Yaqub, Kiran and Jamil, Mutiullah and Saeed, Muhammad Nauman and Ubaid, Saqib and Mehmood, Arif and Gracia Villar, Mónica and Silva Alvarado, Eduardo René and Ashraf, Imran and Mishra, Pramita UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, monica.gracia@uneatlantico.es, eduardo.silva@funiber.org, UNSPECIFIED, UNSPECIFIED (2026) Framework to Detect Adulterated Honey Using Hyperspectral Imaging and Machine Learning. Journal of Spectroscopy, 2026 (1). ISSN 2314-4920
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Journal of Spectroscopy - 2026 - Yaqub - Framework to Detect Adulterated Honey Using Hyperspectral Imaging and Machine.pdf Download (2MB) | Preview |
Abstract
Honey is a natural product renowned for its nutritional and medicinal properties. However, the increasing market demand in Pakistan has led to frequent instances of adulterated and fraudulent honey. Conventional detection methods are widely used but are often ineffective in accurately identifying adulteration. This study proposes a novel approach to detecting adulteration in honey, specifically the mixing of sugar, using machine learning (ML) techniques in conjunction with hyperspectral imaging (HSI). By leveraging HSI, we capture distinct spectral features of both pure and adulterated honey. These spectral features are processed and analyzed using ML algorithms trained on a dataset comprising pure and mixed honey samples. Experiments involve binary, as well as, multiclass (5 classes) honey samples with different levels of adulteration. In addition, data balancing is also carried out using synthetic minority oversampling technique (SMOTE). The results prove the proposed approach to be efficient in detecting adulteration across various honey varieties, with linear regression achieved 99.9% while support vector machine and multilayer perceptron obtaining a 98.7% accuracy for binary class. For the multiple classes of honey, the multilayer perceptron shows a 96.67% accuracy outperforming the remaining ML and deep learning models. Among the deep learning models, the convolutional neural network (CNN) achieved the best performance with an accuracy of 94.67% after applying the SMOTE × 3 (three times of over samples of original samples of each class) data balancing strategy, whereas recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models showed comparatively lower performance.
| Item Type: | Article |
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| Uncontrolled Keywords: | adulterated honey | hyperspectral imaging | image processing | machine learning |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Articles and books Fundación Universitaria Internacional de Colombia > Research > Articles and books Ibero-american International University > Research > Articles and books Universidad Internacional do Cuanza > Research > Articles and books University of La Romana > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 04 Sep 2026 06:20 |
| Last Modified: | 04 Sep 2026 06:20 |
| URI: | https://repositorio.funiber.org/id/eprint/29684 |
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