Classification of Wheat Grains in Different Quality Categories by Near Infrared Spectroscopy and Support Vector Machine

For the purpose of rapid, simple and accurate identification of quality of wheat grains, this study proposed a recognition method which is an integration of near infrared spectroscopy and support vector machine (SVM). The spectral data of wheat samples were analyzed in order to eliminate abnormal data, and then Mahalanobis distance method was used to identify abnormal samples. After deleting those abnormal samples, principal component analysis was done to prove the feasibility of classifying wheat by near infrared technologies. The remaining 111 wheat samples were divided into calibration set and prediction set by sample set partitioning based on joint X-Y distance algorithm, then, the first derivative, second derivative, standard normal variate (SNV) transformation and their combinations were used to preprocess spectra for obtaining the optimal pretreatment method before modeling. Finally, SVM and back propagation neural network classification model were established with the spectral data preprocessed by second derivative plus SNV and first derivative plus SNV, respectively. Prediction results of SVM model showed that the recognition accuracy rate of strong gluten wheat and weak gluten wheat both achieved 100% and the recognition accuracy rate of medium gluten wheat also reached 81.82%, which proved that SVM classification model with the spectra data preprocessed by the second derivative plus SNV achieved the best results and realized rapid and accurate identification and classification of wheat quality.