selection, crossover, and mutation operations were executed, with high-fitness individuals retained to accelerate convergence. After training, the ANN rapidly scored and ranked each generation of chromosomes produced by the GA. This coupled GA–ANN procedure continued until the maximum generation criterion was met, ultimately identifying the optimal combination of process parameters that maximized lipase activity. 2.7. Preparation of crude extracellular lipase solution The fermentation broth was centrifuged at 12,000 rpm for 20 min at 4°C, and the supernatant was collected. The supernatant was then filtered through a hydrophilic PVDF membrane with a pore size of 0.22 µm to obtain the crude enzyme solution. 2.8. Effects of temperature and pH on extracellular lipase activity The effect of temperature on lipase activity was evaluated by incubating the crude enzyme at 30–90°C (with 10°C intervals) for 15 min, and the maximum measured activity was defined as 100%. Thermal stability was assessed within the same temperature range (30–90°C, at 10°C intervals) by sampling every 2 h to determine the residual activity of the crude lipase. For pH stability, the crude enzyme solution was incubated in different buffer systems at 4°C for 1 h, and the residual activity was measured under each pH condition. The buffer systems used were: sodium citrate buffer (pH 5.0–7.0), Tris-HCl buffer (pH 7.0–9.0), Tris-HCl buffer (pH 9.0–12.0), and Gly-NaOH buffer (pH 9.0–11.0). Extracellular lipase activity under different media and cultivation conditions was determined according to the method described in Section 2.2 . All enzyme activity data were expressed as relative activity, with the maximum observed value set as 100%. 2.9. Effects of metal ions or chemical reagents on enzyme activity To evaluate the effects of metal ions and chemical reagents on enzyme activity, Na⁺, Ca²⁺, Mg²⁺, Fe³⁺, Mn²⁺, Co²⁺, and Cu²⁺ (all supplied as chlorides) were individually added to the reaction system. Similarly, isopropanol (IPA), sodium dodecyl sulfate (SDS), dimethyl sulfoxide (DMSO), Triton X-100, and β-mercaptoethanol (BME) were tested separately. A reaction system without any added metal ions or chemical reagents served as the control. Extracellular lipase activity under different media and cultivation conditions was determined according to the method described in Section 2.2 . All enzyme activity values were expressed as relative activity, with the activity of the control defined as 100%. 2.10. Data and Statistics All statistical analyses were performed using IBM SPSS Statistics 27. Data significance was evaluated by one-way analysis of variance (ANOVA). Data visualization was primarily conducted with Origin 2024, while selected model-fitting plots were generated using MATLAB R2023a. Experimental results are presented as mean ± standard error ( \\(\\:\\stackrel{\\prime }{x}\\pm\\:SE\\) ), and all experiments were carried out in triplicate. 3. Results and Analysis 3.1. Screening and identification of strains A lipase-producing strain, designated C14802, was isolated using TBA medium. After 24 h of growth on LB agar plates, the colonies appeared circular, opaque white, with smooth, raised surfaces and entire margins (Fig. 1A). Gram staining identified the strain as Gram-negative (Fig. 2B). When cultured on TBA medium for 12 h, a yellow halo was observed around the colonies (Fig. 1C), indicating the ability of the strain to hydrolyze tributyrin in the medium. Phylogenetic tree analysis revealed that strain C14802 clustered in the same branch as Serratia marcescens HB10 (Fig. 1D), suggesting a close evolutionary relationship. Based on morphological characteristics, physiological and biochemical tests, and phylogenetic analysis, strain C14802 was identified as Serratia marcescens. 3.2. Optimization of lipase production culture medium and culture conditions For strain C41802, the highest lipase activity was observed at a fermentation time of 36 h (Fig. 2I), while an inoculum size of 5% yielded the maximum enzyme activity (Fig. 2J). In medium supplemented with 1% olive oil, lipase activity reached 235.17 ± 10.25 U/mL, which was significantly higher than that obtained with other oils (P < 0.05) (Fig. 2A). When glucose was used as the carbon source, the maximum enzyme production was achieved at 339.89 ± 17.99 U/mL, significantly higher than with other carbon sources (P < 0.05) (Fig. 2C), with the optimal concentration determined to be 8 g/L (Fig. 2D). Both beef extract and yeast extract significantly enhanced lipase activity, yielding 402.74 ± 17.29 U/mL and 395.79 ± 16.54 U/mL, respectively, with beef extract showing a slightly superior effect (Fig. 2E); the optimal concentration was 6 g/L (Fig. 2F). Among the inorganic salts tested, K₂HPO₄ was identified as the most effective for lipase production, with an optimal concentration of 2 g/L, resulting in an activity of 317.96 ± 14.03 U/mL (Fig. 2G, H). The optimal fermentation temperature for lipase production was 30 °C (Fig. 2K), with only minor differences observed among other temperature treatments, indicating that strain C41802 possesses a relatively broad temperature tolerance for lipase production. The highest enzyme yield under different pH conditions was obtained at pH 5, reaching 310.59 ± 8.86 U/mL (Fig. 2L), suggesting that the strain can effectively produce lipase even in mildly acidic environments. 3.3 Plackett-Burman test results The results of the Plackett–Burman (PB) design indicated that factors A (glucose), B (beef extract), E (pH), F (temperature), G (time), and H (inoculum size) had significant effects on the response value (Table 1). The order of factor importance was B > G > H > E > A > F. Analysis of the Pareto chart (Fig. 3) revealed clear differences in the effects of individual factors on enzyme activity. Among them, factor B (beef extract) and factor G (time) exhibited highly significant positive effects (t-values far exceeding the Bonferroni limit of 7.0406), identifying them as the key contributors to enhanced enzyme activity. Factors H (inoculum size), E (pH), A (glucose), and F (temperature) also showed significant positive effects (t-values between the t-value limit of 3.18245 and the Bonferroni limit). In contrast, the positive effect of factor D (K₂HPO₄) and the negative effect of factor C (olive oil) were not significant (t-values below the t-value limit). The resulting regression linear equation (1) was as follows: （1） Tabe.1 Plackett-Burman test factor level and significance analysis Source SS df MS F P Sig. Importance Ranking Model 2.234E+05 8 27925.83 34.13 0.0073 ** Significance A 22847.35 1 22847.35 27.92 0.0132 * Significance 5 B 87204.04 1 87204.04 106.57 0.0019 ** Significance 1 C 886.84 1 886.84 1.08 0.3744 D 4121.99 1 4121.99 5.04 0.1105 E 25195.20 1 25195.20 30.79 0.0115 * Significance 4 F 13221.31 1 13221.31 16.16 0.0277 * Significance 6 G 37178.58 1 37178.58 45.44 0.0067 ** Significance 2 H 32751.35 1 32751.35 40.02 0.0080 ** Significance 3 Residual 2454.84 3 818.28 Cor Total 2.259E+05 11 R 2 0.9891 R adj 2 0.9601 Note：“*” P ≤0.05；“**” P ≤0.01. 3.4 RSM model fitting and statistical evaluation Based on the PB results, the three most influential factors—beef extract (A), inoculum size (B), and cultivation time (C)—were selected for a central composite design (CCD); the design levels and experimental data are provided in Supplementary Table S4. ANOVA of the RSM–CCD model (Table 2) showed high significance with , indicating that the model effectively captured the main, interaction, and quadratic effects on the response. Among the tested terms, inoculum size (B), interactions , , , and quadratic terms and were significant ( ); in contrast, beef extract (A), cultivation time (C), and the quadratic term were not significant ( ). Multiple regression analysis yielded the following empirical second-order polynomial relating the response to the coded variables (Equation 2): （2） Tabe.2 RSM-CCD-Testergebnisse Source Sum of squares df Mean square F value p value Model 1.319E+05 9 14660.93 46.68 < 0.0001 significant A-Beef Extract 880.80 1 880.80 2.80 0.1179 B-Inoculum size 25078.47 1 25078.47 79.85 < 0.0001 C-Time 171.25 1 171.25 0.5453 0.4734 AB 38479.40 1 38479.40 122.53 < 0.0001 AC 5390.59 1 5390.59 17.16 0.0012 BC 19786.08 1 19786.08 63.00 < 0.0001 A 2 301.57 1 301.57 0.960 0.3450 B 2 4448.83 1 4448.83 14.17 0.0024 C 2 37647.32 1 37647.32 119.88 < 0.0001 Residual 4082.65 13 314.05 Lack of Fit 158.70 5 31.74 0.0647 0.9960 not significant Pure Error 3923.95 8 490.49 R 2 0.97 Adjusted R² 0.9492 CV (%) 5.57 3.5. ANN-GA modeling and optimization Based on the 23 datasets obtained from the RSM–CCD design (Supplementary Table S4), the data were divided into training (70%), validation (15%), and testing (15%) sets to construct a three-layer backpropagation neural network (BP–NN) model. The target scatter plots for training (Fig. 4A, R = 0.98449), validation (Fig. 4B, R = 0.9972), testing (Fig. 4C, R = 0.99092), and overall performance (Fig. 4D, R = 0.98447) showed regression coefficients close to 1, indicating a high degree of agreement between predicted and experimental values and confirming the reliability of the model. During training, the mean squared error (MSE) decreased progressively with increasing epochs, reaching the optimal validation performance (MSE = 88.3194) at the third epoch (Fig. 4F). The error histogram (Fig. 4E) demonstrated that prediction errors ranged from −39.94 to 176.5 and fluctuated around zero, reflecting rapid convergence and good stability of the model, making it suitable for subsequent analysis.To further optimize the BP–NN, a genetic algorithm (GA) was applied with 200 iterations, an initial population size of 30, a crossover probability of 0.8, and a mutation probability of 0.05. The GA–BP–NN hybrid model ultimately identified the optimal process parameters as beef extract 3.12 g/L, inoculum size 3.49%, and fermentation time 26.16 h (Fig. 4G). 3.6. Prediction data verification of RSM-CCD and GA-ANN To evaluate the accuracy and reliability of the integrated modeling approach, the RSM–CCD and GA–ANN models were comparatively analyzed, and their predictive capabilities were experimentally validated. As shown in Table 3, under the predicted optimal conditions of the RSM–CCD model, the experimentally measured lipase activity was close to the predicted value, with a relative error of 2.54%, demonstrating good predictive performance. In contrast, the GA–ANN model exhibited nearly identical predicted and experimental values (relative error 0.67%), with a mean squared error (MSE) significantly lower than that of the RSM–CCD model, indicating superior fitting accuracy and stability. These results suggest that for the optimization of complex, highly nonlinear fermentation processes, the GA–ANN model offers clear advantages over the traditional RSM–CCD approach, achieving an experimentally validated lipase activity that was 14.98 U/mL higher than that obtained with RSM–CCD. Tabe.3 Optimal conditions and verification results of the model Model Beef extract（g/L） Inoculum size （%） Time (h) Enzyme activity（U/mL） 相对误差 MSE Actual value Predicted value （%） RSM-CCD GA-ANN 3.6 3.1 4.2 3.5 26.5 26.2 598.41 613.39 614.03 617.52 2.54 0.67 314.05 1.27 3.7. Optimum temperature and temperature stability The crude lipase from strain C14802 exhibited the highest activity at 70 °C (Fig. 5A). When incubated above 70 °C for 2 h, the enzyme activity was almost completely lost. At 60 °C, the relative activity approached complete loss only after 12 h of incubation, whereas at 50 °C, 52.56% of the relative activity was still retained after 12 h. Interestingly, incubation at 30 °C and 40 °C for 2–4 h resulted in a slight increase in activity, suggesting that the enzyme requires a longer equilibration period to reach stable activity at lower temperatures. Overall, these results indicate that the enzyme undergoes rapid inactivation above 70 °C, while exhibiting good thermal stability below 60 °C, highlighting its promising potential for industrial applications and further research. 3.8. Optimum pH and pH stability As shown in Fig. 6, the crude lipase exhibited its optimal activity at pH 7.0 in sodium citrate buffer. When the pH was maintained between 7.0 and 9.0, more than 60% of the relative activity was retained. After incubation of the crude enzyme solution for 1 h under different pH conditions, the relative activity was markedly reduced (<20%) at pH 5.0–6.0. In contrast, within the pH range of 7.0–10.0, the enzyme retained a high proportion of its activity, with only a slight decrease compared to the pre-incubation level. At pH 11.0, however, the relative activity declined sharply from 62.9% to 21.4%. Collectively, these results indicate that the enzyme is better adapted to neutral and mildly alkaline environments, exhibiting good pH stability in the neutral (6.0–8.0) and weakly alkaline (8.0–9.0) ranges. 3.9. Effects of metal ions and chemicals on enzyme activity As shown in Fig. 7, the effects of different metal ions and chemical reagents on enzyme activity varied significantly. Among the metal ions (Fig. 7A), compared with the control (CK), Na⁺, Mg²⁺, and Mn²⁺ exhibited only weak inhibitory effects, with relative activities of 96.32%, 98.04%, and 99.65%, respectively. In contrast, Co²⁺ strongly inhibited enzyme activity, with relative activity reduced to 33.69%. Conversely, Ca²⁺, Fe³⁺, and Cu²⁺ enhanced enzyme activity, with Fe³⁺ showing the most pronounced effect, increasing relative activity to 123.45%.For chemical reagents (Fig. 7B), β-mercaptoethanol, DMSO, Triton X‑100, and IPA had minimal effects on enzyme activity, with no significant differences compared to the control. Notably, SDS significantly enhanced enzyme activity, with relative activity reaching 110.41%. 4. Discussion Single-factor optimization experiments revealed that the initial pH of 5 in the fermentation medium yielded the highest extracellular lipase production by strain C14802. This finding contrasts with the neutral or slightly alkaline conditions typically preferred by most bacterial fermentations, but is consistent with reports on certain Serratia marcescens strains (Kaira et al., 2015 ; Elistratova et al., 2022 ). Such acid tolerance enables the strain to maintain high levels of lipase secretion under low-pH conditions, providing a theoretical basis for its potential application in acidic reaction systems, such as lipid degradation in acidic industrial wastewater and catalytic transformations under acidic environments. With respect to optimization strategies for Serratia lipases, traditional single-factor approaches can preliminarily identify key determinants of enzyme production (Gao et al., 2004 ; Nwachukwu et al., 2017 ; Begam et al., 2012 ; Issa et al., 2024 ). However, by neglecting interactions among multiple factors, they fail to accurately capture the integrated effects of multi-parameter systems. Response surface methodology (RSM) improves optimization precision to some extent by considering two-factor interactions (Venil et al., 2009 ; Krishnankutty, 2024 ). Nevertheless, when dealing with three or more variables or highly nonlinear relationships, the predictive accuracy of RSM becomes limited. To overcome this, the present study employed artificial neural networks (ANN) for nonlinear modeling of fermentation data, coupled with genetic algorithms (GA) for global optimization. The GA–ANN model demonstrated superior predictive accuracy and optimization performance compared with RSM, not only accurately fitting complex nonlinear relationships but also providing a more reliable theoretical basis for further process optimization. The extracellular lipase of strain C14802 exhibited an optimal temperature of 70°C and an optimal pH of 7, similar to the lipase from S. marcescens isolated from industrial wastewater by Ali et al. ( 2022 ), which showed an optimum at 75°C and pH 8. A relatively high optimum temperature suggests enhanced catalytic efficiency under thermophilic conditions, while good thermal stability supports sustained catalytic activity during prolonged incubation. In terms of pH stability, the crude enzyme in this study displayed greater stability under neutral conditions, consistent with the properties of most neutral lipases, but also implying potential structural features conferring broader acid–alkali adaptability. Regarding the effects of metal ions and chemical reagents, Na⁺ inhibition of lipase activity may result from altered ionic strength around the enzyme or shielding of charged groups at the active site, thereby reducing catalytic efficiency (Page and Di Cera, 2006 ). The strong inhibitory effect of Co²⁺ is likely due to competitive replacement of essential binding sites or strong coordination with critical thiol residues, leading to disruption of the catalytic conformation (Yadav R). The activating effect of Ca²⁺ on extracellular lipase was consistent with previous findings (Zhu et al., 2010). Interestingly, Fe³⁺ and Cu²⁺ at certain concentrations enhanced enzyme activity in this study (by 23.45% and 7.93%, respectively), in contrast to inhibitory effects reported elsewhere (Matsumae H; Eddehech A et al., 2024). Metal ions have been reported to enhance catalytic activity in other enzymes (e.g., proteases, cellulases) by stabilizing conformations or participating in electron transfer (El’vina P. M. et al., 1998; Prejanò et al., 2020 ; Tokmina-Lukaszewska et al., 2023 ). These divergent results suggest that lipases from different S. marcescens strains may exhibit strain-dependent responses to metal ions. Further studies involving enzyme purification, biochemical characterization, and structural analysis will be required to elucidate the underlying mechanisms. 5. Conclusions In this study, a high-efficiency extracellular lipase-producing strain, Serratia marcescens C14802, was successfully isolated and identified from kitchen waste. Both the response surface methodology (RSM–CCD) and the hybrid artificial neural network–genetic algorithm (ANN–GA) models effectively predicted and optimized lipase production, demonstrating strong modeling capabilities. Among them, the ANN–GA model, using enzyme activity as the predictive index, outperformed the RSM–CCD model, highlighting the superiority of artificial intelligence-based approaches in optimizing complex fermentation systems. Optimization by the ANN–GA model yielded the following optimal fermentation conditions: olive oil 10 g/L, glucose 8 g/L, beef extract 3.1 g/L, K₂HPO₄ 2 g/L, inoculum size 3.5%, cultivation time 26.2 h, temperature 30°C, and initial pH 5. Under these conditions, lipase production reached 613.39 U/mL, representing a 6.45-fold increase compared with the basal medium. The crude extracellular lipase exhibited an optimal temperature of 70°C and an optimal pH of 7, with good thermal stability. Moreover, the enzyme showed dependency on Ca²⁺, Fe³⁺, and Cu²⁺, while maintaining high tolerance to β-mercaptoethanol, DMSO, Triton X-100, and IPA. These favorable characteristics provide both theoretical and practical support for the potential application of this enzyme in industrial processes such as biodiesel production and oily wastewater treatment, particularly under harsh conditions involving high temperature, extreme pH, or chemical inhibitors. Declarations Acknowledgments Gratefully acknowledge the Guangxi Key Laboratory of Polysaccharide Materials and Modification for providing experimental infrastructure support, and the Guangxi Engineering Technology Research Center for Industrialization of Marine Microbial Resources for access to computational and analytical instrumentation. Author Contributions Xiaodong Tian : Conceived and conducted the study; drafted the original manuscript. Chuyun Huang and Shen Wang : Assisted with experiments and data analysis. Zheng Zhang : Provided guidance, project administration, supervision, review, and editing. Mingguo Jiang : Conceptualization, writing—review and editing, funding acquisition, project administration, and supervision. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 32460009), the 2024 Guangxi Science Popularization and Rural Revitalization Program—Science and Technology Courtyard (Funding for the Guangxi Liangqing Papaya Science and Technology Courtyard), and the 2024 Graduate Education Innovation Program of Guangxi University for Nationalities (Grant No. gxmzu-chxs2024240). Research involving human participants and/or animals Not applicable. Competing Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Yahia E M, Mourad M. Food waste at the consumer level[M]//Preventing food losses and waste to achieve food security and sustainability. 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07:20:17\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":806604,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMorphological characteristics and phylogenetic analysis of strain C41802\\u003c/p\\u003e\\n\\u003cp\\u003eA: Morphological characteristics on LB plate; B: Gram staining results; C: Lipolysis ability; D: Phylogenetic tree constructed based on 16S rRNA gene sequence\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/8cd7e8e70a689727cbaa9734.png\"},{\"id\":95949529,\"identity\":\"296f7bb4-ba1d-4c4d-b825-ecdece6ea7ea\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 18:53:30\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":168800,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSingle factor optimization results\\u003c/p\\u003e\\n\\u003cp\\u003eA:Inducer Type; B:Inducer concentration; C:Carbon source; D:Carbon source concentration; E:Nitrogen source; F:Nitrogen source concentration; G:Types of inorganic salts; H:Inorganic salt concentration; I: Fermentation time; J:Inoculum size; K: Fermentation temperature; L;pH\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/8f50a09ac3dfaedce2a227a2.png\"},{\"id\":96244988,\"identity\":\"30a491ab-0d33-49ab-abe9-f5edb8ba6f71\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:19:41\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":73271,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePato chart of PB test results\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/160610291bb12998ab9254ad.png\"},{\"id\":95949531,\"identity\":\"50d6da0d-c5a1-4fb6-b48c-e343cb08bf49\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 18:53:30\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":206770,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe model performance verification and analysis: scatter plot between experimental and predictive data by ANN modeling for (A) training, (B) testing, (C) validation, and (D) overall data fitting; (E) performance; (F) error histogram. (G) The variation diagram of fitness values based on multiple generations and (H) the comparison diagram of the best and average fitness values.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/262e244ac43c320900751fb2.png\"},{\"id\":96243663,\"identity\":\"5d755b03-d459-4c76-9aa4-ec2ef0c03b4c\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:16:49\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":106207,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffect of temperature on lipase\\u003c/p\\u003e\\n\\u003cp\\u003eA:30℃-90℃ Optimum temperature；B：0h-12h Temperature stability\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/508a1f709cf53a71e98130eb.png\"},{\"id\":96246310,\"identity\":\"7d016b56-7d58-4f6b-ad95-96c763015f46\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:25:22\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":84290,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffect of pH on lipase\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/1580ec89155245f0f10db84a.png\"},{\"id\":96245702,\"identity\":\"e131e6ec-ed55-4db7-ae92-f41facac5fd4\",\"added_by\":\"auto\",\"created_at\":\"2025-11-19 07:21:59\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":60375,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEffects of metal ions and chemicals on enzyme activity\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/2b96db6582fcd6dfdd060856.png\"},{\"id\":105755558,\"identity\":\"30fb3eea-7827-465d-a97d-a1f491c5e018\",\"added_by\":\"auto\",\"created_at\":\"2026-03-30 16:28:01\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2364794,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/39a813d0-0888-436c-8833-fa1678a6c92a.pdf\"},{\"id\":95949534,\"identity\":\"a1b18118-bbcf-4360-a719-ddba128e5f7e\",\"added_by\":\"auto\",\"created_at\":\"2025-11-14 18:53:30\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30904,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterials.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7922278/v1/188891d6e3d66f4ffc2aaf4d.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Optimization of fermentation conditions and enzymatic properties of extracellular lipase produced by Serratia marcescens derived from food waste\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eFood waste primarily consists of edible food discarded or spoiled due to over-preparation, improper storage, and inefficient operations in households or the catering industry (Yahia \\u0026amp; Mourad, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). It typically comprises carbohydrates, proteins, and fats, with its specific composition varying based on the mixture of different food types (Lelicińska-Serafin et al., 2020). The global generation of food waste is substantial, with estimates suggesting that approximately 1.3\\u0026nbsp;billion metric tons are produced annually (Huang et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). According to authoritative estimates, global annual food loss and waste amount to roughly 1.3 to 1.4\\u0026nbsp;billion metric tons, imposing significant resource pressures on the environment. This massive volume incurs remarkable economic and social costs: direct financial losses from food waste alone are estimated at around 1 trillion USD annually (Marimuthu et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Among the various components of food waste, oils are of particular concern due to their difficulty in natural degradation and potential environmental risks, presenting new challenges for subsequent processing and resource utilization.\\u003c/p\\u003e\\u003cp\\u003eFood waste contains a significant amount of lipids, which are difficult to degrade naturally (Lin et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). The unsaturated fatty acids in these lipids undergo auto-oxidation, producing peroxides that further decompose into a complex mixture of volatile aldehydes, ketones, and organic acids, resulting in unpleasant odors (Frankel E N, 2005; Liu et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). This not only poses environmental hazards but also constitutes a potential threat to public safety (Kumar et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Improper handling can lead to the accumulation and transformation of lipids, causing severe harm to ecosystems. Therefore, developing effective methods for the harmless treatment of lipids in food waste has become an urgent priority.Currently, common methods for lipid treatment include anaerobic fermentation, physicochemical separation, wet hydrothermal processing, and microbial degradation. Anaerobic fermentation is relatively complex and time-consuming, generating substantial amounts of wastewater during the process (He et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Physicochemical separation methods such as flotation, adsorption, and magnetic adsorption often require the addition of flocculants or adsorbents, leading to high operational costs and potential secondary pollution (Iskander et al., 2024; Khalidi-Idrissi et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Wet hydrothermal processing relies on high temperature and pressure to transfer solid-phase lipids to the liquid phase but is energy-intensive, incomplete in extraction, and involves a cumbersome process (Munir et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).In contrast, microbial methods utilizing lipases to degrade waste oils offer significant advantages: they are cost-effective, involve mild conditions, are environmentally friendly, and enable the resource utilization of oils (Vishnoi et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Lipases, as biological catalysts capable of hydrolyzing oils, show great potential in food waste management (Zhao et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Bhatia et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Their products, glycerol and fatty acids, can be further converted into biofuels or other high-value chemicals.\\u003c/p\\u003e\\u003cp\\u003eWithin the broader family of microbial lipases, most enzymes utilized in biotechnological applications and organic chemistry are derived from natural bacteria, fungi, and recombinant strains. The efficacy of microbial lipases is highly dependent on factors such as temperature, pH, and substrate specificity, which serve as critical parameters in industrial production and find wide application in food processing, fine chemicals, biodiesel synthesis, and other fields (Treichel et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Chandra et al., 2010). However, the performance of natural microorganisms in enzyme production and lipid degradation is often suboptimal, necessitating additional optimization measures to enhance enzyme activity in lipase-producing strains.One popular approach for optimization involves the use of Genetic Algorithm-Artificial Neural Network (GA-ANN) methods. This technique plays a crucial role in increasing lipase yield and improving lipid degradation efficiency, serving as an effective strategy to boost microbial lipid degradation capabilities. For instance, Lau et al. (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) successfully optimized the lipase production conditions for \\u003cem\\u003eBurkholderia cenocepacia\\u003c/em\\u003e using GA-ANN, achieving a lipase activity of 225 U/mL, representing a 1.6-fold increase compared to pre-optimization levels. In another study, Benhoula-M et al employed Response Surface Methodology to investigate bacterial lipase production from olive oil wastewater, achieving a maximum lipase activity of 8.82 U/mL. These studies highlight that precise control and optimization of cultivation conditions can significantly enhance the lipase production capacity of specific microbial strains.\\u003c/p\\u003e\\u003cp\\u003eIn this study, a bacterium capable of producing extracellular lipase was isolated from food waste. The fermentation conditions were precisely optimized using the GA-ANN method, and the optimized process parameters were evaluated through experimental results, demonstrating a significant enhancement in lipase activity. Additionally, some physicochemical properties of the crude extracellular lipase were characterized to deepen our understanding of its characteristics.The optimization results provide a basis for establishing suitable fermentation production parameters and offer valuable references for determining optimal kinetics and economic parameters in subsequent scale-up studies of this bioprocess. Furthermore, the strain identified in this study serves as an excellent biological resource for lipase production and contributes to the recycling and utilization of resources.This research not only enhances the efficiency of lipase production but also paves the way for broader applications in industrial biotechnology, particularly in the context of sustainable resource management.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1. Isolation and identification of lipase-producing bacteria\\u003c/h2\\u003e\\u003cp\\u003eIn this study, food waste samples and food waste wastewater were collected from the Food Waste Bin at the cafeteria of Guangxi University for Nationalities in Xixiangtang District, Nanning City, Guangxi Zhuang Autonomous Region (coordinates: 22\\u0026deg;50\\u0026prime;26\\u0026Prime;N, 108\\u0026deg;11\\u0026prime;23\\u0026Prime;E). After collection, the samples were stored in sterile bags. In the laboratory, the samples were serially diluted to 10⁻⁷ using sterile water and spread on enrichment medium plates. The plates were incubated at 36\\u0026deg;C for 48 hours, and colonies with distinct morphologies were selected for two rounds of purification on single plates. Single colonies were then inoculated onto tributyrin agar (TBA) medium and incubated at 36\\u0026deg;C for 12 hours. Lipase production capability was assessed by observing the clear zones around the colonies. The biochemical characteristics of the strains were determined according to Bergey's Manual of Determinative Bacteriology (Bergey \\u0026amp; Holt, 1994), followed by Gram staining. Colonies were cultured on Luria Bertani (LB) medium for 24 hours to observe colony morphology. Genomic DNA was extracted using a DNA extraction kit (Sangon Biotech, Shanghai Co., Ltd.), and the 16S rRNA gene sequence was amplified using universal primers 27F/1492R. The PCR products were sequenced, and the 16S rRNA gene sequences were compared using NCBI BLASTn. Sequences with high similarity were downloaded for phylogenetic analysis. Phylogenetic trees were constructed using MEGA 12.0.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2. Determination of extracellular lipase\\u003c/h2\\u003e\\u003cp\\u003eExtracellular lipase activity was measured using the p-nitrophenylpalmitate (pNPP) assay, based on the method described by Winkler et al. with slight modifications. The following steps were used: 30 mg of pNPP was dissolved in 10 mL of isopropanol and then mixed with 90 mL of 0.05 M phosphate buffer (pH 8.0, containing 207 mg of sodium deoxycholate and 100 mg of gum arabic) to prepare the substrate solution. 2.4 mL of freshly prepared substrate solution, preheated at 37\\u0026deg;C, was added to 0.1 mL of cell-free supernatant and mixed. The mixture was incubated at 37\\u0026deg;C for 15 minutes and then cooled on ice for 5 minutes to terminate the reaction. Enzyme activity was calculated by measuring the OD₄₁₀ and comparing it with a no-enzyme control. One unit (U) of enzyme activity was defined as the activity required to enzymatically release 1 \\u0026micro;mol of p-nitrophenol from the substrate per minute.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3. Optimization of culture medium and fermentation conditions for lipase production\\u003c/h2\\u003e\\u003cp\\u003eIn this study, a basal fermentation medium (olive oil emulsion 5 g/L, peptone 10 g/L, K₂HPO₄ 1 g/L, KH₂PO₄ 0.5 g/L) was used as the initial reference system. Preliminary single-factor experiments were conducted to optimize inoculum size (1%, 3%, 5%, 7%, 9%) and cultivation time (12 h, 24 h, 36 h, 48 h, 60 h), thereby establishing the basic operational parameters for subsequent optimization. Based on the determined inoculum size and cultivation time, further single-factor experiments were systematically performed to evaluate the effects of different inducers (olive oil, peanut oil, sesame oil, rapeseed oil, and soybean oil; all oils were pre-emulsified), carbon sources (glucose, maltose, sucrose, lactose, soluble starch), nitrogen sources (ammonium sulfate, ammonium chloride, ammonium dihydrogen phosphate, sodium nitrate, beef extract, yeast extract), and inorganic salts (ferrous sulfate, magnesium sulfate, potassium dihydrogen phosphate, dipotassium hydrogen phosphate, copper sulfate) on extracellular lipase production, in order to identify the optimal medium formulation. Finally, based on this optimized formulation, the initial pH (4, 5, 6, 7, 8, 9) and incubation temperature (26\\u0026deg;C, 29\\u0026deg;C, 32\\u0026deg;C, 35\\u0026deg;C, 38\\u0026deg;C) were further refined, leading to the establishment of fermentation conditions most favorable for extracellular lipase production.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4. Plackett-Burman design and response surface optimization\\u003c/h2\\u003e\\u003cp\\u003eBased on the results of single-factor experiments, a Plackett\\u0026ndash;Burman (PB) design was performed using Design-Expert 12.0 (Stat-Ease, Inc., Minneapolis, USA) (see Supplementary Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Eight variables\\u0026mdash;glucose (A), beef extract (B), olive oil (C), KH₂PO₄ (D), pH (E), temperature (F), cultivation time (G), and inoculum size (H)\\u0026mdash;were coded at two levels (\\u0026minus;\\u0026thinsp;1, +\\u0026thinsp;1) to identify the most significant fa ctors influencing lipase production. The three variables with the greatest impact were subsequently subjected to response surface methodology (RSM) optimization. A five-level coded design was employed (see Supplementary Table S2), generating a total of 23 experimental runs (including axial and replicated center points). The data were fitted to a second-order polynomial model to evaluate main effects, interaction effects, and quadratic terms.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.5. Artificial neural network\\u003c/h2\\u003e\\u003cp\\u003eThe data obtained from the response surface experiments were normalized and subsequently used for artificial neural network (ANN) training in MATLAB R2023a (The MathWorks, Inc., Natick, MA, USA). A feed-forward neural network based on error backpropagation was constructed, and the Levenberg\\u0026ndash;Marquardt (LM) algorithm was employed for optimization. Beef extract concentration (g/L), inoculum size (%), and cultivation time (h) were used as input variables, while lipase activity (U/mL) served as the output variable to establish the predictive ANN model. The ANN was trained using the RSM CCD dataset, which was randomly divided into 70% for training, 15% for validation, and 15% for testing to perform BP learning and generalization assessment. Signals generated by the hidden layer were propagated to the output layer, where the predicted responses were compared with the experimentally measured values for the given input dataset. The mean squared error (MSE) across all datasets was calculated to evaluate model performance. The backpropagation (BP) training algorithm was applied to minimize the error function by adjusting weights and biases (Nasab et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.6.Genetic algorithm\\u003c/h2\\u003e\\u003cp\\u003eAfter establishing and validating the ANN model, a genetic algorithm (GA) was employed to optimize the multi-parameter space of the fermentation process. Each chromosome encoded three decision variables\\u0026mdash;nitrogen source (beef extract concentration), cultivation time, and inoculum size\\u0026mdash;all continuously coded within the ranges defined by the RSM CCD design. The trained GA\\u0026ndash;ANN model was used as the fitness evaluator: for each candidate solution (chromosome), forward prediction was performed, and the predicted lipase activity was assigned as the individual\\u0026rsquo;s fitness value. Using the MATLAB Genetic Algorithm Toolbox, the population size, crossover rate, and mutation rate were specified, and the initial population was generated. During iterative generations, selection, crossover, and mutation operations were executed, with high-fitness individuals retained to accelerate convergence. After training, the ANN rapidly scored and ranked each generation of chromosomes produced by the GA. This coupled GA\\u0026ndash;ANN procedure continued until the maximum generation criterion was met, ultimately identifying the optimal combination of process parameters that maximized lipase activity.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.7. Preparation of crude extracellular lipase solution\\u003c/h2\\u003e\\u003cp\\u003eThe fermentation broth was centrifuged at 12,000 rpm for 20 min at 4\\u0026deg;C, and the supernatant was collected. The supernatant was then filtered through a hydrophilic PVDF membrane with a pore size of 0.22 \\u0026micro;m to obtain the crude enzyme solution.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.8. Effects of temperature and pH on extracellular lipase activity\\u003c/h2\\u003e\\u003cp\\u003eThe effect of temperature on lipase activity was evaluated by incubating the crude enzyme at 30\\u0026ndash;90\\u0026deg;C (with 10\\u0026deg;C intervals) for 15 min, and the maximum measured activity was defined as 100%. Thermal stability was assessed within the same temperature range (30\\u0026ndash;90\\u0026deg;C, at 10\\u0026deg;C intervals) by sampling every 2 h to determine the residual activity of the crude lipase. For pH stability, the crude enzyme solution was incubated in different buffer systems at 4\\u0026deg;C for 1 h, and the residual activity was measured under each pH condition. The buffer systems used were: sodium citrate buffer (pH 5.0\\u0026ndash;7.0), Tris-HCl buffer (pH 7.0\\u0026ndash;9.0), Tris-HCl buffer (pH 9.0\\u0026ndash;12.0), and Gly-NaOH buffer (pH 9.0\\u0026ndash;11.0). Extracellular lipase activity under different media and cultivation conditions was determined according to the method described in Section \\u003cspan refid=\\\"Sec4\\\" class=\\\"InternalRef\\\"\\u003e2.2\\u003c/span\\u003e. All enzyme activity data were expressed as relative activity, with the maximum observed value set as 100%.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.9. Effects of metal ions or chemical reagents on enzyme activity\\u003c/h2\\u003e\\u003cp\\u003eTo evaluate the effects of metal ions and chemical reagents on enzyme activity, Na⁺, Ca\\u0026sup2;⁺, Mg\\u0026sup2;⁺, Fe\\u0026sup3;⁺, Mn\\u0026sup2;⁺, Co\\u0026sup2;⁺, and Cu\\u0026sup2;⁺ (all supplied as chlorides) were individually added to the reaction system. Similarly, isopropanol (IPA), sodium dodecyl sulfate (SDS), dimethyl sulfoxide (DMSO), Triton X-100, and β-mercaptoethanol (BME) were tested separately. A reaction system without any added metal ions or chemical reagents served as the control. Extracellular lipase activity under different media and cultivation conditions was determined according to the method described in Section \\u003cspan refid=\\\"Sec4\\\" class=\\\"InternalRef\\\"\\u003e2.2\\u003c/span\\u003e. All enzyme activity values were expressed as relative activity, with the activity of the control defined as 100%.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.10. Data and Statistics\\u003c/h2\\u003e\\u003cp\\u003eAll statistical analyses were performed using IBM SPSS Statistics 27. Data significance was evaluated by one-way analysis of variance (ANOVA). Data visualization was primarily conducted with Origin 2024, while selected model-fitting plots were generated using MATLAB R2023a. Experimental results are presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard error (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\stackrel{\\\\prime }{x}\\\\pm\\\\:SE\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), and all experiments were carried out in triplicate.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results and Analysis\",\"content\":\"\\u003cp\\u003e3.1. Screening and identification of strains\\u003c/p\\u003e\\n\\u003cp\\u003eA lipase-producing strain, designated C14802, was isolated using TBA medium. After 24 h of growth on LB agar plates, the colonies appeared circular, opaque white, with smooth, raised surfaces and entire margins (Fig. 1A). Gram staining identified the strain as Gram-negative (Fig. 2B). When cultured on TBA medium for 12 h, a yellow halo was observed around the colonies (Fig. 1C), indicating the ability of the strain to hydrolyze tributyrin in the medium. Phylogenetic tree analysis revealed that strain C14802 clustered in the same branch as \\u003cem\\u003eSerratia marcescens\\u003c/em\\u003e HB10 (Fig. 1D), suggesting a close evolutionary relationship. Based on morphological characteristics, physiological and biochemical tests, and phylogenetic analysis, strain C14802 was identified as \\u003cem\\u003eSerratia marcescens.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e3.2. Optimization of lipase production culture medium and culture conditions\\u003c/p\\u003e\\n\\u003cp\\u003eFor strain C41802, the highest lipase activity was observed at a fermentation time of 36 h (Fig. 2I), while an inoculum size of 5% yielded the maximum enzyme activity (Fig. 2J). In medium supplemented with 1% olive oil, lipase activity reached 235.17 \\u0026plusmn; 10.25 U/mL, which was significantly higher than that obtained with other oils (P \\u0026lt; 0.05) (Fig. 2A). When glucose was used as the carbon source, the maximum enzyme production was achieved at 339.89 \\u0026plusmn; 17.99 U/mL, significantly higher than with other carbon sources (P \\u0026lt; 0.05) (Fig. 2C), with the optimal concentration determined to be 8 g/L (Fig. 2D). Both beef extract and yeast extract significantly enhanced lipase activity, yielding 402.74 \\u0026plusmn; 17.29 U/mL and 395.79 \\u0026plusmn; 16.54 U/mL, respectively, with beef extract showing a slightly superior effect (Fig. 2E); the optimal concentration was 6 g/L (Fig. 2F). Among the inorganic salts tested, K₂HPO₄ was identified as the most effective for lipase production, with an optimal concentration of 2 g/L, resulting in an activity of 317.96 \\u0026plusmn; 14.03 U/mL (Fig. 2G, H). The optimal fermentation temperature for lipase production was 30 \\u0026deg;C (Fig. 2K), with only minor differences observed among other temperature treatments, indicating that strain C41802 possesses a relatively broad temperature tolerance for lipase production. The highest enzyme yield under different pH conditions was obtained at pH 5, reaching 310.59 \\u0026plusmn; 8.86 U/mL (Fig. 2L), suggesting that the strain can effectively produce lipase even in mildly acidic environments.\\u003c/p\\u003e\\n\\u003cp\\u003e3.3 Plackett-Burman test results\\u003c/p\\u003e\\n\\u003cp\\u003eThe results of the Plackett\\u0026ndash;Burman (PB) design indicated that factors A (glucose), B (beef extract), E (pH), F (temperature), G (time), and H (inoculum size) had significant effects on the response value (Table 1). The order of factor importance was B \\u0026gt; G \\u0026gt; H \\u0026gt; E \\u0026gt; A \\u0026gt; F. Analysis of the Pareto chart (Fig. 3) revealed clear differences in the effects of individual factors on enzyme activity. Among them, factor B (beef extract) and factor G (time) exhibited highly significant positive effects (t-values far exceeding the Bonferroni limit of 7.0406), identifying them as the key contributors to enhanced enzyme activity. Factors H (inoculum size), E (pH), A (glucose), and F (temperature) also showed significant positive effects (t-values between the t-value limit of 3.18245 and the Bonferroni limit). In contrast, the positive effect of factor D (K₂HPO₄) and the negative effect of factor C (olive oil) were not significant (t-values below the t-value limit). The resulting regression linear equation (1) was as follows:\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg width=\\\"541\\\" height=\\\"17\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1763145928.png\\\" alt=\\\"image\\\"\\u003e\\u0026nbsp; \\u0026nbsp; （1）\\u003c/p\\u003e\\n\\u003cp\\u003eTabe.1 \\u0026nbsp;Plackett-Burman test factor level and significance analysis\\u003c/p\\u003e\\n\\u003cdiv align=\\\"\\\"\\u003e\\n \\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eSource\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003edf\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003eMS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eF\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSig.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003eImportance Ranking\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eModel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2.234E+05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e27925.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e34.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0073 \\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e22847.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e22847.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e27.92\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0132\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e87204.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e87204.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e106.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0019 \\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e886.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e886.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e1.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.3744\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e4121.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e4121.99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e5.04\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.1105\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e25195.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e25195.20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e30.79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0115\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e13221.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e13221.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e16.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0277\\u003cstrong\\u003e*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eG\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e37178.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e37178.58\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e45.44\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0067 \\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eH\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e32751.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e32751.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e40.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e0.0080 \\u003cstrong\\u003e**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003eSignificance\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eResidual\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2454.84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e818.28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003eCor Total\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e2.259E+05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.9891\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 75px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eR\\u003csub\\u003eadj\\u003c/sub\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.9601\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 31px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 69px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 70px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\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\\u003eNote：\\u0026ldquo;*\\u0026rdquo; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026le;0.05；\\u0026ldquo;**\\u0026rdquo; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026le;0.01.\\u003c/p\\u003e\\n\\u003cp\\u003e3.4 RSM model fitting and statistical evaluation\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the PB results, the three most influential factors\\u0026mdash;beef extract (A), inoculum size (B), and cultivation time (C)\\u0026mdash;were selected for a central composite design (CCD); the design levels and experimental data are provided in Supplementary Table S4. ANOVA of the RSM\\u0026ndash;CCD model (Table 2) showed high significance with \\u003cimg width=\\\"65\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1763145958.png\\\" alt=\\\"image\\\"\\u003e, indicating that the model effectively captured the main, interaction, and quadratic effects on the response. Among the tested terms, inoculum size (B), interactions \\u003cimg width=\\\"17\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595871.png\\\" alt=\\\"image\\\"\\u003e, \\u003cimg width=\\\"17\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595955.png\\\" alt=\\\"image\\\"\\u003e, \\u003cimg width=\\\"17\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595950.png\\\" alt=\\\"image\\\"\\u003e, and quadratic terms \\u003cimg width=\\\"15\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595973.png\\\" alt=\\\"image\\\"\\u003eand \\u003cimg width=\\\"15\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595874.png\\\" alt=\\\"image\\\"\\u003ewere significant (\\u003cimg width=\\\"51\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595829.png\\\" alt=\\\"image\\\"\\u003e); in contrast, beef extract (A), cultivation time (C), and the quadratic term \\u003cimg width=\\\"14\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1763145959.png\\\" alt=\\\"image\\\"\\u003ewere not significant (\\u003cimg width=\\\"51\\\" height=\\\"19\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595943.png\\\" alt=\\\"image\\\"\\u003e). Multiple regression analysis yielded the following empirical second-order polynomial relating the response to the coded variables (Equation 2):\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cimg width=\\\"624\\\" height=\\\"37\\\" src=\\\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img176314595967.png\\\" alt=\\\"image\\\"\\u003e（2）\\u003c/p\\u003e\\n\\u003cp\\u003eTabe.2 RSM-CCD-Testergebnisse\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"718\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eSource\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003eSum of squares\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003edf\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003eMean square\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eF\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003ep\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eModel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.319E+05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e14660.93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e46.68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003esignificant\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eA-Beef Extract\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e880.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e880.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e2.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.1179\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eB-Inoculum size\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e25078.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e25078.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e79.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eC-Time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e171.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e171.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.5453\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.4734\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eAB\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e38479.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e38479.40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e122.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eAC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e5390.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e5390.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e17.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.0012\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eBC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e19786.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e19786.08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e63.00\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eA\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e301.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e301.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.960\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.3450\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eB\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e4448.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e4448.83\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e14.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.0024\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eC\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e37647.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e37647.32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e119.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eResidual\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e4082.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e314.05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003eLack of Fit\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e158.70\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e31.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.0647\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e0.9960\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003enot significant\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003ePure Error\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e3923.95\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e490.49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 132px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.97\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eAdjusted R\\u0026sup2;\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.9492\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eCV\\u0026nbsp;\\u003c/em\\u003e(%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e5.57\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e3.5. ANN-GA modeling and optimization\\u003c/p\\u003e\\n\\u003cp\\u003eBased on the 23 datasets obtained from the RSM\\u0026ndash;CCD design (Supplementary Table S4), the data were divided into training (70%), validation (15%), and testing (15%) sets to construct a three-layer backpropagation neural network (BP\\u0026ndash;NN) model. The target scatter plots for training (Fig. 4A, R = 0.98449), validation (Fig. 4B, R = 0.9972), testing (Fig. 4C, R = 0.99092), and overall performance (Fig. 4D, R = 0.98447) showed regression coefficients close to 1, indicating a high degree of agreement between predicted and experimental values and confirming the reliability of the model. During training, the mean squared error (MSE) decreased progressively with increasing epochs, reaching the optimal validation performance (MSE = 88.3194) at the third epoch (Fig. 4F). The error histogram (Fig. 4E) demonstrated that prediction errors ranged from \\u0026minus;39.94 to 176.5 and fluctuated around zero, reflecting rapid convergence and good stability of the model, making it suitable for subsequent analysis.To further optimize the BP\\u0026ndash;NN, a genetic algorithm (GA) was applied with 200 iterations, an initial population size of 30, a crossover probability of 0.8, and a mutation probability of 0.05. The GA\\u0026ndash;BP\\u0026ndash;NN hybrid model ultimately identified the optimal process parameters as beef extract 3.12 g/L, inoculum size 3.49%, and fermentation time 26.16 h (Fig. 4G).\\u003c/p\\u003e\\n\\u003cp\\u003e3.6. Prediction data verification of RSM-CCD and GA-ANN\\u003c/p\\u003e\\n\\u003cp\\u003eTo evaluate the accuracy and reliability of the integrated modeling approach, the RSM\\u0026ndash;CCD and GA\\u0026ndash;ANN models were comparatively analyzed, and their predictive capabilities were experimentally validated. As shown in Table 3, under the predicted optimal conditions of the RSM\\u0026ndash;CCD model, the experimentally measured lipase activity was close to the predicted value, with a relative error of 2.54%, demonstrating good predictive performance. In contrast, the GA\\u0026ndash;ANN model exhibited nearly identical predicted and experimental values (relative error 0.67%), with a mean squared error (MSE) significantly lower than that of the RSM\\u0026ndash;CCD model, indicating superior fitting accuracy and stability. These results suggest that for the optimization of complex, highly nonlinear fermentation processes, the GA\\u0026ndash;ANN model offers clear advantages over the traditional RSM\\u0026ndash;CCD approach, achieving an experimentally validated lipase activity that was 14.98 U/mL higher than that obtained with RSM\\u0026ndash;CCD.\\u003c/p\\u003e\\n\\u003cp\\u003eTabe.3 Optimal conditions and verification results of the model\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"639\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eModel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003eBeef extract（g/L）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003eInoculum size\\u003c/p\\u003e\\n \\u003cp\\u003e（%）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003eTime\\u003c/p\\u003e\\n \\u003cp\\u003e(h)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" valign=\\\"top\\\" style=\\\"width: 203px;\\\"\\u003e\\n \\u003cp\\u003eEnzyme activity（U/mL）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 72px;\\\"\\u003e\\n \\u003cp\\u003e相对误差\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 53px;\\\"\\u003e\\n \\u003cp\\u003eMSE\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 89px;\\\"\\u003e\\n \\u003cp\\u003eActual value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003ePredicted value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 72px;\\\"\\u003e\\n \\u003cp\\u003e（%）\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 53px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eRSM-CCD\\u003c/p\\u003e\\n \\u003cp\\u003eGA-ANN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 92px;\\\"\\u003e\\n \\u003cp\\u003e3.6\\u003c/p\\u003e\\n \\u003cp\\u003e3.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 93px;\\\"\\u003e\\n \\u003cp\\u003e4.2\\u003c/p\\u003e\\n \\u003cp\\u003e3.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 49px;\\\"\\u003e\\n \\u003cp\\u003e26.5\\u003c/p\\u003e\\n \\u003cp\\u003e26.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 89px;\\\"\\u003e\\n \\u003cp\\u003e598.41\\u003c/p\\u003e\\n \\u003cp\\u003e613.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e614.03\\u003c/p\\u003e\\n \\u003cp\\u003e617.52\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 72px;\\\"\\u003e\\n \\u003cp\\u003e2.54\\u003c/p\\u003e\\n \\u003cp\\u003e0.67\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 53px;\\\"\\u003e\\n \\u003cp\\u003e314.05\\u003c/p\\u003e\\n \\u003cp\\u003e1.27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e3.7. Optimum temperature and temperature stability\\u003c/p\\u003e\\n\\u003cp\\u003eThe crude lipase from strain C14802 exhibited the highest activity at 70 \\u0026deg;C (Fig. 5A). When incubated above 70 \\u0026deg;C for 2 h, the enzyme activity was almost completely lost. At 60 \\u0026deg;C, the relative activity approached complete loss only after 12 h of incubation, whereas at 50 \\u0026deg;C, 52.56% of the relative activity was still retained after 12 h. Interestingly, incubation at 30 \\u0026deg;C and 40 \\u0026deg;C for 2\\u0026ndash;4 h resulted in a slight increase in activity, suggesting that the enzyme requires a longer equilibration period to reach stable activity at lower temperatures. Overall, these results indicate that the enzyme undergoes rapid inactivation above 70 \\u0026deg;C, while exhibiting good thermal stability below 60 \\u0026deg;C, highlighting its promising potential for industrial applications and further research.\\u003c/p\\u003e\\n\\u003cp\\u003e3.8. Optimum pH and pH stability\\u003c/p\\u003e\\n\\u003cp\\u003eAs shown in Fig. 6, the crude lipase exhibited its optimal activity at pH 7.0 in sodium citrate buffer. When the pH was maintained between 7.0 and 9.0, more than 60% of the relative activity was retained. After incubation of the crude enzyme solution for 1 h under different pH conditions, the relative activity was markedly reduced (\\u0026lt;20%) at pH 5.0\\u0026ndash;6.0. In contrast, within the pH range of 7.0\\u0026ndash;10.0, the enzyme retained a high proportion of its activity, with only a slight decrease compared to the pre-incubation level. At pH 11.0, however, the relative activity declined sharply from 62.9% to 21.4%. Collectively, these results indicate that the enzyme is better adapted to neutral and mildly alkaline environments, exhibiting good pH stability in the neutral (6.0\\u0026ndash;8.0) and weakly alkaline (8.0\\u0026ndash;9.0) ranges.\\u003c/p\\u003e\\n\\u003cp\\u003e3.9. Effects of metal ions and chemicals on enzyme activity\\u003c/p\\u003e\\n\\u003cp\\u003eAs shown in Fig. 7, the effects of different metal ions and chemical reagents on enzyme activity varied significantly. Among the metal ions (Fig. 7A), compared with the control (CK), Na⁺, Mg\\u0026sup2;⁺, and Mn\\u0026sup2;⁺ exhibited only weak inhibitory effects, with relative activities of 96.32%, 98.04%, and 99.65%, respectively. In contrast, Co\\u0026sup2;⁺ strongly inhibited enzyme activity, with relative activity reduced to 33.69%. Conversely, Ca\\u0026sup2;⁺, Fe\\u0026sup3;⁺, and Cu\\u0026sup2;⁺ enhanced enzyme activity, with Fe\\u0026sup3;⁺ showing the most pronounced effect, increasing relative activity to 123.45%.For chemical reagents (Fig. 7B), \\u0026beta;-mercaptoethanol, DMSO, Triton X‑100, and IPA had minimal effects on enzyme activity, with no significant differences compared to the control. Notably, SDS significantly enhanced enzyme activity, with relative activity reaching 110.41%.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eSingle-factor optimization experiments revealed that the initial pH of 5 in the fermentation medium yielded the highest extracellular lipase production by strain C14802. This finding contrasts with the neutral or slightly alkaline conditions typically preferred by most bacterial fermentations, but is consistent with reports on certain \\u003cem\\u003eSerratia marcescens\\u003c/em\\u003e strains (Kaira et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Elistratova et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Such acid tolerance enables the strain to maintain high levels of lipase secretion under low-pH conditions, providing a theoretical basis for its potential application in acidic reaction systems, such as lipid degradation in acidic industrial wastewater and catalytic transformations under acidic environments.\\u003c/p\\u003e\\u003cp\\u003eWith respect to optimization strategies for \\u003cem\\u003eSerratia\\u003c/em\\u003e lipases, traditional single-factor approaches can preliminarily identify key determinants of enzyme production (Gao et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Nwachukwu et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Begam et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Issa et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). However, by neglecting interactions among multiple factors, they fail to accurately capture the integrated effects of multi-parameter systems. Response surface methodology (RSM) improves optimization precision to some extent by considering two-factor interactions (Venil et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Krishnankutty, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Nevertheless, when dealing with three or more variables or highly nonlinear relationships, the predictive accuracy of RSM becomes limited. To overcome this, the present study employed artificial neural networks (ANN) for nonlinear modeling of fermentation data, coupled with genetic algorithms (GA) for global optimization. The GA\\u0026ndash;ANN model demonstrated superior predictive accuracy and optimization performance compared with RSM, not only accurately fitting complex nonlinear relationships but also providing a more reliable theoretical basis for further process optimization.\\u003c/p\\u003e\\u003cp\\u003eThe extracellular lipase of strain C14802 exhibited an optimal temperature of 70\\u0026deg;C and an optimal pH of 7, similar to the lipase from \\u003cem\\u003eS. marcescens\\u003c/em\\u003e isolated from industrial wastewater by Ali et al. (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), which showed an optimum at 75\\u0026deg;C and pH 8. A relatively high optimum temperature suggests enhanced catalytic efficiency under thermophilic conditions, while good thermal stability supports sustained catalytic activity during prolonged incubation. In terms of pH stability, the crude enzyme in this study displayed greater stability under neutral conditions, consistent with the properties of most neutral lipases, but also implying potential structural features conferring broader acid\\u0026ndash;alkali adaptability.\\u003c/p\\u003e\\u003cp\\u003eRegarding the effects of metal ions and chemical reagents, Na⁺ inhibition of lipase activity may result from altered ionic strength around the enzyme or shielding of charged groups at the active site, thereby reducing catalytic efficiency (Page and Di Cera, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). The strong inhibitory effect of Co\\u0026sup2;⁺ is likely due to competitive replacement of essential binding sites or strong coordination with critical thiol residues, leading to disruption of the catalytic conformation (Yadav R). The activating effect of Ca\\u0026sup2;⁺ on extracellular lipase was consistent with previous findings (Zhu et al., 2010). Interestingly, Fe\\u0026sup3;⁺ and Cu\\u0026sup2;⁺ at certain concentrations enhanced enzyme activity in this study (by 23.45% and 7.93%, respectively), in contrast to inhibitory effects reported elsewhere (Matsumae H; Eddehech A et al., 2024). Metal ions have been reported to enhance catalytic activity in other enzymes (e.g., proteases, cellulases) by stabilizing conformations or participating in electron transfer (El\\u0026rsquo;vina P. M. et al., 1998; Prejan\\u0026ograve; et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Tokmina-Lukaszewska et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These divergent results suggest that lipases from different \\u003cem\\u003eS. marcescens\\u003c/em\\u003e strains may exhibit strain-dependent responses to metal ions. Further studies involving enzyme purification, biochemical characterization, and structural analysis will be required to elucidate the underlying mechanisms.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eIn this study, a high-efficiency extracellular lipase-producing strain, Serratia marcescens C14802, was successfully isolated and identified from kitchen waste. Both the response surface methodology (RSM\\u0026ndash;CCD) and the hybrid artificial neural network\\u0026ndash;genetic algorithm (ANN\\u0026ndash;GA) models effectively predicted and optimized lipase production, demonstrating strong modeling capabilities. Among them, the ANN\\u0026ndash;GA model, using enzyme activity as the predictive index, outperformed the RSM\\u0026ndash;CCD model, highlighting the superiority of artificial intelligence-based approaches in optimizing complex fermentation systems. Optimization by the ANN\\u0026ndash;GA model yielded the following optimal fermentation conditions: olive oil 10 g/L, glucose 8 g/L, beef extract 3.1 g/L, K₂HPO₄ 2 g/L, inoculum size 3.5%, cultivation time 26.2 h, temperature 30\\u0026deg;C, and initial pH 5. Under these conditions, lipase production reached 613.39 U/mL, representing a 6.45-fold increase compared with the basal medium. The crude extracellular lipase exhibited an optimal temperature of 70\\u0026deg;C and an optimal pH of 7, with good thermal stability. Moreover, the enzyme showed dependency on Ca\\u0026sup2;⁺, Fe\\u0026sup3;⁺, and Cu\\u0026sup2;⁺, while maintaining high tolerance to β-mercaptoethanol, DMSO, Triton X-100, and IPA. These favorable characteristics provide both theoretical and practical support for the potential application of this enzyme in industrial processes such as biodiesel production and oily wastewater treatment, particularly under harsh conditions involving high temperature, extreme pH, or chemical inhibitors.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eAcknowledgments\\u003c/p\\u003e\\n\\u003cp\\u003eGratefully acknowledge the Guangxi Key Laboratory of Polysaccharide Materials and Modification for providing experimental infrastructure support, and the Guangxi Engineering Technology Research Center for Industrialization of Marine Microbial Resources for access to computational and analytical instrumentation.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eXiaodong Tian\\u003c/strong\\u003e: Conceived and conducted the study; drafted the original manuscript.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eChuyun Huang\\u003c/strong\\u003e and \\u003cstrong\\u003eShen Wang\\u003c/strong\\u003e: Assisted with experiments and data analysis.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eZheng Zhang\\u003c/strong\\u003e: Provided guidance, project administration, supervision, review, and editing.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eMingguo Jiang\\u003c/strong\\u003e: Conceptualization, writing\\u0026mdash;review and editing, funding acquisition, project administration, and supervision.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 32460009), \\u0026nbsp;the 2024 Guangxi Science Popularization and Rural Revitalization Program\\u0026mdash;Science and Technology Courtyard (Funding for the Guangxi Liangqing Papaya Science and Technology Courtyard), and the 2024 Graduate Education Innovation Program of Guangxi University for Nationalities (Grant No. gxmzu-chxs2024240).\\u003c/p\\u003e\\n\\u003cp\\u003eResearch involving human participants and/or animals\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting Interest Statement\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eYahia E M, Mourad M. Food waste at the consumer level[M]//Preventing food losses and waste to achieve food security and sustainability. Burleigh Dodds Science Publishing, 2020: 341-366.\\u003c/li\\u003e\\n \\u003cli\\u003eLelicińska-Serafin K, Manczarski P, Rolewicz-Kalińska A. An insight into post-consumer food waste characteristics as the key to an organic recycling method selection in a circular economy[J]. Energies, 2023, 16(4): 1735.\\u003c/li\\u003e\\n \\u003cli\\u003eHuang J, Pan Y, Liu L, et al. High salinity slowed organic acid production from acidogenic fermentation of kitchen wastewater by shaping functional bacterial community[J]. Journal of Environmental Management, 2022, 310: 114765.\\u003c/li\\u003e\\n \\u003cli\\u003eMarimuthu S, Saikumar A, Badwaik L S. Food losses and wastage within food supply chain: A critical review of its generation, impact, and conversion techniques[J]. 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Msphere, 2022, 7(6): e00212-22.\\u003c/li\\u003e\\n \\u003cli\\u003eGao L, Xu J H, Li X J, et al. Optimization of Serratia marcescens lipase production for enantioselective hydrolysis of 3-phenylglycidic acid ester[J]. Journal of Industrial Microbiology and Biotechnology, 2004, 31(11): 525-530.\\u003c/li\\u003e\\n \\u003cli\\u003eNwachukwu E, Ejike EN, Ejike BU, et al. Characterization and Optimization of soil microorganisms (Serratia marcescens)[J]. Int J Curr Microbiol App Sci, 2017, 6(12): 1215-1231.\\u003c/li\\u003e\\n \\u003cli\\u003eBegam M S, Pradeep F S, Pradeep B V. Production, purification, characterization and applications of lipase from Serratia marcescens MBB05[J]. Asian J Pharm Clin Res, 2012, 5(4): 237-245.\\u003c/li\\u003e\\n \\u003cli\\u003eIssa H K, Abou-Dobara M I, El-Sayed A, et al. Optimization, purification and characterization of extracellular lipase produced by Serratia marcescens EGHK-19[J]. Egyptian Journal of Botany, 2024, 64(4): 107-119.\\u003c/li\\u003e\\n \\u003cli\\u003eVenil C, Sangeetha Kamatshi N, Lakshmanaperumalsamy P. Statistical optimization of medium components for the production of lipase by Serratia marcescens SB08[J]. The Internet Journal of Microbiology, 2009, 7(1).\\u003c/li\\u003e\\n \\u003cli\\u003eKrishnankutty V. Optimization of lipase production by response surface methodology from Serratia marcescens VT 1 isolated from oil contaminated soil[J]. Biologia, 2024, 79(5): 1471-1486.\\u003c/li\\u003e\\n \\u003cli\\u003eAli S R, Sultana S S, Rajak S, et al. Serratia sp. scl1: isolation of a novel thermostable lipase producing microorganism which holds industrial importance[J]. Antonie van Leeuwenhoek, 2022, 115(11): 1335-1348.\\u003c/li\\u003e\\n \\u003cli\\u003ePage M J, Di Cera E. Role of Na+ and K+ in enzyme function[J]. Physiological reviews, 2006, 86(4): 1049-1092.\\u003c/li\\u003e\\n \\u003cli\\u003eYadav R P, Saxena R K, Gupta R, et al. Purification and characterization of a regiospecific lipase from Aspergillus terreus[J]. Biotechnology and applied biochemistry, 1998, 28(3): 243-249.\\u003c/li\\u003e\\n \\u003cli\\u003eZhu Qixia, Chen Ying, Zhang Bo, Huang Ribo. Cloning, expression and enzymatic properties of Serratia marcescens lipase gene[J]. Biotechnology, 2010, 20(5): 12-16\\u003c/li\\u003e\\n \\u003cli\\u003eMatsumae H, Shibatani T. Purification and characterization of the lipase from Serratia marcescens Sr41 8000 responsible for asymmetric hydrolysis of 3-phenylglycidic acid esters[J]. Journal of fermentation and bioengineering, 1994, 77(2): 152-158.\\u003c/li\\u003e\\n \\u003cli\\u003eEddehech A, Zarai Z, Aloui F, et al. Production, purification and biochemical characterization of a thermoactive, alkaline lipase from a newly isolated Serratia sp. W3 Tunisian strain[J]. International journal of biological macromolecules, 2019, 123: 792-800.\\u003c/li\\u003e\\n \\u003cli\\u003eEl\\u0026apos;vina P M, Vertlib M G, Budnikov G K. Metal ions as enzyme effectors[J]. Russian chemical reviews, 1998, 67(3): 225-232\\u003c/li\\u003e\\n \\u003cli\\u003ePrejan\\u0026ograve; M, Alberto M E, Russo N, et al. The effects of the metal ion substitution into the active site of metalloenzymes: A theoretical insight on some selected cases[J]. Catalysts, 2020, 10(9): 1038.\\u003c/li\\u003e\\n \\u003cli\\u003eTokmina-Lukaszewska M, Huang Q, Berry L, et al. Fe protein docking transduces conformational changes to MoFe nitrogenase active site in a nucleotide-dependent manner[J]. Communications Chemistry, 2023, 6(1): 254.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"world-journal-of-microbiology-and-biotechnology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"wibi\",\"sideBox\":\"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)\",\"snPcode\":\"11274\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11274/3\",\"title\":\"World Journal of Microbiology and Biotechnology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"lipase, enzyme production optimization, artificial neural network, genetic algorithm, enzymatic properties\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7922278/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7922278/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eA strain designated C41802, capable of high lipase production, was isolated from a food waste environment. Based on morphological characteristics, physiological and biochemical tests, and phylogenetic analysis, the strain was identified as \\u003cem\\u003eSerratia marcescens\\u003c/em\\u003e. To optimize lipase production by C41802, fermentation conditions were refined using an Artificial Neural Network (ANN) combined with a Genetic Algorithm (GA), establishing a GA-ANN model. The enzymatic properties of the extracellular crude lipase were then characterized under these optimal conditions. Results demonstrated that the ANN-GA model surpassed the Response Surface Methodology with Central Composite Design (RSM-CCD) in predicting optimal fermentation conditions and maximum enzyme yield, with a relative error of merely 0.67% for the ANN-GA model compared to 2.54% for RSM-CCD. Optimal fermentation conditions, as determined by the GA-ANN model, included: olive oil at 10 g/L, glucose at 8 g/L, tryptone at 3.1 g/L, K₂HPO₄ at 2 g/L, an inoculum size of 3.5%, cultivation time of 26.2 hours, temperature at 30\\u0026deg;C, and initial pH at 5, achieving a lipase yield of 613.39 U/mL, which is 6.45 times higher than before optimization. Under these optimized conditions, the extracellular crude lipase produced by S. marcescens C41802 exhibited an optimal temperature of 70\\u0026deg;C and pH of 7. After incubation at 50\\u0026deg;C for 12 hours, the enzyme retained 52.56% of its relative activity, indicating substantial potential for industrial applications.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\",\"manuscriptTitle\":\"Optimization of fermentation conditions and enzymatic properties of extracellular lipase produced by Serratia marcescens derived from food waste\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-14 18:53:25\",\"doi\":\"10.21203/rs.3.rs-7922278/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-11-25T20:59:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-25T17:54:09+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-08T15:41:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"155296823753152669143294618609720071791\",\"date\":\"2025-11-06T07:15:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-06T05:58:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"160824396867077352860334577424113831757\",\"date\":\"2025-11-05T05:12:56+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"296094776274112935988235559910410608696\",\"date\":\"2025-11-04T19:02:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-11-04T17:10:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-25T17:52:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-25T05:10:54+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"World Journal of Microbiology and Biotechnology\",\"date\":\"2025-10-22T10:00:10+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"world-journal-of-microbiology-and-biotechnology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"wibi\",\"sideBox\":\"Learn more about [World Journal of Microbiology and Biotechnology](https://www.springer.com/journal/11274)\",\"snPcode\":\"11274\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11274/3\",\"title\":\"World Journal of Microbiology and Biotechnology\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"4525939d-84de-472b-bbee-553d6cacfcd6\",\"owner\":[],\"postedDate\":\"November 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-30T16:25:04+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7922278\",\"link\":\"https://doi.org/10.1007/s11274-026-04866-5\",\"journal\":{\"identity\":\"world-journal-of-microbiology-and-biotechnology\",\"isVorOnly\":false,\"title\":\"World Journal of Microbiology and Biotechnology\"},\"publishedOn\":\"2026-03-25 16:09:17\",\"publishedOnDateReadable\":\"March 25th, 2026\"},\"versionCreatedAt\":\"2025-11-14 18:53:25\",\"video\":\"\",\"vorDoi\":\"10.1007/s11274-026-04866-5\",\"vorDoiUrl\":\"https://doi.org/10.1007/s11274-026-04866-5\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7922278\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7922278\",\"identity\":\"rs-7922278\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}