The Application of Classical Least Square Algorithm in the Quantitative Analysis of Lime in Wheat Flour by ATR-MIR Spectroscopy

Abstract : In this thesis, classical least square (CLS) regression was applied in the attenuate total reflection mid-infrared (ATR-MIR) spectra data processing of lime in wheat flour to develop the corrected peak height model of the raw spectra, the corrected peak area model of the raw spectra, the corrected peak height model of the 2nd derivative spectra and the corrected peak area model of the 2nd derivative spectra respectively. The result indicated that the correlation coefficients of the four models mentioned above are 0.9648, 0.9696, -0.9646 and -0.9599 respectively. F-test result indicated that a very remarkable correlation exists between the estimated and specified values of the calibration set and external validation set. The detection limits of the four models mentioned above are 3.51 %, 3.21 %, 3.51 % and 3.69 % respectively, which can fulfill the demand for the rapid quality safe screening of wheat flour in the market. This method, to some extent, can provide some references for not only the design and manufacturing of the special MIR instrument for the quality safe control of wheat flour in the market but also the quantitative determination of banned additives in wheat flour.
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Communication dans un congrès
Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-369, pp.8-16, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27278-3_2〉
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Dong Wang, Donghai Han, Zhihong Ma, Ligang Pan, Liu Zhao, et al.. The Application of Classical Least Square Algorithm in the Quantitative Analysis of Lime in Wheat Flour by ATR-MIR Spectroscopy. Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-369, pp.8-16, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27278-3_2〉. 〈hal-01360958〉

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