Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method

Polysaccharide is the main active compound of Lilium, and showed many activities, such as hypoglycemic, anti-oxidant, immune-modulatory. There are three types' Lilium in China market, i.e. Lilium lancifolium Thunb (JD), Lilium davidiivar. Unicolor Salisb (L. davidii var)(LZBH), and Lilium brownii F.E. Brown var. viridulum Baker (BH). Near infrared spectroscopy (NIR) technique has become popular in the fields of quality control, due to its advantages, such as fast, non-destructive, and can detect several ingredients, simultaneously. In this study, a classification model was established based on NIR technique and random forest method to accurately distinguish three types' Lilium species, and the classification accuracy reached 94.37%. Furthermore, taking the effects of neighbor wavelength into account, a new weighted partial least square algorithm was proposed to establish an accurate and quantitative model for predicting the polysaccharide contents of these samples. In the model establishing process, some signal pre-treatment methods were optimized, and the validation results with highest determination coefficient (R-2) and low root mean square errors of prediction (RMSEP) were, 0.9455 and 0.9098, respectively. The obtained results showed that combined NIR technique with chemometrics was an effective and green method for quality control. (C) 2020 Published by Elsevier B.V.