Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

Omar Elhamdaoui; Aimen El Orche; Amine Cheikh; Brahim Mojemmi; Rachid Nejjari; Mustapha Bouatia

doi:10.1155/2020/8816249

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools

J Anal Methods Chem. 2020 Dec 23:2020:8816249. doi: 10.1155/2020/8816249. eCollection 2020.

Authors

Omar Elhamdaoui¹, Aimen El Orche², Amine Cheikh³, Brahim Mojemmi¹, Rachid Nejjari⁴, Mustapha Bouatia¹

Affiliations

¹ Laboratory of Analytical Chemistry, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco.
² Laboratory of Chemical Processes and Applied Materials, Faculty of Science and Technology, Sultan Moulay Slimane University, Beni-Mellal, Morocco.
³ Faculty of Pharmacy, Abulcasis University, Rabat, Morocco.
⁴ Laboratory of Pharmacognosy, Team of Formulation and Quality Control of Health Products, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco.

Abstract

In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000-600 cm^-1, with a spectral resolution of 4 cm^-1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R ² greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.