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Spoilage evaluation of raw Atlantic salmon (Salmo salar) stored under modified atmospheres by multivariate statistics and augmented ordinal regression

Kuuliala, L., Sader, M., Solimeo, A., Pérez-Fernández, R., Vanderroost, M., De Baets, B., De Meulenaer, B., Ragaert, P., Devlieghere, F.
International journal of food microbiology 2019 v.303 pp. 46-57
Salmo salar, air, anaerobic conditions, data collection, dimethyl sulfide, ethanol, food quality, hydrogen sulfide, ketones, mass spectrometry, modified atmosphere packaging, monitoring, multivariate analysis, perishable foods, seafoods, sensory evaluation, spoilage, storage conditions, volatile organic compounds
The development of quality monitoring systems for perishable food products like seafood requires extensive data collection under specified packaging and storage conditions, followed by advanced data analysis and interpretation. Even though the benefits of using volatile organic compounds as food quality indices have been recognized, few studies have focused on real-time quantification of the seafood volatilome and subsequent systematic identification of the most important spoilage indicators. In this study, spoilage of Atlantic salmon (Salmo salar) stored under modified atmospheres (% CO2/O2/N2) and air was characterized by performing multivariate statistical analysis and augmented ordinal regression modelling for data collected by microbiological, chemical and sensory analyses. Out of 25 compounds quantified by selected-ion flow-tube mass spectrometry, ethanol, dimethyl sulfide and hydrogen sulfide were found characteristic under anaerobic conditions (0/0/100 and 60/0/40), whereas spoilage under air was primarily associated with the production of alcohols and ketones. Under high-O2 MAP (60/40/0), only 3-methylbutanal fulfilled the identification criteria. Overall, this manuscript presents a systematic and widely applicable methodology for the identification of most potential seafood spoilage indicators within the context of intelligent packaging technology development. In particular, parallel application of statistics and modelling was found highly beneficial for the performance of the quality characterization process and for the practical applicability of the obtained results in food quality monitoring.