Exploring the spoilage microbiota of raw and processed meats under different storage environments and its related profiling methods: a review

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Résumé

Raw and processed meats are potentially rich ecosystems colonized by a variety of
microorganisms whose growth and viability are strongly dependent on storage
conditions. Furthermore, for their own adaptations, microorganisms can release
exocellular compounds which contribute to meat spoilage due to changes in
physicochemical parameters according to preservation methods. Therefore, deeper
knowledge of such microorganisms at species level might help to better determine and
standardize the best preservation system that may delay the spoilage and extend the
meat shelf-life. For this purpose, culture-independent methods have emerged as
complementary to culture-dependent methods for a better knowledge at species level
of viable spoilage microorganisms in meat ecosystems stored under various conditions.
This review reported the most applied methods to identify meat spoilage microbiota
and highlighted their limitations in raw and processed meat ecosystems. The use of
next generation sequencing (ngS)-based metabarcoding is revealed to be among the
most effective techniques to completely profile the microbial ecosystem of processed
and stored meats in different environments. Recommendations are formulated to
combine ngS tools with an artificial intelligence approach such as machine learning
through convolutional neural networks to predict spoilage and monitor meat quality
samples in real time.

Mots-clés

Meats; spoilage,microbiota,culture-dependent and,independent methods,storage conditions; next,generation sequencing