Spectral interval combination optimization (ICO) on rapid quality assessment of plant: a validation study.

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 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 plant. Moreover, this study provided a supplementary verification of the proposed variable selection method for the further applications.