Dataset Information

Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics.

ABSTRACT: Sidr honey is vulnerable to adulteration with low-grade honey and sugar syrups, which compromises its nutritional and medicinal value, demanding fast and reliable analytical tools for quality assessment. In this study, fluorescence spectroscopy was employed to assess the quality of a honey samples, specifically, Sidr, unifloral (Acacia) and multifloral (Acacia, Carisa and Justicia) honey. Fluorescence spectroscopy revealed characteristic spectral signatures of Sidr honey, compared to Acacia and multifloral honey. In addition, cane sugar syrup was artificially added to Sidr honey at different concentrations. These spectral signatures were exploited for the machine-assisted classification of Sidr, sugar syrup and different concentrations of Sidr-sugar mixture. The bagging classification algorithm generated values of sensitivity and specificity close to unity, indicating its ability for efficient discrimination of the samples. Fluorescence spectroscopy in tandem with chemometrics could potentially be used as a rapid analytical tool to identify Sidr honey and its sugar adulteration.

Supplementary information

The online version contains supplementary material available at 10.1007/s00217-022-04008-9.

SUBMITTER: Ali H

PROVIDER: S-EPMC8994421 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics.

Ali Hina H Rafique Khalid K Ullah Rahat R Saleem M M Ahmad Iftikhar I

European food research & technology. 20220409 7

Sidr honey is vulnerable to adulteration with low-grade honey and sugar syrups, which compromises its nutritional and medicinal value, demanding fast and reliable analytical tools for quality assessment. In this study, fluorescence spectroscopy was employed to assess the quality of a honey samples, specifically, Sidr, unifloral (Acacia) and multifloral (Acacia, Carisa and Justicia) honey. Fluorescence spectroscopy revealed characteristic spectral signatures of Sidr honey, compared to Acacia and ...[more]

PMID: 35431646

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Project description:Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

Dataset Information

Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics.

Supplementary information

Publications

Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics.

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