Comparative Study on Calibration Models Using NIR Spectroscopy Data

Abstract

The quality of pork is largely influenced by moisture, fat, and protein. In the meat industry, the establishment of a fast and accurate prediction system is always welcomed. Near infrared spectroscopy (NIRS) can satisfy the requirements of the evaluation. An automatic routine based on support vector regression (SVR), a backpropagation neural network (BPNN), and principal component analysis–backpropagation neural network (PCA–BPNN) was developed to predict three components of pork using 16 combinations of pretreatment (convolution function-based moving average, detrending based on the standard normal variate, and multiplicative scatter correction). Model comparisons were implemented to evaluate the influence of pretreatment and calibration models on the prediction ability of models. The correction method and smoothing methods can significantly reduce the model prediction error. Most of the SVR models have high prediction accuracy and are suitable for predicting moisture and protein. The BPNN and PCA–BPNN are more suitable for dealing with nonlinearity between fat and NIR observations.