Abstract:

Simulated annealing algorithm (SAA) is a random search algorithm for global optimization. In order to improvethe accuracy and robustness of near-infrared spectroscopy (NIR) in detecting wheat flour quality, this paper proposed aquantitative prediction model using global optimization based on SAA combined with partial least squares (PLS). In thisalgorithm, a comparative analysis was made in different parameter settings of cooling schedule. According to the ash contentgradients in flour, the NIR spectra of 126 samples were selected randomly to establish an SAA-PLS model. Results showedthat 70 wave numbers were picked out of 2 074 wave numbers using SAA. The quantitative model established using partialleast squares exhibited a correlation coefficient (CC) of 0.976 0, a root mean square error of cross validation (RMSECV) of0.022, and a root mean square error of prediction (RMSEP) of 0.030 1, while the CC, RMSECV and RMSEP values of thePLS model based on the full wave spectra was 0.778 5, 0.066 6 and 0.076 8, respectively. These results indicated that it wasfeasible to establish a quantitative model for predicting ash content using wavelength optimization based on SAA, which wassuperior in accuracy and robustness to the full-spectrum model.

Key words: simulated annealing algorithm, partial least squares method, flour, near-infrared spectroscopy, quantitative analysis