Visible feature engineering to detect fraud in black and red peppers.
Artificial intelligence.
Feature engineering
Fraud detection
Image processing
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 Oct 2024
25 Oct 2024
Historique:
received:
10
04
2024
accepted:
15
10
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
Visible imaging is a fast, cheap, and accurate technique in the assessment of food quality and safety. The technique was used in the present research to detect sea foam adulterant levels in black and red peppers. The fraud levels included 0, 5, 15, 30, and 50%. Sample preparation, image acquisition and preprocessing, and feature engineering (feature extraction, selection, and classification) were the conducted steps in the present research. The efficient features were classified using artificial neural networks and support vector machine methods. The classifiers were evaluated using the specificity, sensitivity, precision, and accuracy metrics. The artificial neural networks had better results than the support vector machine method for the classification of different adulterant levels in black pepper with the metrics' values of 98.89, 95.67, 95.56, and 98.22%, respectively. Reversely, the support vector machine method had higher metrics' values (99.46, 98.00, 97.78, and 99.11%, respectively) for red pepper. The results showed the ability of visible imaging and machine learning methods to detect fraud levels in black and red pepper.
Identifiants
pubmed: 39455689
doi: 10.1038/s41598-024-76617-1
pii: 10.1038/s41598-024-76617-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25417Informations de copyright
© 2024. The Author(s).
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