Determination and Visualization of Peimine and Peiminine Content in Fritillaria thunbergii Bulbi Treated by Sulfur Fumigation Using Hyperspectral Imaging with Chemometrics.

Juan He,Yong He,And Chu Zhang

doi:10.3390/molecules22091402

Abstract

Rapid, non-destructive, and accurate quantitative determination of the effective components in traditional Chinese medicine (TCM) is required by industries, planters, and regulators. In this study, near-infrared hyperspectral imaging was applied for determining the peimine and peiminine content in Fritillaria thunbergii bulbi under sulfur fumigation. Spectral data were extracted from the hyperspectral images. High-performance liquid chromatography (HPLC) was conducted to determine the reference peimine and peiminine content. The successive projection algorithm (SPA), weighted regression coefficient (Bw), competitive adaptive reweighted sampling (CARS), and random frog (RF) were used to select optimal wavelengths, while the partial least squares (PLS), least-square support vector machine (LS–SVM) and extreme learning machine (ELM) were used to build regression models. Regression models using the full spectra and optimal wavelengths obtained satisfactory results with the correlation coefficient of calibration (rc), cross-validation (rcv) and prediction (rp) of most models being over 0.8. Prediction maps of peimine and peiminine content in Fritillaria thunbergii bulbi were formed by applying regression models to the hyperspectral images. The overall results indicated that hyperspectral imaging combined with regression models and optimal wavelength selection methods were effective in determining peimine and peiminine content in Fritillaria thunbergii bulbi, which will help in the development of an online detection system for real-world quality control of Fritillaria thunbergii bulbi under sulfur fumigation.

Highlights

Fritillaria thunbergii Miq. (Zhebeimu) is a famous traditional Chinese medicine (TCM) planted in Zhejiang Province, China
For partial least squares (PLS) model, parameter is the optimal number of latent variables (LVs); for least-squares support vector machine (LS–support vector machine (SVM)) model, parameter is the kernel width γ and the regularization parameter σ2 ; and for extreme learning machine (ELM) model, parameter is the number of nodes in the hidden layer. b successive projection algorithm (SPA) refers to successive projections algorithm; Bw refers to weighted regression coefficients; RF refers to random frog; and CARS refers to competitive adaptive reweighted sampling
We proposed a rapid and non-destructive method for determination of peimine and peiminine content in Fritillaria thunbergii bulbi by hyperspectral imaging

Summary

Introduction

Fritillaria thunbergii Miq. (Zhebeimu) is a famous traditional Chinese medicine (TCM) planted in Zhejiang Province, China. (Zhebeimu) is a famous traditional Chinese medicine (TCM) planted in Zhejiang Province, China. The bulbus of Fritillaria thunbergii Miq. is used as medicine as it has curative effects in clearing heat, resolving phlegm, relieving cough, and detoxifying [1]. Peimine and Peiminine are major alkaloids in the Fritillaria thunbergii bulbi, which play important roles in these curative effects. Determination of peimine and peiminine content in the Fritillaria thunbergii bulbi is important for grading, processing, and trading of the Fritillaria thunbergii bulbi. Sulfur fumigation (SF) is a widely-used traditional method to prolong traditional Chinese medicine preservation [2]. SF may add uncertain side effects to traditional Chinese medicine and is restricted by the Chinese government, it is still widely used due to its relatively low cost. It is important to detect chemical components of Fritillaria thunbergii bulbi under SF for quality control and sorting

Objectives

Methods

Results

Conclusion