Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging

Abstract

Hyperspectral imaging technique in the spectral wavelength range of 400–1000 nm was implemented in this study to determine the total volatile basic nitrogen (TVB-N) contents of grass carp fillets during the frozen storage. The quantitative calibration models were built between the spectral data extracted from the hyperspectral images and the reference measured TVB-N values by using partial least squares regression (PLSR) and least squares support vector machines (LS-SVM). The LS-SVM model using full spectral range had a better performance than the PLSR model for prediction of TVB-N value with the corresponding coefficients of determination (R 2 P ) of 0.916 and 0.905, and root-mean-square errors of prediction (RMSEP) of 2.346% and 2.749%, respectively. Nine optimal wavelengths (420, 466, 523, 552, 595, 615, 717, 850 and 955 nm) were selected using successive projections algorithm (SPA), and R 2 P values of 0.902 and 0.891 with the corresponding RMSEP of 2.782% and 2.807% were obtained from the new optimized models established based on the selected valuable wavelengths. The best SPA-LS-SVM model was used to achieve the visualization map of TVB-N content distribution of the tested fish fillet samples. The results of this study indicated that hyperspectral imaging technique as an objective and promising tool is capable of determining TVB-N values for evaluation of fish freshness quality in a rapid and non-destructive way. The study showed that VIS–NIR hyperspectral imaging technique was an effective and powerful tool for rapid and non-destructive determination and assessment of fish fillet freshness for the fish industry. • Hyperspectral imaging was used to predict TVB-N content of fish fillet. • TVB-N contents were determined by using spectral features of hyperspectral image. • SPA was used for optimal wavelength selection in hyperspectral imaging system. • TVB-N content distribution maps were created and visualized.