# Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics
###### Abstract  
The classification of wheat grain varieties is of great value because its high purity is the yield and quality guarantee. In this study, hyperspectral imaging combined with the chemometric methods was applied to explore and implement the varieties classification of wheat seeds. The hyperspectral images of all the samples covering 874-1734 nm bands were collected. Exploratory analysis was first carried out while using principal component analysis (PCA) and linear discrimination analysis (LDA). Spectral preprocessing methods including standard normal variate (SNV), multiplicative scatter correction (MSC), and wavelet transform (WT) were introduced, and their effects on discriminant models were studied to eliminate the interference of instrumental and environmental factors. PCA loading, successive projections algorithm (SPA), and random frog (RF) were applied to extract feature wavelengths for redundancy elimination owing to the possibility of existing redundant spectral information. Classification models were developed based on full wavelengths and feature wavelengths using LDA, support vector machine (SVM), and extreme learning machine (ELM). This optimal model was finally utilized to generate visualization map to observe the classification performance intuitively. When comparing with other models, ELM based on full wavelengths achieved the best accuracy up to 91.3%. The overall results suggested that hyperspectral imaging was a potential tool for the rapid and accurate identification of wheat varieties, which could be conducted in large-scale seeds classification and quality detection in modern seed industry. hyperspectral imaging; chemometrics; wheat seeds; variety classification; discriminant model; preprocessing; feature wavelengths +
Footnote †: journal: Applied  
Traditional method relying on manual inspection and requiring enormous professional knowledge is subjective and time-consuming. In recent years, machine vision technology has emerged as an automatic and subjective method for identifying seed varieties. For example, [PERSON] et al. identified three varieties of rice seeds by extracting morphological and textural features of samples while using machine vision technology [3]. [PERSON] et al. classified eight wheat classes by extracting textural features employing a machine vision system with a monochrome camera [4]. However, machine vision technology could not achieve satisfactory results when the external characteristics such as appearance, morphology and texture of different varieties of seeds were little difference. Hence, the internal information could be considered to differentiate them, owing to the difference in the internal contents and components of different varieties of seeds [5; 6]. Imaging and spectroscopic technology exactly reflect the internal characteristics of samples by their optical properties, which compensate for the shortcomings of machine vision technology. Integrating the two technologies in one system, hyperspectral imaging (HSI) could simultaneously obtain spectral and spatial information and be applied to distinguish wheat varieties [7; 8]. HSI with the aid of chemometrics has been widely applied in various fields because of the advantage of nondestructive and rapid detection [9; 10]. For grain seeds, [PERSON] et al. classified single rice seed while using hyperspectral imaging combined with convolutional neural network and compared k-nearest neighbors (KNN) with support vector machine (SVM) models [11]. [PERSON] et al. distinguished 17 maize varieties using hyperspectral imaging combined with feature transformation methods integrating the least squares SVM model [12]. For wheat seeds, the classification using HSI mainly concerns the identification of mycotoxin contamination and foreign materials. [PERSON] et al. recognized mildew-contaminated wheat samples under six deoxynivalenol (DON) content levels based on hyperspectral imaging combined with chemometric methods and established several discriminant analysis [13]. [PERSON] et al.