Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy
HUI Guang-yan1, SUN Lai-jun1*, WANG Jia-nan1, WANG Le-kai2, DAI Chang-jun2
1. Key Laboratory of Electronics Engineering, College of Heilongjiang Province, Heilongjiang University, Harbin 150080, China 2. Cereal and Products Quality Supervisory Inspection and Test Center of Ministry of Agriculture, Harbin 150080, China
Abstract：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.
Key words：Hardness;Wheat;Visible-near infrared spectroscopy;Successive projections algorithm;Radial basis function
惠光艳1，孙来军1*，王佳楠1，王乐凯2，戴常军2 . 可见-近红外光谱的小麦硬度预测模型预处理方法的研究 [J]. 光谱学与光谱分析, 2016, 36(07): 2111-2116.
HUI Guang-yan1, SUN Lai-jun1*, WANG Jia-nan1, WANG Le-kai2, DAI Chang-jun2 . Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(07): 2111-2116.
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