Raw Material Quality Analysis of Single‑cell Protein Production from Baijiu Distiller’s Grains via Dual‑strain Fermentation Based on Metabolomics

doi:10.13560/j.cnki.biotech.bull.1985.2026-0567

Abstract

Abstract:

Objective This study aimed to investigate the process conditions for producing single-cell protein (SCP) from Baijiu distiller’s grains through solid-state fermentation with Candida utilis and Saccharomyces cerevisiae, and to analyze the changes in metabolite profiles of the fermented products based on metabolomics, as well as their associations with protein quality, flavor characteristics, and safety. Method Nongxiang-type Baijiu distiller’s grain served as the substrate. Single-factor and Box-Behnken experimental designs were used to optimize the fermentation process. Metabolomics analysis identified differences in volatile and non‑volatile metabolites between dual‑strain fermented samples and the blank control. Nutrient composition determination, in vitro digestion tests, and mycotoxin detection were performed to complete the systematic quality evaluation. Result The optimal fermentation parameters were determined as 15% wheat bran supplementation, 30 ℃, 15% inoculum size, 58% moisture content, pH 5.5, and 3-day fermentation duration. After fermentation, acid-soluble protein (ASP) increased markedly by 35.03%, while crude protein (CP) and true protein (TP) increased by 16.64% and 29.32%, respectively. Metabolomics analysis identified 92 volatile flavor compounds across 9 chemical classes. Ester contents increased substantially, while undesirable flavor substances such as hexanal decreased significantly. A total of 296 differential metabolites were screened, among which lipids and terpenoid derivatives were significantly up‑regulated, while aromatic amino acids and anthraquinones were significantly down‑regulated. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis revealed the activation of four metabolic pathways, including glutamic acid biosynthesis and 2-oxocarboxylic acid metabolism. In vitro digestibility improved significantly, mycotoxin tests showed negative results, and the product met national feed hygiene standards. Conclusion The solid-state fermentation of liquor residue using both Candida utilis and Saccharomyces cerevisiae can significantly improves protein quality and flavor characteristics, and providing metabolomics-based data support and a novel quality evaluation perspective for the high-value utilization of Baijiu distiller’s grains.

Key words: Baijiu distiller’s grains, single-cell protein, dual-bacteria synergistic fermentation, metabolomics, quality evaluation

GUI Yuan, YANG Ting-fei, WANG Feng-qing, LIU Jun, LIU Gen-qiao, LI Zhong-xuan, LIAO Yu-ting, WANG Chuan, LI Li, ZHOU Zheng-fu. Raw Material Quality Analysis of Single‑cell Protein Production from Baijiu Distiller’s Grains via Dual‑strain Fermentation Based on Metabolomics[J]. Biotechnology Bulletin, 2026, 42(11): 283-296.

Figures/Tables 12

References 39

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方差来源 Variance source	平方和 Sum of squares	自由度 Degrees of freedom	均方 Mean square	F值 F-value	P值 P-value	显著性 Significance
模型	31.42	9	3.49	245.78	<0.000 1	**
A-温度（℃）	1.02	1	1.02	72.15	<0.000 1	**
B-接种量（%）	2.13	1	2.13	149.62	<0.000 1	**
C-时间（d）	1.10	1	1.10	77.12	<0.000 1	**
AB	0.007 9	1	0.007 9	0.553 1	0.481 3	ns
AC	0.228 2	1	0.228 2	16.07	0.005 1	**
BC	0.832 6	1	0.832 6	58.62	0.000 1	**
A²	17.24	1	17.24	1 213.61	<0.000 1	**
B²	4.98	1	4.98	350.89	<0.000 1	**
C²	1.76	1	1.76	123.91	<0.000 1	**
残差	0.099 4	7	0.014 2
失拟误差	0.017 8	3	0.005 9	0.290 7	0.831 0	ns
纯误差	0.081 6	4	0.020 4
总误差	31.52	16
R²：0.996 8， R²_Adj：0.970 6， R²_Pre：0.986 8

方差来源 Variance source	平方和 Sum of squares	自由度 Degrees of freedom	均方 Mean square	F值 F-value	P值 P-value	显著性 Significance
模型	31.42	9	3.49	245.78	<0.000 1	**
A-温度（℃）	1.02	1	1.02	72.15	<0.000 1	**
B-接种量（%）	2.13	1	2.13	149.62	<0.000 1	**
C-时间（d）	1.10	1	1.10	77.12	<0.000 1	**
AB	0.007 9	1	0.007 9	0.553 1	0.481 3	ns
AC	0.228 2	1	0.228 2	16.07	0.005 1	**
BC	0.832 6	1	0.832 6	58.62	0.000 1	**
A²	17.24	1	17.24	1 213.61	<0.000 1	**
B²	4.98	1	4.98	350.89	<0.000 1	**
C²	1.76	1	1.76	123.91	<0.000 1	**
残差	0.099 4	7	0.014 2
失拟误差	0.017 8	3	0.005 9	0.290 7	0.831 0	ns
纯误差	0.081 6	4	0.020 4
总误差	31.52	16
R²：0.996 8， R²_Adj：0.970 6， R²_Pre：0.986 8

方差来源 Variance source	平方和 Sum of squares	自由度 Degrees of freedom	均方 Mean square	F值 F-value	P值 P-value	显著性 Significance
模型	5.78	9	0.642 6	59.75	<0.000 1	**
A-温度（℃）	1.61	1	1.61	150.13	<0.000 1	**
B-接种量（%）	0.016 0	1	0.016 0	1.49	0.261 9	ns
C-时间（d）	0.187 9	1	0.187 9	17.47	0.004 1	**
AB	0.014 1	1	0.014 1	1.31	0.289 4	ns
AC	0.116 6	1	0.116 6	10.84	0.013 2	*
BC	0.033 3	1	0.033 3	3.10	0.121 7	ns
A²	2.21	1	2.21	205.59	<0.000 1	**
B²	0.036 9	1	0.036 9	3.43	0.106 4	ns
C²	1.29	1	1.29	119.70	<0.000 1	**
残差	0.075 3	7	0.010 8
失拟误差	0.047 0	3	0.015 7	2.22	0.228 4	ns
纯误差	0.028 3	4	0.007 1
总误差	5.86	16
R²：0.987 2， R²_Adj：0.970 6， R²_Pre：0.864 0

方差来源 Variance source	平方和 Sum of squares	自由度 Degrees of freedom	均方 Mean square	F值 F-value	P值 P-value	显著性 Significance
模型	5.78	9	0.642 6	59.75	<0.000 1	**
A-温度（℃）	1.61	1	1.61	150.13	<0.000 1	**
B-接种量（%）	0.016 0	1	0.016 0	1.49	0.261 9	ns
C-时间（d）	0.187 9	1	0.187 9	17.47	0.004 1	**
AB	0.014 1	1	0.014 1	1.31	0.289 4	ns
AC	0.116 6	1	0.116 6	10.84	0.013 2	*
BC	0.033 3	1	0.033 3	3.10	0.121 7	ns
A²	2.21	1	2.21	205.59	<0.000 1	**
B²	0.036 9	1	0.036 9	3.43	0.106 4	ns
C²	1.29	1	1.29	119.70	<0.000 1	**
残差	0.075 3	7	0.010 8
失拟误差	0.047 0	3	0.015 7	2.22	0.228 4	ns
纯误差	0.028 3	4	0.007 1
总误差	5.86	16
R²：0.987 2， R²_Adj：0.970 6， R²_Pre：0.864 0