药学研究2026，Vol.45Issue(2)：163-169,7.DOI:10.13506/j.cnki.jpr.2026.02.007
基于高光谱成像的丹参饮片多指标成分原位快速定量方法
Hyperspectral imaging for rapid in-situ quantification of multi-index components in Salvia miltiorrhiza slices
摘要
Abstract
Objective This study aimed to develop an in-situ and rapid quantitative analytical method for five key components of Salvia miltiorrhiza slices using hyperspectral imaging(HSI),and to systematically evaluate the impact of different spectral band selections and feature wavelength screening strategies on the performance of partial least squares regression(PLSR)models.Methods Hyperspectral images in the visible-near-infrared and near-infrared bands were acquired from 115 batches of Salvia miltiorrhiza slices.The contents of five key components—salvianolic acid B,tanshinone I,cryptotanshinone,tanshinone ⅡA,and total tanshinones—were quantified.Spectral data from the VNIR,NIR,and fused full-band ranges were extracted.These spectra were preprocessed using six methods,including multiplicative scatter correction and standard normal variate transformation.Feature wavelengths were then selected by competitive adaptive reweighted sampling,successive projections algorithm,and uninformative variable elimination.Finally,partial least squares regression models were developed and evaluated based on the correlation coefficient,root mean square error of the validation set,and residual predictive deviation.Results The partial least squares regression model,built using full-band spectra and refined by competitive adaptive reweighted sampling,achieved optimal predictive performance.For all five analytes,the validation correlation coefficients(Rv)ranged from 0.92 to 0.96,and the residual predictive deviation values exceeded 3.5,demonstrating enhanced model robustness and reliable predictive capability.Conclusion The integration of full-band hyperspectral data with CARS feature selection enables rapid,nondestructive,and accurate quantification of five key components in Salvia miltiorrhiza slices.This approach provides a scientific basis and technical support for intelligent quality control and rapid on-site testing of traditional Chinese medicinal slices.关键词
高光谱成像/丹参/指标成分/预测模型/原位定量Key words
Hyperspectral imaging/Salvia miltiorrhiza/Marker compounds/Prediction model/In-situ quantification分类
医药卫生引用本文复制引用
秦梦廷,崔伟亮,任小英,李慧芬,董钰宁,桑梦娇,汪冰,林永强..基于高光谱成像的丹参饮片多指标成分原位快速定量方法[J].药学研究,2026,45(2):163-169,7.基金项目
国家重点研发计划-中医药现代化专项(No.2023YFC3504102) （No.2023YFC3504102）
药品监管科学全国重点实验室课题(No.2025SKLDRS0338) （No.2025SKLDRS0338）
中药监管科学研究项目(No.ZYJGKX202410) （No.ZYJGKX202410）
山东省自然科学基金项目(No.ZR2020MH374) （No.ZR2020MH374）
