A novel spectral variable selection method, named as interval combination optimization (ICO), was proposed in the previous study of us. In the present study, ICO coupled with near infrared (NIR) spectroscopy was applied to the rapid determination of four primary constituents including total sugar, reducing sugar, total nitrogen and nicotine in Nicotiana plant. Partial least squares regressions was performed after ICO algorithm. The full spectrum was divided into forty equal-width intervals, and the interval with lower root mean squared error of cross-validation was selected for further analysis. As a result, only 155 variables were retained from 1555 variables for each constituent. Particularly, as a variables selection method, ICO improved the prediction accuracy of calibration model and obtained a satisfactory result compared with full-spectrum data. Results revealed that NIR combined with ICO could be efficiently used for rapid analysis of quality associated constituents of Nicotiana plant. Moreover, this study provided a supplementary verification of the proposed variable selection method for the further applications.
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The authors would like to thank Prof. Suqin Sun (Tsinghua University, Beijing, China) for the critical review of the manuscript. The authors are also grateful to Yunnan Tobacco Industry for providing samples used in this work. This research was supported by the National Natural Science Foundation of China (NSFC) and Fundamental Research Funds for the Central Universities.
This study was funded by the National Natural Science Foundation of China (NSFC) (No. 31301685), and Fundamental Research Funds for the Central Universities (2652015164; 3142017100).
Compliance with ethical standards
Conflict of interest
The authors declare that there is no conflict of interests.
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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