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首页> 外文期刊>Spanish Journal of Agricultural Research >Screening of transgenic maize using near infrared spectroscopy and chemometric techniques
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Screening of transgenic maize using near infrared spectroscopy and chemometric techniques

机译：使用近红外光谱和化学计量学技术筛选转基因玉米

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The applicability of near infrared (NIR) spectroscopy combined with chemometrics was examined to develop fast, low-cost and non-destructive spectroscopic methods for classification of transgenic maize plants. The transgenic maize plants containing both cry1Ab/cry2Aj-G10evo proteins and their non-transgenic parent were measured in the NIR diffuse reflectance mode with the spectral range of 700–1900 nm. Three variable selection algorithms, including weighted regression coefficients, principal component analysis -loadings and second derivatives were used to extract sensitive wavelengths that contributed the most discrimination information for these genotypes. Five classification methods, including K-nearest neighbor, Soft Independent Modeling of Class Analogy, Naive Bayes Classifier, Extreme Learning Machine (ELM) and Radial Basis Function Neural Network were used to build discrimination models based on the preprocessed full spectra and sensitive wavelengths. The results demonstrated that ELM had the best performance of all methods, even though the model’s recognition ability decreased as the variables in the training of neural networks were reduced by using only the sensitive wavelengths. The ELM model calculated on the calibration set showed classification rates of 100% based on the full spectrum and 90.83% based on sensitive wavelengths. The NIR spectroscopy combined with chemometrics offers a powerful tool for evaluating large number of samples from maize hybrid performance trials and breeding programs.
机译：研究了近红外（NIR）光谱与化学计量学相结合的适用性，以开发出快速，低成本和无损光谱的方法来对转基因玉米植物进行分类。含有cry1Ab / cry2Aj-G10evo蛋白及其非转基因亲本的转基因玉米植株在NIR漫反射模式下测量，光谱范围为700-1900 nm。三种变量选择算法，包括加权回归系数，主成分分析-载荷和二阶导数，被用于提取敏感波长，这些波长为这些基因型贡献了最多的判别信息。使用五种分类方法，包括K近邻法，类比法的软独立建模，朴素贝叶斯分类器，极限学习机（ELM）和径向基函数神经网络，基于预处理的全光谱和敏感波长建立判别模型。结果表明，即使仅通过使用敏感波长来减少神经网络训练中的变量，该模型的识别能力也会下降，但ELM在所有方法中均具有最佳性能。在校准集上计算的ELM模型显示，基于全光谱的分类率为100％，基于敏感波长的分类率为90.83％。近红外光谱与化学计量学相结合，为评估来自玉米杂交性能试验和育种计划的大量样品提供了强大的工具。

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