The development of green analytical methods to monitor adulteration in honey by UV-visible spectroscopy and chemometrics models

• Main Authors: Elhamdaoui, Omar, El Orche, Aimen, Bouchafra, Houda, El Karbane, Miloud, Cheikh, Amine, Bouatia, Mustapha
• Format: Conference Proceeding
• Language: English
• Subjects: Agribusiness; Agricultural production; Algorithms; Analytical chemistry; Analytical methods; Calibration; Chemometrics; Control methods; Food production; Honey; Learning algorithms; Machine learning; Mathematical analysis; Mathematical models; Nondestructive testing; Quality control; Regression analysis; Spectroscopy; Statistical analysis; Support vector machines; Sustainable development
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202021102011

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.