NIR spectroscopy combined with multivariate analysis after the appropriate spectral data pre-treatment has been proved to be a very powerful tool for judgment of the relative pattern of the objects that have very similar properties. In this study, 500 GMO soybean seeds and, 500 non-GMO ones were measured in NIR reflectance mode. Principal component analysis (PCA), and discriminant analysis (DA) were applied to classify soybean with different genes into two groups (GMO and non-GMO). Calibrations were developed using DA regression with the cross-validation technique. The results show that differences between GMO and non-GMO soybeans do exist and excellent classification can be obtained after optimizing spectral pre-treatment. The raw spectra with DA model after the second derivative pre-treatment had the best satisfactory calibration and prediction abilities, with 97% accuracy. The results in the present study show NIR spectroscopy together with chemometrics techniques could be used to differentiate GMO soybean, which offers the benefit of avoiding time-consuming, costly and laborious chemical and sensory analysis.

• Myoung-Gun Choung(Dept. of Herbal Medicine Resource, Kangwon National University) Corresponding Author
• Young-Sun Hwang(Dept. of Herbal Medicine Resource, Kangwon National University)
• Hyeon-Jin Lee(Dept. of Herbal Medicine Resource, Kangwon National University)
• Su-San-Na Choi(Dept. of Herbal Medicine Resource, Kangwon National University)
• Su-Jin Lee(Dept. of Herbal Medicine Resource, Kangwon National University)
• Sung-Taeg Kang(National Institute of Crop Science, Rural Development Administration)
• Won-Young Han(Yeongnam Agricultural Research Institute, NICS, RDA)
• In-Youl Baek(Yeongnam Agricultural Research Institute, NICS, RDA)
• Keum-Yong Park(Yeongnam Agricultural Research Institute, NICS, RDA)