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

Rachineni, Kavitha; Kakita, Veera Mohana Rao; Awasthi, Neeraj Praphulla; Shirke, Vrushali Siddesh; Hosur, Ramakrishna V.; Shukla, Satish Chandra

doi:10.1016/j.crfs.2022.01.008

Cited by 36 publications

(12 citation statements)

References 35 publications

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“…However, the sugar content of the honey depends on the botanical and geographical regions. Honey is prone to adulteration by the direct addition of a certain amount of sucrose syrup into the honey and also the addition of chemical colour to attract consumers [6,7].…”

Section: Introductionmentioning

confidence: 99%

Physicochemical Properties of a New Green Honey from Banggi Island, Sabah

et al. 2022

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Green honey is exclusively available on the island of Banggi in Sabah, and its uniqueness sees the commodity being sold at a high market price. Therefore, green honey is prone to adulteration by unscrupulous individuals, possibly compromising the health of those consuming this food commodity for its curative properties. Moreover, an established standard for reducing sugar in green honey is unavailable. Ipso facto, the study aimed to profile green honey’s physical and chemical properties, such as its pH, moisture content, free acidity, ash content, electroconductivity, hydroxymethylfurfural (HMF), total phenolic content, total flavonoid content, DPPH, colour, total sugar content, total protein content, and heavy metals as well as volatile organic compounds, the data of which are profoundly valuable in safeguarding consumers’ safety while providing information for its quality certification for local consumption and export. The results revealed that the honey’s physicochemical profile is comparable to other reported kinds of honey. The honey’s naturally green colour is because of the chlorophyll from the nectar from various flowers on the island. The raw honey showed free acidity between 28 and 33 Meq/100 g, lower than the standard’s 50 Meq/100 g. The hydroxymethylfurfural content is the lowest compared to other reported honey samples, with the total phenolic content between 16 and 19 mg GAE/100 g. The honey’s reducing sugar content is lower (~37.9%) than processed ones (56.3%) because of water removal. The protein content ranged from 1 to 2 gm/kg, 4- to 6-fold and 2-fold higher than local and manuka honey, respectively. The exceptionally high content of trans-4-hydroxyproline in raw honey is its source of collagen and other healing agents. Interestingly, low levels of arsenic, lead, nickel, cadmium, copper, and cobalt were detected in the honey samples, presumably due to their subterranean hives. Nevertheless, the honey is fit for general consumption as the concentrations were below the maxima in the Codex Alimentarius Commission of 2001.

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Section: Introductionmentioning

confidence: 99%

Physicochemical Properties of a New Green Honey from Banggi Island, Sabah

et al. 2022

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“…The limitations of conventional methods for verifying the botanical and geographical origins of honey emphasize the necessity for more dependable and contemporary analytical approaches. Cutting-edge analytical instruments and sensor arrays, including chromatography [14], mass spectrometry (MS) [15] techniques, vibrational spectroscopy such as infrared (IR) [16] and Raman spectroscopy methods [17], nuclear magnetic resonance (NMR) [18], are joined in research to assess sugar profiles, mineral content, phenolic and flavonoid compositions, aroma characteristics, and amino acid compositions [19]. The application of these tools is crucial in ensuring the precision of honey origin authentication.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Turkish pine honey from multi-floral honey through MALDI-MS-based N-glycomics and machine learning

Masri,

Aksoy,

Duman

et al. 2024

Food Measure

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Honey, a multifaceted blend of sugars, amino acids, vitamins, proteins, and minerals, exhibits compositional variability dependent upon the floral source. While previous studies have attempted to categorize honey, the use of glycomic profiles for honey classification remains an unexplored avenue. This investigation seeks to establish a methodology for distinguishing honey types, specifically multi-floral and pine honey, employing mass spectrometry-based glycomic analysis in tandem with machine learning. In this search, seven samples of pine honey and eight samples of multi-floral honey were obtained from diverse regions of Turkey. Subsequently, the proteins within these honey samples were extracted, and glycans were enzymatically released. The released glycans were labeled with 2-aminobenzoic acid (2-AA) and subjected to analysis via matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The glycan profiles of pine and multi-floral honey were determined through these analytical procedures, revealing a total of 76 distinct N-glycan structures. Among these, 13 N-glycan profiles consistently established at high levels across experimental replicates and were incorporated in subsequent analyses. Following the quantification of individual glycan abundances, statistically significant differences in glycan profiles were determined. Notably, N-glycans Hex5HexNAc2, Hex4HexNAc3, and Hex5HexNAc3 displayed considerable differences. Using the 13 N-glycan profiles, an accuracy rate of 93.5% was obtained from machine learning analysis, which increased to 100% when incorporating the identified significantly changed glycans. The most productive models were identified as “subspace and fine k-nearest neighbors (KNN).” The findings underscore the potential of mass spectrometry-based glycomics in conjunction with machine learning as a robust tool for precise honey type classification and its prospective utility in quality control and honey product authentication.

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“…479 NMR spectroscopy was employed in conjunction with supervised machine learning models, which map input data and via an inferred function produces output data to detect in an automatic fashion adulteration in honey, such as invert sugar, i.e., hydrolyzed sucrose. 480 The classification methods included a logistic regression classifier, DNN, and a light gradient boosting machine; interestingly, by combining the results through a voting method using all of the classifiers, the tested data sets were correctly identified, whether they came from samples containing adulterated or pure honey. One can foresee that machine learning approaches will have great potential to complement already existing NMR chemical shift prediction methods based on increment rules (vide supra, section 6.2).…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning methods related to NMR spectroscopy are to this end presently being developed based on data-driven approaches and density functional theory quantum chemical computed values − of organic molecules as well as by using deep neural networks (DNN) for peak picking of biomolecular NMR spectra . NMR spectroscopy was employed in conjunction with supervised machine learning models, which map input data and via an inferred function produces output data to detect in an automatic fashion adulteration in honey, such as invert sugar, i.e., hydrolyzed sucrose . The classification methods included a logistic regression classifier, DNN, and a light gradient boosting machine; interestingly, by combining the results through a voting method using all of the classifiers, the tested data sets were correctly identified, whether they came from samples containing adulterated or pure honey.…”

Section: Discussionmentioning

confidence: 99%

Primary Structure of Glycans by NMR Spectroscopy

Fontana

Widmalm

2023

Chem. Rev.

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Glycans, carbohydrate molecules in the realm of biology, are present as biomedically important glycoconjugates and a characteristic aspect is that their structures in many instances are branched. In determining the primary structure of a glycan, the sugar components including the absolute configuration and ring form, anomeric configuration, linkage(s), sequence, and substituents should be elucidated. Solution state NMR spectroscopy offers a unique opportunity to resolve all these aspects at atomic resolution. During the last two decades, advancement of both NMR experiments and spectrometer hardware have made it possible to unravel carbohydrate structure more efficiently. These developments applicable to glycans include, inter alia, NMR experiments that reduce spectral overlap, use selective excitations, record tilted projections of multidimensional spectra, acquire spectra by multiple receivers, utilize polarization by fast-pulsing techniques, concatenate pulse-sequence modules to acquire several spectra in a single measurement, acquire pure shift correlated spectra devoid of scalar couplings, employ stable isotope labeling to efficiently obtain homo-and/or heteronuclear correlations, as well as those that rely on dipolar cross-correlated interactions for sequential information. Refined computer programs for NMR spin simulation and chemical shift prediction aid the structural elucidation of glycans, which are notorious for their limited spectral dispersion. Hardware developments include cryogenically cold probes and dynamic nuclear polarization techniques, both resulting in enhanced sensitivity as well as ultrahigh field NMR spectrometers with a 1 H NMR resonance frequency higher than 1 GHz, thus improving resolution of resonances. Taken together, the developments have made and will in the future make it possible to elucidate carbohydrate structure in great detail, thereby forming the basis for understanding of how glycans interact with other molecules.

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

Cited by 36 publications

References 35 publications

Physicochemical Properties of a New Green Honey from Banggi Island, Sabah

Physicochemical Properties of a New Green Honey from Banggi Island, Sabah

Distinguishing Turkish pine honey from multi-floral honey through MALDI-MS-based N-glycomics and machine learning

Primary Structure of Glycans by NMR Spectroscopy

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