[Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy].

Grain hardness is an important quality parameter of wheat which has great influence on the classification, usage and composition research of wheat. To achieve rapid and accurate detection of wheat hardness, radial basis function (RBF) neural network model was built to predict the hardness of unknown samples on the basis of analyzing the absorptive characteristics of the composition of wheat grain in infrared, besides, the effects of different spectral pretreatment methods on the predictive accuracy of models were emphatically analyzed. 111 wheat samples were collected from major wheat-producing areas in China; then, spectral data were obtained by scanning samples. Mahalanobis distance method was used to identify and eliminated abnormal spectra. The optimized method of sample set partitioning based on joint X-Y distance (SPXY) was used to divide sample set with the number of calibration set samples being 84 and prediction set samples being 24. Successive projections algorithm (SPA) was employed to extract 47 spectral features from 262. SPA, first derivatives, second derivatives, standard normal variety (SNV) and their combinations were applied to preprocess spectral data, and the interplay of different prediction methods was analyzed to find the optimal prediction combination. Radial basis function (RBF) was built with preprocessed spectral data of calibration set being as inputs and the corresponding hardness data determined via hardness index (HI) method being as outputs. Results showed that the model got the best prediction accuracy when using the combination of SNV and SPA to preprocess spectral data, with the discriminant coefficient (R2), standard error of prediction (SEP) and ratio of performance to standard deviate (RPD) being 0.844, 3.983 and 2.529, respectively, which indicated that the RBF neural network model built based on visible-near infrared spectroscopy (Vis-NIR) could accurately predict wheat hardness, having the advantages of easy, fast and nondestructive compared with the traditional method. It provides a more convenient and practical method for estimating wheat hardness.