# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
## 3 Results and Discussion
### Classification Results and Analysis of Discriminant Models Based on Full Wavelengths  
The discrimination models, LDA, SVM, and ELM, were established based on full wavelengths to classify five varieties of wheat seeds quantitatively using all wheat seeds sample, because of the distinguishable feasibility of part of random sample. The whole data set was divided into 10 groups and every time one group was taken for the test set, the other nine for the train set. Ten different data groups were input into the model, respectively, and their average accuracy was served as the final result. Three preprocessing methods were introduced and their influence on the discriminant model were analyzed because the spectral information contains lots of noise. The performance of all models and the relevant parameters were summarized in Table 1. As shown in Table 1, the three models based on full spectra all achieved good accuracies over 80% on predication set, which indicated a clear and available classification. Dealing by a linear segmentation, LDA got a lower performance than SVM while using hyperplane and ELM using neural network. SVM reached accuracy of 90.13% and 87.81% for calibration set and prediction set, respectively, which were close to the accuracy of 91.30% and 86.26% obtained by ELM model. However, it could be also seen from Table 1 that the models' classification accuracies all slightly decreased after introducing the preprocessing methods. Among them, WT caused the most performance degradation, followed by MSC, and finally SNV, which was fairly close to the performance of the discriminant model based on the raw spectra. According to [PERSON], the highest accuracy to classify contaminants and wheat was obtained by the classifier with SNV than MSC. Moreover, combined with both techniques, the modelling performance were also better than that based on raw spectra [17]. [PERSON] et al. concluded that the higher accuracy using SVM with WT was acquired than other preprocessing methods in prediction set to identify seed varieties [30]. However, none of the pretreatment methods used in this study, including WT, SNV, and MSC, improved the performance of the discriminant model and achieved satisfactory results. By conducting studies above, it could be indicated that not all of the preprocessing methods could improve the classification performance well. For different spectral data, it is not always possible to obtain better results employing the model that was preprocessed by same technique. The reasons were as follows: (1) the spectral information was inevitably disturbed in the process of noise elimination; (2) the transformation and disposal of spectral characteristics might reduce the credibility of data; and, (3) the inhomogeneity of noise random distribution in each band might be ignored. Thus, appropriate preprocessing method or only the original spectra should be selected in accordance with specific data set in the specific application. Besides, the running time using LDA and ELM was much shorter than SVM whose spent time, including optimal parameter selection, was more than one day. In conclusion, the pretreatment of spectra did not have significant impact on the performance of the models. When compared with LDA and SVM, the ELM model had greater advantages in both operational speed and classification accuracy.