文献类型： 外文期刊

作者： Wang Hai-long ¹ ; Yang Xiang-dong ² ; Zhang Chu ¹ ; Guo Dong-quan ² ; Bao Yi-dan ¹ ; He Yong ¹ ; Liu Fei ¹ ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China

2.Jilin Acad Agr Sci, Agr Biotechnol Res Ctr, Changchun 130033, Peoples R China

关键词： Near-infrared hyperspectral imaging;Transgenic soybean;PLS-DA;x-loading weights

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2016 年 36 卷 6 期

页码：

收录情况： SCI

摘要： Near-infrared hyperspectral imaging technology combined with chemometrics was applied for rapid and non-invasive transgenic soybeans variety identification. Three different non-GMO parent soybeans ( HC6, JACK, TL1) and their transgenic soybeans were chosen as the research object. The developed hyperspectral imaging system was used to acquire the hyperspectral images in the spectral range of 874 similar to 1 734 nm with 256 bands of soybeans, and the reflectance spectra were extracted from the. region of interest (ROD in the images. After eliminating the obvious noises, the moving average(MA) was applied as smooth pretreatment, and the wavelengths from 941 similar to 1 646 nm were used for later analysis. Partial least squares-discriminant analysis (PLS-DA)was employed as pattern recognition method to class the three different non-GMO parent soybeans. The classification accuracy of both the calibration set and the prediction set were 97.50% and 100% for the HC6, 100% and 100% for the JACK, 96.25% and 92.50% for the TL1, which indicated that hyperspectral imaging technology could identify the varieties of the non-GMO parent soybeans. Then PLS-DA was applied to classify non-GMO parent soybean and its transgenic soybean cultivars for building discriminant models. For the full spectra, the classification accuracy of both the calibration set and the prediction set were 99.17% and 99.17% for the HC6 and its transgenic soybean cultivars, 87.19% and 81.25% for the JACK and its trans genic soybean cultivars, 99.17% and 98.33% for the TL1 and its transgenic soybean cultivars, respectively. The sensitive wavelengths were selected by x-loading weights, and the classification accuracy of the calibration set and prediction set of PLS-DA models based on sensitive wavelengths were 72.50% and 80% for the HC6 and its transgenic soybean cultivars, 80.63% and 79.38% for the JACK and its transgenic soybean cultivars, 85% and 85% for the TL1 and its transgenic soybean cultivars, respectively. These results showed that the pattern recognition for non-GMO parent soybean and their transgenic soybeans was feasible, and the selected sensitive wavelengths could be used for the pattern recognition of non-GMO parent soybeans and trans genic soybeans. The overall results indicated that it was feasible to use near-infrared hyperspectral imaging technology for the pattern recognition of the non-GMO parent soybeans varieties, non-GMO parent soybean and its transgenic soybeans. This study also provided a new alternative for rapid and non-destructive accurate identification of transgenic soybean.