Identifying Type of Sugar Adulterants in Honey: Combined Application of NMR Spectroscopy and Supervised Machine Learning Classification

Overview

Journal Curr Res Food Sci

Date 2022 Feb 10

PMID 35141528

Authors

Kavitha Rachineni

Veera Mohana Rao Kakita

Neeraj Praphulla Awasthi

Vrushali Siddesh Shirke

Ramakrishna V Hosur

Satish Chandra Shukla

Affiliations

Soon will be listed here.

Abstract

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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References

Feng L, Wu B, Zhu S, He Y, Zhang C . Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins. Front Nutr. 2021; 8:680357. PMC: 8247466. DOI: 10.3389/fnut.2021.680357. View

Fakhlaei R, Selamat J, Khatib A, Razis A, Sukor R, Ahmad S . The Toxic Impact of Honey Adulteration: A Review. Foods. 2020; 9(11). PMC: 7692231. DOI: 10.3390/foods9111538. View

Tolles J, Meurer W . Logistic Regression: Relating Patient Characteristics to Outcomes. JAMA. 2016; 316(5):533-4. DOI: 10.1001/jama.2016.7653. View

Wang L, Li J, Li T, Liu H, Wang Y . Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh . ACS Omega. 2021; 6(30):19665-19674. PMC: 8340397. DOI: 10.1021/acsomega.1c02317. View

Zhu L, Spachos P, Pensini E, Plataniotis K . Deep learning and machine vision for food processing: A survey. Curr Res Food Sci. 2021; 4:233-249. PMC: 8079277. DOI: 10.1016/j.crfs.2021.03.009. View

Machado A, Miguel M, Vilas-Boas M, Figueiredo A . Honey Volatiles as a Fingerprint for Botanical Origin-A Review on their Occurrence on Monofloral Honeys. Molecules. 2020; 25(2). PMC: 7024207. DOI: 10.3390/molecules25020374. View

Schievano E, Tonoli M, Rastrelli F . NMR Quantification of Carbohydrates in Complex Mixtures. A Challenge on Honey. Anal Chem. 2017; 89(24):13405-13414. DOI: 10.1021/acs.analchem.7b03656. View

Spiteri C, Lia F, Farrugia C . Determination of the Geographical Origin of Maltese Honey Using H NMR Fingerprinting. Foods. 2020; 9(10). PMC: 7601949. DOI: 10.3390/foods9101455. View

Puscion-Jakubik A, Borawska M, Socha K . Modern Methods for Assessing the Quality of Bee Honey and Botanical Origin Identification. Foods. 2020; 9(8). PMC: 7466300. DOI: 10.3390/foods9081028. View

10.

Bertelli D, Lolli M, Papotti G, Bortolotti L, Serra G, Plessi M . Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance. J Agric Food Chem. 2010; 58(15):8495-501. DOI: 10.1021/jf101460t. View

11.

Soares S, Amaral J, Oliveira M, Mafra I . A Comprehensive Review on the Main Honey Authentication Issues: Production and Origin. Compr Rev Food Sci Food Saf. 2020; 16(5):1072-1100. DOI: 10.1111/1541-4337.12278. View

12.

Lolli M, Bertelli D, Plessi M, Sabatini A, Restani C . Classification of Italian honeys by 2D HR-NMR. J Agric Food Chem. 2008; 56(4):1298-304. DOI: 10.1021/jf072763c. View

13.

Dong H, Xiao K, Xian Y, Wu Y . Authenticity determination of honeys with non-extractable proteins by means of elemental analyzer (EA) and liquid chromatography (LC) coupled to isotope ratio mass spectroscopy (IRMS). Food Chem. 2017; 240:717-724. DOI: 10.1016/j.foodchem.2017.08.008. View

14.

da Silva P, Gauche C, Gonzaga L, Costa A, Fett R . Honey: Chemical composition, stability and authenticity. Food Chem. 2015; 196:309-23. DOI: 10.1016/j.foodchem.2015.09.051. View

15.

Spiteri M, Jamin E, Thomas F, Rebours A, Lees M, Rogers K . Fast and global authenticity screening of honey using ¹H-NMR profiling. Food Chem. 2015; 189:60-6. DOI: 10.1016/j.foodchem.2014.11.099. View

16.

Olawode E, Tandlich R, Cambray G . ¹H-NMR Profiling and Chemometric Analysis of Selected Honeys from South Africa, Zambia, and Slovakia. Molecules. 2018; 23(3). PMC: 6017463. DOI: 10.3390/molecules23030578. View

17.

Yan S, Wang S, Qiu J, Li M, Li D, Xu D . Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level. Talanta. 2021; 226:122195. DOI: 10.1016/j.talanta.2021.122195. View

18.

Liang N, Sun S, Zhang C, He Y, Qiu Z . Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food. Crit Rev Food Sci Nutr. 2020; 62(11):2963-2984. DOI: 10.1080/10408398.2020.1862045. View