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Open AccessArticle

Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics

by Yidan Bao 1,2, Chunxiao Mi 1,2, Na Wu 1,2, Fei Liu 1,2,* and Yong He 1,2
1
College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
2
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(19), 4119; https://doi.org/10.3390/app9194119
Received: 20 August 2019 / Revised: 18 September 2019 / Accepted: 23 September 2019 / Published: 2 October 2019
(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)
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. View Full-Text
Keywords: hyperspectral imaging; chemometrics; wheat seeds; variety classification; discriminant model; preprocessing; feature wavelengths hyperspectral imaging; chemometrics; wheat seeds; variety classification; discriminant model; preprocessing; feature wavelengths
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Bao, Y.; Mi, C.; Wu, N.; Liu, F.; He, Y. Rapid Classification of Wheat Grain Varieties Using Hyperspectral Imaging and Chemometrics. Appl. Sci. 2019, 9, 4119.

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