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Published on: November 30, 2022
Aged Pickle Brine Influences Flavor and Bacteria Characteristics of Pickled Chili Peppers
Chenshuo Wang1, Kaishuang Wei1, Zhiji Qin1
1College of Pharmacy and Food, Southwest Minzu University, Chengdu 610041, China.
Abstract:
Aging duration of the brine is considered one of the core variables determining the fermentation quality of pickled chili peppers. This study systematically analyzed the dynamic changes in volatile and non-volatile metabolites and bacteria community structure in fermented pickled chili peppers fermented with different pickle brines (aged 0, 5, 15, 25, and 50 years). Results indicate that brine age was significantly associated with both the flavor compound composition and bacteria diversity of fermented pickled chili peppers. GC-MS identified 127 volatile compounds, and RF-based exploratory marker analysis identified 10 candidate volatile markers associated with brine aging. 1H-NMR analysis screening identified six candidate non-volatile metabolites, whose dynamic changes were associated with bacterial degradation of proteins and carbohydrates. The selected marker panels showed internal discriminative consistency within the current dataset, indicating that brine ageing duration was associated with distinguishable metabolic profiles in fermented pickled chili peppers. α-diversity indices showed both species richness and community diversity shifted dynamically. Bacillota dominated the bacterial community, with Levilactobacillus as the most abundant genus. Spearman correlation analysis revealed that Levilactobacillus was significantly positively correlated with 4-ethyl-2-methoxyphenol, 4-ethylphenol, methylguanidine, and propylene glycol, while negatively correlated with β-Ionone and ethanol. Meanwhile, ethanol was positively associated with Weissella, Oceanobacillus, and unclassified_f_Bacillaceae. These findings provide a theoretical basis for understanding the role of aged brine in pickled chili peppers and support the potential application of multi-omics combined with machine learning to assist in fermented food discrimination. However, it is worth noting that, as the initial metabolic composition of the brines was not characterized, the observed differences should be interpreted as a brine-age-associated composite effect rather than being attributed exclusively to the fermentation process.
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