Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively

Abstract

Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. In this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simultaneously, their K values were determined by high performance liquid chromatography (HPLC). A back propagation artificial neural network (BP-ANN) prediction model of K value was established. The precision of BP-ANN was further improved after optimization by the algorithm of Adaptive boosting (AdaBoost), whose root mean square error of prediction (RMSEP) and correlation coefficient (R_P) were 9.89% and 0.84 respectively in the prediction set, indicating that the non-linear models (BP-ANN and BP-AdaBoost) were superior to the linear principal component regression (PCR) model. The topological neural network architecture was much more suitable for analyzing complicated regression relationship between K value and THz spectra. It can be concluded that the THz spectral coupled with BP-AdaBoost algorithm is capable of predicting the pork K value.

Keywords:
pork; K value; THz spectroscopy; chemometry; BP-ANN adaptive boosting; non-destruction