扬州大学学报（农业与生命科学版）2024，Vol.45Issue(3)：82-89,8.DOI:10.16872/j.cnki.1671-4652.2024.03.009
近红外技术结合参数轨迹策略快速评价土茯苓质量
A processing-trajectory strategy for rapid quality assessment of Rhizoma smilacis Glabrae by NIR combined with PLS modeling
摘要
Abstract
To establish a method for rapid determination of astilbin in Smilax glabra by near infrared spectroscopy(NIRS)combined with processing-trajectory strategy,so as to evaluate the quality of Smilax glabra from different pro-duction areas.NIR spectra of Smilax glabra from different production areas were collected by near infrared spectroscopy,and the content of astilbin,and the main bioactive component in Smilax glabra,was determined by high performance liq-uid chromatography(HPLC).The quantitative model of astilbin was established by partial least squares(PLS)combined with the collected NIR spectra and the content of astilbin.For the parameter optimization of astilbin PLS model,a pro-cessing-trajectory strategy that integrated four pretreatment methods,latent factors setting from 1 to 10 and interval par-tial least squares regression(iPLS),backward interval partial least squares(BiPLS)-based variable selection was pro-posed.The results showed that the processing-trajectory strategy proved to be more efficient and accurate than the com-monly used step-by-step strategy.Three good models with better predictive performance were obtained through the new strategy.Furthermore,PLS-DA model was built to differentiate the geographical origin of the Rhizoma smilacis Glabrae samples.NIR coupled with the novel processing-trajectory strategy of PLS model can be a rapid,reliable and cost-effec-tive method for raw material testing and product quality control.关键词
土茯苓/近红外光谱/偏最小二乘法/参数轨迹策略/质量控制Key words
Rhizoma smilacis Glabrae/near infrared spectroscopy/partial least squares/processing-trajectory strategy分类
农业科技引用本文复制引用
吕蒙莹,万夏芸,帅锦浩,王杨,刘晓庆,严欢,赵娜..近红外技术结合参数轨迹策略快速评价土茯苓质量[J].扬州大学学报（农业与生命科学版）,2024,45(3):82-89,8.基金项目
国家自然科学基金资助项目(81903906、81960769) （81903906、81960769）
江苏省高等学校自然科学基金面上项目(23KJB360018) （23KJB360018）
中国博士后科学基金资助项目(2018M642346) （2018M642346）
江苏省中医药管理局科研项目(MS2022094) （MS2022094）
国家留学基金委员会资助项目(201809300008) （201809300008）
扬州大学"青蓝工程"优秀骨干青年教师项目(2023-08) （2023-08）
扬州大学人才引进基金项目(2021-08) （2021-08）
扬州大学技术创新与培育基金项目(2019CXJ175) （2019CXJ175）
