Microbial Community Succession and Its Correlation with Quality Characteristics during Gray Sufu Fermentation.

Gray sufu, a traditional fermented food derived from soybeans, undergoes a complex fermentation process. This study aimed to investigate the dynamics of the microbial community during sufu fermentation and its relationship with key quality characteristics. Through systematic sampling of sufu at different phases of fermentation, 143 bacterial genera and 84 fungal genera involved in the process were identified. Among these, Chishuiella, Enterococcus, Lactococcus, and Weissella emerged as the predominant bacterial communities. After seven days of ripening fermentation, Trichosporon supplanted Diutina as the predominant fungus, accounting for more than 84% of all fungi. Using redundancy analysis, significant correlations between microbiota and physicochemical properties were uncovered. Chishuiella and Empedobacter displayed positive relationships with pH, soluble protein, and amino nitrogen content. In addition, five biogenic amines were detected, and it was determined that tyramine accounted for more than 75% of the total biogenic amines in the final gray sufu products. Spearman correlation analysis revealed significant positive relationships between Lactococcus, Enterococcus, Tetragenococcus, Halanaerobium, and Trichosporon and the five biogenic amines examined. These findings shed light on the complex interactions between microorganisms and biogenic amines during the fermentation of gray sufu, thereby facilitating the development of microbial regulation strategies for better quality control.

Simultaneous identification of animal-derived components in meats using high-throughput sequencing in combination with a custom-built mitochondrial genome database.

Currently, the inspection and supervision of animal ingredients relies primarily upon specific amplification-dependent methods, whose efficiency and accuracy are being seriously challenged by the increasing diversity and complexity of meat products. High-throughput sequencing (HTS) technology was employed to develop an alternative method to detect animal-derived ingredients in meat products. A custom-built database containing 2,354 complete mitochondrial genomic sequences from animals, an identification analysis pipeline based on short-sequence alignment, and a web-based server were built to facilitate this detection. The entire process, including DNA extraction, gene amplification, and sequencing, was established and optimized for both marker gene (part of the CYTB gene)-based detection and total DNA-based detection. Using simulated samples containing various levels of pig, cattle, sheep, chicken, rabbit, and mice ingredients, the detection capability and accuracy of this method were investigated. The results of this study indicated that the method is capable of detecting animal components in meats that are present at levels as low as 1%. Our method was then tested using 28 batches of real meat products such as raw meat slices, raw meat mince, cooked dried meat, cooked meat sausage, and other supermarket samples, with a traditional qPCR method as the control. The results demonstrated an accuracy of 97.65% for the qualitative detection method, which indicate that the developed method is reliable for the detection of animal components. The method is also effective for the identification of unknown food samples containing mixed animal components, which suggests a good future in application.