Application of successive projections algorithm to nondestructive determination of pork pH value

Using a few variables from the fresh pork spectra to construct the model of pH prediction is capable of decreasing of calculated amount, which has a vital significance to determine and monitor pork pH values. In this study, successive projection algorithm (SPA) was proposed to select feature wavelength from pork to determine the pH values. The performance of the model which constructed by variables selected of SPA was compared with various models including the model of partial least squares regression (PLSR) based on full spectrum (5 000-10 440 cm-1), the model constructed by variables selected of stepwise multiple linear regression (SMLR) and the model constructed by variables selected of genetic algorithm (GA). A total of 37 variables, only 2.6 percent in the full spectrum, selected by SPA were employed to construct the model with 0.870 as the correlation coefficient and 0.094 as the root of mean square error of calibration set and 0.892 as the correlation coefficient and 0.085 as the root of mean square error of validation set. While it was nearly to the PLSR model with preprocess of multiplicative signal correction, the SPA-MLR model is more accurate than SMLR model and GA model. The results confirm that SPA can be applied to select few variables from huge information space of NIR spectroscopy to build simple model to determine fresh pork pH values.