Detection of small yellow croaker freshness by hyperspectral imaging

Abstract

Rapid discrimination of fresh and repeatedly thawed small yellow croaker is of great significance for monitoring quality and ensuring consumer safety. In this study, we selected 160 fish samples of similar size and divided them equally into four groups (fresh, freeze-thaw once, freeze-thaw twice and freeze-thaw three times). Hyperspectral images were acquired from fresh and repeatedly frozen-thawed fish samples. The grey scale co-occurrence matrix (GLCM) was then applied to extract texture information from the first three principal component (PC) images, and a library for support vector machines (LIBSVM) were employed to discriminate fresh and repeatedly frozen-thawed fish samples using spectral characteristics, texture features and their fusion, respectively. The results indicated that LIBSVM model using the fused data showed the highest classification accuracy of 96.88%, and freshness degradation of fish samples after three freeze-thaw cycles was observed in the second PC image. In the freshness level for validation model, PLSR model achieved good performance withRV2= 0.90 and RPD= 3.17. The current findings demonstrate that hyperspectral imaging(HSI) is feasible for non-destructive determination of small yellow croaker freshness, providing technical guidance for the storage and marketing of aquatic products.