Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy
The accurate detection of honey adulteration is paramount for maintaining the quality and authenticity of honey products. In this study, we introduce a novel feature selection method, termed Optimal Subspace Wavelength Reduction (OSWR), and integrate it with reflectance Visible-Near Infrared (Vis-NI...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10363203/ |
| _version_ | 1797376326686998528 |
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| author | Mokhtar Al-Awadhi Ratnadeep Deshmukh |
| author_facet | Mokhtar Al-Awadhi Ratnadeep Deshmukh |
| author_sort | Mokhtar Al-Awadhi |
| collection | DOAJ |
| description | The accurate detection of honey adulteration is paramount for maintaining the quality and authenticity of honey products. In this study, we introduce a novel feature selection method, termed Optimal Subspace Wavelength Reduction (OSWR), and integrate it with reflectance Visible-Near Infrared (Vis-NIR) spectroscopy to enhance the discrimination between pure and adulterated honey and predict adulteration levels. OSWR efficiently addresses the dimensionality challenge of large spectral datasets, reducing 2151 wavelengths to a compact and informative set of 39 wavelengths. We comprehensively evaluate machine learning (ML) models, focusing on OSWR as a pivotal component of our methodology. Our results reveal remarkable success in discriminating among pure honey, adulterated honey, and sugar syrup, with an impressive classification accuracy of 96.67% achieved using OSWR, coupled with Standard Normal Variate (SNV) preprocessing, Linear Discriminant Analysis (LDA) feature extraction, and K-Nearest Neighbors (KNN) classification. Furthermore, this study demonstrates the effectiveness of OSWR for predicting adulteration levels, where it achieves an accuracy of as high as 92.67% when coupled with SNV, LDA, and KNN. This work highlights the potential of OSWR as a feature selection method in the context of honey adulteration detection. Through the integration of Vis-NIR spectroscopy and OSWR, our approach offers a tool for enhancing honey products’ quality and authenticity assessment, potentially simplifying spectral data analysis. |
| first_indexed | 2024-03-08T19:36:56Z |
| format | Article |
| id | doaj.art-05b0df28f80c45a1ba70d602e12cd73d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:36:56Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-05b0df28f80c45a1ba70d602e12cd73d2023-12-26T00:06:47ZengIEEEIEEE Access2169-35362023-01-011114422614424310.1109/ACCESS.2023.334373110363203Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection SpectroscopyMokhtar Al-Awadhi0https://orcid.org/0000-0003-2603-3831Ratnadeep Deshmukh1https://orcid.org/0000-0002-1257-2300Department of Information Technology, Faculty of Engineering and IT, Taiz University, Taiz, YemenDepartment of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, IndiaThe accurate detection of honey adulteration is paramount for maintaining the quality and authenticity of honey products. In this study, we introduce a novel feature selection method, termed Optimal Subspace Wavelength Reduction (OSWR), and integrate it with reflectance Visible-Near Infrared (Vis-NIR) spectroscopy to enhance the discrimination between pure and adulterated honey and predict adulteration levels. OSWR efficiently addresses the dimensionality challenge of large spectral datasets, reducing 2151 wavelengths to a compact and informative set of 39 wavelengths. We comprehensively evaluate machine learning (ML) models, focusing on OSWR as a pivotal component of our methodology. Our results reveal remarkable success in discriminating among pure honey, adulterated honey, and sugar syrup, with an impressive classification accuracy of 96.67% achieved using OSWR, coupled with Standard Normal Variate (SNV) preprocessing, Linear Discriminant Analysis (LDA) feature extraction, and K-Nearest Neighbors (KNN) classification. Furthermore, this study demonstrates the effectiveness of OSWR for predicting adulteration levels, where it achieves an accuracy of as high as 92.67% when coupled with SNV, LDA, and KNN. This work highlights the potential of OSWR as a feature selection method in the context of honey adulteration detection. Through the integration of Vis-NIR spectroscopy and OSWR, our approach offers a tool for enhancing honey products’ quality and authenticity assessment, potentially simplifying spectral data analysis.https://ieeexplore.ieee.org/document/10363203/Chemometric analysisfeature selectionhoney adulterationmachine learningoptimal subspace wavelength reductionVis-NIR spectroscopy |
| spellingShingle | Mokhtar Al-Awadhi Ratnadeep Deshmukh Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy IEEE Access Chemometric analysis feature selection honey adulteration machine learning optimal subspace wavelength reduction Vis-NIR spectroscopy |
| title | Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy |
| title_full | Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy |
| title_fullStr | Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy |
| title_full_unstemmed | Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy |
| title_short | Enhancing Honey Adulteration Detection With Optimal Subspace Wavelength Reduction in Vis-NIR Reflection Spectroscopy |
| title_sort | enhancing honey adulteration detection with optimal subspace wavelength reduction in vis nir reflection spectroscopy |
| topic | Chemometric analysis feature selection honey adulteration machine learning optimal subspace wavelength reduction Vis-NIR spectroscopy |
| url | https://ieeexplore.ieee.org/document/10363203/ |
| work_keys_str_mv | AT mokhtaralawadhi enhancinghoneyadulterationdetectionwithoptimalsubspacewavelengthreductioninvisnirreflectionspectroscopy AT ratnadeepdeshmukh enhancinghoneyadulterationdetectionwithoptimalsubspacewavelengthreductioninvisnirreflectionspectroscopy |