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

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 R2 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.

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics

“…Recently, several spectroscopy methods, such as Raman spectroscopy [ 6 ], NIR spectroscopy on Apis mellifera [ 7 , 8 , 9 ], and stingless bee honey [ 1 , 9 ] were conducted to determine the adulteration. The combination of NIR spectroscopy application and chemometric methods [ 10 , 11 ] shows a simple, rapid, and nondestructive method on adulterated honey. Moreover, honey consistency is possibly fluid, viscous, or partially to completely crystalline [ 12 ].…”

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

“…By training the models with a broader range of spectral information, the variations attributed to sample preparation and Vis-NIRs analysis would be mitigated. It should be noted that there are studies based on spectroscopy data in which better results were obtained with PLS than with SVR [ 53 ], while in other cases, the opposite is reported [ 54 ] or even similar performances [ 55 ].…”

Investigating the potential of Fourier transform mid-infrared spectroscopy combined with chemometrics for detecting camel’s milk adulteration

“…This method is particularly useful when dealing with large amounts of quantitative data, as it extracts crucial information from the data table by projecting it onto a set of new orthogonal variables known as PCs. These PCs, which represent the largest variations between characteristics, are calculated to be independent of one another and provide an overview of the data structure by revealing the relationships between objects and detecting anomalous features (Roggo et al, 2007;El Orche et al, 2020). The outcome of HC analysis, however, is a dendrogram that illustrates the grouping of samples in a hierarchical manner and measures the similarity between them based on their different attributes (Miller et al, 2000).…”

“…For instance, El Mouftari et al (2021) investigated the adulteration of oleaster oil by olive oil using ATR-FTIR and chemometrics, and the best results were obtained by using PLSR with R-square of 0.995. Likewise, for the quantification of honey adulteration using MIR and chemometrics, including PLSR and SVM, the PLSR have induced higher efficiency compared to the SVM regression (Elhamdaoui et al, 2020).…”