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Studies from Zhejiang University Describe New Findings in Chemometrics

May 7, 2014

By a News Reporter-Staff News Editor at Biotech Week -- Current study results on Chemometrics have been published. According to news originating from Hangzhou, People's Republic of China, by NewsRx correspondents, research stated, "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."

Our news journalists obtained a quote from the research from Zhejiang University, "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)."

According to the news editors, the research concluded: "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."

For more information on this research see: Comparison Of Fourier Transform Near-infrared, Visible Near-infrared, Mid-infrared, And Raman Spectroscopy As Non-invasive Tools For Transgenic Rice Discrimination. Transactions of the ASABE, 2014;57(1):141-150. Transactions of the ASABE can be contacted at: Amer Soc Agricultural & Biological Engineers, 2950 Niles Rd, St Joseph, MI 49085-9659, USA (see also Chemometrics).

The news correspondents report that additional information may be obtained from W. Xu, Zhejiang University, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, People's Republic of China. Additional authors for this research include X. Liu, L. Xie and Y. Ying.

Keywords for this news article include: Asia, Hangzhou, Machine Learning, Emerging Technologies, People's Republic of China

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Source: Biotech Week

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