Abstract
The total viable count (TVC) is a traditional method for evaluating seafood freshness. This study aimed to predict the TVC of Fujian oysters stored at 4℃ by using the colorimetric sensing array (CSA) technique combined with imaging and visible near-infrared (Vis-NIR) information channels. An optimal quantitative prediction model based on partial least squares (PLS) was established by combining feature variable selection with a data fusion strategy. All three data-fusion strategies showed better predictive performance than the individual datasets. The high-level fusion strategy performed the best, with a root mean square error of prediction (RMSEP) of 0.17, mean absolute error of prediction (MAEP) of 0.14, coefficient of determination of prediction (Rp2) of 0.9822, and residual prediction deviation (RPD) of 7.37. The experimental results showed that CSA combined with multiple detection channels could be effectively used to predict the TVC of Fujian oysters. In this study, a reliable and comprehensive evaluation model was established to achieve a rapid and accurate prediction of oyster freshness.
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