Application of hyperspectral imaging technology to discriminate different geographical origins of Jatropha curcas L. seeds

Abstract

A rapid and non-invasive method was investigated to identify the geographical origin of Jatropha curcas L. seeds in China by using near-infrared hyperspectral imaging technique on the wavelength between 874 and 1734nm. Two hundred and forty J. curcas L. seed samples from four different geographical origins (Jiangsu, Sichuan, Hainan and Taiwan) in China were studied and all of them were scanned by a pushbroom hyperspectral imaging system. Then the obtained data sets were analyzed by spectral and image processing technique respectively. Successive projections algorithm (SPA) was used for selecting effective wavelengths. Dimension reduction was carried out on the region of interest (ROI) image by principal component analysis (PCA). The first principal component (PC) explained over 92% of variances of all spectral bands. Gray-level co-occurrence matrix (GLCM) analysis was implemented on the principal component (PC) image to extract 5 textural feature variables in total. Moreover, 7 morphological features of samples were computed additionally. Then least squares-support vector machine (LS-SVM) classification models were built based on the extracted spectral, textural, morphological, combined spectral and textural, combined spectral and morphological, combined textural and morphological, combined spectral, textural and morphological features, respectively. The satisfactory results show the correct discrimination rate of 93.75% for the prediction samples based on spectral and morphological features. The study demonstrated that hyperspectral image technique can be a reliable tool for discriminating different geographical origins of J. curcas L. seeds. The above results indicated that this objective and non-destructive method can be utilized for quality control purposes and seed breeding in future.