Abstract: In order to optimize the calibration set for near-infrared modeling of the protein content in wheat flour, the binary dragonfly algorithm (BDA) was used to select representative samples from the primary calibration set divided by the traditional Kennard/Stone (K/S) method. Based on the representative samples, a partial least square regression (PLSR) model for estimating the protein content in wheat flour was established, and the prediction set was employed to evaluate the stability and prediction performance of the model. The results indicated that an optimal calibration set with 30 samples was selected finally by BDA, and the proposed model exhibited a coefficient of determination of prediction (Rp2) of 0.956 4 and a root mean square errors of prediction (RMSEP) of 0.278 1, which increased by 1.87% and decreased by 15.57% compared with those (0.938 8 and 0.329 4) from K/S partition of 100 primary calibration sets, respectively. The average number of calibration sets selected from 10 BDA experiments was 30.2, and the protein content of wheat flour was predicted better by the 10 models developed than that obtained based on the primary calibration set. Therefore, BDA can select a small number of representative calibration set samples based on which a PLSR model with good robustness and high prediction accuracy for the protein content of wheat flour can be established. The proposed method can provide an efficient tool for calibration set selection in near-infrared spectroscopic analysis of the quality of wheat flour.

Key words: dragonfly algorithm; near-infrared spectroscopy; optimization of calibration set; protein content of wheat flour