Establishment of Quantitative Model to Predict the Freshness of Crucian Carp (Carassius auratus) Based on Near-infrared Spectroscopy
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Abstract:
To rapidly determine the freshness of crucian carp (Carassius auratus), near-infrared (NIR) diffuse reflectance spectroscopy- based quantitative analysis coupled with chemometric methods was used to collect spectral data in the range of 1000~1799 nm for 144 carp samples. Freshness quality indexes including pH, total volatile basic nitrogen (TVB-N) content, the thiobarbituric acid (TBA) value, and the K value were measured for all samples. After the optimum spectral pretreatment method and suitable spectra bands were determined, quantitative prediction models for crucian carp freshness were established using partial least squares (PLS) regression, principal component analysis (PCA) combined with back propagation artificial neural network (BP-ANN), and PLS combined with BP-ANN. The ranges of the four indicator values for crucian carp samples were wide and met the assumptionss for modeling. When pH was used as the freshness indicator, the prediction model developed using PLS combined with BP-ANN was the best, and the correlation coefficient was 0.9945. When the TVB-N content, TBA value, and K value were used as freshness indicators, PLS prediction models were the best, and the corresponding correlation coefficients were 0.9857, 0.9985, and 0.9952, respectively. The established quantitative models for the four freshness indicators all had strong prediction capabilities.
