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. 2021 Feb 9;26(4):915.
doi: 10.3390/molecules26040915.

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

Affiliations

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

Diding Suhandy et al. Molecules. .

Abstract

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190-400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky-Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250-400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.

Keywords: Indonesian honey; UV spectroscopy; authentication; botanical origin; entomological origin; geographical origin.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Geographical origin of Indonesian honeys used in this study.
Figure 2
Figure 2
The average original (a) and pre-processed (b) spectra of the Indonesian honey with different botanical, entomological, and geographical origins over the range of 190–400 nm.
Figure 3
Figure 3
The score plot of the first two PCs (principal components; PC1 × PC2) for both the original (a) and pre-processed spectra (b) between 250–400 nm for the six different types of honey collected.
Figure 4
Figure 4
A plot of Hotelling’s T2 versus Q-residual of honey samples using the third principal component (PC3) from PCA (principal component analysis) calculation at the 95% significance level.
Figure 5
Figure 5
X-loadings plots for all three components for pre-processed spectra.
Figure 6
Figure 6
The discrimination power plot of wavelengths obtained by prediction sample sets using pre-processed spectra over the range of 250–400 nm (the black dashed line at discrimination power = 3 is a threshold for selecting important wavelengths).
Figure 7
Figure 7
Cooman’s plot of the classification result of six pairwise SIMCA models for prediction sample sets using SIMCA models developed in each class using pre-processed spectral data over the range of 250–400 nm (acacia versus Jambi (a), acacia versus longan (b), acacia versus durian (c), acacia versus rubber tree (d), Jambi versus Muara Enim (e), and longan versus durian (f)). (the significance level for the Cooman’s plot is 95%). (Rubber tree: black triangle; longan: purple plus; durian: brown open squares; Jambi: blue cross; Muara Enim: green open circle; acacia: red diamonds).
Figure 7
Figure 7
Cooman’s plot of the classification result of six pairwise SIMCA models for prediction sample sets using SIMCA models developed in each class using pre-processed spectral data over the range of 250–400 nm (acacia versus Jambi (a), acacia versus longan (b), acacia versus durian (c), acacia versus rubber tree (d), Jambi versus Muara Enim (e), and longan versus durian (f)). (the significance level for the Cooman’s plot is 95%). (Rubber tree: black triangle; longan: purple plus; durian: brown open squares; Jambi: blue cross; Muara Enim: green open circle; acacia: red diamonds).

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