Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics
<b>Background</b>: Honey is one of the most adulterated foods worldwide, and several analytical methods have been developed over the last decade to detect syrup additions to honey. These include approaches based on stable isotopes and the specific detection of individual marker compounds...
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MDPI AG
2024-11-01
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| Series: | Metabolites |
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| author | Jule Hansen Christof Kunert Kurt-Peter Raezke Stephan Seifert |
| author_facet | Jule Hansen Christof Kunert Kurt-Peter Raezke Stephan Seifert |
| author_sort | Jule Hansen |
| collection | DOAJ |
| description | <b>Background</b>: Honey is one of the most adulterated foods worldwide, and several analytical methods have been developed over the last decade to detect syrup additions to honey. These include approaches based on stable isotopes and the specific detection of individual marker compounds or foreign enzymes. Proton nuclear magnetic resonance (<sup>1</sup>H-NMR) spectroscopy is applied as a rapid and comprehensive screening method, which also enables the detection of quality parameters and the analysis of the geographical and botanical origin. However, especially for the detection of foreign sugars, <sup>1</sup>H-NMR has insufficient sensitivity. <b>Methods</b>: Since untargeted liquid chromatography–mass spectrometry (LC-MS) is more sensitive, we used this approach for the detection of positive and negative ions in combination with a recently developed data processing workflow for routine laboratories based on bucketing and random forest for the detection of rice, beet and high-fructose corn syrup in honey. <b>Results</b>: We show that the distinction between pure and adulterated honey is possible for all three syrups, with classification accuracies ranging from 98 to 100%, while the accuracy of the syrup content estimation depends on the respective syrup. For rice and beet syrup, the deviations from the true proportion were in the single-digit percentage range, while for high-fructose corn syrup they were much higher, in some cases exceeding 20%. <b>Conclusions</b>: The approach presented here is very promising for the robust and sensitive detection of syrup in honey applied in routine laboratories. |
| format | Article |
| id | doaj-art-aae3535ebe4a405880fdf0e6e282be42 |
| institution | OA Journals |
| issn | 2218-1989 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Metabolites |
| spelling | doaj-art-aae3535ebe4a405880fdf0e6e282be422025-08-20T01:53:57ZengMDPI AGMetabolites2218-19892024-11-01141163310.3390/metabo14110633Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and ChemometricsJule Hansen0Christof Kunert1Kurt-Peter Raezke2Stephan Seifert3Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, GermanyEurofins Food Integrity Control Services GmbH, Berliner Str. 2, 27721 Ritterhude, GermanyEurofins Food Integrity Control Services GmbH, Berliner Str. 2, 27721 Ritterhude, GermanyHamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany<b>Background</b>: Honey is one of the most adulterated foods worldwide, and several analytical methods have been developed over the last decade to detect syrup additions to honey. These include approaches based on stable isotopes and the specific detection of individual marker compounds or foreign enzymes. Proton nuclear magnetic resonance (<sup>1</sup>H-NMR) spectroscopy is applied as a rapid and comprehensive screening method, which also enables the detection of quality parameters and the analysis of the geographical and botanical origin. However, especially for the detection of foreign sugars, <sup>1</sup>H-NMR has insufficient sensitivity. <b>Methods</b>: Since untargeted liquid chromatography–mass spectrometry (LC-MS) is more sensitive, we used this approach for the detection of positive and negative ions in combination with a recently developed data processing workflow for routine laboratories based on bucketing and random forest for the detection of rice, beet and high-fructose corn syrup in honey. <b>Results</b>: We show that the distinction between pure and adulterated honey is possible for all three syrups, with classification accuracies ranging from 98 to 100%, while the accuracy of the syrup content estimation depends on the respective syrup. For rice and beet syrup, the deviations from the true proportion were in the single-digit percentage range, while for high-fructose corn syrup they were much higher, in some cases exceeding 20%. <b>Conclusions</b>: The approach presented here is very promising for the robust and sensitive detection of syrup in honey applied in routine laboratories.https://www.mdpi.com/2218-1989/14/11/633syrup adulterationhoneyroutine analysisuntargeted LC-MSmachine learningrandom forest |
| spellingShingle | Jule Hansen Christof Kunert Kurt-Peter Raezke Stephan Seifert Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics Metabolites syrup adulteration honey routine analysis untargeted LC-MS machine learning random forest |
| title | Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics |
| title_full | Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics |
| title_fullStr | Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics |
| title_full_unstemmed | Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics |
| title_short | Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography–Mass Spectrometry and Chemometrics |
| title_sort | detection of sugar syrups in honey using untargeted liquid chromatography mass spectrometry and chemometrics |
| topic | syrup adulteration honey routine analysis untargeted LC-MS machine learning random forest |
| url | https://www.mdpi.com/2218-1989/14/11/633 |
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