Learning algorithms for identification of whisky using portable Raman spectroscopy
Reliable identification of high-value products such as whisky is vital due to rising issues of brand substitution and quality control in the industry. We have developed a novel framework that can perform whisky analysis directly from raw spectral data with no human intervention by integrating machin...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Current Research in Food Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665927124000558 |
| _version_ | 1827221781550727168 |
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| author | Kwang Jun Lee Alexander C. Trowbridge Graham D. Bruce George O. Dwapanyin Kylie R. Dunning Kishan Dholakia Erik P. Schartner |
| author_facet | Kwang Jun Lee Alexander C. Trowbridge Graham D. Bruce George O. Dwapanyin Kylie R. Dunning Kishan Dholakia Erik P. Schartner |
| author_sort | Kwang Jun Lee |
| collection | DOAJ |
| description | Reliable identification of high-value products such as whisky is vital due to rising issues of brand substitution and quality control in the industry. We have developed a novel framework that can perform whisky analysis directly from raw spectral data with no human intervention by integrating machine learning models with a portable Raman device. We demonstrate that machine learning models can achieve over 99% accuracy in brand or product identification across twenty-eight commercial samples. To demonstrate the flexibility of this approach, we utilized the same algorithms to quantify ethanol concentrations, as well as measuring methanol levels in spiked whisky samples. To demonstrate the potential use of these algorithms in a real-world environment we tested our algorithms on spectral measurements performed through the original whisky bottle. Through the bottle measurements are facilitated by a beam geometry hitherto not applied to whisky brand identification in conjunction with machine learning. Removing the need for decanting greatly enhances the practicality and commercial potential of this technique, enabling its use in detecting counterfeit or adulterated spirits and other high-value liquids. The techniques established in this paper aim to function as a rapid and non-destructive initial screening mechanism for detecting falsified and tampered spirits, complementing more comprehensive and stringent analytical methods. |
| first_indexed | 2024-04-24T12:49:54Z |
| format | Article |
| id | doaj.art-59f4de0be0fd40febc59ace8e45b471e |
| institution | Directory Open Access Journal |
| issn | 2665-9271 |
| language | English |
| last_indexed | 2025-03-21T16:28:03Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Food Science |
| spelling | doaj.art-59f4de0be0fd40febc59ace8e45b471e2024-06-17T05:57:41ZengElsevierCurrent Research in Food Science2665-92712024-01-018100729Learning algorithms for identification of whisky using portable Raman spectroscopyKwang Jun Lee0Alexander C. Trowbridge1Graham D. Bruce2George O. Dwapanyin3Kylie R. Dunning4Kishan Dholakia5Erik P. Schartner6Centre of Light for Life (CLL) and Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, Adelaide, 5005, SA, Australia; School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia; School of Biological Sciences, The University of Adelaide, Adelaide, 5005, SA, AustraliaCentre of Light for Life (CLL) and Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, Adelaide, 5005, SA, Australia; School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia; School of Biological Sciences, The University of Adelaide, Adelaide, 5005, SA, AustraliaSUPA School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS, Fife, United KingdomSUPA School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS, Fife, United KingdomSchool of Biological Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia; Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, 5005, SA, AustraliaCentre of Light for Life (CLL) and Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, Adelaide, 5005, SA, Australia; SUPA School of Physics and Astronomy, University of St Andrews, St Andrews, KY16 9SS, Fife, United Kingdom; School of Biological Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia; Corresponding author. Centre of Light for Life (CLL) and Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, Adelaide, 5005, SA, Australia.Centre of Light for Life (CLL) and Institute for Photonics and Advanced Sensing (IPAS), The University of Adelaide, Adelaide, 5005, SA, Australia; School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia; Robinson Research Institute, School of Biomedicine, The University of Adelaide, Adelaide, 5005, SA, AustraliaReliable identification of high-value products such as whisky is vital due to rising issues of brand substitution and quality control in the industry. We have developed a novel framework that can perform whisky analysis directly from raw spectral data with no human intervention by integrating machine learning models with a portable Raman device. We demonstrate that machine learning models can achieve over 99% accuracy in brand or product identification across twenty-eight commercial samples. To demonstrate the flexibility of this approach, we utilized the same algorithms to quantify ethanol concentrations, as well as measuring methanol levels in spiked whisky samples. To demonstrate the potential use of these algorithms in a real-world environment we tested our algorithms on spectral measurements performed through the original whisky bottle. Through the bottle measurements are facilitated by a beam geometry hitherto not applied to whisky brand identification in conjunction with machine learning. Removing the need for decanting greatly enhances the practicality and commercial potential of this technique, enabling its use in detecting counterfeit or adulterated spirits and other high-value liquids. The techniques established in this paper aim to function as a rapid and non-destructive initial screening mechanism for detecting falsified and tampered spirits, complementing more comprehensive and stringent analytical methods.http://www.sciencedirect.com/science/article/pii/S2665927124000558Raman spectroscopyMachine learningWhiskyBrand identification |
| spellingShingle | Kwang Jun Lee Alexander C. Trowbridge Graham D. Bruce George O. Dwapanyin Kylie R. Dunning Kishan Dholakia Erik P. Schartner Learning algorithms for identification of whisky using portable Raman spectroscopy Current Research in Food Science Raman spectroscopy Machine learning Whisky Brand identification |
| title | Learning algorithms for identification of whisky using portable Raman spectroscopy |
| title_full | Learning algorithms for identification of whisky using portable Raman spectroscopy |
| title_fullStr | Learning algorithms for identification of whisky using portable Raman spectroscopy |
| title_full_unstemmed | Learning algorithms for identification of whisky using portable Raman spectroscopy |
| title_short | Learning algorithms for identification of whisky using portable Raman spectroscopy |
| title_sort | learning algorithms for identification of whisky using portable raman spectroscopy |
| topic | Raman spectroscopy Machine learning Whisky Brand identification |
| url | http://www.sciencedirect.com/science/article/pii/S2665927124000558 |
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