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特征變量篩選在近紅外光譜測定綠茶湯中茶多酚的應(yīng)用
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國家自然科學(xué)基金資助項目(30971685)、江蘇省自然科學(xué)基金資助項目(BK2009216)、福建漳州市藍(lán)火計劃資助項目(Z2010016)和江蘇大學(xué)博士創(chuàng)新計劃資助項目(CX10B_020X)


Application of Characteristic Variables Selection in Determination of Polyphenols Content in Green Tea Infusion by Near Infrared Spectroscopy
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    摘要:

    利用化學(xué)計量學(xué)方法從綠茶湯近紅外光譜中提取茶多酚光譜信息，建立茶多酚近紅外光譜定量分析模型。光譜采集使用5mm光程的石英比色皿，利用聯(lián)合區(qū)間偏最小二乘法(siPLS)篩選特征光譜區(qū)間，然后在篩選的光譜區(qū)間內(nèi)進(jìn)一步利用遺傳算法(GA)優(yōu)選特征變量。結(jié)果表明，siPLS篩選的特征光譜區(qū)間避開了水的強吸收峰影響，利用GA在篩選的特征光譜區(qū)間內(nèi)優(yōu)選出166個特征變量建立PLS模型，模型預(yù)測集均方根誤差為0.685%，相對標(biāo)準(zhǔn)差為5.26%，相對分析誤差為3.22，所建模型能達(dá)到精度要求，可用于實際檢測。

    Abstract:

    The feasibility to extract the polyphenols spectra information from near infrared spectroscopy of green tea infusion using chemometrics methods was attempted. A cylindrical quartz glass tube with an optical path of 5mm was used. First, synergy interval PLS (siPLS) was implemented to select efficient spectral regions from SNV preprocessed spectra. Then, optimal variables were selected using genetic algorithm (GA) from these selected spectral regions by siPLS. This study showed that the selected characteristic spectral intervals were not within the range of the strong absorbance for water. When 166 variables selected by GA and eight PLS factors were included, the optimal model (siPLS-GA) was achieved with PRMSEP=0.685%, PRSD=5.26% and PRPD=3.22 in the prediction set. The results showed that the performance of siPLS-GA model could be employed to measure polyphenols content in green tea infusion by near infrared spectroscopy in practice.

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吳瑞梅,岳鵬翔,趙杰文,黃星奕,陳全勝.特征變量篩選在近紅外光譜測定綠茶湯中茶多酚的應(yīng)用[J].農(nóng)業(yè)機械學(xué)報,2011,42(12):154-157,163. Wu Ruimei,Yue Pengxiang, Zhao Jiewen,Huang Xingyi,Chen Quansheng. Application of Characteristic Variables Selection in Determination of Polyphenols Content in Green Tea Infusion by Near Infrared Spectroscopy[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(12):154-157,163.

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  • 在線發(fā)布日期: 2011-12-19
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