Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

• Published in: Current research in food science Vol. 5; pp. 272 - 277
• Main Authors: Rachineni, Kavitha, Rao Kakita, Veera Mohana, Awasthi, Neeraj Praphulla, Shirke, Vrushali Siddesh, Hosur, Ramakrishna V., Chandra Shukla, Satish
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-01-2022; Elsevier
• Subjects: Deep learning; Honey; Machine learning; NMR; Sugar Adulteration; Supervised classification; Deep learning; Honey; Sugar Adulteration; NMR; Supervised classification; Machine learning
• ISSN: 2665-9271; 2665-9271
• DOI: 10.1016/j.crfs.2022.01.008

Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes.
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•NMR helps to identify the fingerprints of honey chemical constituents.•Combined NMR and ML tools can determine the type of adulteration in honey.•Supervised classification schemes, Logistic regression, DNN, and LGBM are utilized.•Corn, brown rice, and jaggery adulterations are discriminated in honey.