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    王俊, 许多宽, 肖勇, 王勇, 陈志华, 陈维建. 基于化学指标的烟叶产区正交偏最小二乘判别分析[J]. 中国烟草科学, 2017, 38(1): 91-96. DOI: 10.13496/j.issn.1007-5119.2017.01.016
    引用本文: 王俊, 许多宽, 肖勇, 王勇, 陈志华, 陈维建. 基于化学指标的烟叶产区正交偏最小二乘判别分析[J]. 中国烟草科学, 2017, 38(1): 91-96. DOI: 10.13496/j.issn.1007-5119.2017.01.016
    WANG Jun, XU Duokuan, XIAO Yong, WANG Yong, CHEN Zhihua, CHEN Weijian. The OPLS-DA Model of Tobacco Producing Areas Based on Chemical Measurements[J]. CHINESE TOBACCO SCIENCE, 2017, 38(1): 91-96. DOI: 10.13496/j.issn.1007-5119.2017.01.016
    Citation: WANG Jun, XU Duokuan, XIAO Yong, WANG Yong, CHEN Zhihua, CHEN Weijian. The OPLS-DA Model of Tobacco Producing Areas Based on Chemical Measurements[J]. CHINESE TOBACCO SCIENCE, 2017, 38(1): 91-96. DOI: 10.13496/j.issn.1007-5119.2017.01.016

    基于化学指标的烟叶产区正交偏最小二乘判别分析

    The OPLS-DA Model of Tobacco Producing Areas Based on Chemical Measurements

    • 摘要: 为构建基于化学指标的不同产区烟叶的模式识别模型,收集2003-2007年5个年度四川、云南和福建3省份共计1262份烤烟样品的21种化学指标,并进行正交偏最小二乘判别分析(OPLS-DA)。结果表明,基于21种化学指标所构建的OPLS-DA模型显著可靠,模型参数R2XR2YQ2值分别为0.429、0.702和0.627;模型提取出2个预测主成分,对四川、云南和福建3省未知烟样识别正确率分别为76.67%、93.33%和93.33%。基于化学指标的烟叶产区OPLS-DA模型,可以有效识别不同产区烟叶样品,筛选出各产区特征指标并解释产区间差异。

       

      Abstract: In order to construct the pattern recognition model of tobacco producing areas based on chemical measurements, 21 chemical measurements from 1262 samples of flue-cured tobacco were collected from 2003 to 2007, and were used to construct a OPLS-DA model. The results showed that the OPLS-DA model was highly significant with the following model performance statistics (R2X=0.429, R2Y=0.702, Q2=0.627). The OPLS-DA of the training set gave a model with two Y-predictive components, and gave the correct rate of 76.67%, 93.33% and 93.33% to distinguish samples from Sichuan, Yunnan and Fujian tobacco producing areas. The OPLS-DA model based on chemical measurements can recognize tobacco samples from different producing areas, and can be used in screening for biomarkers for different areas and providing interpretations for regional difference of tobacco samples.

       

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