Comparison of Fourier Transform Near-Infrared, Visible Near-Infrared, Mid-Infrared, and Raman Spectroscopy as Non-Invasive Tools for Transgenic Rice Discrimination

Abstract. Spectroscopic techniques combined with multivariate analysis have been proven to be effective tools for the discrimination of objects with similar properties. In this work, a comparison of various spectroscopic techniques for identifying genetically modified organisms (GMOs) was performed, including Fourier transform near-infrared (FT-NIR), visible near-infrared (VIS-NIR), mid-infrared (MIR), and Raman spectroscopy. Transgenic rice (Huahui-1) and its parent (Minghui 63) were chosen as subjects in this study. The obtained spectra were analyzed using three common chemometrics methods: principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), and discriminant analysis (DA). The highest classification accuracy (100%) was obtained using Raman (applying PLSDA model) and VIS-NIR (applying DA and PLSDA model) spectroscopy. The accuracies obtained by MIR and FT-NIR reached 96.7% (using DA model) and 95.7% (using PLSDA model), respectively. These results indicate that FT-NIR, VIS-NIR, Raman, and MIR spectroscopy together with chemometrics methods could be effective in differentiating transgenic rice.