Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey

Honey is a widely appreciated and consumed natural product which is highly susceptible to fraudulent practices involving different sample attributes such as the botanical species or the geographical production regions, as well as possible adulterations. In the present work, the potential of non-targ...

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Autores: Mostoles, Danica, Egido, Carla, Mara, Alessandro, Sanna, Gavino, Sentellas, Sonia, Saurina, Javier, Núñez Burcio, Oscar
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2025
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/217969
Acceso en línea:https://hdl.handle.net/2445/217969
Access Level:acceso embargado
Palabra clave:Mel d'abelles
Cromatografia de líquids d'alta resolució
Quimiometria
Honey
High performance liquid chromatography
Chemometrics
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spelling Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of HoneyMostoles, DanicaEgido, CarlaMara, AlessandroSanna, GavinoSentellas, SoniaSaurina, JavierNúñez Burcio, OscarMel d'abellesCromatografia de líquids d'alta resolucióQuimiometriaHoneyHigh performance liquid chromatographyChemometricsHoney is a widely appreciated and consumed natural product which is highly susceptible to fraudulent practices involving different sample attributes such as the botanical species or the geographical production regions, as well as possible adulterations. In the present work, the potential of non-targeted HPLC-UV fingerprints as honey chemical descriptors to assess their geographical origin authentication involving a high number of samples belonging to nine different countries (and 4 continents) was evaluated by partial least squares-discriminant analysis (PLS-DA). Accurate discrimination between Spanish and Italian samples independently of the botanical varieties involved (multifloral, rosemary, and eucalyptus) was accomplished, as well as for the botanical species discrimination when considering each country independently. The best classification performance for 157 honey samples produced in 9 countries was accomplished when HPLC-UV fingerprints were submitted to a classification decision tree performed by consecutive PLS-DA models built using hierarchical model builder (HMB), with sensitivity and specificity values (for calibration and cross-validation) higher than 87.5 and 78.6%, respectively, and with classification errors below 17.0%. Prediction capabilities improved for samples belonging to New Zealand, Costa Rica, The Netherlands, and China, with classification errors below 8.3%, while it worsened for the other sample groups (classification errors in the range 17.4-27.4% for the samples belonging to Spain, Italy, France, and Serbia). Japanese samples showed the worse prediction errors (37.5%) as the “unknown” samples used were mostly misclassified as Chinese samples. Elsevier B.V.2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/217969Articles publicats en revistes (Enginyeria Química i Química Analítica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: https://doi.org/https://doi.org/10.1016/j.microc.2025.112669Microchemical Journal, 2025, vol. 209https://doi.org/https://doi.org/10.1016/j.microc.2025.112669cc-by-nc-nd (c) Elsevier B.V., 2025http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccessoai:diposit.ub.edu:2445/2179692026-05-27T06:46:51Z
dc.title.none.fl_str_mv Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
title Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
spellingShingle Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
Mostoles, Danica
Mel d'abelles
Cromatografia de líquids d'alta resolució
Quimiometria
Honey
High performance liquid chromatography
Chemometrics
title_short Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
title_full Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
title_fullStr Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
title_full_unstemmed Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
title_sort Potential of High-Performance Liquid Chromatography with Ultraviolet Detection (HPLC-UV) Fingerprints to Assess the Geographical Production Origin and Authenticity of Honey
dc.creator.none.fl_str_mv Mostoles, Danica
Egido, Carla
Mara, Alessandro
Sanna, Gavino
Sentellas, Sonia
Saurina, Javier
Núñez Burcio, Oscar
author Mostoles, Danica
author_facet Mostoles, Danica
Egido, Carla
Mara, Alessandro
Sanna, Gavino
Sentellas, Sonia
Saurina, Javier
Núñez Burcio, Oscar
author_role author
author2 Egido, Carla
Mara, Alessandro
Sanna, Gavino
Sentellas, Sonia
Saurina, Javier
Núñez Burcio, Oscar
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Mel d'abelles
Cromatografia de líquids d'alta resolució
Quimiometria
Honey
High performance liquid chromatography
Chemometrics
topic Mel d'abelles
Cromatografia de líquids d'alta resolució
Quimiometria
Honey
High performance liquid chromatography
Chemometrics
description Honey is a widely appreciated and consumed natural product which is highly susceptible to fraudulent practices involving different sample attributes such as the botanical species or the geographical production regions, as well as possible adulterations. In the present work, the potential of non-targeted HPLC-UV fingerprints as honey chemical descriptors to assess their geographical origin authentication involving a high number of samples belonging to nine different countries (and 4 continents) was evaluated by partial least squares-discriminant analysis (PLS-DA). Accurate discrimination between Spanish and Italian samples independently of the botanical varieties involved (multifloral, rosemary, and eucalyptus) was accomplished, as well as for the botanical species discrimination when considering each country independently. The best classification performance for 157 honey samples produced in 9 countries was accomplished when HPLC-UV fingerprints were submitted to a classification decision tree performed by consecutive PLS-DA models built using hierarchical model builder (HMB), with sensitivity and specificity values (for calibration and cross-validation) higher than 87.5 and 78.6%, respectively, and with classification errors below 17.0%. Prediction capabilities improved for samples belonging to New Zealand, Costa Rica, The Netherlands, and China, with classification errors below 8.3%, while it worsened for the other sample groups (classification errors in the range 17.4-27.4% for the samples belonging to Spain, Italy, France, and Serbia). Japanese samples showed the worse prediction errors (37.5%) as the “unknown” samples used were mostly misclassified as Chinese samples. 
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/217969
url https://hdl.handle.net/2445/217969
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/https://doi.org/10.1016/j.microc.2025.112669
Microchemical Journal, 2025, vol. 209
https://doi.org/https://doi.org/10.1016/j.microc.2025.112669
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Elsevier B.V., 2025
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv cc-by-nc-nd (c) Elsevier B.V., 2025
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
dc.source.none.fl_str_mv Articles publicats en revistes (Enginyeria Química i Química Analítica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,81155

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