# Degradation of Anti-Nutritional Factors in Maize Gluten Feed by Fermentation with Bacillus subtilis: A Focused Study on Optimizing Fermentation Conditions

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Sample Preparation

^{8}cfu/mL, was used for MGF fermentation.

#### 2.2. Fermentation of Maize Gluten Feed

^{3}, with a circular opening diameter of 6 cm), covered with breathable sealing film to allow air exchange, and then autoclaved at 121 °C for 20 min (Panasonic MLS-3751L-PC, Kadoma, Japan). After cooling to room temperature, the samples were inoculated with the seed culture, supplemented with a specific volume of sterile water, and thoroughly mixed under sterile conditions in an ultra-clean workbench. The containers were then incubated in a biochemical incubator (LHS-HC-Ι, Shanghai Bluepard Experimental Instrument Co., Ltd., Shanghai, China). After a designated fermentation period, the fermented MGF was vacuum freeze-dried, followed by milling and sieving through an 80-mesh screen.

#### 2.3. Determination of Phytic Acid

#### 2.4. Determination of Water-Unextractable Arabinoxylan

#### 2.5. Optimization Experimental Design

#### 2.5.1. Single-Factor Test

#### 2.5.2. Plackett–Burman Design

_{1}) and WU-AX (Y

_{2}), thereby identifying the key independent variables for further optimization. Based on the results of the single-factor tests (Supplementary Table S1), the seven factors were evaluated at two levels: low (−1) and high (+1). The design involved 12 experimental runs, each with a different combination of factor levels, along with a 13th run under baseline conditions. All experiments were performed in triplicate, and the mean PA and WU-AX contents in the fermented samples were recorded as the dependent variables (responses). A first-order polynomial model was applied to fit the Plackett–Burman design, assuming no interactions between variables, as shown in Formula (1).

_{0}denotes the intercept, β

_{i}corresponds to the linear regression coefficient, and X

_{i}refers to the coded independent variable.

#### 2.5.3. Central Composite Design

_{1}, X

_{4}, and X

_{7}respectively, were selected from the Plackett–Burman design for further factorial optimization. Each variable was tested at five coded levels (−α, −1, 0, +1, +α, with α = 2), while the remaining factors from the Plackett–Burman design were held at their optimal levels. A total of 19 experimental runs were designed, including 5 replicates at the central point, with all runs performed in triplicate. The degradation of anti-nutritional substances was analyzed using a second-order polynomial equation, and the data were fitted through a multiple regression procedure. The mathematical relationship between the response variables Y

_{1}(PA content) and Y

_{2}(WU-AX content) and the significant independent variables X

_{1}, X

_{4}, and X

_{7}was expressed by the following quadratic polynomial equation (Formula (2)):

_{1}) and the contents of WU-AX (Y

_{2}); β

_{0}is the constant coefficient, β

_{i}represents the linear coefficients, β

_{ii}represents the quadratic coefficients, β

_{ij}represents the interaction coefficients, and X

_{i}and X

_{j}are the coded values of the independent variables.

#### 2.6. Enzymatic Activity Analysis

#### 2.6.1. Phytase Activity Assay

#### 2.6.2. Xylanase Activity Assay

#### 2.6.3. Cellulase Activity Assay

#### 2.6.4. Protease Activity Assay

#### 2.7. Protein Nutritional Analysis

#### 2.8. In Vitro Minerals Digestion

_{3}, followed by the addition of 10 mL pancreatin solution (20 g/L), and incubated at 37 °C for 30 min. The mixture was centrifuged at 10,000 r/min for 10 min at 4 °C, and the supernatant was filtered through a 0.45 μm filter. The mineral contents (Fe, Mn, Cu, Zn) in the filtrate and samples were analyzed by ICP-OES. Mineral bioavailability was calculated using formula (Formula (6)).

#### 2.9. Statistical Analysis

## 3. Results and Discussion

#### 3.1. Effects of Independent Factors on PA and WU-AX

#### 3.2. Screening of Significant Factors Using Plackett–Burman Design

_{1}, content of PA; and Y

_{2}, content of WU-AX) are presented in Table 1, with the ANOVA results shown in Table 2. Generally, variables with a p-value less than 0.05 are considered significant parameters at the 95% confidence interval [19]. The findings revealed that the factors exhibited similar significance for both responses, with X

_{1}(fermentation time), X

_{4}(inoculum dose), and X

_{7}(material-to-liquid ratio) being statistically significant (p ≤ 0.05). In contrast, X

_{2}(fermentation temperature), X

_{3}(initial pH), X

_{5}(particle size), and X

_{6}(substrate filling rate) were determined to be non-significant.

_{1}represents the phytic acid (PA) content, Y

_{2}denotes the water-unextractable arabinoxylan (WU-AX) content, and X

_{1}, X

_{2}, X

_{3}, X

_{4}, X

_{5}, X

_{6}, and X

_{7}correspond to the coded variables of fermentation time, fermentation temperature, initial pH, inoculum dose, particle size, substrate filling rate, and the material-to-liquid ratio, respectively.

_{1}(PA content) in decreasing order of significance were as follows: X

_{1}(fermentation time) > X

_{7}(material-to-liquid ratio) > X

_{4}(inoculum dose) > X

_{6}(substrate filling rate) > X

_{2}(fermentation temperature) > X

_{5}(particle size) > X

_{3}(initial pH). For response Y

_{2}(WU-AX content), the ranking was X

_{1}> X

_{4}> X

_{7}> X

_{5}> X

_{6}> X

_{3}> X

_{2}. A Pareto chart can present the effect of factors on responses and check the statistical significance; thus, it was employed here to identify the significant factors [34]. The relative size of the effect degree of each parameter on the PA and WU-AX contents was evaluated by comparing the t-value of the effect. The resulting Pareto chart plotted by the t-value of the effect versus each parameter is shown in Figure 2. A parameter with a t-value higher than the t-value limit line indicated that it had a confidence level greater than 95% and could be considered as significant [35]. In addition, a Bonferroni limit line (5.74) and t-value limit line (2.77) were applied to determine the extremely significant (the t-value was above the Bonferroni limit line), significant (t-value was between the Bonferroni limit line and the t-value limit line), and insignificant (below the t-value limit line) coefficients of different factors [35]. The t-value of the fermentation time, material-to-liquid ratio, and inoculum dose on both responses were above the t-value limit line, which indicated that the three factors were considered as significant factors. Consequently, the fermentation time (X

_{1}), inoculum dose (X

_{4}), and material-to-liquid ratio (X

_{7}) were selected for the further optimization of fermentation conditions. Both fermentation time and inoculum dose have been identified as critical parameters in other fermentation processes as well. In light of the Plackett–Burman design results, and considering fermentation efficiency and cost considerations, the non-significant variables—fermentation temperature, initial pH, particle size, and substrate filling rate—were fixed at 31 °C, pH 6.5, 189 μm, and 2.63%, respectively.

#### 3.3. Statistical Analysis of Central Composite Design

_{1}) and WU-AX content (Y

_{2}) are presented in Table 3. Multiple regression analysis was carried out on the experimental data, and the second-order polynomial stepwise equations were obtained, as shown in Equations (9) and (10).

_{1}represents the phytic acid (PA) content, Y

_{2}denotes the water-unextractable arabinoxylan (WU-AX) content, and X

_{1}, X

_{4}, and X

_{7}are the coding variables of fermentation time, inoculum dose, and the material-to-liquid ratio, respectively.

_{1}, X

_{4}, and X

_{7}) and their interactions on the response variables. ANOVA was performed to assess the significance of the central composite design model and validate the accuracy of the fitting curve [36]. The model coefficients were evaluated using F-values and p-values, with a higher F-value and smaller p-value (p ≤ 0.05) indicating greater model significance [19,34,36]. The ANOVA results, along with goodness-of-fit and model adequacy, are presented in Table 4. The model coefficients were validated based on F-values and p-values. As shown, the F-values and corresponding low p-values for both the PA and WU-AX responses confirmed the high significance of the models. The lack of fit for both response models was insignificant (p > 0.05), with values of 0.2272 and 0.6234 for PA and WU-AX, respectively. This suggests that no outliers were present in the data, and higher-order terms were unnecessary, confirming the appropriateness of the selected models. The high coefficient of determination (R

^{2}), 0.9469 for PA and 0.9712 for WU-AX, indicates that the factor terms explain 94.69% and 97.12% of the variance in the models for PA and WU-AX, respectively, implying the models are reliable. Furthermore, the R

^{2}values were close to their respective adjusted R

^{2}values, further demonstrating the high explanatory power of the regression models used in this study [19]. The coefficient of variation (CV) values, which reflect the degree of variability in the mean response, were 2.45% for PA and 1.67% for WU-AX, indicating low variability between the predicted and experimental responses. Therefore, the mathematical models established in this study have been proven reliable and can be utilized for subsequent prediction and optimization steps.

_{1}, X

_{4}, and X

_{7}) and the quadratic terms (X

_{1}

^{2}, X

_{4}

^{2}, and X

_{7}

^{2}) were statistically significant for both PA and WU-AX responses (p ≤ 0.05). The two-factor interaction term X

_{1}X

_{4}had a significant effect (p ≤ 0.05) on PA, while the interaction term X

_{1}X

_{7}showed a significant influence (p ≤ 0.05) on WU-AX. Notably, X

_{1}had the most significant effect, followed by X

_{7}and X

_{4}for the PA response. In contrast, for the WU-AX response, the order of variable influence was X

_{1}> X

_{4}> X

_{7}(Table 4). These significant effects of X

_{1}, X

_{4}, and X

_{7}are consistent with the results of the significance analysis in the Plackett–Burman design. The values of the interaction terms further indicated strong interactions between the independent variables, particularly X

_{1}X

_{4}for the PA response and X

_{1}X

_{7}for the WU-AX response.

#### 3.4. Optimum Conditions and Authenticity of Predictive Model

#### 3.5. Changes in Enzymatic Activity Before and After Fermentation

#### 3.6. Comparison of the Nutritional Values of MGF and FMGF

## 4. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Zhang, R.; Ma, S.; Li, L.; Zhang, M.; Tian, S.; Wang, D.; Liu, K.; Liu, H.; Zhu, W.; Wang, X. Comprehensive Utilization of Corn Starch Processing By-Products: A Review. Grain Oil Sci. Technol.
**2021**, 4, 89–107. [Google Scholar] [CrossRef] - Guo, Y.; Wang, K.; Wu, B.; Wu, P.; Duan, Y.; Ma, H. Production of ACE Inhibitory Peptides from Corn Germ Meal by an Enzymatic Membrane Reactor with a Novel Gradient Diafiltration Feeding Working-Mode and in vivo Evaluation of Antihypertensive Effect. J. Funct. Foods
**2020**, 64, 103584. [Google Scholar] [CrossRef] - Rocha-Villarreal, V.; Hoffmann, J.F.; Vanier, N.L.; Serna-Saldivar, S.O.; García-Lara, S. Hydrothermal Treatment of Maize: Changes in Physical, Chemical, and Functional Properties. Food Chem.
**2018**, 263, 225–231. [Google Scholar] [CrossRef] [PubMed] - Lyu, Z.; Li, Y.; Liu, H.; Li, E.; Li, P.; Zhang, S.; Wang, F.; Lai, C. Net Energy Content of Rice Bran, Defatted Rice Bran, Corn Gluten Feed, and Corn Germ Meal Fed to Growing Pigs Using Indirect Calorimetry. J. Anim. Sci.
**2018**, 96, 1877–1888. [Google Scholar] [CrossRef] [PubMed] - Ortiz de Erive, M.; Wang, T.; He, F.; Chen, G. Development of High-Fiber Wheat Bread Using Microfluidized Corn Bran. Food Chem.
**2020**, 310, 125921. [Google Scholar] [CrossRef] - Bloot, A.P.M.; Kalschne, D.L.; Amaral, J.A.S.; Baraldi, I.J.; Canan, C. A Review of Phytic Acid Sources, Obtention, and Applications. Food Rev. Int.
**2023**, 39, 73–92. [Google Scholar] [CrossRef] - Shi, C.; Zhang, Y.; Lu, Z.; Wang, Y. Solid-State Fermentation of Corn-Soybean Meal Mixed Feed with Bacillus subtilis and Enterococcus faecium for Degrading Antinutritional Factors and Enhancing Nutritional Value. J. Anim. Sci. Biotechnol.
**2017**, 8, 50. [Google Scholar] [CrossRef] - Sun, X.; Ma, L.; Lux, P.E.; Wang, X.; Stuetz, W.; Frank, J.; Liang, J. The Distribution of Phosphorus, Carotenoids and Tocochromanols in Grains of Four Chinese Maize (Zea mays L.) Varieties. Food Chem.
**2022**, 367, 130725. [Google Scholar] [CrossRef] - Noureddini, H.; Malik, M.; Byun, J.; Ankeny, A.J. Distribution of Phosphorus Compounds in Corn Processing. Bioresour. Technol.
**2009**, 100, 731–736. [Google Scholar] [CrossRef] - Sun, H.; Cozannet, P.; Ma, R.; Zhang, L.; Huang, Y.K.; Preynat, A.; Sun, L.-h. Effect of Concentration of Arabinoxylans and a Carbohydrase Mixture on Energy, Amino Acids and Nutrients Total Tract and Ileal Digestibility in Wheat and Wheat by-Product-Based Diet for Pigs. Anim. Feed Sci. Technol.
**2020**, 262, 114380. [Google Scholar] [CrossRef] - Huang, M.; Bai, J.; Buccato, D.G.; Zhang, J.; He, Y.; Zhu, Y.; Yang, Z.; Xiao, X.; Daglia, M. Cereal-Derived Water-Unextractable Arabinoxylans: Structure Feature, Effects on Baking Products and Human Health. Foods
**2024**, 13, 2369. [Google Scholar] [CrossRef] [PubMed] - Wang, J.; Bai, J.; Fan, M.; Li, T.; Li, Y.; Qian, H.; Wang, L.; Zhang, H.; Qi, X.; Rao, Z. Cereal-Derived Arabinoxylans: Structural Features and Structure–Activity Correlations. Trends Food Sci. Technol.
**2020**, 96, 157–165. [Google Scholar] [CrossRef] - Rosicka-Kaczmarek, J.; Komisarczyk, A.; Nebesny, E.; Makowski, B. The Influence of Arabinoxylans on the Quality of Grain Industry Products. Eur. Food Res. Technol.
**2016**, 242, 295–303. [Google Scholar] [CrossRef] - Bautil, A.; Verspreet, J.; Buyse, J.; Goos, P.; Bedford, M.R.; Courtin, C.M. Age-Related Arabinoxylan Hydrolysis and Fermentation in the Gastrointestinal Tract of Broilers Fed Wheat-Based Diets. Poult. Sci.
**2019**, 98, 4606–4621. [Google Scholar] [CrossRef] [PubMed] - Endalew, H.W.; Atlabachew, M.; Karavoltsos, S.; Sakellari, A.; Aslam, M.F.; Allen, L.; Griffiths, H.; Zoumpoulakis, P.; Kanellou, A.; Yehuala, T.F.; et al. Effect of Fermentation on Nutrient Composition, Antinutrients, and Mineral Bioaccessibility of Finger Millet Based Injera: A Traditional Ethiopian Food. Food Res. Int.
**2024**, 190, 114635. [Google Scholar] [CrossRef] [PubMed] - Watanakij, N.; Visessanguan, W.; Petchkongkaew, A. Aflatoxin B
_{1}-Degrading Activity from Bacillus subtilis BCC 42005 Isolated from Fermented Cereal Products. Food Addit. Contam. Part A**2020**, 37, 1579–1589. [Google Scholar] [CrossRef] - Iqbal, S.; Begum, F.; Rabaan, A.A.; Aljeldah, M.; Al Shammari, B.R.; Alawfi, A.; Alshengeti, A.; Sulaiman, T.; Khan, A. Classification and Multifaceted Potential of Secondary Metabolites Produced by Bacillus subtilis Group: A Comprehensive Review. Molecules
**2023**, 28, 927. [Google Scholar] [CrossRef] - Suprayogi, W.P.S.; Ratriyanto, A.; Akhirini, N.; Hadi, R.F.; Setyono, W.; Irawan, A. Changes in Nutritional and Antinutritional Aspects of Soybean Meals by Mechanical and Solid-State Fermentation Treatments with Bacillus subtilis and Aspergillus oryzae. Bioresour. Technol. Rep.
**2022**, 17, 100925. [Google Scholar] [CrossRef] - Bruno Siewe, F.; Kudre, T.G.; Narayan, B. Optimisation of Ultrasound-Assisted Enzymatic Extraction Conditions of Umami Compounds from Fish by-Products Using the Combination of Fractional Factorial Design and Central Composite Design. Food Chem.
**2021**, 334, 127498. [Google Scholar] [CrossRef] - Buddrick, O.; Jones, O.A.H.; Cornell, H.J.; Small, D.M. The Influence of Fermentation Processes and Cereal Grains in Wholegrain Bread on Reducing Phytate Content. J. Cereal Sci.
**2014**, 59, 3–8. [Google Scholar] [CrossRef] - Douglas, S.G. A Rapid Method for the Determination of Pentosans in Wheat Flour. Food Chem.
**1981**, 7, 139–145. [Google Scholar] [CrossRef] - Rouau, X.; Surget, A. A Rapid Semi-Automated Method for the Determination of Total and Water-Extractable Pentosans in Wheat Flours. Carbohydr. Polym.
**1994**, 24, 123–132. [Google Scholar] [CrossRef] - Hernández-Espinosa, N.; Posadas-Romano, G.; Dreisigacker, S.; Crossa, J.; Crespo, L.; Ibba, M.I. Efficient Arabinoxylan Assay for Wheat: Exploring Variability and Molecular Marker Associations in Wholemeal and Refined Flour. J. Cereal Sci.
**2024**, 117, 103897. [Google Scholar] [CrossRef] [PubMed] - Akpoilih, B.U.; Adeshina, I.; Chukwudi, C.F.; Abdel-Tawwab, M. Evaluating the Inclusion of Phytase Sources to Phosphorus-Free Diets for GIFT Tilapia (Oreochromis niloticus): Growth Performance, Intestinal Morphometry, Immune-Antioxidant Responses, and Phosphorus Utilization. Anim. Feed Sci. Technol.
**2023**, 303, 115678. [Google Scholar] [CrossRef] - Dhaver, P.; Pletschke, B.; Sithole, B.; Govinden, R. Optimization, Purification, and Characterization of Xylanase Production by a Newly Isolated Trichoderma Harzianum Strain by a Two-Step Statistical Experimental Design Strategy. Sci. Rep.
**2022**, 12, 17791. [Google Scholar] [CrossRef] - Al Talebi, Z.A.; Al-Kawaz, H.S.; Mahdi, R.K.; Al-Hassnawi, A.T.; Alta’ee, A.H.; Hadwan, A.M.; Khudhair, D.A.; Hadwan, M.H. An Optimized Protocol for Estimating Cellulase Activity in Biological Samples. Anal. Biochem.
**2022**, 655, 114860. [Google Scholar] [CrossRef] - Wang, Y.; Xu, K.; Lu, F.; Wang, Y.; Ouyang, N.; Ma, H. Application of Ultrasound Technology in the Field of Solid-State Fermentation: Increasing Peptide Yield through Ultrasound-Treated Bacterial Strain. J. Sci. Food Agric.
**2021**, 101, 5348–5358. [Google Scholar] [CrossRef] - Zhang, Y.; Ishikawa, M.; Koshio, S.; Yokoyama, S.; Dossou, S.; Wang, W.; Zhang, X.; Shadrack, R.S.; Mzengereza, K.; Zhu, K.; et al. Optimization of Soybean Meal Fermentation for Aqua-Feed with Bacillus Subtilis Natto Using the Response Surface Methodology. Fermentation
**2021**, 7, 306. [Google Scholar] [CrossRef] - Pearce, K.N.; Karahalios, D.; Friedman, M. Ninhydrin Assay For Proteolysis in Ripening Cheese. J. Food Sci.
**1988**, 53, 432–435. [Google Scholar] [CrossRef] - Kamble, D.B.; Singh, R.; Rani, S.; Kaur, B.P.; Upadhyay, A.; Kumar, N. Optimization and Characterization of Antioxidant Potential, in vitro Protein Digestion and Structural Attributes of Microwave Processed Multigrain Pasta. J. Food Process. Preserv.
**2019**, 43, e14125. [Google Scholar] [CrossRef] - Kumar, A.; Lal, M.K.; Kar, S.S.; Nayak, L.; Ngangkham, U.; Samantaray, S.; Sharma, S.G. Bioavailability of Iron and Zinc as Affected by Phytic Acid Content in Rice Grain. J. Food Biochem.
**2017**, 41, e12413. [Google Scholar] [CrossRef] - Zhang, L.; Yang, Y.; Sun, J.; Shen, Y.; Wei, D.; Zhu, J.; Chu, J. Microbial Production of 2,3-Butanediol by a Mutagenized Strain of Serratia Marcescens H30. Bioresour. Technol.
**2010**, 101, 1961–1967. [Google Scholar] [CrossRef] [PubMed] - Terlabie, N.N.; Sakyi-Dawson, E.; Amoa-Awua, W.K. The Comparative Ability of Four Isolates of Bacillus subtilis to Ferment Soybeans into Dawadawa. Int. J. Food Microbiol.
**2006**, 106, 145–152. [Google Scholar] [CrossRef] [PubMed] - Dayana Priyadharshini, S.; Bakthavatsalam, A.K. Optimization of Phenol Degradation by the Microalga Chlorella Pyrenoidosa Using Plackett-Burman Design and Response Surface Methodology. Bioresour. Technol.
**2016**, 207, 150–156. [Google Scholar] [CrossRef] - Chen, F.; Zhang, Q.; Fei, S.; Gu, H.; Yang, L. Optimization of Ultrasonic Circulating Extraction of Samara Oil from Acer Saccharum Using Combination of Plackett–Burman Design and Box–Behnken Design. Ultrason. Sonochem.
**2017**, 35, 161–175. [Google Scholar] [CrossRef] - Xi, J.; Xiang, B.; Deng, Y. Comparison of Batch and Circulating Processes for Polyphenols Extraction from Pomelo Peels by Liquid-Phase Pulsed Discharge. Food Chem.
**2021**, 340, 127918. [Google Scholar] [CrossRef] - Chen, W.; Xu, D. Phytic Acid and Its Interactions in Food Components, Health Benefits, and Applications: A Comprehensive Review. Trends Food Sci. Technol.
**2023**, 141, 104201. [Google Scholar] [CrossRef] - Dahiya, S.; Kumar, A.; Singh, B. Enhanced Endoxylanase Production by Myceliophthora thermophila Using Rice Straw and Its Synergism with Phytase in Improving Nutrition. Process Biochem.
**2020**, 94, 235–242. [Google Scholar] [CrossRef] - Tse, T.; Schendel, R.R. Cereal Grain Arabinoxylans: Processing Effects and Structural Changes during Food and Beverage Fermentations. Fermentation
**2023**, 9, 914. [Google Scholar] [CrossRef] - Liu, Y.; Li, H.; Liu, W.; Ren, K.; Li, X.; Zhang, Z.; Huang, R.; Han, S.; Hou, J.; Pan, C. Bioturbation Analysis of Microbial Communities and Flavor Metabolism in a High-Yielding Cellulase Bacillus subtilis Biofortified Daqu. Food Chem X
**2024**, 22, 101382. [Google Scholar] [CrossRef] - Reynaud, Y.; Lopez, M.; Riaublanc, A.; Souchon, I.; Dupont, D. Hydrolysis of Plant Proteins at the Molecular and Supra-Molecular Scales during in vitro Digestion. Food Res. Int.
**2020**, 134, 109204. [Google Scholar] [CrossRef] [PubMed] - Zhu, X.; Wang, L.; Zhang, Z.; Ding, L.; Hang, S. Combination of Fiber-Degrading Enzymatic Hydrolysis and Lactobacilli Fermentation Enhances Utilization of Fiber and Protein in Rapeseed Meal as Revealed in Simulated Pig Digestion and Fermentation in vitro. Anim. Feed Sci. Technol.
**2021**, 278, 115001. [Google Scholar] [CrossRef]

**Figure 1.**Effects of fermentation time (

**a**) and initial pH (

**b**) on the PA and WU-AX contents. PA, phytic acid; WU-AX, water-unextractable arabinoxylan.

**Figure 2.**Pareto chart illustrating the effects of seven variables on the responses of Y

_{1}(

**a**) and Y

_{2}(

**b**). Variables with t-values exceeding the critical value of 2.77 are considered statistically significant. X

_{1}, fermentation time, hour; X

_{2}, fermentation temperature, °C; X

_{3}, initial pH; X

_{4}, inoculum dose, %; X

_{5}, particle size, μm; X

_{6}, substrate filling rate, %; X

_{7}, material-to-liquid ratio; Y

_{1}, the content of phytic acid (PA), mg/g; Y

_{2}, the content of water-unextractable arabinoxylan (WU-AX), mg/g.

**Figure 3.**Response-surface plots illustrating the effects on PA content and the interactions between (

**a**) fermentation time and inoculum dose, (

**c**) fermentation time and the material-to-liquid ratio, and (

**e**) inoculum dose and the material-to-liquid ratio. Corresponding 2D contour plots depict the interactions between (

**b**) fermentation time and inoculum dose, (

**d**) fermentation time and the material-to-liquid ratio, and (

**f**) inoculum dose and the material-to-liquid ratio.

**Figure 4.**Response-surface plots illustrating the effects on WU-AX content and the interactions between (

**a**) fermentation time and inoculum dose, (

**c**) fermentation time and the material-to-liquid ratio, and (

**e**) inoculum dose and the material-to-liquid ratio. Corresponding 2D contour plots depict the interactions between (

**b**) fermentation time and inoculum dose, (

**d**) fermentation time and the material-to-liquid ratio, and (

**f**) inoculum dose and the material-to-liquid ratio.

**Figure 5.**Effects of fermentation on the activities of phytase (

**a**), xylanase (

**b**), cellulase (

**c**), and protease (

**d**). MGF, maize germ feed; FMGF, fermented maize germ feed; *** means significance level of p < 0.001.

Factors | Responses | ||||||||
---|---|---|---|---|---|---|---|---|---|

Run | X_{1} | X_{2} | X_{3} | X_{4} | X_{5} | X_{6} | X_{7} | Y_{1} | Y_{2} |

1 | 96 (+) | 28 (−) | 7 (+) | 20 (+) | 189 (−) | 3.42 (+) | 1:2.5 (−) | 8.6 ± 0.54 | 88.9 ± 0.94 |

2 | 96 (+) | 28 (−) | 6 (−) | 10 (−) | 30 (+) | 3.42 (+) | 1:3.5 (+) | 8.3 ± 0.26 | 90.0 ± 1.88 |

3 | 48 (−) | 34 (+) | 6 (−) | 10 (−) | 189 (−) | 3.42 (+) | 1:3.5 (+) | 9.1 ± 0.20 | 92.3 ± 1.01 |

4 | 48 (−) | 28 (−) | 6 (−) | 10 (−) | 189 (−) | 1.84 (−) | 1:2.5 (−) | 9.9 ± 0.36 | 97.9 ± 1.40 |

5 | 72 (0) | 31 (0) | 6.5 (0) | 15 (0) | 123 (0) | 2.63 (0) | 1:3 (0) | 8.3 ± 0.30 | 87.8 ± 1.24 |

6 | 48 (−) | 34 (+) | 7 (+) | 10 (−) | 30 (+) | 1.84 (−) | 1:2.5 (−) | 10.0 ± 0.38 | 98.3 ± 0.84 |

7 | 96 (+) | 34 (+) | 6 (−) | 20 (+) | 30 (+) | 1.84 (−) | 1:3.5 (+) | 8.2 ± 0.33 | 86.9 ± 0.96 |

8 | 48 (−) | 28 (−) | 7 (+) | 20 (+) | 30 (+) | 1.84 (−) | 1:3.5 (+) | 8.7 ± 0.31 | 91.8 ± 0.91 |

9 | 96 (+) | 28 (−) | 7 (+) | 10 (−) | 189 (−) | 1.84 (−) | 1:3.5 (+) | 8.4 ± 0.07 | 89.3 ± 0.57 |

10 | 96 (+) | 34 (+) | 6 (−) | 20 (+) | 189 (−) | 1.84 (−) | 1:2.5 (−) | 8.5 ± 0.31 | 88.8 ± 1.08 |

11 | 96 (+) | 34 (+) | 7 (+) | 10 (−) | 30 (+) | 3.42 (+) | 1:2.5 (−) | 8.8 ± 0.74 | 92.5 ± 0.59 |

12 | 48 (−) | 34 (+) | 7 (+) | 20 (+) | 189 (−) | 3.42 (+) | 1:3.5 (+) | 8.9 ± 0.40 | 91.8 ± 0.43 |

13 | 48 (−) | 28 (−) | 6 (−) | 20 (+) | 30 (+) | 3.42 (+) | 1:2.5 (−) | 9.0 ± 0.43 | 93.6 ± 0.92 |

_{1}, fermentation time, hour; X

_{2}, fermentation temperature, °C; X

_{3}, initial pH; X

_{4}, inoculum dose, %; X

_{5}, particle size, μm; X

_{6}, substrate filling rate, %; X

_{7}, material-to-liquid ratio; Y

_{1}, the content of phytic acid (PA), mg/g; Y

_{2}, the content of water-unextractable arabinoxylan (WU-AX), mg/g. Values of Y

_{1}and Y

_{2}are given as means ± standard deviation (n = 3).

PA (Phytic Acid) | ||||||

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | p-Value | Significance |

Model | 3.4036 | 7 | 0.4862 | 10.4564 | 0.0194 | * |

X_{1} | 1.8723 | 1 | 1.8723 | 40.2645 | 0.0032 | ** |

X_{2} | 0.0385 | 1 | 0.0385 | 0.8287 | 0.4142 | |

X_{3} | 0.0096 | 1 | 0.0096 | 0.2072 | 0.6726 | |

X_{4} | 0.5461 | 1 | 0.5461 | 11.7448 | 0.0266 | * |

X_{5} | 0.0147 | 1 | 0.0147 | 0.3161 | 0.6040 | |

X_{6} | 0.0901 | 1 | 0.0901 | 1.9384 | 0.2363 | |

X_{7} | 0.8321 | 1 | 0.8321 | 17.8953 | 0.0134 | * |

WU-AX (Water-Unextractable Arabinoxylan) | ||||||

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | p-Value | Significance |

Model | 129.6758 | 7 | 18.5251 | 17.4466 | 0.0075 | ** |

X_{1} | 70.7616 | 1 | 70.7616 | 66.6420 | 0.0012 | ** |

X_{2} | 0.0645 | 1 | 0.0645 | 0.0608 | 0.8174 | |

X_{3} | 0.8640 | 1 | 0.8640 | 0.8137 | 0.4180 | |

X_{4} | 28.9541 | 1 | 28.9541 | 27.2685 | 0.0064 | ** |

X_{5} | 1.3736 | 1 | 1.3736 | 1.2937 | 0.3189 | |

X_{6} | 1.2545 | 1 | 1.2545 | 1.1815 | 0.3382 | |

X_{7} | 26.4033 | 1 | 26.4033 | 24.8662 | 0.0076 | ** |

_{1}, fermentation time, hour; X

_{2}, fermentation temperature, °C; X

_{3}, initial pH; X

_{4}, inoculum dose, %; X

_{5}, particle size, μm; X

_{6}, substrate filling rate, %; X

_{7}, material-to-liquid ratio; statistical significance: * p < 0.05, ** p < 0.01.

**Table 3.**Central composite design with experimental responses for the PA and WU-AX contents in MGF under different fermentation conditions.

Factors | Responses | ||||
---|---|---|---|---|---|

Run | X_{1} | X_{4} | X_{7} | Y_{1} | Y_{2} |

5 | 48 (−1) | 10 (−1) | 1:3.5 (1) | 9.7 ± 0.27 | 94.8 ± 0.70 |

1 | 48 (−1) | 10 (−1) | 1:2.5 (−1) | 10.2 ± 0.46 | 102.2 ± 1.59 |

12 | 72 (0) | 25 (2) | 1:3 (0) | 8.9 ± 0.32 | 89.5 ± 1.25 |

13 | 72 (0) | 15 (0) | 1:2 (−2) | 9.4 ± 0.13 | 90.9 ± 1.13 |

4 | 96 (1) | 20 (1) | 1:2.5 (−1) | 9.3 ± 0.17 | 87.1 ± 0.32 |

16 (C) | 72 (0) | 15 (0) | 1:3 (0) | 8.3 ± 0.30 | 83.5 ± 1.46 |

10 | 120 (2) | 15 (0) | 1:3 (0) | 8.4 ± 0.30 | 89.7 ± 0.80 |

8 | 96 (1) | 20 (1) | 1:3.5 (1) | 8.6 ± 0.25 | 84.6 ± 0.65 |

3 | 48 (−1) | 20 (1) | 1:2.5 (−1) | 9.2 ± 0.70 | 97.0 ± 0.73 |

2 | 96 (1) | 10 (−1) | 1:2.5 (−1) | 9.2 ± 0.07 | 91.7 ± 0.97 |

7 | 48 (−1) | 20 (1) | 1:3.5 (1) | 9.0 ± 0.36 | 88.9 ± 0.99 |

6 | 96 (1) | 10 (−1) | 1:3.5 (1) | 8.7 ± 0.31 | 92.8 ± 1.05 |

19 (C) | 72 (0) | 15 (0) | 1:3 (0) | 8.3 ± 0.29 | 87.9 ± 0.56 |

17 (C) | 72 (0) | 15 (0) | 1:3 (0) | 8.2 ± 0.46 | 84.5 ± 0.72 |

14 | 72 (0) | 15 (0) | 1:4 (2) | 8.6 ± 0.25 | 86.8 ± 0.91 |

9 | 24 (−2) | 15 (0) | 1:3 (0) | 10.4 ± 0.44 | 105.5 ± 0.78 |

11 | 72 (0) | 5 (−2) | 1:3 (0) | 10.1 ± 0.19 | 99.9 ± 1.08 |

18 (C) | 72 (0) | 15 (0) | 1:3 (0) | 8.4 ± 0.28 | 85.8 ± 1.24 |

15 (C) | 72 (0) | 15 (0) | 1:3 (0) | 8.6 ± 0.61 | 85.3 ± 0.51 |

_{1}, fermentation time, h; X

_{4}, inoculum dose, %; X

_{7}, material-to-liquid ratio; Y

_{1}, phytic acid (PA) content, mg/g; Y

_{2}, water-unextractable arabinoxylan (WU-AX) content, mg/g. Values of Y

_{1}and Y

_{2}are given as means ± standard deviation (n = 3).

**Table 4.**ANOVA of central composite design for the PA and WU-AX contents in MGF under different fermentation conditions.

Source | PA | WU-AX | ||||
---|---|---|---|---|---|---|

Sum of Squares | F-Value | p-Value | Sum of Squares | F-Value | p-Value | |

Model | 7.8627 | 17.8433 | 0.0001 | 696.2109 | 33.6951 | <0.0001 |

X_{1} | 2.5440 | 51.9596 | <0.0001 | 213.1600 | 92.4500 | <0.0001 |

X_{4} | 0.9312 | 19.0195 | 0.0018 | 125.3280 | 54.3563 | <0.0001 |

X_{7} | 0.9702 | 19.8160 | 0.0016 | 39.4384 | 17.1049 | 0.0025 |

X_{1} × X_{4} | 0.2813 | 5.7443 | 0.0401 | 0.3200 | 0.1388 | 0.7181 |

X_{1} × X_{7} | 0.0008 | 0.0163 | 0.9011 | 24.7808 | 10.7477 | 0.0096 |

X_{4} × X_{7} | 0.0145 | 0.2951 | 0.6001 | 2.2261 | 0.9655 | 0.3515 |

X_{1}^{2} | 1.7536 | 35.8157 | 0.0002 | 229.7474 | 99.6442 | <0.0001 |

X_{4}^{2} | 2.0730 | 42.3401 | 0.0001 | 134.6781 | 58.4115 | <0.0001 |

X_{7}^{2} | 0.7016 | 14.3295 | 0.0043 | 20.0899 | 8.7132 | 0.0162 |

Residual | 0.4407 | 20.7511 | ||||

Lack of Fit | 0.3247 | 2.2411 | 0.2272 | 10.0901 | 0.7572 | 0.6234 |

C.V.% | 2.45 | 1.67 | ||||

Pure Error | 0.1159 | 10.6610 | ||||

Cor Total | 8.3034 | 719.9620 | ||||

R^{2} | 0.9469 | 0.9712 | ||||

R^{2}-adjusted | 0.8939 | 0.9424 |

_{1}, X

_{4}, and X

_{7}represent the linear effects of fermentation time (h), inoculum dose (%), and the material-to-liquid ratio, respectively. X

_{1}

^{2}, X

_{4}

^{2}, and X

_{7}

^{2}denote the quadratic effects, while X

_{1}× X

_{4}, X

_{1}× X

_{7}, and X

_{4}× X

_{7}represent the interaction effects.

Items | MGF | FMGF | p-Value | Change (%) |
---|---|---|---|---|

Protein (%) | 27.1 ± 0.13 | 28.63 ± 0.08 | <0.001 | 5.57 |

PDI (%) | 37.9 ± 1.02 | 46.67 ± 0.58 | <0.001 | 23.17 |

DH (%) | 2.3 ± 0.11 | 3.26 ± 0.12 | <0.001 | 43.61 |

IVPD (%) | 44.3 ± 1.07 | 58.48 ± 0.78 | <0.001 | 31.92 |

Minerals’ bioavailability | ||||

Fe (%) | 22.8 ± 1.51 | 33.68 ± 2.15 | <0.001 | 47.72 |

Mn (%) | 38.3 ± 1.53 | 53.23 ± 1.38 | <0.001 | 39.05 |

Cu (%) | 46.0 ± 1.04 | 57.54 ± 1.06 | <0.001 | 25.20 |

Zn (%) | 12.6 ± 1.23 | 16.67 ± 0.95 | <0.001 | 31.88 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sun, X.; Ma, L.; Xuan, Y.; Liang, J.
Degradation of Anti-Nutritional Factors in Maize Gluten Feed by Fermentation with *Bacillus subtilis*: A Focused Study on Optimizing Fermentation Conditions. *Fermentation* **2024**, *10*, 555.
https://doi.org/10.3390/fermentation10110555

**AMA Style**

Sun X, Ma L, Xuan Y, Liang J.
Degradation of Anti-Nutritional Factors in Maize Gluten Feed by Fermentation with *Bacillus subtilis*: A Focused Study on Optimizing Fermentation Conditions. *Fermentation*. 2024; 10(11):555.
https://doi.org/10.3390/fermentation10110555

**Chicago/Turabian Style**

Sun, Xiaohong, Lei Ma, Yaoquan Xuan, and Jianfen Liang.
2024. "Degradation of Anti-Nutritional Factors in Maize Gluten Feed by Fermentation with *Bacillus subtilis*: A Focused Study on Optimizing Fermentation Conditions" *Fermentation* 10, no. 11: 555.
https://doi.org/10.3390/fermentation10110555