Optimization of Lipase Activity in Aspergillus niger C2J6 Whole Cells Using Choline Chloride Ethylene Glycol Deep Eutectic Solvent

Optimization of Lipase Activity in Aspergillus niger C2J6 Whole Cells Using Choline Chloride Ethylene Glycol Deep Eutectic Solvent | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimization of Lipase Activity in Aspergillus niger C2J6 Whole Cells Using Choline Chloride Ethylene Glycol Deep Eutectic Solvent Qingxiu Ma, Mingxiang Tang, Yinsong Wan, Mengzhen Zhang, Qian Mu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6024054/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Deep eutectic solvents (DES), recognized as tunable green solvents, show significant potential for enhancing enzyme activity in biocatalytic applications. This study investigated the effect of choline chloride–ethylene glycol DES on the lipase activity of Aspergillus niger C2J6 whole cells, employing a self-isolated endophytic strain. By varying the molar ratio (1:2–1:4) and water content (0–80%), the highest lipase activity (142.31%) was observed at a 1:1.55 molar ratio with 46% water content. Mathematical models were developed to connect DES composition with key properties, including surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity. Statistical analysis revealed that among the physicochemical properties of DES, polarity exhibited the most significant impact on enzymatic activity, followed by viscosity, surface tension, and conductivity. This study provides valuable insights for designing optimized DES systems to improve biocatalytic efficiency and precision. Biological sciences/Biochemistry Biological sciences/Biochemistry/Biocatalysis Biological sciences/Biochemistry/Enzymes Deep eutectic solvent Physicochemical properties Lipase Enzyme activity Causal inference Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The synergistic relationship between the characteristics of the reaction medium and enzyme activity remains a central focus in the design and optimization of biocatalytic reaction systems 1 . The selection of the reaction medium significantly influences reaction efficiency, selectivity, enzyme activity, and stability 2 . Organic solvents, commonly used as reaction media in many traditional industrial processes, can adversely affect enzymatic catalytic activity and stability through various mechanisms 3 . Strongly polar organic solvents (such as DMSO and DMF) compete with water molecules on the enzyme surface and with internal hydrogen bonds, disrupting the natural folded structure of enzymes. The three-dimensional structure of enzymes is fundamental to their function; alterations to this structure typically result in loss of catalytic activity and may even lead to enzyme denaturation. Non-polar solvents (such as n-hexane) inhibit catalytic efficiency by interfering with the hydrophobic core of enzymes, increasing conformational rigidity and reducing molecular flexibility 4 . While aqueous media effectively dissolve polar substrates, they exhibit limitations in dissolving hydrophobic substrates, imposing further constraints on reaction efficiency 5 . Additionally, organic solvents may affect the thermal stability of enzymes, making them more susceptible to inactivation at elevated temperatures. Beyond these functional limitations, the potential environmental pollution associated with organic solvent use renders them less than ideal for biocatalysis based on modern green chemistry principles 6,7 .Consequently, the pursuit of new, efficient, and environmentally friendly reaction media has emerged as a critical research direction 8 . Deep eutectic solvents (DESs) have garnered significant attention across multiple scientific disciplines in recent years due to their green characteristics, including facile preparation, low toxicity, cost-effectiveness, designability, and biodegradability 9,10 . In enzymatic catalytic reactions, DESs significantly enhance catalytic efficiency by optimizing enzyme-substrate interactions and improving enzyme solubility in poorly polar or hydrophobic substrates 11 . Furthermore, DESs have demonstrated extensive application potential in several other fields, such as extraction processes for isolating bioactive compounds from plant materials, sustainable electrolytes for energy storage devices and electroplating processes in electrochemistry, enhancement of drug solubility and delivery systems in the pharmaceutical industry, and synthesis of nanomaterials in materials science 12–14 .Among these, choline chloride–ethylene glycol (ChCl-EG) DES is particularly notable. Notably, in the field of biocatalysis 15 , this DES enhances solubility and addresses the limitations of traditional media for hydrophobic substrates 16 . Additionally, its low volatility and toxicity create a mild and stable environment 17 that stabilizes enzyme conformation, enhances activity, and improves catalytic efficiency 18 . The physicochemical properties of ChCl-EG DES, such as viscosity, polarity, and conductivity, are largely influenced by the composition ratio of its components 19 , which in turn affects enzyme activity by altering the solubility and dispersibility of substrates and enzymes 20,21 . Optimizing these properties can preserve enzyme activity, facilitate effective substrate-enzyme binding, and enhance both the rate and selectivity of catalytic reactions 22,23 . Previous research on the biocatalysis of ChCl-EG DES has yielded promising results, demonstrating its superior performance compared to traditional media in applications such as ester synthesis and drug intermediate conversion 24 . However, most studies have focused on correlations between individual or limited physicochemical properties of fixed DES systems and enzyme activity without exploring their causal relationships 8 . Although DESs have been extensively studied for their effects on enzyme activity, the use of whole cells, particularly endophytes, within DES remains largely unexplored. Unlike purified enzyme preparations, whole-cell systems offer dual advantages of reduced purification costs and a complete metabolic network with natural cofactors, creating stable microenvironments for catalysis 25 . While most DES biocatalysis studies have utilized purified enzymes, this approach faces limitations of complex extraction processes, high costs, and poor stability in DES environments 26 . In contrast, endophytic fungal whole-cell system employs the cell wall as a natural protective barrier, enhancing enzyme stability in DES 25 . Additionally, endophytic fungi possess unique niche adaptability and metabolite diversity, potentially exhibiting catalytic properties distinct from purified enzymes under DES conditions 27 . This approach addresses a significant research gap and opens new avenues for DES-mediated biocatalysis. However, there remains a paucity of research on the catalytic behavior of endophyte whole cells within DES. Exploring the catalytic mechanisms of endophyte whole cells in deep eutectic solvents can not only enhance the theoretical understanding of enzyme catalysis but also provide technical support for practical industrial applications. This research explores the effects of choline chloride-ethylene glycol DES on lipase activity in Aspergillus niger C2J6 ( A. niger ) whole cells and optimizes DES composition for enhanced biocatalytic efficiency. Unlike previous studies that focused on simple correlations, this study employed multivariate statistical methods to reveal the causal relationships between DES composition, physicochemical properties, and enzyme activity. The findings provide both theoretical insights and practical guidance for the precise regulation of DES in biocatalytic reactions, supporting the green transformation of biochemical industries. 2. Materials and Methods 2.1 Materials A. niger C2J6 strain is an endophytic fungus isolated from grapes provided by the Fruit and Vegetable Processing Laboratory of the School of Food Science, Shihezi University, Shihezi City, Xinjiang, China. Choline chloride (≥ 98%), ethylene glycol (≥ 98%), Nile red (≥ 98%), anhydrous ethanol (≥ 99.7%), and poly (vinyl alcohol) were obtained from Shanghai Macklin Biochemical Technology Co., Ltd. (China). KH 2 PO 4 (≥ 99.5%), MgSO 4 ·7H 2 O (≥ 99.0%), and peptone were sourced from Tianjin Xinbote Chemical Co., Ltd. (China). Olive oil was purchased from Kerry Grain and Oil Co., Ltd. (China). All reagents and solvents were purchased from commercial suppliers and used without further purification. 2.2 Preparation of DES The preparation of the DES is based on the intermolecular interaction between the hydrogen bond acceptor (HBA) and the hydrogen bond donor (HBD). Studies have shown that the molar ratio of HBA to HBD of 1:2 usually represents the minimum HBD requirement for stable DES formation 28 , and the ratio of 1:3 and 1:4 is helpful to systematically study the effect of increasing hydrogen bond donor concentration on the physicochemical properties of DES 29 . Based on this theoretical basis, DES was prepared by heating and stirring method in this study. Choline chloride (ChCl) was selected as HBA and ethylene glycol (EG) as HBD, and mixed according to the molar ratio of 1:2 (labeled DES1), 1:3 (labeled DES2) and 1:4 (labeled DES3). The mixtures were heated and stirred at 80°C for 2 hours to form uniform and transparent liquids, then cooled to room temperature and observed to confirm successful preparation 30,31 . In addition, given that the properties of DES show significant changes with the addition of water from slight influence at low concentration (<10%) to complete destruction of eutectic network at high concentration, water was added to the prepared DES in different proportions to comprehensively study its regulatory effect on DES performance 32 . A series of DES systems with water content of 0%, 20%, 40%, 60% and 80% (v/v) were prepared to systematically evaluate their characteristics under different water content conditions. 2.3 Preparation of whole-cell catalysts A. niger C2J6 spores were prepared as 10 7 /mL spore suspensions and inoculated into the fermentation medium at a 2% inoculum volume. The fermentation medium composed of 5% olive oil emulsion (V 2% polyvinyl alcohol : V olive oil 3:1), 5% peptone (w/v),1% KH 2 PO 4 (w/v), and 0.5% MgSO 4 ·7H 2 O (w/v) was incubated on a shaking table at 28℃ and 150 rpm for 48 h. After incubation, the fungal cells were collected by centrifugation (8,000 rpm, 30 min) to obtain wet whole cells, which were washed three times with pure water and then freeze-dried 33 . The freeze-dried cells were subsequently crushed at 12,000 rpm to produce whole-cell biocatalysts and stored at -4℃. 2.4 Determination of lipase activity in the catalyst in whole cells The lipase determination method described by Kim et al. 34 was slightly modified. Specifically, 200 μL DES was added to 0.01 g of the whole-cell catalyst, with water serving as the blank control. The mixture was vortexed and incubated at room temperature for 15 min. Subsequently, 100 μL of this mixture was transferred, followed by the addition of 100 μL of a 10 mM p-NPP substrate solution dissolved in acetonitrile. Subsequently, 0.9 mL of 50 mM Tris-HCl buffer (pH 8) was added, and the reaction was performed in a water bath at 40°C for 10 min. Immediately thereafter, 4.9 mL of 0.5 M EDTA was added to terminate the reaction. The sample was then centrifuged at 8,000 rpm for 5 min at 4°C, and 200 μL of the supernatant was transferred to a 96-well plate. Whole-cell lipase activity was determined by measuring the absorbance at 405 nm using ReadMax1900. The optimal temperature (40°C) and reaction time (10 minutes) for Aspergillus niger C2J6 whole-cell lipase in the DES environment were determined through preliminary experiments, ensuring that measurements were conducted within the linear range of enzymatic activity. One unit (U) of enzyme activity was defined as the amount of whole cells required to catalyze the hydrolysis of p-NPP to produce 1 μmol of p-NP per minute at 40°C, expressed as U/mL. Lipase activity was determined using Eq. (1). where U is lipase activity (U/mL), c is the concentration of p-nitrophenol (μmol/mL), V 1 is the total volume of the reaction liquid (mL), t is the reaction time (min), V 2 is the volume of the enzyme liquid to be measured (mL), and n is the dilution ratio. Relative activity (%) was defined as the ratio of whole-cell lipase activity pretreated with DES to that of the blank control (pure water) under equivalent conditions. This measure was employed to evaluate enzyme activity in various DES systems. Eq. (2) was used to calculate relative activity. where U a is the lipase activity of the DES pretreatment (U/mL), and U b is the lipase activity of the blank control group (U/mL). 2.5 Determination of physicochemical properties of DES The surface tension of the DES systems was measured using a DCAT25 surface tensiometer (Dataphysics, Germany) based on the Wilhelmy plate method, with an accuracy of ± 0.1 mN/m 35 . The conductivity of the DES was determined using a DDS-307 conductivity meter (Leizi™, China) with an accuracy of ±0.01 μS/cm 8 . The density was measured using a 5 mL density bottle, and the mass was recorded using an electronic analytical balance (accuracy ±0.0001 g) 36 . The viscosity of the DES systems was determined based on the principle of cone-plate rheometry using an MCR302 rheometer (Anton Paar, Austria) equipped with a CP50-1 cone-plate geometry operating at a constant shear rate of 50 s⁻¹ with a 1 mm gap 37 . The refractive index was measured using a 2WAJ Abbe refractometer (China), with an accuracy of ±0.0002 38 . The polarity was determined using Nile red as a solvatochromic probe 39 . Nile red solution (1 g/L) in ethanol was prepared and stored at 4°C. The DES was placed in a quartz cuvette, and 150 µL of Nile red solution was added before evaporating the solvent under high-purity nitrogen. An ultraviolet spectrophotometer was used to scan and record the maximum absorption wavelength of DES (λ max ). The polarity parameter was calculated as E NR = 25891 / λ max , where E NR is in kcal/mol and λ max is in nm. The water activity of the DES was measured using an ST-3A intelligent water activity measuring instrument (China), with an accuracy of ±0.015 40 . 2.6 Statistical analysis All experimental treatments were conducted in triplicate, and the results are expressed as mean values. The experimental data were comprehensively analyzed using MATLAB R2024b (MathWorks Corp., USA). First, statistical analysis was performed, and significance was assessed using a Duncan multiple range test following the multivariate analysis of variance, with p < 0.05, indicating significant differences. Subsequently, a multiple regression analysis was conducted, and the model significance was evaluated using variance analysis. Model validity was verified using statistical parameters, such as the mean square, degrees of freedom, sum of squares, F-value, and P-value. A P-value of less than 0.01 indicated that the model was statistically significant. Data were standardized using Z-score normalization. Principal component analysis (PCA) was employed for multivariate statistical analysis to identify correlations among variables. Finally, the coefficients of the multiple linear regression equation were used in the matrix calculations to deduce causal relationships between variables. 3. Results and discussion In this study, the effects of the ChCl–EG molar ratio and water content on the physicochemical properties of DES were investigated, and the activity of A. niger C2J6 whole-cell lipase in these DES systems was experimentally determined. Subsequently, multiple statistical methods were employed to elucidate the causal relationships between the physicochemical properties of DES and lipase activity. 3.1 Effect of DES on lipase activity in whole cells of A. niger For lipase in A. niger C2J6 whole cells, DES molar ratio and water content were two critical parameters that could directly influence enzyme activity (Fig. 1 a). The relative activity of lipase in DES followed the order DES1 > DES2 > DES3 ( p < 0.01), with the highest relative activity observed for DES at a molar ratio of 1:2, representing increases of 9% and 23% compared to the ratios of 1:3 and 1:4, respectively. These results indicated that the molar ratio of HBA to HBD significantly affected lipase activity in whole cells. In addition, the enzyme activity in pure DES (0% water content) increased by 20.49%, 19.80%, and 8.87% for DES1, DES2, and DES3, respectively, relative to the control group with 100% water content. However, pure DES did not yield the highest activity. Instead, when the water content reached 40%, the catalytic activity was maximized, increasing to 142.05%, 133.75%, and 119.18% for DES1, DES2, and DES3, respectively, surpassing that of the blank control group without DES by 42.05%, 33.75%, and 19.18%, respectively. Juneidi 41 et al. found that when the concentration of DES was 40% v/v, the activity of Burkholderia cepacia lipase (BCL) increased by 230% compared to the untreated control. However, the whole-cell lipase used in this experiment exhibited relatively lower activity, a discrepancy that may be attributed to differences in enzyme preparation purity. Commercially purchased pure enzymes undergo rigorous purification and processing techniques, resulting in more pristine active sites that can bind substrates and catalyze reactions more efficiently. In contrast, the whole-cell systems isolated and screened in this study typically have not undergone similar sophisticated modifications, and the enzyme molecules may be subject to interference from other cellular components, leading to reduced catalytic efficiency and overall activity levels. Conversely, enzyme activity at 80% water content was lower than that in pure DES at 0% water content. These findings suggested that a moderate increase in water content enhanced lipase activity to its maximum, while further increases reduced enzymatic performance. Nevertheless, compared to the 100% water control, all three DES, with water contents ranging from 0–80%, consistently enhanced lipase activity, demonstrating that DES systems were superior to water for maintaining lipase activity in whole cells. The positive effect of DES on lipase activity may be related to the influence of polyols on the enzyme catalytic mechanism. In the initial step of the lipase-catalyzed reaction, the hydroxyl group of the serine residue in the active site attacks the carbonyl carbon of the substrate to form a tetrahedral intermediate. Within DES, the polyol hydroxyl groups may form hydrogen bonds with the enzyme’s active site hydroxyl group, potentially increasing the nucleophilicity of the serine residue, thereby promoting a more rapid substrate attack 42 . To illustrate the correlation between the molar ratio, water content, and enzyme activity, the molar ratio (x 1 ) and water content (x 2 ) were designated as independent variables, and relative activity (y) was defined as the dependent variable. Binary linear, binary quadratic, and binary cubic regression analyses were conducted successively to determine the most suitable model. Owing to the varying ranges and units of the molar ratio, water content, and enzyme activity, variables with broader value ranges may dominate the regression results, potentially obscuring the contributions of those with narrower ranges and resulting in misleading conclusions. To eliminate these dimensional effects, the data for molar ratio, water content, and relative enzyme activity were standardized before performing regression analysis (Table 1 ) and significance tests for the regression equations (Tables 2 – 4 ). Based on these standardized data, response surface diagrams of the models were drawn (Fig. 1 b–d). Subsequently, a significance test of each variable coefficient in the optimal model was performed to evaluate the importance of variables such as molar ratio and water content on relative activity (Table 5 ). Table 1 The binary regression model equations describing the relationship between the ChCl-EG DES molar ratio, moisture content, and lipase relative activity. model R 2 R 2 adj binary linear regression y=-0.6492x 1 -0.0320x 2 -0.1998x 1 x 2 0.4225 0.3950 Bivariate quadratic regression y=-0.6492x 1 -0.0320x 2 -0.1998x 1 x 2 -0.1975x 1 2 -0.7254x 2 2 + 0.9023 0.8407 0.8203 Binary cubic regression y = 0.5130x 2 -0.1997x 1 x 2 -0.1975x 1 2 -0.7254x 2 2 -0.0274x 1 2 x 2 + 0.0851x 1 x 2 2 -0.4994x 1 3 -0.3117x 2 3 + 0.9023 0.8794 0.8483 Table 2 ANOVA results for the binary linear regression model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity. Sum of squares DoF Mean square F-value Prob>F Model 20.3065 3 6.7688 1471.4783 0.0013 Residual 23.6935 41 0.5779 Lack of fit 23.5561 11 2.1415 525 < 0.0001 error 0.1374 30 0.0046 Total 44 44 Table 3 ANOVA results for the binary quadratic regression model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity. Sum of squares DoF Mean square F-value Prob > F Model 36.9911 5 7.3982 1608.3043 < 0.0001 Residual 7.0089 39 0.1797 Lack of fit 6.8715 9 0.7635 165.9782 < 0.0001 error 0.1374 30 0.0046 Total 44 44 Table 4 ANOVA results for the binary cubic regression model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity. Sum of squares DoF Mean square F-value Prob>F Model 38.6917 9 4.2991 934.5870 < 0.0001 Residual 5.3038 35 0.1515 Lack of fit 5.1709 5 1.0342 224.8261 < 0.0001 error 0.1374 30 0.0046 Total 44 44 Table 5 Significance test of partial regression coefficients in the bivariate quadratic model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity. Value standard error t-value Prob>|t| intercept 0.90233 0.13299 6.78497 < 0.0001 x 1 -0.64921 0.06391 -10.15829 < 0.0001 x 2 -0.03195 0.06391 -0.49994 0.6199 x 1 x 2 -0.19975 0.06463 -3.09056 0.0037 x 1 2 -0.19747 0.0914 -2.16042 0.0369 x 2 2 -0.72537 0.07725 -9.38993 < 0.0001 A comparison of the determination coefficients (R 2 ) in Table 1 indicated that the relationship between molar ratio, water content, and relative activity aligned more closely with a multivariate nonlinear regression model. Tables 2 – 5 present the significance tests of partial regression coefficients for the binary linear, binary quadratic, and binary cubic models, respectively. The F-values of the models were 1471.4783, 1608.3043, and 934.5870, respectively, and their P-values were all less than 0.0001, demonstrating that the models were statistically significant. Although the binary cubic model had a larger R 2 than the binary quadratic model, according to the analysis of variance of the regression equation, the F model of the binary quadratic model was higher than that of the binary cubic model, and the F Lack of fit of the binary quadratic model was lower than that of the binary cubic model. The results suggest that the binary quadratic model is more suitable for describing the relationship between molar ratio, water content, and relative activity. Therefore, given the same significance level in the F-test, the simpler binary quadratic model could be used to predict and optimize the relative activity, whereas the binary cubic model and binary linear model presenting lower significance were not adopted. In this model, x 1 , x 1 x 2 , and x 2 2 are significant terms, whereas x 2 and x 1 2 are not. As shown in Fig. 1 c, the enzyme activity was negatively correlated with the molar ratio in the range between 2 and 4, positively correlated with the water content from 0 to 50%, and negatively correlated with the water content from 50–100%. The optimal DES composition for achieving the highest relative activity was determined to be a molar ratio of 1:1.55 and a water content of 46.28%, resulting in a relative activity of 136.41%. For the verification test, the conditions were set at a molar ratio of 1:1.55 and a water content of 46%. Three repeated experiments yielded a relative activity of 142.31% with a relative error of 4.15% compared to the predicted value of 136.41%, indicating a good fit between the quadratic regression model and the actual enzyme activity. The true values of enzyme activity and the values predicted by the regression equation were evenly distributed around the Y = x curve (Fig. 1 e), further confirming the accuracy of the model predictions. 3.2 Physicochemical properties The surface tension (γ), conductivity (σ), density (ρ), viscosity (µ), refractive index (n), polarity (E NR ), and water activity (Aw) of DES1, DES2, and DES3 at molar ratios of 1:2, 1:3, and 1:4, respectively, were examined by varying the water content from 0–80%. Surface tension reflected the mutual attraction and interaction forces among the components of the solvent system and can be utilized to determine the interaction strength between a given solvent and other compounds during the mixing process 43 . When the molar ratio of ChCl to EG was shifted from 1:2 to 1:4, the surface tension of the DES decreased as the proportion of EG increased (Fig. 2 a). This occurred because EG had a relatively low surface tension; thus, increasing its proportion reduced the overall surface tension of the system. The water content also significantly influenced the DES surface tension. As the water content increased from 0 to 80%, the surface tension generally increased because water possessed a relatively high surface tension and increasingly dominated the overall system as its content grew. These observations were consistent with those reported by Aravena et al. 44 The relationship between the molar ratio (x 1 ), water content (x 2 ), and surface tension (y 1 ) followed regression Eq. ( 1 ), with R 2 = 0.9749. y 1 =-4.2140x 1 + 0.1220x 2 -0.0227x 1 x 2 + 0.5120x 1 2 + 0.0008x 2 2 + 61.8459 (1) DES exhibited excellent conductivity, reflecting the mobility of ions within the system 8 . As the molar ratio increased, the DES conductivity increased, and it further increased with higher water content, reaching a maximum at 60% water content, which was approximately five times the conductivity of pure DES. However, at 80% water content, conductivity decreased (Fig. 2 b). This occurred because the addition of water leads to ionic dissociation of the DES components; when the water content is excessively high, the complete dissociation of Cl − from choline chloride disrupts the fundamental DES structure. Further addition of water diluted the ion concentration, thereby reducing conductivity 45 . These findings were consistent with the trends observed in water-based ethylamine/glycol DES by Alfurayj et al. 46 The relationship between molar ratio (x 1 ), water content (x 2 ), and conductivity (y 2 ) followed regression Eq. ( 2 ), with R 2 = 0.9709. y 2 =-3.9197x 1 + 0.6410x 2 + 0.0865x 1 x 2 + 0.8147x 1 2 -0.0065x 2 2 + 10.0955 (2) Density is an important parameter for DES in industrial applications. At room temperature, the density of DES with varying water contents ranged from 1.05 to 1.2 g/cm 3 , exceeding that of water. As the molar ratio increased from 1:2 to 1:3 or 1:4, the density gradually decreased with increasing molar fraction of EG. However, the density change was minor (within 0.003 g/cm 3 ), indicating that the molar ratio had a limited effect (Fig. 2 c), which is consistent with the results reported by Han et al. 47 . The addition of water further influenced DES density. Because water generally had a lower density than ChCl and EG, increasing the water content typically led to a linear decrease in the overall solvent density. This effect can be attributed to the altered molecular packing caused by water molecules in the DES, ultimately reducing its density 48 . The relationship between molar ratio (x 1 ) and moisture content (x 2 ) to density (y 3 ) followed regression Eq. (3), with R 2 = 0.9909. y 3 =-0.0037x 1 -0.0012x 2 + 0.0003x 1 2 + 1.1758 (3) Viscosity is an important parameter describing molecular interactions in liquids, including the strength of hydrogen bonds, van der Waals forces, and presence of free volumes 49 . Generally, the viscosity of DES is influenced by its components, molar ratios, water content, and other factors. As shown in Fig. 2 d, the pure DES exhibited relatively high viscosity owing to the formation of hydrogen bond networks among its components, which reduced the mobility of the DES molecules. Electrostatic and van der Waals interactions may also contribute to the high viscosity 50 , affecting mass transfer, solute solubility, dispersion, and stability. Moreover, changes in the HBA-to-HBD molar ratio had a minimal effect on viscosity, whereas the addition of water significantly decreased viscosity. However, once the water content reached a certain level, the viscosity tended to stabilize, which is consistent with the findings of Meredith et al. 51 . Consequently, water can be used to fine-tune the characteristics of DES 52 . The relationship between molar ratio (x 1 ), moisture content (x 2 ), and viscosity (y 4 ) followed regression Eq. (4), with R 2 = 0.9726. y 4 =-3.5291x 1 -0.8664x 2 + 0.0461x 1 x 2 + 0.1000x 1 2 -0.0057x 2 2 + 33.6754 (4) The refractive index can be another key descriptor of the DES physical properties, providing insights into intermolecular interactions and available free space 53 . As shown in Fig. 2 e, the refractive indices of all three DES decreased as the molar ratio increased. This trend may occur because higher EG concentrations allow light to travel more rapidly, resulting in a lower refractive index compared to DES with lower EG concentrations 38 . Similarly, the addition of water decreased the refractive index, likely because of the lower refractive index of water relative to the standard DES components. Thus, increasing the water content reduced the overall refractive index of the DES. The relationship between the molar ratio (x 1 ) and moisture content (x 2 ) of DES and the refractive index (y 5 ) can be described by regression Eq. (5), with R 2 = 0.9938. y 5 =-0.0083x 1 -0.0010x 2 + 0.0004x 1 2 + 1.4715 (5) Polarity is a crucial parameter for characterizing the complex interactions between solvent and solute molecules, including hydrogen bonding and van der Waals forces, and serves as a key criterion for solvent selection 54 . As the molar ratio increased from 1:2 to 1:4, the E NR value gradually decreased, indicating that the polarity of the DES system was enhanced. This enhancement was attributable to the hydroxyl groups (-OH) in EG, which were polar and thus increased the overall polarity of the solvent (Fig. 2 f). Similarly, increasing the water content (0–80%) further elevated the polarity of the DES, as water is a strongly polar solvent. This result aligned with the polarity trends of water-based choline chloride–glycol mixtures reported by Gabriele et al. 55 . The relationship between molar ratio (x 1 ), water content (x 2 ), and polarity (y 6 ) followed regression Eq. (6), with R 2 = 0.9697. y 6 =-0.2901x 1 -0.0495x 2 + 0.0005x 1 x 2 + 0.0126x 1 2 -0.0002x 2 2 + 51.2597 (6) Water activity is an important thermodynamic property that describes the effective water content related to the reaction rates, water, and microbial growth in DES 56 . As the EG molar ratio increased, the water activity of DES rose gradually, though not substantially. In addition, the water activity of all DES samples increased consistently as the added water content increased, indicating that the changes in water activity depended only on the amount of added water rather than the molar ratio of DES components (Fig. 2 g). These findings are consistent with those reported by Florindo et al. 57 concerning the relationship between water activity and water content. The correlation between molar ratio (x 1 ), water content (x 2 ), and water activity (y 7 ) is described by regression Eq. (7), with R 2 = 0.9995. y 7 = 0.0240x 1 + 0.0181x 2 -0.0030x 1 2 -0.0001x 2 2 + 0.0056 (7) 3.3 Causal inference of physicochemical properties of DES and lipase activity A thorough understanding of the physicochemical properties of DES is essential for their precise scientific design as effective biocatalysts. According to Sections 3.1 and 3.2 , variations in the water content of DES not only affected lipase activity in whole cells but also influenced the physicochemical properties of the DES itself. Therefore, to accurately regulate lipase activity, it is necessary to thoroughly analyze the internal relationships between these physicochemical properties and lipase activity at different levels of hydration. First, PCA was performed on the molar ratio, moisture content, and physicochemical properties of the DES, including surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity. Because PCA is a multivariate linear combination analysis of the original variables, if a nonlinear relationship exists among these variables, it cannot effectively represent the correlations between them. Based on the initial projection (Fig. 1 a) and response surface (Fig. 1 c), the data for relative activity at water contents ranging from 0 to 80% were divided into two simple linear intervals, one exhibiting a monotonic increase and the other a monotonic decrease, with the highest relative activity serving as the stationary point. PCA was then performed separately at each interval (0–40% and 40–80%) to identify and extract the physicochemical properties that showed significant changes. The results indicated that the cumulative contribution of the first two principal components exceeded 80% (Fig. 3 a–c), confirming the reliability of PC1 and PC2. In the segmented analysis, only the correlation between conductivity and water content exhibited significant changes between the 0–40% and 40–80% ranges, with the correlation decreasing. Conversely, the correlations between water content and the indices remained largely unchanged across the two segments (Fig. 3 b–c). Subsequently, PCA was conducted on the physicochemical indicators and relative activity of DES, with the first two principal components contributing more than 80% (Fig. 3 d–f), confirming the reliability of PC1 and PC2. According to Fig. 3 e–f, in the segmented analysis, only the correlations of surface tension and conductivity with relative activity exhibited substantial changes, whereas the relationships between other physicochemical properties and relative activity remained relatively stable. Based on the PCA results, conductivity and surface tension were identified as the primary factors influencing the nonlinear variation in relative activity. Subsequently, scatter plots of surface tension, conductivity, density, viscosity, refractive index, polarity, water activity, and lipase activity at 0–80% water content were generated. In both the 0–40% and 40–80% ranges, the surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity exhibited positive and negative linear correlations with enzyme activity, forming distinct clusters (Fig. 4 a–g). However, the clustering relationship between the surface tension, conductivity, and relative activity differed from that of the other parameters. Although some clustering was observed, no clear boundary was identified at the extremum of relative activity for surface tension and conductivity. In contrast, density, viscosity, refractive index, polarity, and water activity demonstrated a pronounced clustering boundary at the extremum of relative activity. These findings indicated that the relationship between surface tension, conductivity, and water content was not linear, whereas the other five parameters may exhibit a linear relationship with relative activity. Therefore, conductivity and surface tension were the main factors responsible for the nonlinearity between lipase activity and water content in the whole cell. Based on the segmented PCA analysis and the projection plots of each index and relative activity, the surface tension and conductivity contributed to the nonlinear relationship between the relative activity and water content, whereas other indices exhibited weaker correlations. In previous multivariate analyses, no definitive statistical evidence was provided to establish clear relationships between the selected properties (surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity) and the relative activity, resulting in insufficient statistics for screening each indicator. However, including all properties in the regression analysis introduced weakly correlated variables that could interfere with the regression of strongly correlated variables. Consequently, regression cannot be performed directly using DES physicochemical indices and relative activity. Consequently, this study attempted to perform regression analyses between molar ratio, moisture content, and relative activity, as well as between molar ratio, moisture content, and various physicochemical properties, supported by experimental data. Based on these results, causal inferences regarding the physicochemical properties and relative activity of DES were drawn using existing regression equations. The methodological process is illustrated in Fig. 5 a. To eliminate dimensional effects and enhance model stability and accuracy, all data were standardized prior to multiple regression analysis. First, a binomial regression was performed with relative activity (z) as the response variable and the molar ratio (x 1 ) and moisture content (x 2 ) as the independent variables, yielding equation F (x 1 , x 2 ) with a partial regression coefficient b 2 . Next, seven physicochemical properties of the DES, including surface tension (y 1 ), electrical conductivity (y 2 ), density (y 3 ), viscosity (y 4 ), refractive index (y 5 ), polarity (y 6 ) and water activity (y 7 ), were individually modeled as the response variables, with the molar ratio (x 1 ) and water content (x 2 ) as the independent variables for binomial regression fitting. This process produced equations f 1 (x 1 ,x 2 ), f 2 (x 1 ,x 2 ), f 3 (x 1 ,x 2 ), f 4 (x 1 ,x 2 ), f 5 (x 1 ,x 2 ), f 6 (x 1 ,x 2 ), and f 7 (x 1 ,x 2 ), which are accompanied by partial regression coefficients bn 1 , bn 2 , bn 3 , bn 4 , bn 5 , bn 6 , and bn 7 . Table 6 shows the regression results. Table 6 Regression equations of the relationship between the molar ratio and water content of DES, their physicochemical properties, and lipase relative activity Function Regression equations R 2 Adjust R 2 F 0.9023 − 0.6492 x 1 -0.0320 x 2 -0.1997 x 1 x 2 -0.1975 x 1 2 -0.7254 x 2 2 0.8407 0.8203 f 1 -0.2520-0.4313x 1 + 0.8644x 2 -0.1366x 1 x 2 + 0.0889x 1 2 + 0.1688x 2 2 0.9749 0.9717 f 2 0.3691 + 0.2893x 1 + 0.8597x 2 + 0.1616x 1 x 2 + 0.0439x 1 2 -0.4215x 2 2 0.9709 0.9671 f 3 0.0425-0.0373x 1 -0.9939x 2 + 0.0010 x 1 x 2 + 0.0051x 1 2 -0.0487x 2 2 0.9909 0.9898 f 4 -0.5139-0.1001x 1 -0.8744x 2 + 0.1213x 1 x 2 + 0.0076x 1 2 -0.5180x 2 2 0.9726 0.9691 f 5 0.0795-0.0920x 1 -0.9889x 2 + 0.0444x 1 x 2 + 0.0082x 1 2 -0.0894x 2 2 0.9938 0.9930 f 6 -0.1267-0.1526x 1 + 0.9796x 2 + 0.0010x 1 x 2 + 0.0082x 1 2 -0.1215x 2 2 0.9697 0.9658 f 7 0.2416 + 0.0148x 1 -0.9675x 2 -0.0007x 1 x 2 -0.0065x 1 2 -0.2406x 2 2 0.9995 0.9995 As shown in Fig. 3 d, among the correlations between surface tension, conductivity, density, viscosity, refractive index, polarity, water activity, and enzyme activity, the correlations of water activity and density with relative activity were notably lower than those of the other indices. In addition, according to Fig. 5 b, the partial regression coefficients of f 3 (x 1 , x 2 ) and f 5 (x 1 , x 2 ) and the coefficients of x 2 2 in bn 3 and bn 5 were − 0.0486 and − 0.0894, respectively, which were lower than those of x 2 2 in the partial regressions of the other properties. This finding indicated that the linear relationships of the density and refractive index with the square term of the water content were not significant. As shown in Fig. 1 a and 1 c, there was a strong nonlinear relationship between water content and relative activity; thus, the density and refractive index were not the primary factors contributing to this nonlinear trend. In summary, water activity, density, and refractive index served as secondary factors, whereas surface tension, conductivity, viscosity, and polarity were the main factors influencing the nonlinear nature of relative activity. Consequently, these secondary factors should be excluded from the calculation of b 1 to focus on primary factors. Moreover, the x 1 2 coefficients in Fig. 5 b for bn 1 -bn 7 were all small, suggesting that the linear relationship between the square term of the molar ratio and each index was insignificant. In contrast, the x 1 2 coefficient in b 2 was not small, implying the possible existence of other DES characteristics that were not examined in this study, which enhanced the significance of x 1 2 . In other words, additional properties may exhibit a nonlinear relationship with the molar ratio. Furthermore, the constant term reflected only the translation of the equation and did not affect the slope. Accordingly, within the experimental scope of this study, after removing the minor factors in bn 1 -bn 7 and b 2 , as well as the constant terms of the major factors and the x 1 2 terms, Fig. 5 c was obtained, and b 1 was calculated (Fig. 5 d). Among the four indices considered, polarity (|3.7660|) > viscosity (|-1.8929|) > surface tension (|1.2771|) > electrical conductivity (|0.9918|). This indicated that a one standard deviation change in polarity, viscosity, surface tension, and conductivity led to changes of 3.766, 1.8929, 1.2771, and 0.9918 standard deviations in the relative activity, respectively. The polarity, surface tension, and conductivity were positively correlated with the relative activity, whereas the viscosity was negatively correlated. This implies that in practical applications, the design of DES should prioritize the adjustment of physicochemical parameters according to their decreasing order of influence, specifically by appropriately increasing polarity, decreasing viscosity, and enhancing surface tension and conductivity, thereby enabling more targeted design of efficient DES systems. According to the hole theory, in low-temperature systems, the smaller average size of holes resulted in reduced ion migration speed and increased viscosity, thereby closely linking the DES conductivity to viscosity 58 . Additionally, reducing the surface tension of the liquid could increase the average size of these holes, enhancing ion migration and ultimately leading to higher conductivity 59 . Simultaneously, a reduction in surface tension can augment the solubilization and dispersion of the substrate, thereby facilitating the interaction between the enzyme and substrate 60 . The high viscosity commonly observed in DES systems can be often attributed to extensive hydrogen bonding between the components 61 . Such hydrogen bond interactions serve as the main driving force regulating enzyme-DES interactions, creating a heterogeneous solvation environment on the enzyme surface and affecting substrate binding 18 . This non-homogeneous solvation environment can exert significant effects on the conformational stability of enzymes. The presence of hydrogen bond networks may constrain the motion of flexible regions within the enzyme, thereby partially stabilizing its catalytically active conformation 62 . This stabilization effect is particularly pronounced under low water content conditions, where the hydrogen bond network is denser and can more effectively restrict excessive molecular movements of the enzyme, thus reducing deformation of the active site caused by thermal fluctuations 63 .Consequently, in this study, when the initial water content was low, the elevated viscosity could reduce ion migration and thus decrease the electrical conductivity, limiting mass transfer and resulting in lower lipase activity. As the water content increased, the viscosity decreased, and the ion mobility improved, thereby increasing the electrical conductivity. According to this principle, lipase activity should increase continuously with increasing water content. However, in practice, lipase activity initially increased and then declined as the water content increased. Makoś-Chełstowska et al. 64 identified through molecular dynamics simulations that the hydrogen bonds in DES could be destroyed once the water content exceeded 50%. Similarly, Zhekenov et al. 65 suggested that the intermolecular interaction network within DES remained intact until the water content reached 50%, at which the point water molecules were integrated into the network. When the water content exceeded 50%, the constituent components of the DES were released from the interaction network. This phenomenon may be attributed to the exposure of the enzyme's active site and its substrate binding capability. At moderate water content, the hydrogen bond network of the DES effectively protects the enzyme's active site while simultaneously allowing appropriate approach and binding of substrate molecules 21 . However, when water content becomes excessive, the hydrogen bond network is disrupted, leading to overexposure of the enzyme's active site, potentially inducing non-specific binding or excessive competition among substrate molecules, thereby reducing the catalytic efficiency of the enzyme 66 . During the reaction, the polarity of the DES also influenced the reaction rate and selectivity 67 . Polarity is crucial for determining DES conductivity, as highly polar solvents allow more ions to carry charges, thereby enhancing electrical conductivity. Conversely, DES with lower polarity had fewer ions for charge transport, leading to poor conductivity 67 . At 46% water content, DES can be effectively diluted, reducing viscosity, appropriately increasing polarity, and maintaining structural stability, thus enhancing ion mobility and promoting mass transfer. This could explain why lipase activity reached a maximum at 46% water content and decreased once water content surpassed this level. Additional specialized studies are required to further elucidate the relationship between the solvent properties of DES and lipase activity in whole cells. Mechanistically, this relationship between polarity and activity may be associated with the electrostatic interactions of the enzyme 68 . In DES with moderate polarity, the enzyme's active site can more effectively attract substrate molecules through electrostatic interactions, thereby promoting the catalytic reaction 68 . However, when polarity becomes excessive, superfluous water molecules may interfere with the electrostatic interactions between the enzyme and substrate, resulting in diminished substrate binding efficiency 69 . These results indicate that changes in the physicochemical properties of solvents, such as polarity, viscosity, surface tension, and conductivity, may collectively influence enzymatic activity by affecting the molecular conformation of enzymes, microenvironment of the active center, diffusion kinetics of reactants, and ionic environment. However, the molecular details of these mechanisms remain insufficiently characterized as they lack in-depth experimental validation and molecular simulation studies. Further research is required to clarify and refine the hypotheses. In addition, this study focused solely on choline chlorine-ethylene glycol, a DES system. Different DES systems may exert distinct effects on enzyme activity owing to variations in their composition and physicochemical properties, warranting further investigation. Despite the use of multivariate statistical analysis, the established model remains a simplified approximation and fails to fully capture the complexity of enzyme-solvent interactions. Subtler mechanisms at the molecular level, such as solvation dynamics and ion-enzyme interactions, may also influence enzyme activity but were not explicitly considered in this study. 4. Conclusion This study provides a comprehensive investigation into the impact of ChCl-EG DES composition, specifically molar ratio and water content, on various physicochemical properties and explores how these changes affect lipase activity in the A . niger C2J6 whole-cell system. Using multivariate statistical techniques such as PCA and regression analysis, this study examined the causal relationships between physicochemical properties and enzyme activity. The results showed that both molar ratio and water content of DES significantly influenced lipase activity, with the relationship being more accurately described by a quadratic regression model. Under optimal conditions (1:1.55 molar ratio and 46% water content), the relative activity reached 142.31%, indicating a well-fitted model with good predictive capability. Multivariate statistical analysis identified the key physicochemical parameters affecting lipase activity. Polarity, viscosity, surface tension, and conductivity are intrinsic factors contributing to the nonlinear relationship between lipase activity and the DES system. This study offers novel insights and methodologies for broadening the application of DES in biocatalytic reactions, particularly in whole-cell catalytic systems. The increased activity of enzymes in our optimized DES formulations reduces waste and replacement costs in industrial processes, thereby contributing to a more sustainable manufacturing process. These findings have significant implications for industrial biocatalysis, as the quantitative understanding of relationships between DES physicochemical properties and enzymatic activity provides data-driven decision support for solvent design in industrial applications, reducing development costs and enhancing production efficiency. The strategy of adjusting DES physicochemical parameters according to their degree of influence can be extended to other enzymatic systems and DES types, while future research may explore broader solvent performance spaces and design novel DES systems with specific functionalities, offering new applications for biocatalytic reactions, particularly in whole-cell catalytic systems. Declarations Conflicts of Interest The authors declare no conflicts of interest. Funding This study was supported by the National Natural Science Foundation of China (grant numbers 32060527 and 31460031). Author Contribution Conceptualization, Q.X. Ma, M.X. Tang and Y. Liu; methodology, Q.X. Ma, M.X. Tang; software, Q.X. Ma, M.X. Tang; validation, Y.S. Wan, H.Y. Yan; formal analysis, Q.X. Ma, M.X. Tang; investigation, M.Z. Zhang, Q. Mu; resources, Y. Liu; data curation, Q.X. Ma; writing—original draft preparation, Q.X. Ma, M.X. Tang; writing—review and editing, Y. Liu; supervision, Y. Liu; project administration, Y. Liu; funding acquisition, Y. Liu. All authors have read and agreed to the published version of the manuscript. Data Availability The data that support the findings of this study are available from the corresponding author, upon reasonable request. References Dudkaitė, V., Kairys, V. & Bagdžiūnas, G. Understanding the activity of glucose oxidase after exposure to organic solvents. J. Mater. Chem. B 11 , 2409–2416 (2023). Leal-Duaso, A., Mayoral, J. 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Ma, C., Laaksonen, A., Liu, C., Lu, X. & Ji, X. The peculiar effect of water on ionic liquids and deep eutectic solvents. Chem. Soc. Rev. 47 , 8685–8720 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 07 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers invited by journal 24 Apr, 2025 Submission checks completed at journal 24 Apr, 2025 First submitted to journal 06 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6024054","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449296149,"identity":"9d599269-64c5-47c1-a197-a981ae5a2915","order_by":0,"name":"Qingxiu Ma","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Qingxiu","middleName":"","lastName":"Ma","suffix":""},{"id":449296151,"identity":"9048ad3f-5bfb-4a55-a02f-7d404fc33464","order_by":1,"name":"Mingxiang Tang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Mingxiang","middleName":"","lastName":"Tang","suffix":""},{"id":449296154,"identity":"332c1af4-148b-4f68-9a97-9ba4908c9bec","order_by":2,"name":"Yinsong Wan","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Yinsong","middleName":"","lastName":"Wan","suffix":""},{"id":449296156,"identity":"b2798c9b-4753-4fa8-b243-8839a491f314","order_by":3,"name":"Mengzhen Zhang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Mengzhen","middleName":"","lastName":"Zhang","suffix":""},{"id":449296157,"identity":"552444b3-853f-402f-b0cd-1ea03f6974d9","order_by":4,"name":"Qian Mu","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Mu","suffix":""},{"id":449296158,"identity":"73db0945-502b-4b0d-a915-75fbf74db5d9","order_by":5,"name":"Haiyan Yan","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Yan","suffix":""},{"id":449296160,"identity":"79385a6a-ef7e-45d9-bfbe-c9b769a88efd","order_by":6,"name":"Ya Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACA/bmg4//8Njw8LM3sEGEDhDSwnMs2YBHJk1OsucAsVokfMwkeGwOGRvcSCBaC1uChETOgcQNN58/e/izjUGO70YC4+cCfFqkmw8YGJy5kzjzdo65gWQbg7HkjQRm6Rn4tMgcS0hI7HmW2Hc7h03CsI0hcQPQhcw8eB2WY3Dg4L/DiQ03jz+TSGxjqCdGi2FjA89hY4EbDGYSB9sYEgwIagEGMjMDDyiQc8wkG85JGM4887BZGp8W+/bm478ZwFF5/JnkjzIbeb7jyQc/49OCDiSAmLGBBA2jYBSMglEwCrABAEApT5AGxDDkAAAAAElFTkSuQmCC","orcid":"","institution":"Shihezi University","correspondingAuthor":true,"prefix":"","firstName":"Ya","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-02-13 14:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6024054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6024054/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04490-7","type":"published","date":"2025-07-01T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81680433,"identity":"16727c52-0969-43b6-808f-a0556e65b0a7","added_by":"auto","created_at":"2025-04-30 08:54:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":804706,"visible":true,"origin":"","legend":"\u003cp\u003eEffects and analysis of ChCl-EG DES on whole-cell lipase relative activity. The Figure (a) shows the effect of the DES molar ratio and moisture content on lipase relative activity. The highest lipase relative activity was obtained at a molar ratio of 1:2 and a moisture content of 40%, with data points represented as mean ± SD, n = 3. Figure (b), figure (c), and figure (d) show the response surfaces of the binary linear regression model, the binary quadratic regression model, and the binary cubic regression model, respectively, for the relationship between the DES molar ratio, moisture content, and lipase relative activity. Figure (e) evaluates the accuracy and reliability of the model by comparing the observed experimental data with the lipase relative activity values predicted by the binary quadratic regression model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6024054/v1/8ad9f288c235854c30094f49.png"},{"id":81680430,"identity":"bb1043e7-f10f-4f07-9f8f-3d8cccc4f76c","added_by":"auto","created_at":"2025-04-30 08:54:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":486332,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of molar ratio and water content on the physicochemical properties of ChCl-EG DES. Figure (a), (b), (c), (d), (e), (f), and (g) respectively describe the changes in physicochemical indicators of DES such as surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity as a function of molar ratio (1:2, 1:3, and 1:4) and water content (0-80%, v/v). Data points represented as mean ± SD, n = 3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6024054/v1/86c82b3b89b1cf76a14c8c7f.png"},{"id":81681436,"identity":"823fd81b-7650-4197-b66c-47a4e7a0d085","added_by":"auto","created_at":"2025-04-30 09:10:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":444175,"visible":true,"origin":"","legend":"\u003cp\u003epresents a series of PCA results. The figure explores the relationships between water content, molar ratio, and seven physicochemical properties through biplots across different ranges of water content, as well as the connections between these properties and lipase relative activity. Figure 3(a) displays a biplot of water content, molar ratio, and seven physicochemical properties within the 0%-80% water content range; Figures 3(b) and 3(c) focus on narrower water content ranges (0%-40% and 40%-80%, respectively) for similar analyses. Figures 3(d) through 3(f) present, the relationships between seven physicochemical properties and lipase relative activity, likewise corresponding to the full range (0%-80%), low water content range (0%-40%), and high water content range (40%-80%), respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6024054/v1/2d3349db171211df71e6859b.png"},{"id":81681434,"identity":"2555c592-a004-43a0-8074-68dc2117dcd6","added_by":"auto","created_at":"2025-04-30 09:10:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":315943,"visible":true,"origin":"","legend":"\u003cp\u003epresents a series of scatter plots comprehensively illustrating the relationships between various physicochemical properties of DES and lipase relative activity across a water content range of 0%-80%. Figure (a)-(g) demonstrate the correlations between lipase relative activity and DES surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity, respectively. The plots utilize green markers to represent the relationships between these parameters and lipase relative activity within the 0%-40% water content range, while red markers indicate the relationships within the 40%-80% water content range.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6024054/v1/66eec5cb13ca70615f22cf54.png"},{"id":81680685,"identity":"6545e939-716f-477d-a244-6ed72474a3b4","added_by":"auto","created_at":"2025-04-30 09:02:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":264740,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Schematic diagram of binary quadratic regression process between the molar ratio and water content of ChCl-EG DES with lipase relative activity and physicochemical properties of DES. (b) Partial regression coefficient matrix obtained from regression equations. (c) Matrix derived after excluding secondary factors and partial regression coefficients. (d) Coefficient calculation for primary factors. The symbol ^ indicates coefficients obtained through linear inference rather than regression.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6024054/v1/fec4cac9c0fd232d9db4e3d4.png"},{"id":86179835,"identity":"c29880e6-9234-4985-a6df-09205c737764","added_by":"auto","created_at":"2025-07-07 16:19:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3381744,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6024054/v1/e4051971-48b0-4ab3-a2c7-981f4b4a3f01.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of Lipase Activity in Aspergillus niger C2J6 Whole Cells Using Choline Chloride Ethylene Glycol Deep Eutectic Solvent","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe synergistic relationship between the characteristics of the reaction medium and enzyme activity remains a central focus in the design and optimization of biocatalytic reaction systems \u003csup\u003e1\u003c/sup\u003e. The selection of the reaction medium significantly influences reaction efficiency, selectivity, enzyme activity, and stability \u003csup\u003e2\u003c/sup\u003e. Organic solvents, commonly used as reaction media in many traditional industrial processes, can adversely affect enzymatic catalytic activity and stability through various mechanisms \u003csup\u003e3\u003c/sup\u003e. Strongly polar organic solvents (such as DMSO and DMF) compete with water molecules on the enzyme surface and with internal hydrogen bonds, disrupting the natural folded structure of enzymes. The three-dimensional structure of enzymes is fundamental to their function; alterations to this structure typically result in loss of catalytic activity and may even lead to enzyme denaturation. Non-polar solvents (such as n-hexane) inhibit catalytic efficiency by interfering with the hydrophobic core of enzymes, increasing conformational rigidity and reducing molecular flexibility \u003csup\u003e4\u003c/sup\u003e. While aqueous media effectively dissolve polar substrates, they exhibit limitations in dissolving hydrophobic substrates, imposing further constraints on reaction efficiency \u003csup\u003e5\u003c/sup\u003e. Additionally, organic solvents may affect the thermal stability of enzymes, making them more susceptible to inactivation at elevated temperatures. Beyond these functional limitations, the potential environmental pollution associated with organic solvent use renders them less than ideal for biocatalysis based on modern green chemistry principles \u003csup\u003e6,7\u003c/sup\u003e.Consequently, the pursuit of new, efficient, and environmentally friendly reaction media has emerged as a critical research direction \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDeep eutectic solvents (DESs) have garnered significant attention across multiple scientific disciplines in recent years due to their green characteristics, including facile preparation, low toxicity, cost-effectiveness, designability, and biodegradability \u003csup\u003e9,10\u003c/sup\u003e. In enzymatic catalytic reactions, DESs significantly enhance catalytic efficiency by optimizing enzyme-substrate interactions and improving enzyme solubility in poorly polar or hydrophobic substrates\u003csup\u003e11\u003c/sup\u003e. Furthermore, DESs have demonstrated extensive application potential in several other fields, such as extraction processes for isolating bioactive compounds from plant materials, sustainable electrolytes for energy storage devices and electroplating processes in electrochemistry, enhancement of drug solubility and delivery systems in the pharmaceutical industry, and synthesis of nanomaterials in materials science\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e.Among these, choline chloride\u0026ndash;ethylene glycol (ChCl-EG) DES is particularly notable. Notably, in the field of biocatalysis\u003csup\u003e15\u003c/sup\u003e, this DES enhances solubility and addresses the limitations of traditional media for hydrophobic substrates \u003csup\u003e16\u003c/sup\u003e. Additionally, its low volatility and toxicity create a mild and stable environment \u003csup\u003e17\u003c/sup\u003e that stabilizes enzyme conformation, enhances activity, and improves catalytic efficiency \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe physicochemical properties of ChCl-EG DES, such as viscosity, polarity, and conductivity, are largely influenced by the composition ratio of its components \u003csup\u003e19\u003c/sup\u003e, which in turn affects enzyme activity by altering the solubility and dispersibility of substrates and enzymes \u003csup\u003e20,21\u003c/sup\u003e. Optimizing these properties can preserve enzyme activity, facilitate effective substrate-enzyme binding, and enhance both the rate and selectivity of catalytic reactions \u003csup\u003e22,23\u003c/sup\u003e. Previous research on the biocatalysis of ChCl-EG DES has yielded promising results, demonstrating its superior performance compared to traditional media in applications such as ester synthesis and drug intermediate conversion \u003csup\u003e24\u003c/sup\u003e. However, most studies have focused on correlations between individual or limited physicochemical properties of fixed DES systems and enzyme activity without exploring their causal relationships \u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough DESs have been extensively studied for their effects on enzyme activity, the use of whole cells, particularly endophytes, within DES remains largely unexplored. Unlike purified enzyme preparations, whole-cell systems offer dual advantages of reduced purification costs and a complete metabolic network with natural cofactors, creating stable microenvironments for catalysis \u003csup\u003e25\u003c/sup\u003e. While most DES biocatalysis studies have utilized purified enzymes, this approach faces limitations of complex extraction processes, high costs, and poor stability in DES environments \u003csup\u003e26\u003c/sup\u003e. In contrast, endophytic fungal whole-cell system employs the cell wall as a natural protective barrier, enhancing enzyme stability in DES \u003csup\u003e25\u003c/sup\u003e. Additionally, endophytic fungi possess unique niche adaptability and metabolite diversity, potentially exhibiting catalytic properties distinct from purified enzymes under DES conditions \u003csup\u003e27\u003c/sup\u003e. This approach addresses a significant research gap and opens new avenues for DES-mediated biocatalysis. However, there remains a paucity of research on the catalytic behavior of endophyte whole cells within DES. Exploring the catalytic mechanisms of endophyte whole cells in deep eutectic solvents can not only enhance the theoretical understanding of enzyme catalysis but also provide technical support for practical industrial applications.\u003c/p\u003e \u003cp\u003eThis research explores the effects of choline chloride-ethylene glycol DES on lipase activity in \u003cem\u003eAspergillus niger\u003c/em\u003e C2J6 (\u003cem\u003eA. niger\u003c/em\u003e) whole cells and optimizes DES composition for enhanced biocatalytic efficiency. Unlike previous studies that focused on simple correlations, this study employed multivariate statistical methods to reveal the causal relationships between DES composition, physicochemical properties, and enzyme activity. The findings provide both theoretical insights and practical guidance for the precise regulation of DES in biocatalytic reactions, supporting the green transformation of biochemical industries.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cem\u003e2.1 Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA.\u0026nbsp;niger\u003c/em\u003e C2J6 strain is an endophytic fungus isolated from grapes provided by the Fruit and Vegetable Processing Laboratory of the School of Food Science, Shihezi University, Shihezi City, Xinjiang, China. Choline chloride (\u0026ge; 98%), ethylene glycol (\u0026ge; 98%), Nile red (\u0026ge; 98%), anhydrous ethanol (\u0026ge; 99.7%), and poly (vinyl alcohol) were obtained from Shanghai Macklin Biochemical Technology Co., Ltd. (China). KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (\u0026ge; 99.5%), MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO (\u0026ge; 99.0%), and peptone were sourced from Tianjin Xinbote Chemical Co., Ltd. (China). Olive oil was purchased from Kerry Grain and Oil Co., Ltd. (China). All reagents and solvents were purchased from commercial suppliers and used without further purification.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Preparation of DES\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe preparation of the DES is based on the intermolecular interaction between the hydrogen bond acceptor (HBA) and the hydrogen bond donor (HBD). Studies have shown that the molar ratio of HBA to HBD of 1:2 usually represents the minimum HBD requirement for stable DES formation\u003csup\u003e28\u003c/sup\u003e, and the ratio of 1:3 and 1:4 is helpful to systematically study the effect of increasing hydrogen bond donor concentration on the physicochemical properties of DES\u003csup\u003e29\u003c/sup\u003e. Based on this theoretical basis, DES was prepared by heating and stirring method in this study. Choline chloride (ChCl) was selected as HBA and ethylene glycol (EG) as HBD, and mixed according to the molar ratio of 1:2 (labeled DES1), 1:3 (labeled DES2) and 1:4 (labeled DES3). The mixtures were heated and stirred at 80\u0026deg;C for 2 hours to form uniform and transparent liquids, then cooled to room temperature and observed to confirm successful preparation \u003csup\u003e30,31\u003c/sup\u003e. In addition, given that the properties of DES show significant changes with the addition of water from slight influence at low concentration (\u0026lt;10%) to complete destruction of eutectic network at high concentration, water was added to the prepared DES in different proportions to comprehensively study its regulatory effect on DES performance\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. A series of DES systems with water content of 0%, 20%, 40%, 60% and 80% (v/v) were prepared to systematically evaluate their characteristics under different water content conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 Preparation of whole-cell catalysts\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA.\u0026nbsp;niger\u003c/em\u003e C2J6 spores were prepared as 10\u003csup\u003e7\u003c/sup\u003e/mL spore suspensions and inoculated into the fermentation medium at a 2% inoculum volume. The fermentation medium composed of 5% olive oil emulsion (V\u003csub\u003e2% polyvinyl alcohol\u003c/sub\u003e: V \u003csub\u003eolive oil\u003c/sub\u003e 3:1), 5% peptone (w/v),1% KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (w/v), and 0.5% MgSO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO (w/v) was incubated on a shaking table at 28℃ and 150 rpm for 48 h. After incubation, the fungal cells were collected by centrifugation (8,000 rpm, 30 min) to obtain wet whole cells, which were washed three times with pure water and then freeze-dried \u003csup\u003e33\u003c/sup\u003e. The freeze-dried cells were subsequently crushed at 12,000 rpm to produce whole-cell biocatalysts and stored at -4℃.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Determination of lipase activity in the catalyst in whole cells\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe lipase determination method described by Kim et al. \u003csup\u003e34\u003c/sup\u003e was slightly modified. Specifically, 200 \u0026mu;L DES was added to 0.01 g of the whole-cell catalyst, with water serving as the blank control. The mixture was vortexed and incubated at room temperature for 15 min. Subsequently, 100 \u0026mu;L of this mixture was transferred, followed by the addition of 100 \u0026mu;L of a 10 mM p-NPP substrate solution dissolved in acetonitrile. Subsequently, 0.9 mL of 50 mM Tris-HCl buffer (pH 8) was added, and the reaction was performed in a water bath at 40\u0026deg;C for 10 min. Immediately thereafter, 4.9 mL of 0.5 M EDTA was added to terminate the reaction. The sample was then centrifuged at 8,000 rpm for 5 min at 4\u0026deg;C, and 200 \u0026mu;L of the supernatant was transferred to a 96-well plate. Whole-cell lipase activity was determined by measuring the absorbance at 405 nm using ReadMax1900. The optimal temperature (40\u0026deg;C) and reaction time (10 minutes) for\u003cem\u003e\u0026nbsp;Aspergillus niger\u003c/em\u003e C2J6 whole-cell lipase in the DES environment were determined through preliminary experiments, ensuring that measurements were conducted within the linear range of enzymatic activity. One unit (U) of enzyme activity was defined as the amount of whole cells required to catalyze the hydrolysis of p-NPP to produce 1 \u0026mu;mol of p-NP per minute at 40\u0026deg;C, expressed as U/mL. Lipase activity was determined using Eq. (1).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAn0AAABwCAYAAABrcsY6AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAA86SURBVHhe7d3Pixv148fxV75/gDrZnrSIbNaDWIloUkVLoQXN0lN70KT14qHYZo5tbTRbT9JKlno07UILvdRkvejBXXYrdMFdRborbFBRcCeIiKeEpfgHzOfw3fcwM8kk2bq1szvPBwSa93uS3WTTmVfeP1Ou67oCAADAnvZ/4QIAAADsPYQ+AACABCD0AQAAJAChDwAAIAEIfQAAAAlA6AMAAEgAQh8AAEACEPoAAAASgNAHAACQAIQ+AACABCD0AQAAJAChDwAAIAEIfQAAAAlA6AMAAEgAQh8AAEACEPoAAAASgNAHAACQAIQ+AACABCD0AQAAJAChDwAAIAEIfQAAAAlA6AMAAEgAQh8AAEACEPoAAAASgNAHAACQAIQ+AACABCD0AQAAJAChDwAAIAEIfQAAAAlA6AMAAEgAQh8AAEACEPoAAAASgNAHAACQAIQ+AACABCD0AQAAJAChDwAAIAEIfQAAAAlA6AMAAEgAQh8AAEACEPoAAAASgNAHAACQAIQ+AACABCD0IRFSqZR3K5VKXvn09HSgrtvtBh4HAMBekXJd1w0XAntNq9XSiy++qFwup9XV1UBdPp+X4zhaWlpSNpsN1AEAsFfQ0oddqdVqaXJyMrJlzrZtzc/Pe/dNmBsbG/MdJXW7XTmOo08++YTABwDY0wh9iK1ms6l8Pu91vU5MTHh1+/fvV7fb7Rv8bNvW6uqqXnnllUB5LpcL3Jekmzdv6uDBgzpz5ky4CgCAPYXQh1iybVu2bev8+fNyXVe1Wi0Q+sbGxrSwsCBJgeBnAt/CwkJPq174frvdVqVSUa1WC5QDALAXMaYPsTMzM6OzZ89qeXlZhw4dClf3MEEvn89HBj75wqEZ0zc5OamjR4/q4sWL4UMBANhzCH2InYmJCVmW1TPhYpCJiQk5jiPHcTQ+Ph6uliRNTU3pypUrcl1XzWZTly5d0sbGRuCYdrutq1evqt1uey2JAADsBXTvIlba7bYcx9Fbb70VrurLjOuzLEvlclnFYrFnjJ/x+OOPS1uPsW1bt27d8upM2dWrV3Xnzh3fowAA2BsIfYiVv//+O1wkbYXBMP9EjoWFBdXrdeXz+b6TO/w++ugjlcvlQNfx2NiYLly4oHq9Hhg7CAA7rdVqaXp6Olw8kqmpKbVarXAxMJJYhr6ZmZnAgrmpVEorKyuBY/yzOlOplKampgL1cdJsNpVOp3teQz/z8/OB15bP5wNLj/TTarVUKpUCs1yHnVBMy1Y6nVYqlVI6nZZt2wPD0n/h0KFDsixLd+/eVbfbVbfb1fT0tIrFYuA4E/gkBcbwDQp+Bw4ckCTduXNH586dC9RJiuwWBoCd0mw2NTMzEzmWeGVlRbZtR57Dz507p5mZGTWbzXAVMJwbU9evX3cluZLcRqMRrnY7nY6byWRcSW65XA5Xx8Ly8rKby+W817G8vBw+JKDRaHivp9PpuI7jeI/v9x64ruuur6+7lmW5uVzOdRzH7XQ6brlcHvi+dDodN5fLuZZleb+Teb9zuZzb6XTCD/lPzc3NeX/bTCbj1mq1nt9pfX3de5/6KZfL7tzcXKBseXnZldRTHlYoFNxCoRAuBoB/pdFoRJ5blpeX3Wq16l0varVa+BCPOYdHXReAKLENfXNzc95FP0qhUHAluY7jhKseKcdx3EKh4GYyGbdYLI4U+hzH8UJXv/Ko12nCUTj8mPJ+JwUTCsPhZ1hYTApCH4Cdtr6+Hnke9zPBb1Doc33Pt76+Hq4CIsWye1eSfv75Z0nSm2++Ga7yLC4uKpPJxK5b7urVqzpx4oQ2NjZGboK/efOmJOn06dOB8vHxcRUKBUnSN998E6ibn5+X4zgqFos9S5S89957kqSvvvoqUN7tdnXt2jVZlqVjx44F6k6dOiVtdT+MyrbtkY4ftoNGXHS7XW1sbHhdy6Pazusb9T0DsHecPn1axWJx6PXqhRdeCBf1lc1mVSgUeq4ZwCCxDX13796VJB0+fDhcJW1dZBWxy8KjVq/Xt73Dw+zsrCTp+eefD1fp6NGjkqQvv/wyUP71119Lkl566aVAuSS99tprku95DRMcDx48GCjX1ng6Sdrc3Bxp/GG73Vaz2dTJkycHhphWq6UjR47o3r17+uuvv8LVsZFKpbRv3z45jqO1tTXt27fPGzc4zBdffKHFxcWhwc+2bV27dk3ffvttuArAHtVsNrW2tqbjx4+Hq3rs378/XBTpxIkTWltbG3j+BfxiG/ru3bsnRYQTSfrhhx8kSUeOHAlX7UqO40iSnnzyyXCVnn76acn3nhhmRqup9/M/j3+m159//ikNmLSQyWQkSb/88ku4qsf4+LiWlpZkWVZk8DOBT5KWlpZivb/t1nCHwG3UtfouX76scrmstbW1yOBnAl+5XFa9Xg9X92i327Jt25ugY95ff3k6ndbMzEz4oQBixCwPFXU9e1Cvvvqq1KdHB4gSy9DXarW0ubkpy7Iiw8nS0pIU0TI2SHhW8Ki3qJlUO8Hfqtbv9Zpvfpubm4HyxcXFQL2f/3n++ecf79+mBfWZZ57xyvzMciX3798PV/WVzWYjg99uCnw7oV6vRwa/7Qa+brerYrGoU6dOqdPpSFsXjlarJdu2deHCBa/87NmzfZe0AfDodbtd71zd7/z+b5hz6uzsbN8vmkBYLEOfacUbNJ5vbW1N8nVJjirckjPqLWp6PfoHv6QFPqNf8Ntu4NPWuoGrq6uBz3e321WlUtHt27c1Pj6usbExr+Ugan1DAI/Wr7/+Kj3EoUiWZUm+nwMMEsvQZ1rxorpuza4NZoIDHr1w8HvQwBduYY3TbVT+4Pfss89uO/CFmZP52tqa6vV6YNKO6fJ/7rnnvDIA8fH9999LW1/kHgbzxc/8HGCQWIY+04oX1XX722+/Sb4JDoiHbDbrBZvNzU198MEH2wp8e0m9Xlcmk/GGKXz88cfhQ0ZmTublcjnQPdRut7W5ualMJvPQLigAgL0jdqGv2+16kxqium6/++47ybfDwm7nb6UZNGs23D1g7g/7hud//pdfflnyje2L8iDvrRlvZlmWLMtSpVLpO7ljkHC3epxu22HbthzH8YJfeIzfdpi/lVlSxzCtfIOGQQAAYMQu9JmurEFdt3fu3JEkvfLKK+GqocJddqPeHuZEjrGxMW9cRj8m1JmZtUb4vp8Jj5ZlBVqB+s309TMDjp966qlw1UDhMXzhMX5J4h/Dt7Gx0TPGb7vM3yT8JcjM2Ita1ggAAL/Yhb5h2u221tbW+i5IPIpw682ot4c9kcO01vRrtfvjjz8kqWeNJxOy+rXamfXwSqVSoNxM8TdBws/MAM1kMtvqlg0Hvmw22zPGb1AL5l7Sb9KGf4zfO++8E37IQGa5nX5fgsyXnzfeeCNcBSAmzJqpGxsb4aodYb5Imp8DDBTeouNRM1vLRG2/ZrY1201bz5ht1AZtw2b2hQ2/7k6n41qW1VPur+u3tc+gLepMXXiLtlqt1rd8ELP3r2VZff8mw+r3kmHb2A2r78fsiVytVgPlnU6n7+cFQLyY/6ujXm7NHuzDtmEzzHOHt+IE+hntU/gfM6GkXC57H2THcbzAt51Q8qiZi3a/C3eYPxR0Oh230+l4ZVGB0ZwgcrmcF/DMz4w6afiDmHne5eVl17Ksbe05O2qgG/W43WzUQDfqcYb5zIf3STZ7MpvQd/36dbdYLAaOARAP22msMMfmcrmhQc6cB7Zz3kayxTL0dTodt1aruZlMxgtMmUzGLZfLI/2nedRMyDG/e79bVHANv+5isTj0Nc/Nzbm5XM57TC6X6wkJYevr697Jxby/USExSqfTcQuFwtDfz936eYVCYehJbLdqNBojB7lyuRz59w8zn4V+LbYmQFqW5dZqtT373gK7nflyPuj/velpCd8GnZdHeV7AL+X+f/MwAAB4SPL5vDKZzI5ObCuVSnIcR6urq+EqoK9dN5ED2K7JyUlvFnY6nfbKV1ZWAjO05+fnA48DgJ1y48YNzc7OBvZC/zfa7bZmZ2d148aNcBUQidCH2Gq320qlUv965u/t27e95W1+//13r/zQoUOqVquSpEajoWPHjnl1ALCTstmsGo2GTp8+Ha56INVqVY1GY1srLQCEPsTWzZs3pT7r02lraZSobpJWq6XJyUnv/tjYmCYmJrx/+/34448qFos9S9sAwE4rlUo6f/68pqamwlXbYtu2jh8/znkL20boQ+y0Wi3l83lduXJF8i2obdu2d8zhw4f7Lvxs1gx89913A+VmJxK/lZUV3bt3T5999lm4CgAeilKppLfffvuBF/yfnp7WmTNnCHx4IEzkQGylUilVq1Vdvnw5XCVJajabOnnypBqNhkqlkhf46vV6zwlxenpalUpF/o/7xMSE3n//fZ05cyZwLAAAexEtfYglM47v9ddfD1d5SqWS1tfXZdu2pqenIwOfn1m9fnp6WhMTEwQ+AEBiEPoQS2Y7umH7K2ezWdXrdVUqFZVKpcjAd+DAAWlrb+d2u61KpeJtkwYAQBIQ+hBLd+/eVS6X65l4EdZsNmXbtmq1mprNZs8YP+Oxxx7z/m2OHx8f98qazWZgaZd8Pr9jSysAABAHhD7E0uLiovL5vHffdMv6mTF99XpdFy9e1NLS0sBZvZL0+eefq9vt6uLFi4HyW7du6ejRo3JdV47jaHNzU5VKJXAMAAC7GRM5EEupVErFYlHNZlMzMzNaWloKhLnwJA4jajJHt9vVvn37JEnr6+tD17aanJzUE088MTBAAgCwm9DSh1iqVquanZ1VOp3W/fv3e8LXTz/9pLm5uZ4xfNlsVktLS7p161ag3HQTV6vVoYHPLOXy4YcfhqsAANi1aOkDfJrNpj799FPduHFjaDgEAGA3IfQBW0zgW1hYGDqBBACA3YbuXUDS/Py8Ll265AW+brfrbd0GAMBeQEsfICmdTmtzczNQVigUtLCwECgDAGC3IvQBAAAkAN27AAAACUDoAwAASABCHwAAQAIQ+gAAABKA0AcAAJAAhD4AAIAEIPQBAAAkAKEPAAAgAQh9AAAACUDoAwAASABCHwAAQAIQ+gAAABKA0AcAAJAAhD4AAIAEIPQBAAAkAKEPAAAgAQh9AAAACUDoAwAASABCHwAAQAIQ+gAAABKA0AcAAJAAhD4AAIAEIPQBAAAkAKEPAAAgAQh9AAAACUDoAwAASABCHwAAQAL8D/ln/GuKyee0AAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere\u0026nbsp;\u003cem\u003eU\u003c/em\u003e is lipase activity (U/mL),\u0026nbsp;\u003cem\u003ec\u003c/em\u003e is the concentration of p-nitrophenol (\u0026mu;mol/mL),\u0026nbsp;\u003cem\u003eV\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e is the total volume of the reaction liquid (mL),\u0026nbsp;\u003cem\u003et\u003c/em\u003e is the reaction time (min),\u0026nbsp;\u003cem\u003eV\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e is the volume of the enzyme liquid to be measured (mL), and\u0026nbsp;\u003cem\u003en\u003c/em\u003e is the dilution ratio.\u003c/p\u003e\n\u003cp\u003eRelative activity (%) was defined as the ratio of whole-cell lipase activity pretreated with DES to that of the blank control (pure water) under equivalent conditions. This measure was employed to evaluate enzyme activity in various DES systems. Eq. (2) was used to calculate relative activity.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eU\u003csub\u003ea\u003c/sub\u003e\u003c/em\u003e is the lipase activity of the DES pretreatment (U/mL), and \u003cem\u003eU\u003csub\u003eb\u003c/sub\u003e\u003c/em\u003e is the lipase activity of the blank control group (U/mL).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5 Determination of physicochemical properties of DES\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe surface tension of the DES systems was measured using a DCAT25 surface tensiometer (Dataphysics, Germany) based on the Wilhelmy plate method, with an accuracy of \u0026plusmn; 0.1 mN/m\u0026nbsp;\u003csup\u003e35\u003c/sup\u003e. The conductivity of the DES was determined using a DDS-307 conductivity meter (Leizi\u0026trade;, China) with an accuracy of \u0026plusmn;0.01 \u0026mu;S/cm \u003csup\u003e8\u003c/sup\u003e. The density was measured using a 5 mL density bottle, and the mass was recorded using an electronic analytical balance (accuracy \u0026plusmn;0.0001 g) \u003csup\u003e36\u003c/sup\u003e. The viscosity of the DES systems was determined based on the principle of cone-plate rheometry using an MCR302 rheometer (Anton Paar, Austria) equipped with a CP50-1 cone-plate geometry operating at a constant shear rate of 50 s⁻\u0026sup1; with a 1 mm gap\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e. The refractive index was measured using a 2WAJ Abbe refractometer (China), with an accuracy of \u0026plusmn;0.0002 \u003csup\u003e38\u003c/sup\u003e. The polarity was determined using Nile red as a solvatochromic probe \u003csup\u003e39\u003c/sup\u003e. Nile red solution (1 g/L) in ethanol was prepared and stored at 4\u0026deg;C. The DES was placed in a quartz cuvette, and 150 \u0026micro;L of Nile red solution was added before evaporating the solvent under high-purity nitrogen. An ultraviolet spectrophotometer was used to scan and record the maximum absorption wavelength of DES (\u0026lambda;\u003csub\u003emax\u003c/sub\u003e). The polarity parameter was calculated as E\u003csub\u003eNR\u003c/sub\u003e = 25891 / \u0026lambda;\u003csub\u003emax\u003c/sub\u003e, where E\u003csub\u003eNR\u003c/sub\u003e is in kcal/mol and \u0026lambda;\u003csub\u003emax\u003c/sub\u003e is in nm. The water activity of the DES was measured using an ST-3A intelligent water activity measuring instrument (China), with an accuracy of \u0026plusmn;0.015 \u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6 Statistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental treatments were conducted in triplicate, and the results are expressed as mean values. The experimental data were comprehensively analyzed using MATLAB R2024b (MathWorks Corp., USA). First, statistical analysis was performed, and significance was assessed using a Duncan multiple range test following the multivariate analysis of variance, with p \u0026lt; 0.05, indicating significant differences. Subsequently, a multiple regression analysis was conducted, and the model significance was evaluated using variance analysis. Model validity was verified using statistical parameters, such as the mean square, degrees of freedom, sum of squares, F-value, and P-value. A P-value of less than 0.01 indicated that the model was statistically significant. Data were standardized using Z-score normalization. Principal component analysis (PCA) was employed for multivariate statistical analysis to identify correlations among variables. Finally, the coefficients of the multiple linear regression equation were used in the matrix calculations to deduce causal relationships between variables.\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eIn this study, the effects of the ChCl\u0026ndash;EG molar ratio and water content on the physicochemical properties of DES were investigated, and the activity of \u003cem\u003eA. niger\u003c/em\u003e C2J6 whole-cell lipase in these DES systems was experimentally determined. Subsequently, multiple statistical methods were employed to elucidate the causal relationships between the physicochemical properties of DES and lipase activity.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effect of DES on lipase activity in whole cells of A. niger\u003c/h2\u003e \u003cp\u003eFor lipase in \u003cem\u003eA. niger\u003c/em\u003e C2J6 whole cells, DES molar ratio and water content were two critical parameters that could directly influence enzyme activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The relative activity of lipase in DES followed the order DES1\u0026thinsp;\u0026gt;\u0026thinsp;DES2\u0026thinsp;\u0026gt;\u0026thinsp;DES3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with the highest relative activity observed for DES at a molar ratio of 1:2, representing increases of 9% and 23% compared to the ratios of 1:3 and 1:4, respectively. These results indicated that the molar ratio of HBA to HBD significantly affected lipase activity in whole cells. In addition, the enzyme activity in pure DES (0% water content) increased by 20.49%, 19.80%, and 8.87% for DES1, DES2, and DES3, respectively, relative to the control group with 100% water content. However, pure DES did not yield the highest activity. Instead, when the water content reached 40%, the catalytic activity was maximized, increasing to 142.05%, 133.75%, and 119.18% for DES1, DES2, and DES3, respectively, surpassing that of the blank control group without DES by 42.05%, 33.75%, and 19.18%, respectively. Juneidi\u003csup\u003e41\u003c/sup\u003e et al. found that when the concentration of DES was 40% v/v, the activity of \u003cem\u003eBurkholderia cepacia\u003c/em\u003e lipase (BCL) increased by 230% compared to the untreated control. However, the whole-cell lipase used in this experiment exhibited relatively lower activity, a discrepancy that may be attributed to differences in enzyme preparation purity. Commercially purchased pure enzymes undergo rigorous purification and processing techniques, resulting in more pristine active sites that can bind substrates and catalyze reactions more efficiently. In contrast, the whole-cell systems isolated and screened in this study typically have not undergone similar sophisticated modifications, and the enzyme molecules may be subject to interference from other cellular components, leading to reduced catalytic efficiency and overall activity levels. Conversely, enzyme activity at 80% water content was lower than that in pure DES at 0% water content. These findings suggested that a moderate increase in water content enhanced lipase activity to its maximum, while further increases reduced enzymatic performance. Nevertheless, compared to the 100% water control, all three DES, with water contents ranging from 0\u0026ndash;80%, consistently enhanced lipase activity, demonstrating that DES systems were superior to water for maintaining lipase activity in whole cells. The positive effect of DES on lipase activity may be related to the influence of polyols on the enzyme catalytic mechanism. In the initial step of the lipase-catalyzed reaction, the hydroxyl group of the serine residue in the active site attacks the carbonyl carbon of the substrate to form a tetrahedral intermediate. Within DES, the polyol hydroxyl groups may form hydrogen bonds with the enzyme\u0026rsquo;s active site hydroxyl group, potentially increasing the nucleophilicity of the serine residue, thereby promoting a more rapid substrate attack \u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo illustrate the correlation between the molar ratio, water content, and enzyme activity, the molar ratio (x\u003csub\u003e1\u003c/sub\u003e) and water content (x\u003csub\u003e2\u003c/sub\u003e) were designated as independent variables, and relative activity (y) was defined as the dependent variable. Binary linear, binary quadratic, and binary cubic regression analyses were conducted successively to determine the most suitable model. Owing to the varying ranges and units of the molar ratio, water content, and enzyme activity, variables with broader value ranges may dominate the regression results, potentially obscuring the contributions of those with narrower ranges and resulting in misleading conclusions. To eliminate these dimensional effects, the data for molar ratio, water content, and relative enzyme activity were standardized before performing regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and significance tests for the regression equations (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on these standardized data, response surface diagrams of the models were drawn (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u0026ndash;d). Subsequently, a significance test of each variable coefficient in the optimal model was performed to evaluate the importance of variables such as molar ratio and water content on relative activity (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe binary regression model equations describing the relationship between the ChCl-EG DES molar ratio, moisture content, and lipase relative activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emodel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebinary linear regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey=-0.6492x\u003csub\u003e1\u003c/sub\u003e-0.0320x\u003csub\u003e2\u003c/sub\u003e-0.1998x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBivariate quadratic regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey=-0.6492x\u003csub\u003e1\u003c/sub\u003e-0.0320x\u003csub\u003e2\u003c/sub\u003e-0.1998x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e-0.1975x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.7254x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.9023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinary cubic regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;0.5130x\u003csub\u003e2\u003c/sub\u003e-0.1997x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e-0.1975x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.7254x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0274x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0851x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.4994x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e-0.3117x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.9023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results for the binary linear regression model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProb\u0026gt;F\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.3065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1471.4783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.6935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eerror\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results for the binary quadratic regression model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.9911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1608.3043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e165.9782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eerror\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA results for the binary cubic regression model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProb\u0026gt;F\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.6917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.2991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e934.5870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e224.8261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eerror\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignificance test of partial regression coefficients in the bivariate quadratic model of the ChCl-EG DES molar ratio, moisture content, and lipase relative activity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003estandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb\u0026gt;|t|\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.78497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.64921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-10.15829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.49994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.09056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.19747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.16042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ex\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.72537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.38993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA comparison of the determination coefficients (R\u003csup\u003e2\u003c/sup\u003e) in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicated that the relationship between molar ratio, water content, and relative activity aligned more closely with a multivariate nonlinear regression model. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the significance tests of partial regression coefficients for the binary linear, binary quadratic, and binary cubic models, respectively. The F-values of the models were 1471.4783, 1608.3043, and 934.5870, respectively, and their P-values were all less than 0.0001, demonstrating that the models were statistically significant. Although the binary cubic model had a larger R\u003csup\u003e2\u003c/sup\u003e than the binary quadratic model, according to the analysis of variance of the regression equation, the F\u003csub\u003emodel\u003c/sub\u003e of the binary quadratic model was higher than that of the binary cubic model, and the F\u003csub\u003eLack of fit\u003c/sub\u003e of the binary quadratic model was lower than that of the binary cubic model. The results suggest that the binary quadratic model is more suitable for describing the relationship between molar ratio, water content, and relative activity. Therefore, given the same significance level in the F-test, the simpler binary quadratic model could be used to predict and optimize the relative activity, whereas the binary cubic model and binary linear model presenting lower significance were not adopted. In this model, x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e, and x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e are significant terms, whereas x\u003csub\u003e2\u003c/sub\u003e and x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e are not. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, the enzyme activity was negatively correlated with the molar ratio in the range between 2 and 4, positively correlated with the water content from 0 to 50%, and negatively correlated with the water content from 50\u0026ndash;100%. The optimal DES composition for achieving the highest relative activity was determined to be a molar ratio of 1:1.55 and a water content of 46.28%, resulting in a relative activity of 136.41%. For the verification test, the conditions were set at a molar ratio of 1:1.55 and a water content of 46%. Three repeated experiments yielded a relative activity of 142.31% with a relative error of 4.15% compared to the predicted value of 136.41%, indicating a good fit between the quadratic regression model and the actual enzyme activity. The true values of enzyme activity and the values predicted by the regression equation were evenly distributed around the Y\u0026thinsp;=\u0026thinsp;x curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), further confirming the accuracy of the model predictions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Physicochemical properties\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe surface tension (γ), conductivity (σ), density (ρ), viscosity (\u0026micro;), refractive index (n), polarity (E\u003csub\u003eNR\u003c/sub\u003e), and water activity (Aw) of DES1, DES2, and DES3 at molar ratios of 1:2, 1:3, and 1:4, respectively, were examined by varying the water content from 0\u0026ndash;80%.\u003c/p\u003e \u003cp\u003eSurface tension reflected the mutual attraction and interaction forces among the components of the solvent system and can be utilized to determine the interaction strength between a given solvent and other compounds during the mixing process \u003csup\u003e43\u003c/sup\u003e. When the molar ratio of ChCl to EG was shifted from 1:2 to 1:4, the surface tension of the DES decreased as the proportion of EG increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This occurred because EG had a relatively low surface tension; thus, increasing its proportion reduced the overall surface tension of the system. The water content also significantly influenced the DES surface tension. As the water content increased from 0 to 80%, the surface tension generally increased because water possessed a relatively high surface tension and increasingly dominated the overall system as its content grew. These observations were consistent with those reported by Aravena et al. \u003csup\u003e44\u003c/sup\u003e The relationship between the molar ratio (x\u003csub\u003e1\u003c/sub\u003e), water content (x\u003csub\u003e2\u003c/sub\u003e), and surface tension (y\u003csub\u003e1\u003c/sub\u003e) followed regression Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9749.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e1\u003c/sub\u003e=-4.2140x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1220x\u003csub\u003e2\u003c/sub\u003e-0.0227x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.5120x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.0008x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;61.8459 (1)\u003c/p\u003e \u003cp\u003eDES exhibited excellent conductivity, reflecting the mobility of ions within the system \u003csup\u003e8\u003c/sup\u003e. As the molar ratio increased, the DES conductivity increased, and it further increased with higher water content, reaching a maximum at 60% water content, which was approximately five times the conductivity of pure DES. However, at 80% water content, conductivity decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This occurred because the addition of water leads to ionic dissociation of the DES components; when the water content is excessively high, the complete dissociation of Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e from choline chloride disrupts the fundamental DES structure. Further addition of water diluted the ion concentration, thereby reducing conductivity \u003csup\u003e45\u003c/sup\u003e. These findings were consistent with the trends observed in water-based ethylamine/glycol DES by Alfurayj et al. \u003csup\u003e46\u003c/sup\u003e The relationship between molar ratio (x\u003csub\u003e1\u003c/sub\u003e), water content (x\u003csub\u003e2\u003c/sub\u003e), and conductivity (y\u003csub\u003e2\u003c/sub\u003e) followed regression Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9709.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e2\u003c/sub\u003e=-3.9197x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.6410x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0865x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.8147x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0065x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;10.0955 (2)\u003c/p\u003e \u003cp\u003eDensity is an important parameter for DES in industrial applications. At room temperature, the density of DES with varying water contents ranged from 1.05 to 1.2 g/cm\u003csup\u003e3\u003c/sup\u003e, exceeding that of water. As the molar ratio increased from 1:2 to 1:3 or 1:4, the density gradually decreased with increasing molar fraction of EG. However, the density change was minor (within 0.003 g/cm\u003csup\u003e3\u003c/sup\u003e), indicating that the molar ratio had a limited effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), which is consistent with the results reported by Han et al. \u003csup\u003e47\u003c/sup\u003e. The addition of water further influenced DES density. Because water generally had a lower density than ChCl and EG, increasing the water content typically led to a linear decrease in the overall solvent density. This effect can be attributed to the altered molecular packing caused by water molecules in the DES, ultimately reducing its density \u003csup\u003e48\u003c/sup\u003e. The relationship between molar ratio (x\u003csub\u003e1\u003c/sub\u003e) and moisture content (x\u003csub\u003e2\u003c/sub\u003e) to density (y\u003csub\u003e3\u003c/sub\u003e) followed regression Eq.\u0026nbsp;(3), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9909.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e3\u003c/sub\u003e=-0.0037x\u003csub\u003e1\u003c/sub\u003e-0.0012x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0003x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;1.1758 (3)\u003c/p\u003e \u003cp\u003eViscosity is an important parameter describing molecular interactions in liquids, including the strength of hydrogen bonds, van der Waals forces, and presence of free volumes \u003csup\u003e49\u003c/sup\u003e. Generally, the viscosity of DES is influenced by its components, molar ratios, water content, and other factors. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, the pure DES exhibited relatively high viscosity owing to the formation of hydrogen bond networks among its components, which reduced the mobility of the DES molecules. Electrostatic and van der Waals interactions may also contribute to the high viscosity \u003csup\u003e50\u003c/sup\u003e, affecting mass transfer, solute solubility, dispersion, and stability. Moreover, changes in the HBA-to-HBD molar ratio had a minimal effect on viscosity, whereas the addition of water significantly decreased viscosity. However, once the water content reached a certain level, the viscosity tended to stabilize, which is consistent with the findings of Meredith et al. \u003csup\u003e51\u003c/sup\u003e. Consequently, water can be used to fine-tune the characteristics of DES \u003csup\u003e52\u003c/sup\u003e. The relationship between molar ratio (x\u003csub\u003e1\u003c/sub\u003e), moisture content (x\u003csub\u003e2\u003c/sub\u003e), and viscosity (y\u003csub\u003e4\u003c/sub\u003e) followed regression Eq.\u0026nbsp;(4), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9726.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e4\u003c/sub\u003e=-3.5291x\u003csub\u003e1\u003c/sub\u003e-0.8664x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0461x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1000x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0057x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;33.6754 (4)\u003c/p\u003e \u003cp\u003eThe refractive index can be another key descriptor of the DES physical properties, providing insights into intermolecular interactions and available free space \u003csup\u003e53\u003c/sup\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, the refractive indices of all three DES decreased as the molar ratio increased. This trend may occur because higher EG concentrations allow light to travel more rapidly, resulting in a lower refractive index compared to DES with lower EG concentrations \u003csup\u003e38\u003c/sup\u003e. Similarly, the addition of water decreased the refractive index, likely because of the lower refractive index of water relative to the standard DES components. Thus, increasing the water content reduced the overall refractive index of the DES. The relationship between the molar ratio (x\u003csub\u003e1\u003c/sub\u003e) and moisture content (x\u003csub\u003e2\u003c/sub\u003e) of DES and the refractive index (y\u003csub\u003e5\u003c/sub\u003e) can be described by regression Eq.\u0026nbsp;(5), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9938.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e5\u003c/sub\u003e=-0.0083x\u003csub\u003e1\u003c/sub\u003e-0.0010x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0004x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;1.4715 (5)\u003c/p\u003e \u003cp\u003ePolarity is a crucial parameter for characterizing the complex interactions between solvent and solute molecules, including hydrogen bonding and van der Waals forces, and serves as a key criterion for solvent selection \u003csup\u003e54\u003c/sup\u003e. As the molar ratio increased from 1:2 to 1:4, the E\u003csub\u003eNR\u003c/sub\u003e value gradually decreased, indicating that the polarity of the DES system was enhanced. This enhancement was attributable to the hydroxyl groups (-OH) in EG, which were polar and thus increased the overall polarity of the solvent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Similarly, increasing the water content (0\u0026ndash;80%) further elevated the polarity of the DES, as water is a strongly polar solvent. This result aligned with the polarity trends of water-based choline chloride\u0026ndash;glycol mixtures reported by Gabriele et al. \u003csup\u003e55\u003c/sup\u003e. The relationship between molar ratio (x\u003csub\u003e1\u003c/sub\u003e), water content (x\u003csub\u003e2\u003c/sub\u003e), and polarity (y\u003csub\u003e6\u003c/sub\u003e) followed regression Eq.\u0026nbsp;(6), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9697.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e6\u003c/sub\u003e=-0.2901x\u003csub\u003e1\u003c/sub\u003e-0.0495x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0005x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0126x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0002x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;51.2597 (6)\u003c/p\u003e \u003cp\u003eWater activity is an important thermodynamic property that describes the effective water content related to the reaction rates, water, and microbial growth in DES \u003csup\u003e56\u003c/sup\u003e. As the EG molar ratio increased, the water activity of DES rose gradually, though not substantially. In addition, the water activity of all DES samples increased consistently as the added water content increased, indicating that the changes in water activity depended only on the amount of added water rather than the molar ratio of DES components (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). These findings are consistent with those reported by Florindo et al. \u003csup\u003e57\u003c/sup\u003e concerning the relationship between water activity and water content. The correlation between molar ratio (x\u003csub\u003e1\u003c/sub\u003e), water content (x\u003csub\u003e2\u003c/sub\u003e), and water activity (y\u003csub\u003e7\u003c/sub\u003e) is described by regression Eq.\u0026nbsp;(7), with R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9995.\u003c/p\u003e \u003cp\u003ey\u003csub\u003e7\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.0240x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0181x\u003csub\u003e2\u003c/sub\u003e-0.0030x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0001x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.0056 (7)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Causal inference of physicochemical properties of DES and lipase activity\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA thorough understanding of the physicochemical properties of DES is essential for their precise scientific design as effective biocatalysts. According to Sections \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e and \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e, variations in the water content of DES not only affected lipase activity in whole cells but also influenced the physicochemical properties of the DES itself. Therefore, to accurately regulate lipase activity, it is necessary to thoroughly analyze the internal relationships between these physicochemical properties and lipase activity at different levels of hydration. First, PCA was performed on the molar ratio, moisture content, and physicochemical properties of the DES, including surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity. Because PCA is a multivariate linear combination analysis of the original variables, if a nonlinear relationship exists among these variables, it cannot effectively represent the correlations between them. Based on the initial projection (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and response surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), the data for relative activity at water contents ranging from 0 to 80% were divided into two simple linear intervals, one exhibiting a monotonic increase and the other a monotonic decrease, with the highest relative activity serving as the stationary point. PCA was then performed separately at each interval (0\u0026ndash;40% and 40\u0026ndash;80%) to identify and extract the physicochemical properties that showed significant changes. The results indicated that the cumulative contribution of the first two principal components exceeded 80% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;c), confirming the reliability of PC1 and PC2. In the segmented analysis, only the correlation between conductivity and water content exhibited significant changes between the 0\u0026ndash;40% and 40\u0026ndash;80% ranges, with the correlation decreasing. Conversely, the correlations between water content and the indices remained largely unchanged across the two segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u0026ndash;c).\u003c/p\u003e \u003cp\u003eSubsequently, PCA was conducted on the physicochemical indicators and relative activity of DES, with the first two principal components contributing more than 80% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed\u0026ndash;f), confirming the reliability of PC1 and PC2. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee\u0026ndash;f, in the segmented analysis, only the correlations of surface tension and conductivity with relative activity exhibited substantial changes, whereas the relationships between other physicochemical properties and relative activity remained relatively stable. Based on the PCA results, conductivity and surface tension were identified as the primary factors influencing the nonlinear variation in relative activity. Subsequently, scatter plots of surface tension, conductivity, density, viscosity, refractive index, polarity, water activity, and lipase activity at 0\u0026ndash;80% water content were generated. In both the 0\u0026ndash;40% and 40\u0026ndash;80% ranges, the surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity exhibited positive and negative linear correlations with enzyme activity, forming distinct clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;g). However, the clustering relationship between the surface tension, conductivity, and relative activity differed from that of the other parameters. Although some clustering was observed, no clear boundary was identified at the extremum of relative activity for surface tension and conductivity. In contrast, density, viscosity, refractive index, polarity, and water activity demonstrated a pronounced clustering boundary at the extremum of relative activity. These findings indicated that the relationship between surface tension, conductivity, and water content was not linear, whereas the other five parameters may exhibit a linear relationship with relative activity. Therefore, conductivity and surface tension were the main factors responsible for the nonlinearity between lipase activity and water content in the whole cell.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the segmented PCA analysis and the projection plots of each index and relative activity, the surface tension and conductivity contributed to the nonlinear relationship between the relative activity and water content, whereas other indices exhibited weaker correlations. In previous multivariate analyses, no definitive statistical evidence was provided to establish clear relationships between the selected properties (surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity) and the relative activity, resulting in insufficient statistics for screening each indicator. However, including all properties in the regression analysis introduced weakly correlated variables that could interfere with the regression of strongly correlated variables. Consequently, regression cannot be performed directly using DES physicochemical indices and relative activity. Consequently, this study attempted to perform regression analyses between molar ratio, moisture content, and relative activity, as well as between molar ratio, moisture content, and various physicochemical properties, supported by experimental data. Based on these results, causal inferences regarding the physicochemical properties and relative activity of DES were drawn using existing regression equations. The methodological process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo eliminate dimensional effects and enhance model stability and accuracy, all data were standardized prior to multiple regression analysis. First, a binomial regression was performed with relative activity (z) as the response variable and the molar ratio (x\u003csub\u003e1\u003c/sub\u003e) and moisture content (x\u003csub\u003e2\u003c/sub\u003e) as the independent variables, yielding equation F (x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e) with a partial regression coefficient b\u003csub\u003e2\u003c/sub\u003e. Next, seven physicochemical properties of the DES, including surface tension (y\u003csub\u003e1\u003c/sub\u003e), electrical conductivity (y\u003csub\u003e2\u003c/sub\u003e), density (y\u003csub\u003e3\u003c/sub\u003e), viscosity (y\u003csub\u003e4\u003c/sub\u003e), refractive index (y\u003csub\u003e5\u003c/sub\u003e), polarity (y\u003csub\u003e6\u003c/sub\u003e) and water activity (y\u003csub\u003e7\u003c/sub\u003e), were individually modeled as the response variables, with the molar ratio (x\u003csub\u003e1\u003c/sub\u003e) and water content (x\u003csub\u003e2\u003c/sub\u003e) as the independent variables for binomial regression fitting. This process produced equations f\u003csub\u003e1\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), f\u003csub\u003e2\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), f\u003csub\u003e3\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), f\u003csub\u003e4\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), f\u003csub\u003e5\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), f\u003csub\u003e6\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), and f\u003csub\u003e7\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e,x\u003csub\u003e2\u003c/sub\u003e), which are accompanied by partial regression coefficients bn\u003csub\u003e1\u003c/sub\u003e, bn\u003csub\u003e2\u003c/sub\u003e, bn\u003csub\u003e3\u003c/sub\u003e, bn\u003csub\u003e4\u003c/sub\u003e, bn\u003csub\u003e5\u003c/sub\u003e, bn\u003csub\u003e6\u003c/sub\u003e, and bn\u003csub\u003e7\u003c/sub\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the regression results.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression equations of the relationship between the molar ratio and water content of DES, their physicochemical properties, and lipase relative activity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression equations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjust R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9023\u0026thinsp;\u0026minus;\u0026thinsp;0.6492 x\u003csub\u003e1\u003c/sub\u003e-0.0320 x\u003csub\u003e2\u003c/sub\u003e-0.1997 x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e-0.1975 x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.7254 x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2520-0.4313x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.8644x\u003csub\u003e2\u003c/sub\u003e-0.1366x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0889x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;0.1688x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3691\u0026thinsp;+\u0026thinsp;0.2893x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.8597x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1616x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0439x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.4215x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0425-0.0373x\u003csub\u003e1\u003c/sub\u003e-0.9939x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0010 x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0051x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0487x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5139-0.1001x\u003csub\u003e1\u003c/sub\u003e-0.8744x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.1213x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0076x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.5180x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9691\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0795-0.0920x\u003csub\u003e1\u003c/sub\u003e-0.9889x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0444x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0082x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.0894x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1267-0.1526x\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.9796x\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0010x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.0082x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.1215x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ef\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2416\u0026thinsp;+\u0026thinsp;0.0148x\u003csub\u003e1\u003c/sub\u003e-0.9675x\u003csub\u003e2\u003c/sub\u003e-0.0007x\u003csub\u003e1\u003c/sub\u003ex\u003csub\u003e2\u003c/sub\u003e-0.0065x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e-0.2406x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed, among the correlations between surface tension, conductivity, density, viscosity, refractive index, polarity, water activity, and enzyme activity, the correlations of water activity and density with relative activity were notably lower than those of the other indices. In addition, according to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, the partial regression coefficients of f\u003csub\u003e3\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e) and f\u003csub\u003e5\u003c/sub\u003e(x\u003csub\u003e1\u003c/sub\u003e, x\u003csub\u003e2\u003c/sub\u003e) and the coefficients of x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e in bn\u003csub\u003e3\u003c/sub\u003e and bn\u003csub\u003e5\u003c/sub\u003e were \u0026minus;\u0026thinsp;0.0486 and \u0026minus;\u0026thinsp;0.0894, respectively, which were lower than those of x\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e in the partial regressions of the other properties. This finding indicated that the linear relationships of the density and refractive index with the square term of the water content were not significant. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, there was a strong nonlinear relationship between water content and relative activity; thus, the density and refractive index were not the primary factors contributing to this nonlinear trend. In summary, water activity, density, and refractive index served as secondary factors, whereas surface tension, conductivity, viscosity, and polarity were the main factors influencing the nonlinear nature of relative activity. Consequently, these secondary factors should be excluded from the calculation of b\u003csub\u003e1\u003c/sub\u003e to focus on primary factors.\u003c/p\u003e \u003cp\u003eMoreover, the x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e coefficients in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb for bn\u003csub\u003e1\u003c/sub\u003e-bn\u003csub\u003e7\u003c/sub\u003e were all small, suggesting that the linear relationship between the square term of the molar ratio and each index was insignificant. In contrast, the x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e coefficient in b\u003csub\u003e2\u003c/sub\u003e was not small, implying the possible existence of other DES characteristics that were not examined in this study, which enhanced the significance of x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e. In other words, additional properties may exhibit a nonlinear relationship with the molar ratio. Furthermore, the constant term reflected only the translation of the equation and did not affect the slope. Accordingly, within the experimental scope of this study, after removing the minor factors in bn\u003csub\u003e1\u003c/sub\u003e-bn\u003csub\u003e7\u003c/sub\u003e and b\u003csub\u003e2\u003c/sub\u003e, as well as the constant terms of the major factors and the x\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e terms, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec was obtained, and b\u003csub\u003e1\u003c/sub\u003e was calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Among the four indices considered, polarity (|3.7660|)\u0026thinsp;\u0026gt;\u0026thinsp;viscosity (|-1.8929|)\u0026thinsp;\u0026gt;\u0026thinsp;surface tension (|1.2771|)\u0026thinsp;\u0026gt;\u0026thinsp;electrical conductivity (|0.9918|). This indicated that a one standard deviation change in polarity, viscosity, surface tension, and conductivity led to changes of 3.766, 1.8929, 1.2771, and 0.9918 standard deviations in the relative activity, respectively. The polarity, surface tension, and conductivity were positively correlated with the relative activity, whereas the viscosity was negatively correlated. This implies that in practical applications, the design of DES should prioritize the adjustment of physicochemical parameters according to their decreasing order of influence, specifically by appropriately increasing polarity, decreasing viscosity, and enhancing surface tension and conductivity, thereby enabling more targeted design of efficient DES systems.\u003c/p\u003e \u003cp\u003eAccording to the hole theory, in low-temperature systems, the smaller average size of holes resulted in reduced ion migration speed and increased viscosity, thereby closely linking the DES conductivity to viscosity \u003csup\u003e58\u003c/sup\u003e. Additionally, reducing the surface tension of the liquid could increase the average size of these holes, enhancing ion migration and ultimately leading to higher conductivity \u003csup\u003e59\u003c/sup\u003e. Simultaneously, a reduction in surface tension can augment the solubilization and dispersion of the substrate, thereby facilitating the interaction between the enzyme and substrate \u003csup\u003e60\u003c/sup\u003e. The high viscosity commonly observed in DES systems can be often attributed to extensive hydrogen bonding between the components \u003csup\u003e61\u003c/sup\u003e. Such hydrogen bond interactions serve as the main driving force regulating enzyme-DES interactions, creating a heterogeneous solvation environment on the enzyme surface and affecting substrate binding \u003csup\u003e18\u003c/sup\u003e. This non-homogeneous solvation environment can exert significant effects on the conformational stability of enzymes. The presence of hydrogen bond networks may constrain the motion of flexible regions within the enzyme, thereby partially stabilizing its catalytically active conformation \u003csup\u003e62\u003c/sup\u003e. This stabilization effect is particularly pronounced under low water content conditions, where the hydrogen bond network is denser and can more effectively restrict excessive molecular movements of the enzyme, thus reducing deformation of the active site caused by thermal fluctuations\u003csup\u003e63\u003c/sup\u003e.Consequently, in this study, when the initial water content was low, the elevated viscosity could reduce ion migration and thus decrease the electrical conductivity, limiting mass transfer and resulting in lower lipase activity. As the water content increased, the viscosity decreased, and the ion mobility improved, thereby increasing the electrical conductivity. According to this principle, lipase activity should increase continuously with increasing water content. However, in practice, lipase activity initially increased and then declined as the water content increased. Makoś-Chełstowska et al. \u003csup\u003e64\u003c/sup\u003e identified through molecular dynamics simulations that the hydrogen bonds in DES could be destroyed once the water content exceeded 50%. Similarly, Zhekenov et al. \u003csup\u003e65\u003c/sup\u003e suggested that the intermolecular interaction network within DES remained intact until the water content reached 50%, at which the point water molecules were integrated into the network. When the water content exceeded 50%, the constituent components of the DES were released from the interaction network. This phenomenon may be attributed to the exposure of the enzyme's active site and its substrate binding capability. At moderate water content, the hydrogen bond network of the DES effectively protects the enzyme's active site while simultaneously allowing appropriate approach and binding of substrate molecules \u003csup\u003e21\u003c/sup\u003e. However, when water content becomes excessive, the hydrogen bond network is disrupted, leading to overexposure of the enzyme's active site, potentially inducing non-specific binding or excessive competition among substrate molecules, thereby reducing the catalytic efficiency of the enzyme \u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring the reaction, the polarity of the DES also influenced the reaction rate and selectivity \u003csup\u003e67\u003c/sup\u003e. Polarity is crucial for determining DES conductivity, as highly polar solvents allow more ions to carry charges, thereby enhancing electrical conductivity. Conversely, DES with lower polarity had fewer ions for charge transport, leading to poor conductivity \u003csup\u003e67\u003c/sup\u003e. At 46% water content, DES can be effectively diluted, reducing viscosity, appropriately increasing polarity, and maintaining structural stability, thus enhancing ion mobility and promoting mass transfer. This could explain why lipase activity reached a maximum at 46% water content and decreased once water content surpassed this level. Additional specialized studies are required to further elucidate the relationship between the solvent properties of DES and lipase activity in whole cells. Mechanistically, this relationship between polarity and activity may be associated with the electrostatic interactions of the enzyme\u003csup\u003e68\u003c/sup\u003e. In DES with moderate polarity, the enzyme's active site can more effectively attract substrate molecules through electrostatic interactions, thereby promoting the catalytic reaction\u003csup\u003e68\u003c/sup\u003e. However, when polarity becomes excessive, superfluous water molecules may interfere with the electrostatic interactions between the enzyme and substrate, resulting in diminished substrate binding efficiency\u003csup\u003e69\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThese results indicate that changes in the physicochemical properties of solvents, such as polarity, viscosity, surface tension, and conductivity, may collectively influence enzymatic activity by affecting the molecular conformation of enzymes, microenvironment of the active center, diffusion kinetics of reactants, and ionic environment. However, the molecular details of these mechanisms remain insufficiently characterized as they lack in-depth experimental validation and molecular simulation studies. Further research is required to clarify and refine the hypotheses. In addition, this study focused solely on choline chlorine-ethylene glycol, a DES system. Different DES systems may exert distinct effects on enzyme activity owing to variations in their composition and physicochemical properties, warranting further investigation. Despite the use of multivariate statistical analysis, the established model remains a simplified approximation and fails to fully capture the complexity of enzyme-solvent interactions. Subtler mechanisms at the molecular level, such as solvation dynamics and ion-enzyme interactions, may also influence enzyme activity but were not explicitly considered in this study.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study provides a comprehensive investigation into the impact of ChCl-EG DES composition, specifically molar ratio and water content, on various physicochemical properties and explores how these changes affect lipase activity in the \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eniger\u003c/em\u003e C2J6 whole-cell system. Using multivariate statistical techniques such as PCA and regression analysis, this study examined the causal relationships between physicochemical properties and enzyme activity. The results showed that both molar ratio and water content of DES significantly influenced lipase activity, with the relationship being more accurately described by a quadratic regression model. Under optimal conditions (1:1.55 molar ratio and 46% water content), the relative activity reached 142.31%, indicating a well-fitted model with good predictive capability. Multivariate statistical analysis identified the key physicochemical parameters affecting lipase activity. Polarity, viscosity, surface tension, and conductivity are intrinsic factors contributing to the nonlinear relationship between lipase activity and the DES system. This study offers novel insights and methodologies for broadening the application of DES in biocatalytic reactions, particularly in whole-cell catalytic systems. The increased activity of enzymes in our optimized DES formulations reduces waste and replacement costs in industrial processes, thereby contributing to a more sustainable manufacturing process. These findings have significant implications for industrial biocatalysis, as the quantitative understanding of relationships between DES physicochemical properties and enzymatic activity provides data-driven decision support for solvent design in industrial applications, reducing development costs and enhancing production efficiency. The strategy of adjusting DES physicochemical parameters according to their degree of influence can be extended to other enzymatic systems and DES types, while future research may explore broader solvent performance spaces and design novel DES systems with specific functionalities, offering new applications for biocatalytic reactions, particularly in whole-cell catalytic systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (grant numbers 32060527 and 31460031).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Q.X. Ma, M.X. Tang and Y. Liu; methodology, Q.X. Ma, M.X. Tang; software, Q.X. Ma, M.X. Tang; validation, Y.S. Wan, H.Y. Yan; formal analysis, Q.X. Ma, M.X. Tang; investigation, M.Z. Zhang, Q. Mu; resources, Y. Liu; data curation, Q.X. Ma; writing\u0026mdash;original draft preparation, Q.X. Ma, M.X. Tang; writing\u0026mdash;review and editing, Y. Liu; supervision, Y. Liu; project administration, Y. Liu; funding acquisition, Y. Liu. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDudkaitė, V., Kairys, V. \u0026amp; Bagdžiūnas, G. Understanding the activity of glucose oxidase after exposure to organic solvents. \u003cem\u003eJ. Mater. Chem. B\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 2409\u0026ndash;2416 (2023).\u003c/li\u003e\n \u003cli\u003eLeal-Duaso, A., Mayoral, J. A. \u0026amp; Pires, E. 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Rev.\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 8685\u0026ndash;8720 (2018).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep eutectic solvent, Physicochemical properties, Lipase, Enzyme activity, Causal inference","lastPublishedDoi":"10.21203/rs.3.rs-6024054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6024054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeep eutectic solvents (DES), recognized as tunable green solvents, show significant potential for enhancing enzyme activity in biocatalytic applications. This study investigated the effect of choline chloride–ethylene glycol DES on the lipase activity of \u003cem\u003eAspergillus\u003c/em\u003e \u003cem\u003eniger\u003c/em\u003e C2J6 whole cells, employing a self-isolated endophytic strain. By varying the molar ratio (1:2–1:4) and water content (0–80%), the highest lipase activity (142.31%) was observed at a 1:1.55 molar ratio with 46% water content. Mathematical models were developed to connect DES composition with key properties, including surface tension, conductivity, density, viscosity, refractive index, polarity, and water activity. Statistical analysis revealed that among the physicochemical properties of DES, polarity exhibited the most significant impact on enzymatic activity, followed by viscosity, surface tension, and conductivity. This study provides valuable insights for designing optimized DES systems to improve biocatalytic efficiency and precision.\u003c/p\u003e","manuscriptTitle":"Optimization of Lipase Activity in Aspergillus niger C2J6 Whole Cells Using Choline Chloride Ethylene Glycol Deep Eutectic Solvent","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 08:54:33","doi":"10.21203/rs.3.rs-6024054/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T15:46:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T14:01:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T19:18:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313650974249353391537688347388571857544","date":"2025-04-26T13:06:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126494213923537717432437692520161569040","date":"2025-04-24T12:56:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-24T12:46:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-24T08:29:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-06T07:01:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a30e809-3b80-4572-89e6-be485a003fd5","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47812648,"name":"Biological sciences/Biochemistry"},{"id":47812649,"name":"Biological sciences/Biochemistry/Biocatalysis"},{"id":47812650,"name":"Biological sciences/Biochemistry/Enzymes"}],"tags":[],"updatedAt":"2025-07-07T16:12:16+00:00","versionOfRecord":{"articleIdentity":"rs-6024054","link":"https://doi.org/10.1038/s41598-025-04490-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:58:11","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-30 08:54:33","video":"","vorDoi":"10.1038/s41598-025-04490-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04490-7","workflowStages":[]},"version":"v1","identity":"rs-6024054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6024054","identity":"rs-6024054","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}