以近红外高光谱成像技术, 结合化学计量学方法, 研究了转基因大豆的快速、 无损检测方法。 实验以3种不同非转基因亲本(HC6, JACK, TL1)及其转基因大豆作为研究对象。 采用高光谱成像系统采集874~1 734 nm波长范围的256个波段范围的高光谱图像, 提取大豆的光谱信息, 剔除明显噪声部分后, 采用Moving Average(MA)平滑预处理的941~1 646 nm范围光谱数据进行分析。 采用偏最小二乘判别分析算法(partial least squares-discriminant analysis, PLS-DA), 对3种非转基因亲本大豆建立模型进行判别分析, 其相应的建模集和预测集的判别正确率分别为97.50%和100%, 100%和100%, 96.25%和92.50%, 结果表明, 高光谱成像技术可用于非转基因大豆的识别。 对非转基因亲本及其转基因大豆进行判别分析, 基于全谱, 3种的建模集和预测集的判别正确率分别为99.17%和99.17%, 87.19%和81.25%, 99.17%和98.33%; 以x-loading weights提取非转基因亲本及其转基因大豆判别分析的特征波长并建立PLS-DA模型, 3种的建模集和预测集的判别正确率分别为72.50%和80%, 80.63%和79.38%, 85%和85%, 该结果表明非转基因亲本与转基因品种的判别分析是可行的, 特征波长的选择也可用于非转基因亲本与转基因品种的判别分析。 研究表明采用近红外高光谱成像技术对非转基因大豆、 非转基因亲本及其转基因大豆进行鉴别是可行的, 为转基因大豆的快速无损准确鉴别提供了一种新方法。

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 874~1 734 nm with 256 bands of soybeans, and the reflectance spectra were extracted from the region of interest (ROI) in the images. After eliminating the obvious noises, the moving average(MA)was applied as smooth pretreatment, and the wavelengths from 941~1 646 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 transgenic 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 transgenic 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.