Assessment of the methane production potential of the anaerobic digestion of ribes fruit

Assessment of the methane production potential of the anaerobic digestion of ribes fruit | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of the methane production potential of the anaerobic digestion of ribes fruit Zhiwei Zhang, Yi Li, Defang Zhang, Rui Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6443865/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Ribes Fruit (RF) is an underutilized resource. Here, the Anaerobic Digestion (AD) process parameters for RF optimized via medium temperature batch AD experiments using Box–Behnken response surface design. The optimal methane production process conditions were an organic loading of 16.15 g Volatile Solids (VS)/L, an inoculum to substrate ratio of 2.05, and an initial pH of 7.46. The measured value of cumulative methane yield under these conditions was 357.42 mL/g VS, which was very close to the model predicted value (346.44 mL/g VS), with a relative error of less than 5%, indicating that the response surface model was valid. The findings also show that RF has strong methane production potential compared to other types of residual biomass, and is easier, more efficient, and more environmentally friendly to use as an AD feedstock. This research highlights RF as a new potential feedstock for biogas engineering and advances the application of RF for AD from theory to practice. Ribes fruit Anaerobic digestion Methane Response surface methodology Figures Figure 1 Figure 2 Figure 3 1. Introduction Ribes (Ribes odoratum ), belonging to Ribes of the Saxifragaceae family, is a deciduous erect shrub that is commonly known as Currant, and has been planted in the northeast and northwest of China since its introduction to China (Lyashenko et al., 2019 ). Due to its strong adaptability and beautiful leaves, ribes has been widely used in urban gardening, and its planting area is increasing. Ribes Fruit (RF) is rich in nutrients such as vitamins, sugars, and organic acids, and has the potential to be developed as a food ingredient (Shaw et al., 2017 ; Tian et al., 2017 ). Currants are already considered a traditional food source in European cuisine, notably jams, jellies, and wines. However, RF is not included in the New Food Ingredients Catalogue (NFIC) in China, and its large-scale development and utilization have been hindered. As the planting area of ribes expands, large quantities of RF, which have a yield of 3 kg per plant, have resulted in resource wastage. If not managed properly, RF can deteriorate rapidly, releasing substances that lead to soil crusting and other forms of environmental pollution (Agrawal et al., 2023 ; Wang et al., 2014 ). Therefore, there is a need to develop innovative storage solutions and utilization strategies to achieve the full potential of RF, mitigate the environmental impacts of RF, and increase the value of this resource. Anaerobic Digestion (AD) technology is a biological treatment method that converts organic matter such as crop residues, livestock manure, processed fruit and vegetable wastes, and kitchen waste into biogas, a clean energy source. With the increasing global interest in biogas production technology, AD is seen as an effective means of converting waste into energy (Almeida et al., 2023 ; Yuan et al., 2023 ). Despite the many advantages of AD technology, relying on traditional feedstocks such as livestock manure and crop residues is insufficient to support the wide application of biogas engineering. To advance the biogas industry, it is essential to discover new feedstock sources to boost production and diversify the input for biogas projects, addressing increasing energy needs and environmental objectives (Dhull et al., 2024 ). RF is considered to be a potential high-quality feedstock for AD processes due to its rich organic matter content. However, the use of RF as a feedstock for methane production through AD has not been reported in the literature. When searching for and evaluating novel AD feedstocks, it is critical to analyze multiple parameters to assess their methane production potential, which can help to determine which levels of factors are most effective in increasing methane production. The high content of readily degradable organic matter in RF, such as polysaccharides, allows for faster hydrolytic acidification during AD. This may lead to a decrease in pH, which may trigger a increase in the accumulation of Volatile Fatty Acids (VFAs), possibly hindering the initiation and proper functioning of AD. To control this process and maintain the stability of the system, pre-adjustment of the initial pH is an effective strategy (Liu et al., 2008 ). In addition to pH, the efficiency of AD is affected by other operational parameters, especially Organic Loading (OL) and the Inoculum to Substrate ratio (I/S). These parameters are particularly important when exploring novel AD feedstocks (Zhu et al., 2022 ; Zhang et al., 2022; Zhang et al., 2019 ). Therefore, the methane production potential of RF in AD can be fully exploited by optimizing the OL, I/S, and the initial pH. Response Surface Methodology (RSM), a mathematical and statistical technique that is widely recognized and applied to the design of AD experiments and the optimization of process parameters, allows the assessment of the effects of independent variables on the production process and the optimization of operating conditions through the development of predictive models (Khalid et al., 2019 ; Cai et al., 2021 ). On this basis, RSM was employed in this study to investigate the interaction effects of the OL, I/S, and initial pH on the Cumulative Methane Yield (CMY) of RF to determine the optimal methane production conditions. This work explored new potential feedstocks for biogas projects and achieved the effective transformation and promotion of biogas production while efficiently utilizing resources. 2. Material and methods 2.1. Substrates and inoculum RF was collected in September 2024 from the experimental base of the Institute of Forestry Science, College of Agriculture and Forestry, Qinghai University. The fruits collected were mature, fresh, and pest-free, meeting the requirements of the experiment. They were then stored at -20°C for preservation. During the experiment, the fruits were crushed into pulp for subsequent experimental operations. The inoculum was obtained from the agricultural biogas digester of the Qinghai Zhiyuan Characteristic Agriculture Co., Ltd., which used sheep manure as the raw material and was operating stably, and then cultured under anaerobic conditions in a constant-temperature water bath (37 ± 0.5°C) for 1–2 weeks to reduce the production of background methane. 2.2. Factorial design In this study, three factors, the OL (10, 15, and 20 g Volatile Solids (VS)/L), the I/S (1.5, 2.0, and 2.5), and the initial pH (7.0, 7.5, and 8.0), were used as the independent variables, and the CMY was the response value. RSM based on Box–Behnken Design (BBD) was used to design a three-factor, three-level response surface method optimization experiment to obtain a coded list of experimental factor levels (Table 1 ). Table 1 Test factor levels and coding Levels Factors OL (g VS/L) I/S Initial pH -1 10 1.5 7 0 15 2 7.5 1 20 2.5 8 Note: OL: Organic Loading; I/S: Inoculum to Substrate ratio. 2.3. AD experiment The AD experiment was conducted using an automatic methane potential analyzer (MultiTalent 203, Nova Skantek, Sweden), which was employed for methane potential testing. According to the experimental design shown in Table 1 , the specific additions of RF, inoculum, and distilled water were calculated (Table 2 ), and the initial pH of the system was adjusted with 1 mol/L sodium hydroxide solution following the addition of the materials in accordance with Table 2 . The experiment consisted of a total of 17 treatment groups with three parallels set up in each group, and a blank treatment with only the addition of inoculum was set up at the same time. During the operation of the system, the methane potential tester automatically recorded the methane production every day and collected the daily methane yield and CMY. The experimental reaction temperature was 37.0 ± 0.5°C and the fermentation cycle was 20 d. Table 2 Amount of each processed material added according to the factorial design Treatment RF addition (g) Inoculum addition (g) RF VS addition (g) Inoculum VS addition (g) Distilled water addition (mL) 1 18.52 81.09 4 6 300.39 2 37.04 162.18 8 12 200.77 3 18.52 135.15 4 10 246.33 4 37.04 270.31 8 20 92.65 5 18.52 108.12 4 8 273.36 6 37.04 216.25 8 16 146.71 7 18.52 108.12 4 8 273.36 8 37.04 216.25 8 16 146.71 9 27.78 121.64 6 9 250.58 10 27.78 202.73 6 15 169.49 11 27.78 121.64 6 9 250.58 12 27.78 202.73 6 15 169.49 13 27.78 162.18 6 12 210.03 14 27.78 162.18 6 12 210.03 15 27.78 162.18 6 12 210.03 16 27.78 162.18 6 12 210.03 17 27.78 162.18 6 12 210.03 Note: RF: Ribes Fruit; VS: Volatile Solids. 2.4. Analytical methods and data analysis The Total Solids (TS) and VS contents of the prepared samples were measured using standard methods (APHA 2005). The pH was determined using a pH meter (pHS-2F, Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China). The total nitrogen and total carbon contents were obtained using an elemental analyzer (Flash SMART, Thermo Fisher Scientific Inc.). The total sugars and polysaccharides were determined using the phenol sulphate method (Yue et al., 2022 ). The crude fiber content was determined using a fiber measuring instrument (F800, Shandong Haineng Scientific Instrument Co.). Methane production was recorded at 24 h using an automatic methane potential analyzer at the AD stage. 2.5. Data analysis and processing Design-Expert 12 software and SPSS Statistics 26 software were employed to analyze the data; Origin 2021 was used for graphing. 3. Results and discussion 3.1. Characteristics of substrates and inoculum The properties of the RF and inoculum are displayed in Table 3 . The TS and VS contents of RF were only 23.31% and 21.60%, respectively. The VS/TS ratio reached 92.66%, indicating that the organic matter content of RF was high and suitable for methane production through AD. Elemental analysis showed that the C/N ratio was 29.07. It has been reported that during AD, the ideal C/N ratio should be maintained in the range of 20 to 30 to ensure that the microorganisms are able to conduct their metabolic activities efficiently (Laiq et al., 2019). It is worth noting that the C/N ratio of RF falls within this recommended range. The crude fiber content of RF is only 1.88%, and low fiber content often means that the substrate is more susceptible to microbial degradation. Additionally, the high total sugar content (12.18%), high polysaccharide content (4.07%), so low fiber content often means that the substrate is more susceptible to microbial degradation. Additionally, the high total sugar content (12.18%), high polysaccharide content (4.07%), and low pH (3.9) of RF may cause the acidification of the AD system and inhibit the reaction, and appropriate measures needed to be taken to maintain the AD system stability. In conclusion, these physicochemical properties indicate that RF can be used as an AD feedstock for methane production. Table 3 Characterization of the substrate and inoculum Parameters RF Inoculum TS (%) 23.31 ± 0.11 25.33 ± 0.24 VS (%) 21.60 ± 0.26 7.40 ± 0.25 VS/TS (%) 92.66 ± 0.68 29.21 ± 0.71 pH 3.94 ± 0.01 7.51 ± 0.12 C (%) 38.08 ± 0.04 ND N (%) 1.31 ± 0.02 ND C/N (%) 29.07 ± 0.42 ND Total sugar (%) 12.18 ± 0.29 ND Polysaccharide (%) 4.07 ± 0.03 ND Crude fiber (%) 1.88 ± 0.15 ND Note: ND: Not Determined; RF: Ribes Fruit. 3.2. Response surface regression model and analysis of variance The BBD and its results are displayed in Table 4 . As shown in Table 4 , the CMY in the 17 experimental groups ranged from 234.62 to 351.26 mL/g VS. The CMY of each experimental group was set as the response value Y, and different levels of coded values OL ( A ), I/S ( B ), and initial pH ( C ) were used as independent variables to establish the fitting equations. The multiple quadratic regression equation was input into Design-Expert 12 software to establish the following: Y = 344.72 + 11.54 A + 9.64 B + 1.44 C − 23.75 AB − 27.70 AC − 17.38 BC − 25.58 A 2 − 29.93 B 2 − 37.35 C 2 . (1) Table 4 Box-Behnken experimental design and corresponding results Group Factor CMY (mL/g VS) OL (g VS/L) I/S Initial pH 1 10 1.5 7.5 249.83 2 20 1.5 7.5 313.65 3 10 2.5 7.5 312.28 4 20 2.5 7.5 281.11 5 10 2 7 234.62 6 20 2 7 319.86 7 10 2 8 299.13 8 20 2 8 273.56 9 15 1.5 7 249.93 10 15 2.5 7 308.32 11 15 1.5 8 281.34 12 15 2.5 8 270.19 13 15 2 7.5 340.25 14 15 2 7.5 351.26 15 15 2 7.5 348.61 16 15 2 7.5 338.34 17 15 2 7.5 345.15 Note: OL: Organic Loading; I/S: Inoculum to Substrate ratio; CMY: Cumulative Methane Yield. As can be seen from Table 5 , the model F-value is 53.01, P < 0.0001, indicating that the whole model regression effect is significant. According to the software, the R 2 is 0.9855, which indicates that this model explains 98.55% of the variation in response values. The misfit term P = 0.2171 > 0.05, reflecting no significant difference, indicating that the model fit is good, and the residuals of the model may be generated by random errors. The difference between the predicted value R 2 (0.8449) and the adjusted R 2 (0.9669) is less than 0.2 and the accuracy is sufficient at 19.8176 > 4, indicating that the model can be used for navigation in the design space. In conclusion, this regression model and equation can be used to analyze and predict the CMY of RF. The P -values of the factors show that A and B have highly significant effects ( P 0.05); the interaction effects of AB, AC, and BC are highly significant ( P < 0.05); and the surface effects of A 2 , C 2 , and B 2 are highly significant ( P < 0.05). The size of the absolute value of the coefficients of each factor in the regression model can reflect the strength of the influence of the factor on the response value. As can be seen from Eq. (1), the absolute value of the coefficients of the three influencing factor terms was A > B > C , indicating that the overall influence of each factor on CMY was in the order of OL > I/S > initial pH. Table 5 Analysis of variance (ANOVA) for the response surface quadratic model. Source Sum square df Mean Square F-values P-values Model 22168.76 9 2463.20 53.01 < 0.0001 significance A -OL 1065.37 1 1065.37 22.93 0.0020 significance B -I/S 744.02 1 744.02 16.01 0.0052 significance C -Initial pH 16.50 1 16.50 0.3552 0.5700 not significant AB 2255.78 1 2255.78 48.55 0.0002 significance AC 3069.71 1 3069.71 66.06 < 0.0001 significance BC 1208.95 1 1208.95 26.02 0.0014 significance A ² 2754.78 1 2754.78 59.29 0.0001 significance B ² 3770.80 1 3770.80 81.15 < 0.0001 significance C ² 5874.09 1 5874.09 126.42 < 0.0001 significance Residual 325.26 7 46.47 Lack of Fit 206.49 3 68.83 2.32 0.2171 not significant Pure Error 118.77 4 29.69 Cor Total 22494.02 16 R 2 = 0.9855 Adjusted R ²=0.9669 Predicted R ²=0.8449 Adeq Precision = 19.8176 Note: * P < 0.05 significant difference, ** P < 0.01 highly significant difference, df : degrees of freedom 3.3. Response surface optimization analysis RSM was utilized to optimize the CMY of RF, and the response surface and contour plots were plotted to investigate the effects of the interactions between different factors on the CMY (Figs. 1–3). The sensitivity of the response values to factor interactions was determined by the steepness of the slope of the response surface, i.e., the steeper the slope, the more sensitive the interactions were; the slower the surface, the smaller the effects of the interactions between factors were. Meanwhile, the contour map can be used to find the maximum response value in the interaction. Figure 1 shows the response surface plot and contour plot between the OL and the initial pH. In Fig. 1a, the slope of the response surface between the OL and the initial pH is larger, indicating that the OL and the initial pH have a higher interaction effect during the AD process. When the OL and the initial pH interacted, the CMY showed the trend of increasing first and then decreasing. As in the present study, Khalid et al. noted that methane production increased with increasing OL and initial pH, but methane production was lower as the OL and initial pH increased to a certain level, suggesting that a range of optimal levels is required for production (Khalid et al., 2019 ). In the present study, the CMY did not fluctuate significantly in response to changes in the initial pH when OL > 18 (Fig. 1a). This may be because RF, as a fruit waste, is prone to acidification during the AD process at high OL, and the initial pH was not sufficient to neutralize the acid produced, resulting in limited methane production. When the initial pH > 7.4, the CMY fluctuated little and did not change significantly with the change of OL (Fig. 1a). Similarly, the results of Cai et al. showed that the CMY values did not exhibit significant differences due to pH variations over the pH range tested (Cai et al., 2021 ). As shown in Fig. 1b, the CMY of the AD of RF was higher when the OL was between 14 g VS/L and 18 g VS/L, and the initial pH was between 7.2 and 7.8. Figure 2 shows the response surface plots and contour plots of the interaction between OL and I/S. CMY was dramatically influenced by the interaction between OL and I/S. This result was consistent with Zhu et al. A higher I/S allowed more anaerobic microorganisms to fully degrade organic matter and convert it into methane, whereas under low I/S conditions, the microorganisms failed to convert a large amount of organic matter to methane in a timely manner due to the low amount of inoculated sludge, resulting in a decrease in the CMY (Zhu et al., 2022 ). When the I/S was a certain level, the CMY also showed a tendency of increasing and then decreasing with the increase of OL. This result was in accordance with Zhang et al. ( 2020 ). As shown in the contour plots, the CMY of the AD of RF was higher when the OL was 12–18 g VS/L and the I/S was 2.0–2.5. As shown in Fig. 3, under the condition of a constant OL, when the I/S was a certain level, the CMY showed a tendency of increasing and then decreasing with the increase of the initial pH. When the initial pH was certain, the CMY also showed a tendency to increase first and then decrease with the increase of the I/S. As displayed in Fig. 3b, the CMY of the AD of RF was higher when the I/S was in the range of 2–2.5 and the initial pH was in the range of 7.2–7.8. The CMY was lower at lower I/S and initial pH values. The following optimal process conditions were obtained through model optimization: an OL of 16.15 g VS/L, an I/S of 2.05, and an initial pH of 7.46. Three replicate AD experiments were conducted with these process parameters to validate the reliability and accuracy of the model. The average value of CMY obtained from the experiment was 357.42 mL/g VS, which was very close to the model prediction (346.44 mL/g VS), and the relative error was less than 5%, indicating that the validation model was effective. Comparing this result with the mesophilic batch AD of other feedstocks in previous investigations showed that the CMY of various types of crop residues ranged from 130.20–222.60 mL/g VS (Li et al., 2019 ; He et al., 2019 ), that of various types of livestock manure ranged from 155.00–323.00 mL/g VS (Kafle et al., 2016), that of processed fruit and vegetable wastes ranged from 124.00–233.00 ml/g VS (Ezieke et al., 2022 ), and that of kitchen waste ranged from 258.44–338.32 mL/g VS (Song et al., 2021 ; Wo et al., 2022 ), while the CMY of RF after optimization was 357.42 mL/g VS in the present study. This suggests that RF has a higher methanogenic potential compared to commonly used AD feedstocks. In addition, the use of crop residues, livestock manure, and kitchen wastes as single feedstock leads to degradation challenges and inhibition, and these single feedstocks need to be supplemented with process conditions such as pre-treatment and anaerobic co-digestion to maintain the stability and continuity of the AD system, thereby increasing the cost and process complexity (Wang et al., 2023 ; Zhao et al., 2021 ; Zhang et al., 2021 ). It has been found that the biomethanization of fruit and vegetable solid wastes, when mixed with sludge yielded the best results. This resulted in methane production of up to 90 L/kg VS. NaHCO₃ acts as a pH regulator, effectively optimizing the reaction conditions (Gomez-Lahoz et al., 2007 ). Compared with other processed fruit and vegetable wastes (Gunaseelan et al., 2004; Ta et al., 2019), RF avoids system acidification due to the accumulation of VFAs by adjusting the initial pH. This feature eliminates the need for additional chemical buffers, thereby reducing treatment costs, simplifying the process, and being environmentally friendly. Therefore, the use of RF as an AD raw material has obvious advantages. 4. Conclusions To resourcefully utilize RF, the methanogenic potential of the AD of RF was evaluated, and the optimal methanogenic conditions obtained using RSM were an OL of 16.15 g VS/L, an I/S of 2.05, and an initial pH of 7.46. Under these conditions, the measured value of the CMY was 357.42 mL/g VS. The AD process of RF is simple, low-cost, efficient, environmentally friendly, and has a higher methane production potential than commonly used single feedstocks (such as crop residues, livestock manure and kitchen wastes). By harnessing RF for AD, we can not only reduce the environmental burden associated with its disposal but also produce a valuable renewable energy source in the form of biogas. Given the clear advantages of using RF as a feedstock for AD, it has the potential to supplement feedstocks for biogas projects. These insights provide valuable guidance for the future utilization of RF. Declarations Acknowledgments We gratefully acknowledge financial support from the Key Translational R&D Program of Qinghai Province (2023-NK-130). We also thank LetPub (www.letpub.com.cn) for providing linguistic assistance during the preparation of this manuscript. References Agrawal A., Chaudhari P.K., Ghosh P., (2023), Anaerobic digestion of fruit and vegetable waste: a critical review of associated challenges, Environmental science and pollution research international , 30 , 24987–25012. 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Zhu Z., Zhang S., Song C., Wang L., Cai F., Chen C., Liu G., (2022), Influences of organic loading, feed-to-inoculum ratio, and different pretreatment strategies on the methane production performance of eggplant stalk, Environmental Science and Pollution Research International , 29 , 85433–85443. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6443865","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442573844,"identity":"a2589f06-6ec8-42e5-a917-407860fc05cd","order_by":0,"name":"Zhiwei Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Zhang","suffix":""},{"id":442574104,"identity":"eb6b269a-add0-4d9c-9fd3-54da5b34788f","order_by":1,"name":"Yi Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Li","suffix":""},{"id":442580983,"identity":"765d1c59-255e-4df4-b439-7d146d233c03","order_by":2,"name":"Defang Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Defang","middleName":"","lastName":"Zhang","suffix":""},{"id":442580984,"identity":"79e1a919-0802-4c5d-8910-2a3c54d8f162","order_by":3,"name":"Rui Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACAwgpwcM4//nBBwkVNURrsZBhbshJNnhw5hixWhgqbNgbEswkH7YwE9Zizn722GeeAgke3oYDaRWJDWwM/O3dCXi1WPbkJc/mAfpFsrHx2I3EHTIMEmfObsDvsAM5xswgLYbNDGk3Es+wAYMil4CW828gWuyPMZgVJLYxE6HlBtQWxh4GMwYitbwxZpwD0jKDJ1ki4cwxHsJ+OZ9jzPDmT5094wz2gx9/VNTI8bf34teCAXhIUz4KRsEoGAWjACsAAMGjQyo/9k+WAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2025-04-14 08:24:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6443865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6443865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80730176,"identity":"21ab25b2-691d-4771-9af8-484fd1d6be01","added_by":"auto","created_at":"2025-04-16 12:25:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1744426,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional Response Surface Methodology (RSM) graphs (a) and two-dimensional contour plots (b) of the correlation between the Organic Loading (OL) and the initial pH during the Anaerobic Digestion (AD) of Ribes Fruit (RF). CMY: Cumulative Methane Yield; VS: Volatile Solids.\u003c/p\u003e","description":"","filename":"Fig.1..png","url":"https://assets-eu.researchsquare.com/files/rs-6443865/v1/f474efac29d4873ab1c822b8.png"},{"id":80729092,"identity":"19455bd0-4af5-42d2-9893-b0d1c7361a44","added_by":"auto","created_at":"2025-04-16 12:17:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1751501,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional Response Surface Methodology (RSM) graphs (a) and two-dimensional contour plots (b) of the correlation between the Organic Loading (OL) and Inoculum to Substrate ratio (I/S) during the Anaerobic Digestion (AD) of Ribes Fruit (RF). CMY: Cumulative Methane Yield; VS: Volatile Solids.\u003c/p\u003e","description":"","filename":"Fig.2..png","url":"https://assets-eu.researchsquare.com/files/rs-6443865/v1/c2abc19931f2d5b173a77091.png"},{"id":80730177,"identity":"b4df7860-1adc-4cac-9393-ebb3ed056e91","added_by":"auto","created_at":"2025-04-16 12:25:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1798417,"visible":true,"origin":"","legend":"\u003cp\u003eThree-dimensional Response Surface Methodology (RSM) graphs (a) and two-dimensional contour plots (b) of the correlation between the Inoculum to Substrate ratio (I/S) and initial pH during the Anaerobic Digestion (AD) of Ribes Fruit (RF). CMY: Cumulative Methane Yield; VS: Volatile Solids.\u003c/p\u003e","description":"","filename":"Fig.3..png","url":"https://assets-eu.researchsquare.com/files/rs-6443865/v1/1d0beaa23311ee0a5ad5a976.png"},{"id":80730827,"identity":"7854cd11-3d16-4e0e-afad-6965a6e1d4f5","added_by":"auto","created_at":"2025-04-16 12:33:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6307456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6443865/v1/68108e48-67d2-4769-b27d-c7d380650a2f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessment of the methane production potential of the anaerobic digestion of ribes fruit\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRibes \u003cem\u003e(Ribes odoratum\u003c/em\u003e), belonging to \u003cem\u003eRibes\u003c/em\u003e of the Saxifragaceae family, is a deciduous erect shrub that is commonly known as Currant, and has been planted in the northeast and northwest of China since its introduction to China (Lyashenko et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Due to its strong adaptability and beautiful leaves, ribes has been widely used in urban gardening, and its planting area is increasing. Ribes Fruit (RF) is rich in nutrients such as vitamins, sugars, and organic acids, and has the potential to be developed as a food ingredient (Shaw et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Currants are already considered a traditional food source in European cuisine, notably jams, jellies, and wines. However, RF is not included in the New Food Ingredients Catalogue (NFIC) in China, and its large-scale development and utilization have been hindered. As the planting area of ribes expands, large quantities of RF, which have a yield of 3 kg per plant, have resulted in resource wastage. If not managed properly, RF can deteriorate rapidly, releasing substances that lead to soil crusting and other forms of environmental pollution (Agrawal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, there is a need to develop innovative storage solutions and utilization strategies to achieve the full potential of RF, mitigate the environmental impacts of RF, and increase the value of this resource.\u003c/p\u003e \u003cp\u003eAnaerobic Digestion (AD) technology is a biological treatment method that converts organic matter such as crop residues, livestock manure, processed fruit and vegetable wastes, and kitchen waste into biogas, a clean energy source. With the increasing global interest in biogas production technology, AD is seen as an effective means of converting waste into energy (Almeida et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yuan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the many advantages of AD technology, relying on traditional feedstocks such as livestock manure and crop residues is insufficient to support the wide application of biogas engineering. To advance the biogas industry, it is essential to discover new feedstock sources to boost production and diversify the input for biogas projects, addressing increasing energy needs and environmental objectives (Dhull et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRF is considered to be a potential high-quality feedstock for AD processes due to its rich organic matter content. However, the use of RF as a feedstock for methane production through AD has not been reported in the literature. When searching for and evaluating novel AD feedstocks, it is critical to analyze multiple parameters to assess their methane production potential, which can help to determine which levels of factors are most effective in increasing methane production. The high content of readily degradable organic matter in RF, such as polysaccharides, allows for faster hydrolytic acidification during AD. This may lead to a decrease in pH, which may trigger a increase in the accumulation of Volatile Fatty Acids (VFAs), possibly hindering the initiation and proper functioning of AD. To control this process and maintain the stability of the system, pre-adjustment of the initial pH is an effective strategy (Liu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition to pH, the efficiency of AD is affected by other operational parameters, especially Organic Loading (OL) and the Inoculum to Substrate ratio (I/S). These parameters are particularly important when exploring novel AD feedstocks (Zhu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., 2022; Zhang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, the methane production potential of RF in AD can be fully exploited by optimizing the OL, I/S, and the initial pH. Response Surface Methodology (RSM), a mathematical and statistical technique that is widely recognized and applied to the design of AD experiments and the optimization of process parameters, allows the assessment of the effects of independent variables on the production process and the optimization of operating conditions through the development of predictive models (Khalid et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On this basis, RSM was employed in this study to investigate the interaction effects of the OL, I/S, and initial pH on the Cumulative Methane Yield (CMY) of RF to determine the optimal methane production conditions. This work explored new potential feedstocks for biogas projects and achieved the effective transformation and promotion of biogas production while efficiently utilizing resources.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Substrates and inoculum\u003c/h2\u003e \u003cp\u003eRF was collected in September 2024 from the experimental base of the Institute of Forestry Science, College of Agriculture and Forestry, Qinghai University. The fruits collected were mature, fresh, and pest-free, meeting the requirements of the experiment. They were then stored at -20\u0026deg;C for preservation. During the experiment, the fruits were crushed into pulp for subsequent experimental operations. The inoculum was obtained from the agricultural biogas digester of the Qinghai Zhiyuan Characteristic Agriculture Co., Ltd., which used sheep manure as the raw material and was operating stably, and then cultured under anaerobic conditions in a constant-temperature water bath (37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C) for 1\u0026ndash;2 weeks to reduce the production of background methane.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Factorial design\u003c/h2\u003e \u003cp\u003eIn this study, three factors, the OL (10, 15, and 20 g Volatile Solids (VS)/L), the I/S (1.5, 2.0, and 2.5), and the initial pH (7.0, 7.5, and 8.0), were used as the independent variables, and the CMY was the response value. RSM based on Box\u0026ndash;Behnken Design (BBD) was used to design a three-factor, three-level response surface method optimization experiment to obtain a coded list of experimental factor levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\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\u003eTest factor levels and coding\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFactors\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOL (g VS/L)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eI/S\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eInitial pH\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e-1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\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\u003eNote: OL: Organic Loading; I/S: Inoculum to Substrate ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. AD experiment\u003c/h2\u003e \u003cp\u003eThe AD experiment was conducted using an automatic methane potential analyzer (MultiTalent 203, Nova Skantek, Sweden), which was employed for methane potential testing. According to the experimental design shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the specific additions of RF, inoculum, and distilled water were calculated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and the initial pH of the system was adjusted with 1 mol/L sodium hydroxide solution following the addition of the materials in accordance with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The experiment consisted of a total of 17 treatment groups with three parallels set up in each group, and a blank treatment with only the addition of inoculum was set up at the same time. During the operation of the system, the methane potential tester automatically recorded the methane production every day and collected the daily methane yield and CMY. The experimental reaction temperature was 37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C and the fermentation cycle was 20 d.\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\u003eAmount of each processed material added according to the factorial design\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTreatment\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRF addition\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(g)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eInoculum\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eaddition (g)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRF VS addition (g)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eInoculum VS addition (g)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eDistilled water addition (mL)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e200.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e246.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e270.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e273.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e146.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e273.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e146.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e250.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e250.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: RF: Ribes Fruit; VS: Volatile Solids.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analytical methods and data analysis\u003c/h2\u003e \u003cp\u003eThe Total Solids (TS) and VS contents of the prepared samples were measured using standard methods (APHA 2005). The pH was determined using a pH meter (pHS-2F, Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China). The total nitrogen and total carbon contents were obtained using an elemental analyzer (Flash SMART, Thermo Fisher Scientific Inc.). The total sugars and polysaccharides were determined using the phenol sulphate method (Yue et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The crude fiber content was determined using a fiber measuring instrument (F800, Shandong Haineng Scientific Instrument Co.). Methane production was recorded at 24 h using an automatic methane potential analyzer at the AD stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data analysis and processing\u003c/h2\u003e \u003cp\u003eDesign-Expert 12 software and SPSS Statistics 26 software were employed to analyze the data; Origin 2021 was used for graphing.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Characteristics of substrates and inoculum\u003c/h2\u003e\n \u003cp\u003eThe properties of the RF and inoculum are displayed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The TS and VS contents of RF were only 23.31% and 21.60%, respectively. The VS/TS ratio reached 92.66%, indicating that the organic matter content of RF was high and suitable for methane production through AD. Elemental analysis showed that the C/N ratio was 29.07. It has been reported that during AD, the ideal C/N ratio should be maintained in the range of 20 to 30 to ensure that the microorganisms are able to conduct their metabolic activities efficiently (Laiq et al., 2019). It is worth noting that the C/N ratio of RF falls within this recommended range. The crude fiber content of RF is only 1.88%, and low fiber content often means that the substrate is more susceptible to microbial degradation. Additionally, the high total sugar content (12.18%), high polysaccharide content (4.07%), so low fiber content often means that the substrate is more susceptible to microbial degradation. Additionally, the high total sugar content (12.18%), high polysaccharide content (4.07%), and low pH (3.9) of RF may cause the acidification of the AD system and inhibit the reaction, and appropriate measures needed to be taken to maintain the AD system stability. In conclusion, these physicochemical properties indicate that RF can be used as an AD feedstock for methane production.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacterization of the substrate and inoculum\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eParameters\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRF\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eInoculum\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTS (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVS (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVS/TS (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC/N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal sugar (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePolysaccharide (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude fiber (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: ND: Not Determined; RF: Ribes Fruit.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Response surface regression model and analysis of variance\u003c/h2\u003e\n \u003cp\u003eThe BBD and its results are displayed in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. As shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, the CMY in the 17 experimental groups ranged from 234.62 to 351.26 mL/g VS. The CMY of each experimental group was set as the response value Y, and different levels of coded values OL (\u003cem\u003eA\u003c/em\u003e), I/S (\u003cem\u003eB\u003c/em\u003e), and initial pH (\u003cem\u003eC\u003c/em\u003e) were used as independent variables to establish the fitting equations. The multiple quadratic regression equation was input into Design-Expert 12 software to establish the following:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;344.72\u0026thinsp;+\u0026thinsp;11.54\u003cem\u003eA\u003c/em\u003e\u0026thinsp;+\u0026thinsp;9.64\u003cem\u003eB\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1.44\u003cem\u003eC\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;23.75\u003cem\u003eAB\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;27.70\u003cem\u003eAC\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;17.38\u003cem\u003eBC\u0026thinsp;\u0026minus;\u003c/em\u003e\u0026thinsp;25.58\u003cem\u003eA\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;29.93\u003cem\u003eB\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;37.35\u003cem\u003eC\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e. (1)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBox-Behnken experimental design and corresponding results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eGroup\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eFactor\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eCMY (mL/g VS)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOL (g VS/L)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eI/S\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eInitial pH\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e313.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e299.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e273.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e249.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e308.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e281.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e270.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e351.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e338.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: OL: Organic Loading; I/S: Inoculum to Substrate ratio; CMY: Cumulative Methane Yield.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs can be seen from Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the model F-value is 53.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, indicating that the whole model regression effect is significant. According to the software, the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e is 0.9855, which indicates that this model explains 98.55% of the variation in response values. The misfit term \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2171\u0026thinsp;\u0026gt;\u0026thinsp;0.05, reflecting no significant difference, indicating that the model fit is good, and the residuals of the model may be generated by random errors. The difference between the predicted value \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (0.8449) and the adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (0.9669) is less than 0.2 and the accuracy is sufficient at 19.8176\u0026thinsp;\u0026gt;\u0026thinsp;4, indicating that the model can be used for navigation in the design space. In conclusion, this regression model and equation can be used to analyze and predict the CMY of RF.\u003c/p\u003e\n \u003cp\u003eThe \u003cem\u003eP\u003c/em\u003e-values of the factors show that A and B have highly significant effects (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on the response values, while C has a non-significant effect (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05); the interaction effects of AB, AC, and BC are highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); and the surface effects of \u003cem\u003eA\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eC\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e, and \u003cem\u003eB\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e are highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The size of the absolute value of the coefficients of each factor in the regression model can reflect the strength of the influence of the factor on the response value. As can be seen from Eq. (1), the absolute value of the coefficients of the three influencing factor terms was \u003cem\u003eA\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eC\u003c/em\u003e, indicating that the overall influence of each factor on CMY was in the order of OL\u0026thinsp;\u0026gt;\u0026thinsp;I/S\u0026thinsp;\u0026gt;\u0026thinsp;initial pH.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnalysis of variance (ANOVA) for the response surface quadratic model.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSum square\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMean Square\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF-values\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-values\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22168.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2463.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003e-OL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1065.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1065.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e-I/S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e744.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e-Initial pH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2255.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2255.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3069.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3069.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1208.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1208.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2754.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2754.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3770.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3770.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5874.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5874.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esignificance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e325.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLack of Fit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enot significant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePure Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22494.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0.9855 Adjusted\u003c/strong\u003e \u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;=0.9669 Predicted\u003c/strong\u003e \u003cstrong\u003eR\u003c/strong\u003e\u003cstrong\u003e\u0026sup2;=0.8449 Adeq Precision\u0026thinsp;=\u0026thinsp;19.8176\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significant difference, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 highly significant difference, \u003cem\u003edf\u003c/em\u003e: degrees of freedom\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Response surface optimization analysis\u003c/h2\u003e\n \u003cp\u003eRSM was utilized to optimize the CMY of RF, and the response surface and contour plots were plotted to investigate the effects of the interactions between different factors on the CMY (Figs.\u0026nbsp;1\u0026ndash;3). The sensitivity of the response values to factor interactions was determined by the steepness of the slope of the response surface, i.e., the steeper the slope, the more sensitive the interactions were; the slower the surface, the smaller the effects of the interactions between factors were. Meanwhile, the contour map can be used to find the maximum response value in the interaction.\u003c/p\u003e\n \u003cp\u003eFigure 1 shows the response surface plot and contour plot between the OL and the initial pH. In Fig. 1a, the slope of the response surface between the OL and the initial pH is larger, indicating that the OL and the initial pH have a higher interaction effect during the AD process. When the OL and the initial pH interacted, the CMY showed the trend of increasing first and then decreasing. As in the present study, Khalid et al. noted that methane production increased with increasing OL and initial pH, but methane production was lower as the OL and initial pH increased to a certain level, suggesting that a range of optimal levels is required for production (Khalid et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the present study, the CMY did not fluctuate significantly in response to changes in the initial pH when OL\u0026thinsp;\u0026gt;\u0026thinsp;18 (Fig. 1a). This may be because RF, as a fruit waste, is prone to acidification during the AD process at high OL, and the initial pH was not sufficient to neutralize the acid produced, resulting in limited methane production. When the initial pH\u0026thinsp;\u0026gt;\u0026thinsp;7.4, the CMY fluctuated little and did not change significantly with the change of OL (Fig. 1a). Similarly, the results of Cai et al. showed that the CMY values did not exhibit significant differences due to pH variations over the pH range tested (Cai et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). As shown in Fig. 1b, the CMY of the AD of RF was higher when the OL was between 14 g VS/L and 18 g VS/L, and the initial pH was between 7.2 and 7.8.\u003c/p\u003e\n \u003cp\u003eFigure 2 shows the response surface plots and contour plots of the interaction between OL and I/S. CMY was dramatically influenced by the interaction between OL and I/S. This result was consistent with Zhu et al. A higher I/S allowed more anaerobic microorganisms to fully degrade organic matter and convert it into methane, whereas under low I/S conditions, the microorganisms failed to convert a large amount of organic matter to methane in a timely manner due to the low amount of inoculated sludge, resulting in a decrease in the CMY (Zhu et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). When the I/S was a certain level, the CMY also showed a tendency of increasing and then decreasing with the increase of OL. This result was in accordance with Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). As shown in the contour plots, the CMY of the AD of RF was higher when the OL was 12\u0026ndash;18 g VS/L and the I/S was 2.0\u0026ndash;2.5.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. 3, under the condition of a constant OL, when the I/S was a certain level, the CMY showed a tendency of increasing and then decreasing with the increase of the initial pH. When the initial pH was certain, the CMY also showed a tendency to increase first and then decrease with the increase of the I/S. As displayed in Fig. 3b, the CMY of the AD of RF was higher when the I/S was in the range of 2\u0026ndash;2.5 and the initial pH was in the range of 7.2\u0026ndash;7.8. The CMY was lower at lower I/S and initial pH values.\u003c/p\u003e\n \u003cp\u003eThe following optimal process conditions were obtained through model optimization: an OL of 16.15 g VS/L, an I/S of 2.05, and an initial pH of 7.46. Three replicate AD experiments were conducted with these process parameters to validate the reliability and accuracy of the model. The average value of CMY obtained from the experiment was 357.42 mL/g VS, which was very close to the model prediction (346.44 mL/g VS), and the relative error was less than 5%, indicating that the validation model was effective.\u003c/p\u003e\n \u003cp\u003eComparing this result with the mesophilic batch AD of other feedstocks in previous investigations showed that the CMY of various types of crop residues ranged from 130.20\u0026ndash;222.60 mL/g VS (Li et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; He et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), that of various types of livestock manure ranged from 155.00\u0026ndash;323.00 mL/g VS (Kafle et al., 2016), that of processed fruit and vegetable wastes ranged from 124.00\u0026ndash;233.00 ml/g VS (Ezieke et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), and that of kitchen waste ranged from 258.44\u0026ndash;338.32 mL/g VS (Song et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wo et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), while the CMY of RF after optimization was 357.42 mL/g VS in the present study. This suggests that RF has a higher methanogenic potential compared to commonly used AD feedstocks. In addition, the use of crop residues, livestock manure, and kitchen wastes as single feedstock leads to degradation challenges and inhibition, and these single feedstocks need to be supplemented with process conditions such as pre-treatment and anaerobic co-digestion to maintain the stability and continuity of the AD system, thereby increasing the cost and process complexity (Wang et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). It has been found that the biomethanization of fruit and vegetable solid wastes, when mixed with sludge yielded the best results. This resulted in methane production of up to 90 L/kg VS. NaHCO₃ acts as a pH regulator, effectively optimizing the reaction conditions (Gomez-Lahoz et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Compared with other processed fruit and vegetable wastes (Gunaseelan et al., 2004; Ta et al., 2019), RF avoids system acidification due to the accumulation of VFAs by adjusting the initial pH. This feature eliminates the need for additional chemical buffers, thereby reducing treatment costs, simplifying the process, and being environmentally friendly. Therefore, the use of RF as an AD raw material has obvious advantages.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eTo resourcefully utilize RF, the methanogenic potential of the AD of RF was evaluated, and the optimal methanogenic conditions obtained using RSM were an OL of 16.15 g VS/L, an I/S of 2.05, and an initial pH of 7.46. Under these conditions, the measured value of the CMY was 357.42 mL/g VS. The AD process of RF is simple, low-cost, efficient, environmentally friendly, and has a higher methane production potential than commonly used single feedstocks (such as crop residues, livestock manure and kitchen wastes). By harnessing RF for AD, we can not only reduce the environmental burden associated with its disposal but also produce a valuable renewable energy source in the form of biogas. Given the clear advantages of using RF as a feedstock for AD, it has the potential to supplement feedstocks for biogas projects. These insights provide valuable guidance for the future utilization of RF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe gratefully acknowledge financial support from the Key Translational R\u0026amp;D Program of Qinghai Province (2023-NK-130). We also thank LetPub (www.letpub.com.cn) for providing linguistic assistance during the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgrawal A., Chaudhari P.K., Ghosh P., (2023), Anaerobic digestion of fruit and vegetable waste: a critical review of associated challenges, \u003cem\u003eEnvironmental science and pollution research international\u003c/em\u003e, \u003cstrong\u003e30\u003c/strong\u003e, 24987\u0026ndash;25012.\u003c/li\u003e\n \u003cli\u003eAlmeida P.S., de Menezes C.A., Augusto I.M.G., Paulinetti A.P., Lovato G., Rodrigues J.A.D., Silva E.L., (2023), Integrated production of hydrogen and methane in a dairy biorefinery using anaerobic digestion: Scale-up, economic and risk analyses, \u003cem\u003eJournal of Environmental Management\u003c/em\u003e, \u003cstrong\u003e348\u003c/strong\u003e, https://doi.org/10.1016/j.jenvman.2023.119215.\u003c/li\u003e\n \u003cli\u003eCai Y., Gallegos D., Zheng Z., Stinner W., Wang X., Pr\u0026ouml;ter J., Sch\u0026auml;fer F., (2021), Exploring the combined effect of total ammonia nitrogen, pH and temperature on anaerobic digestion of chicken manure using response surface methodology and two kinetic models, \u003cem\u003eBioresource Technology\u003c/em\u003e, \u003cstrong\u003e337\u003c/strong\u003e, https://doi.org/10.1016/j.biortech.2021.125328.\u003c/li\u003e\n \u003cli\u003eDhull P., Kumar S., Yadav N., Lohchab R.K., (2024), A comprehensive review on anaerobic digestion with focus on potential feedstocks, limitations associated and recent advances for biogas production, \u003cem\u003eEnvironmental Science and Pollution Research International\u003c/em\u003e, 10.1007/s11356-024-33736-6, Advance online publication, https://doi.org/10.1007/s11356-024-33736-6.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEzieke A.H., Serrano A., Clarke W., Villa-Gomez D.K., (2022), Bottom ash from smouldered digestate and coconut coir as an alkalinity supplement for the anaerobic digestion of fruit waste,\u003cem\u003e\u0026nbsp;Chemosphere\u003c/em\u003e, \u003cstrong\u003e296\u003c/strong\u003e, https://doi.org/10.1016/j.chemosphere.2022.134049.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGomez-Lahoz C., Fern\u0026aacute;ndez-Gim\u0026eacute;nez B., Garcia-Herruzo F., Rodriguez-Maroto J.M., Vereda-Alonso C., (2007), Biomethanization of mixtures of fruits and vegetables solid wastes and sludge from a municipal wastewater treatment plant. 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Here, the Anaerobic Digestion (AD) process parameters for RF optimized via medium temperature batch AD experiments using Box\u0026ndash;Behnken response surface design. The optimal methane production process conditions were an organic loading of 16.15 g Volatile Solids (VS)/L, an inoculum to substrate ratio of 2.05, and an initial pH of 7.46. The measured value of cumulative methane yield under these conditions was 357.42 mL/g VS, which was very close to the model predicted value (346.44 mL/g VS), with a relative error of less than 5%, indicating that the response surface model was valid. The findings also show that RF has strong methane production potential compared to other types of residual biomass, and is easier, more efficient, and more environmentally friendly to use as an AD feedstock. This research highlights RF as a new potential feedstock for biogas engineering and advances the application of RF for AD from theory to practice.\u003c/p\u003e","manuscriptTitle":"Assessment of the methane production potential of the anaerobic digestion of ribes fruit","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 12:17:23","doi":"10.21203/rs.3.rs-6443865/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bc0ae1bf-ef87-4275-add4-029db8393ab0","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-16T12:17:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-16 12:17:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6443865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6443865","identity":"rs-6443865","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}