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

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.