{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T08:46:54Z","timestamp":1791967614853,"version":"4.3.4"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T00:00:00Z","timestamp":1717977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Honey adulteration is a growing concern due to its health benefits and high nutritional content. Traditional methods like Melissopalynology are ineffective in detecting adulterated honey. This research presents a comparative study of machine learning algorithms for detecting adulteration in honey. The study uses hyperspectral imaging, a promising tool for food quality assurance, to classify and predict adulteration in honey. The proposed model relies on hyper-spectrum images and improves the accuracy of existing models using hyperparameter tuning. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples. The study found that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy. The results showed that between 5% and 10% of adulterated honey samples are misclassified, with C1 Clover honey being the most frequently misclassified. This study aims to develop an efficient and accurate honey counterfeit detection technology using machine learning technologies such as Artificial Neural Networks (ANN), Support-vector machines (SVM), K Nearest Neighbors, Random Forests, and Decision trees. The proposed model relies on hyper-spectrum images and overcomes generalization to unknown honey types of problems. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples from seven different brands with 12 different botanical origin labels. Feature reduction techniques, such as feature ranking-based feature selection, and autoencoder techniques are employed to classify the botanical origins of honey. The model parameters are enhanced or tuned by the training process, and hyperparameters are adjusted by running the whole training data. The researchers used Python, and well-known algorithms like ANN, SVM, KNN, random forests, and decision trees. The results show that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy.<\/jats:p>","DOI":"10.1371\/journal.pdig.0000536","type":"journal-article","created":{"date-parts":[[2024,6,10]],"date-time":"2024-06-10T13:22:21Z","timestamp":1718025741000},"page":"e0000536","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":53,"title":["Detection of honey adulteration using machine learning"],"prefix":"10.1371","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8144-1315","authenticated-orcid":true,"given":"Esmael","family":"Ahmed","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,6,10]]},"reference":[{"key":"pdig.0000536.ref001","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.tifs.2017.08.014","article-title":"Food 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