Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

Xiaolei Zhang,Xiaoli Li,Yong He,Fei Liu

doi:10.3390/s121217234

Abstract

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds.

Highlights

Effective variety discrimination of maize seeds is increasingly vital for the growing food industry owing to the appearance on the market of more and more new maize varieties like Sweet maize, Waxy maize, Popcorn, Dent maize and Amylomaize, during these years
Further treatments would be needed and the latent features of the spectra could be applied for the variety discrimination of maize seeds
The above excellent discrimination results suggested that visible and near infrared (VIS-NIR) hyperspectral imaging technique combined with principal component analysis (PCA)-gray level co-occurrence matrix (GLCM) feature extraction and least squares-support vector machine (LS-SVM) could be successfully applied for conducting fast variety identification of commercial maize seeds

Summary

Introduction

Effective variety discrimination of maize seeds is increasingly vital for the growing food industry owing to the appearance on the market of more and more new maize varieties like Sweet maize, Waxy maize, Popcorn, Dent maize and Amylomaize, during these years. Different varieties of maize seeds have different characteristics and qualities. Types of maize are commonly classified depending on their quality parameters, such as oil content, sweetness, and degree of waxiness. The traditional and prevailing methods for seed cultivar identification, like grain morphology, fluorescent scanning, protein electrophoresis and DNA molecular markers are time consuming, expensive, complex to use and subject to human error and inconsistency. To overcome these shortcomings, an approach for quickly and reliably identifying maize seed varieties would be highly desirable and beneficial from both technical and economical points of view. Is this work automatic variety identification based on hyperspectral imaging technique was investigated

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Results

Conclusion