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- Title
Detection of honey adulteration using machine learning.
- Authors
Ahmed, Esmael
- Abstract
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. Author summary: Honey adulteration, the practice of adding substances such as sugar syrups to honey, poses significant challenges to food safety and consumer trust. In our study, we employ machine learning and hyperspectral imaging to develop a robust method for detecting adulterated honey. Using advanced algorithms, including artificial neural networks (ANN), support vector machines (SVM), K-nearest neighbors (KNN), random forests, and decision trees, we analyze honey samples to identify and quantify adulteration. Our findings demonstrate that these machine learning techniques, particularly when enhanced through hyperparameter tuning, achieve high classification accuracy, with ANN models surpassing 98% accuracy. This indicates a powerful potential for these technologies in ensuring the integrity of honey products. Additionally, our study addresses both binary classification (pure vs. adulterated honey) and multi-class classification (various levels of adulteration), providing comprehensive insights into the detection process. This research offers valuable contributions to the field of food safety by proposing a reliable and efficient approach to combat honey fraud. The integration of machine learning with hyperspectral imaging represents a promising advancement in quality assurance for the honey industry, ultimately protecting consumers and maintaining market integrity.
- Subjects
HONEY analysis; RANDOM forest algorithms; PREDICTION models; DATABASE management; NATURAL language processing; FOOD contamination; SUPPORT vector machines; ARTIFICIAL neural networks; SPECTRUM analysis; MACHINE learning; COMPARATIVE studies; QUALITY assurance; DECISION trees; DIGITAL image processing; ALGORITHMS; REGRESSION analysis
- Publication
PLoS Digital Health, 2024, Vol 3, Issue 6, p1
- ISSN
2767-3170
- Publication type
Article
- DOI
10.1371/journal.pdig.0000536