The formation of cheese flavor mainly results from the production of volatile compounds by microorganisms. We investigated how fine-tuning cheese-making process parameters changed the cheese volatilome in a semi-hard cheese inoculated with Lactococcus (L.) lactis, Lactiplantibacillus (L.) plantarum, and Propionibacterium (P.) freudenreichii. A standard (Std) cheese was compared with three variants of technological itineraries: a shorter salting time (7 h vs 10 h, Salt7h), a shorter stirring time (15 min vs 30 min, Stir15min), or a higher ripening temperature (16 °C vs 13 °C, Rip16°C). Bacterial counts were similar in the four cheese types, except for a 1.4 log 10 reduction of L. lactis counts in Rip16°C cheeses after 7 weeks of ripening. Compared to Std, Stir15min and Rip16°C increased propionibacterial activity, causing higher concentrations of acetic, succinic, and propanoic acids and lower levels of lactic acid. Rip16°C accelerated secondary proteolysis and volatile production. We thus demonstrated that fine-tuning process parameters could modulate the cheese volatilome by influencing specific bacterial metabolisms.

Cheese organoleptic properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This digital twin accurately predicted the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration raised additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added-value of dynamic metabolic modeling for the comprehension of fermented food processes.

Dairy products are nutritious and are increasingly consumed as an important dietary component in China. Exploring the factors that affect the nutritional quality of dairy products and ensuring their safety have become the main focus of dairy research. The composition of metabolites in dairy products is large and complex. The levels and types of metabolites vary according to various factors in the process from factory to human dining table. Therefore, metabolites might be used to assess the nutritional value, traceability and authenticity, and physiological function of dairy products. This review's main goal is to introduce the most recent developments and applications of metabolomics as an efficient tool for comprehensively characterising the composition and dynamic changes of metabolites in the area of food science and nutrition research in the process of getting dairy products from factory to human. The examples are taken from the most relevant metabolomics work published from 2018 to 2022, focusing on potential marker metabolites and metabolic mechanisms related to dairy product quality, authenticity/traceability and dairy intake monitoring. The future direction of metabolomics in the field of dairy science was also discussed. This information will provide a reference for the further application of metabolomics technology to Chinese dairy products to develop their quality, safety and nutritional value.