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Spoilage-related microbiota in fish and crustaceans during storage: Research progress and future trends.

Shuai ZhuangHui HongLongteng ZhangYongkang Luo
Published in: Comprehensive reviews in food science and food safety (2020)
Fish and crustaceans are highly perishable due to microbial growth and metabolism. Recent studies found that the spoilage process of fish and crustaceans is highly related to their microbiota composition. Microbiota of fish and crustaceans changes dramatically during storage and can be influenced by many factors (e.g., aquaculture environment, handling process, storage temperature, and various quality control techniques). Among them, many quality control techniques have exhibited efficient effects on inhibiting spoilage bacteria, regulating microbiota composition, and retarding quality deterioration. In this article, we elucidate the relationship between microbiota composition and fish/crustacean spoilage, demonstrate influencing factors of fish/crustaceans microbiota, and review various quality control techniques (especially plant-derived preservatives) including their preservative effects on microbiota and quality of fish and crustaceans. Besides, present and future trends of various detective methods used in microbiota analysis are also compared in this review, so as to provide guides for future microbiota studies. To conclude, novel preservation techniques (especially plant-derived preservatives) and hurdle technologies are expected to achieve comprehensive inhibitory effects on spoilage bacteria. Efficient delivery systems are promising in improving the compatibility of plant-derived preservatives with fish/crustaceans and enhancing their preservative effects. Besides, spoilage mechanisms of fishery products that involve complex metabolisms and microbial interactions need to be further elucidated, by using omics technologies like metagenomics, metatranscriptomics, and metabolomics.
Keyphrases
  • quality control
  • microbial community
  • signaling pathway
  • current status
  • single cell
  • data analysis