文章基本信息

标题：The development of green analytical methods to monitor adulteration in honey by UV-visible spectroscopy and chemometrics models
本地全文：下载
作者：Omar Elhamdaoui ; Aimen El Orche ; Houda Bouchafra 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2020
卷号：211
页码：1-5
DOI：10.1051/e3sconf/202021102011
出版社：EDP Sciences
摘要：The development of green and environmentally friendly analytical methods for agri-food products is an essential element to be treated by green analytical chemistry. In this study, UV-Visible spectroscopy, combined with a mathematical and statistical or chemometrics algorithm, has been developed to monitor honey quality. Partial Least Squares Regression (PLS-R) and Support Vector Machine Learning Regression (SVM-R) showed an adequate quantification of the percentage of impurity. The use of these models demonstrates a high ability to predict the quality of honey. R-square’s high value shows this ability, and the low value of root mean square error of calibration and cross-validation (RMSECV, RMSEC). The results indicate that UV-Visible spectroscopy allied with the Chemometrics algorithms can provide a quick, non-destructive, green, and reliable method to control the quality and predict honey’s adulteration level.