Screening of transgenic maize using near infrared spectroscopy and chemometric techniques
文献类型： 外文期刊
作者： Feng, Xuping 1 ; Yin, Haijun 3 ; Zhang, Chu 1 ; Peng, Cheng 4 ; He, Yong 1 ;
作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China
2.China Jiliang Univ, Coll Life Sci, Hangzhou 310018, Zhejiang, Peoples R China
3.Jiangsu Mingtian Seeds Sci & Thchnol Co LTD, Nanjing 210014, Jiangsu, Peoples R China
4.Zhejiang Acad Agr Sci, Inst Qual & Stand Agroprod, Hangzhou 310021, Zhejiang, Peoples R China
关键词： facile screening method; Zea mays; transgenic maize selection; discrimination model
期刊名称：SPANISH JOURNAL OF AGRICULTURAL RESEARCH （ 影响因子：1.238； 五年影响因子：1.731 ）
ISSN： 1695-971X
年卷期： 2018 年 16 卷 2 期
页码：
收录情况： SCI
摘要： 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.
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