ZHANG Cihai，RUAN Yibin，PENG Qianrong，et al. Determination of latent variable number of NIR quantitative model for nicotine by model population analysis combined with regression vector method‍［J］. Tobacco Science & Technology，2021，54（S1）：9-13.. DOI: 10.16135/j.issn.1002-0861.2021.2022.0037
Citation: ZHANG Cihai，RUAN Yibin，PENG Qianrong，et al. Determination of latent variable number of NIR quantitative model for nicotine by model population analysis combined with regression vector method‍［J］. Tobacco Science & Technology，2021，54（S1）：9-13.. DOI: 10.16135/j.issn.1002-0861.2021.2022.0037
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Determination of latent variable number of NIR quantitative model for nicotine by model population analysis combined with regression vector method

    Abstract
  • In order to establish a stable near infrared quantitative model to predict the nicotine content in Guizhou tobacco leaves, model population analysis combined with regression vector method was adopted to determine the optimal number of latent variables for the NIR model, and the model stability parameter （S）, which was calculated by PLS regression vectors, was used as an additional indicator. The results showed that the number of latent variables obtained by this method was 5. Root mean square error of calibration （RMSEC） was closer to root mean square error of prediction （RMSEP） through the verification by independent validation sets. This method solved the problem that the number of latent variables cannot be determined when it was hard to determine the minimum of root mean square error of cross validation （RMSECV） or maximum of coefficient of determination （QCV2）.
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