Intelligent assessment of the histamine level in mackerel (Scomber australasicus) using near-infrared spectroscopy coupled with a hybrid variable selection strategy

Abstract

Determination of the histamine level in fish is essential not only because it is an indicator of fish freshness but also because this prevents the risk of histamine intoxication in consumers. This study used the strategy of near-infrared (NIR) spectroscopy coupled with a hybrid variable selection for rapid and nondestructive assessment of the histamine level in mackerel. To effectively identify the highly informative spectral variables, a three-step hybrid strategy, combining backward interval partial least squares, selectivity ratio and flower pollination algorithm, was developed. The optimized variables were fitted to the multivariate calibration models of partial least squares model (PLS), radial basis function neural network (RBFNN), and wavelet neural network (WNN). The best model was obtained by the optimized WNN model using the hybrid variable selection method, with R-squared (RP2) value and root mean squared error for prediction were, 0.79 and 70 mg/kg for flesh side dataset, and 0.76 and 75 mg/kg for skin side dataset. The obtained results for the skin side dataset significantly outperformed the PLS(RP2=0.58) and RBFNN (RP2=0.47) calibration models.