Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms
Rapid, onsite, and simple detection of honey integrity in real time provides consumers with the assurance that the honey purchased is free from adulteration such as sugar syrup. In this study, smartphone based camera technique was used to differentiate authentic honey from adulterated ones (using au...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-08-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524000583 |
| _version_ | 1797214802089607168 |
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| author | Ernest Teye Charles L.Y. Amuah Francis Padi Lamptey Francisca Obeng Regina Nyorkeh |
| author_facet | Ernest Teye Charles L.Y. Amuah Francis Padi Lamptey Francisca Obeng Regina Nyorkeh |
| author_sort | Ernest Teye |
| collection | DOAJ |
| description | Rapid, onsite, and simple detection of honey integrity in real time provides consumers with the assurance that the honey purchased is free from adulteration such as sugar syrup. In this study, smartphone based camera technique was used to differentiate authentic honey from adulterated ones (using authentic honey samples from different locations and adulterated samples made by spiking authentic ones with sugar syrup in the laboratory) by employing two supervised machine learning algorithms. Experimental results showed that the prediction model based on Random Forest (RF) was superior to K-nearest neighbour (KNN). The optimum results were assessed based on the prediction rate, specificity, and sensitivity. The performance of the RF model was 100 % accuracy while the specificity of 100 %, and sensitivity of 100 %. These findings could be exploited for reliable and rapid classification of honey integrity in Ghana and West Africa in general. This will further improve consumers’ confidence in the honey trade due to the ease and availability of smartphone technology. |
| first_indexed | 2024-04-24T11:19:57Z |
| format | Article |
| id | doaj.art-2195a47e75ee43fcb5642b06345665c5 |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-24T11:19:57Z |
| publishDate | 2024-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-2195a47e75ee43fcb5642b06345665c52024-04-11T04:42:12ZengElsevierSmart Agricultural Technology2772-37552024-08-018100453Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithmsErnest Teye0Charles L.Y. Amuah1Francis Padi Lamptey2Francisca Obeng3Regina Nyorkeh4University of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana, Food and Drug Integrity Research Group; Corresponding author.University of Cape Coast, College of Agriculture and Natural Sciences, School of Physical Sciences, Department of Physics, Laser and Fibre Optics Centre, Cape Coast, GhanaUniversity of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana, Food and Drug Integrity Research Group; Department of Food Science and Postharvest Technology, Cape Coast Technical University, P. O. Box DL 50, Cape Coast, GhanaUniversity of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana, Food and Drug Integrity Research GroupUniversity of Cape Coast, College of Agriculture and Natural Sciences, School of Agriculture, Department of Agricultural Engineering, Cape Coast, Ghana, Food and Drug Integrity Research GroupRapid, onsite, and simple detection of honey integrity in real time provides consumers with the assurance that the honey purchased is free from adulteration such as sugar syrup. In this study, smartphone based camera technique was used to differentiate authentic honey from adulterated ones (using authentic honey samples from different locations and adulterated samples made by spiking authentic ones with sugar syrup in the laboratory) by employing two supervised machine learning algorithms. Experimental results showed that the prediction model based on Random Forest (RF) was superior to K-nearest neighbour (KNN). The optimum results were assessed based on the prediction rate, specificity, and sensitivity. The performance of the RF model was 100 % accuracy while the specificity of 100 %, and sensitivity of 100 %. These findings could be exploited for reliable and rapid classification of honey integrity in Ghana and West Africa in general. This will further improve consumers’ confidence in the honey trade due to the ease and availability of smartphone technology.http://www.sciencedirect.com/science/article/pii/S2772375524000583HoneyAdulterationSmartphone cameraChemometricFraud |
| spellingShingle | Ernest Teye Charles L.Y. Amuah Francis Padi Lamptey Francisca Obeng Regina Nyorkeh Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms Smart Agricultural Technology Honey Adulteration Smartphone camera Chemometric Fraud |
| title | Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms |
| title_full | Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms |
| title_fullStr | Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms |
| title_full_unstemmed | Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms |
| title_short | Artificial intelligence for honey integrity in Ghana: A feasibility study on the use of smartphone images coupled with multivariate algorithms |
| title_sort | artificial intelligence for honey integrity in ghana a feasibility study on the use of smartphone images coupled with multivariate algorithms |
| topic | Honey Adulteration Smartphone camera Chemometric Fraud |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524000583 |
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