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<p>Honey is a natural sweetener produced by honeybees and is widely appreciated by consumers because of its multiple beneficial properties. Because of its high value, honey is placed as a targeted product for fraudulent practices. In this work, LC-LRMS fingerprinting was employed for classifying honey samples from 10 countries. Good classification and prediction performance were achieved based on a classification decision tree by consecutive paired PLS-DA models using a hierarchical model builder (HMB), obtaining sensitivity and specificity values higher than 83.3% and 92.6%, respectively, except for the case of China versus Japan. Tentative association of some phenolic compounds was accomplished, which provides useful chemical markers for country discrimination. For instance, methoxyphenylacetic acid, previously identified in New Zealander honeys, was tentatively annotated to <i>m/z</i> 165.0, detected in honey from New Zealand and Australia. The prediction of “unknown” samples was successful for most cases, obtaining sensitivity and specificity values of 100% for most countries. Good classification based on the continent of production was also accomplished, obtaining perfect discrimination among samples produced in Oceania and good classification performance was observed in Asian and European samples. Finally, the obtained fingerprints demonstrated to be useful chemical descriptors to quantify, as a proof of concept, adulterated Spanish honey with honey from Italy, China, and Serbia using partial least squares (PLS) regression, obtaining internal and external validation prediction errors lower than 23%.</p>

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Authentication of Honey Geographical Origin Using Liquid Chromatography–Low-Resolution Mass Spectrometry (LC-LRMS) Fingerprints

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Danica Mostoles,
  • Andrea Mara,
  • Gavino Sanna,
  • Javier Saurina,
  • Sònia Sentellas,
  • Oscar Núñez

摘要

Honey is a natural sweetener produced by honeybees and is widely appreciated by consumers because of its multiple beneficial properties. Because of its high value, honey is placed as a targeted product for fraudulent practices. In this work, LC-LRMS fingerprinting was employed for classifying honey samples from 10 countries. Good classification and prediction performance were achieved based on a classification decision tree by consecutive paired PLS-DA models using a hierarchical model builder (HMB), obtaining sensitivity and specificity values higher than 83.3% and 92.6%, respectively, except for the case of China versus Japan. Tentative association of some phenolic compounds was accomplished, which provides useful chemical markers for country discrimination. For instance, methoxyphenylacetic acid, previously identified in New Zealander honeys, was tentatively annotated to m/z 165.0, detected in honey from New Zealand and Australia. The prediction of “unknown” samples was successful for most cases, obtaining sensitivity and specificity values of 100% for most countries. Good classification based on the continent of production was also accomplished, obtaining perfect discrimination among samples produced in Oceania and good classification performance was observed in Asian and European samples. Finally, the obtained fingerprints demonstrated to be useful chemical descriptors to quantify, as a proof of concept, adulterated Spanish honey with honey from Italy, China, and Serbia using partial least squares (PLS) regression, obtaining internal and external validation prediction errors lower than 23%.