Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient

This paper introduces a method for determining the authenticity of commercial cereal bars based on trace element fingerprints. In this regard, 120 cereal bars were prepared using microwave-assisted acid digestion and the concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, S...

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Autores principales: Pérez Rodríguez, Michael, Hidalgo, Melisa Jazmin, Mendoza, Alberto, González, Lucy T., Longoria Rodríguez, Francisco, Goicoechea, Héctor Casimiro, Pellerano, Roberto Gerardo
Formato: Artículo
Lenguaje:Inglés
Publicado: Elsevier 2025
Materias:
Cereal bars
Trace elements
Fingerprinting
ICP-MS
Authentication
LDA
Acceso en línea:http://repositorio.unne.edu.ar/handle/123456789/59136
Aporte de:
RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE) de Universidad Nacional del Nordeste
id I48-R184-123456789-59136
record_format dspace
spelling I48-R184-123456789-591362025-12-05T11:20:13Z Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient Pérez Rodríguez, Michael Hidalgo, Melisa Jazmin Mendoza, Alberto González, Lucy T. Longoria Rodríguez, Francisco Goicoechea, Héctor Casimiro Pellerano, Roberto Gerardo Cereal bars Trace elements Fingerprinting ICP-MS Authentication LDA This paper introduces a method for determining the authenticity of commercial cereal bars based on trace element fingerprints. In this regard, 120 cereal bars were prepared using microwave-assisted acid digestion and the concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, Sn, Sr, V, and Zn were later measured by ICP-MS. Results confirmed the suitability of the analyzed samples for human consumption. Multielemental data underwent autoscaling preprocessing for then applying PCA, CART, and LDA to input data set. LDA model accomplished the highest classification modeling performance with a success rate of 92%, making it the suitable model for reliable cereal bar prediction. The proposed method demonstrates the potential of trace element fingerprints in distinguishing cereal bar samples according to their type (conventional and gluten-free) and principal ingredient (fruit, yogurt, chocolate), thereby contributing to global efforts for food authentication. 2025-12-05T11:11:38Z 2025-12-05T11:11:38Z 2023-06-07 Artículo Pérez Rodríguez, Michael, et al., 2023. Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient. Food Chemistry: X. Ámsterdam: Países Bajos, vol. 18, p. 1-7. E-ISSN 2772-753X. DOI https://doi.org/10.1016/j.fochx.2023.100744 http://repositorio.unne.edu.ar/handle/123456789/59136 en https://doi.org/10.1016/j.fochx.2023.100744 openAccess http://creativecommons.org/licenses/by-nc-nd/2.5/ar/ application/pdf p. 1-7 application/pdf Elsevier Food Chemistry: X, 2023, vol. 18, p. 1-7.
institution Universidad Nacional del Nordeste
institution_str I-48
repository_str R-184
collection RIUNNE - Repositorio Institucional de la Universidad Nacional del Nordeste (UNNE)
language Inglés
topic Cereal bars
Trace elements
Fingerprinting
ICP-MS
Authentication
LDA
spellingShingle Cereal bars
Trace elements
Fingerprinting
ICP-MS
Authentication
LDA
Pérez Rodríguez, Michael
Hidalgo, Melisa Jazmin
Mendoza, Alberto
González, Lucy T.
Longoria Rodríguez, Francisco
Goicoechea, Héctor Casimiro
Pellerano, Roberto Gerardo
Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
topic_facet Cereal bars
Trace elements
Fingerprinting
ICP-MS
Authentication
LDA
description This paper introduces a method for determining the authenticity of commercial cereal bars based on trace element fingerprints. In this regard, 120 cereal bars were prepared using microwave-assisted acid digestion and the concentrations of Al, Ba, Bi, Cd, Co, Cr, Cu, Fe, Li, Mn, Mo, Ni, Pb, Rb, Se, Sn, Sr, V, and Zn were later measured by ICP-MS. Results confirmed the suitability of the analyzed samples for human consumption. Multielemental data underwent autoscaling preprocessing for then applying PCA, CART, and LDA to input data set. LDA model accomplished the highest classification modeling performance with a success rate of 92%, making it the suitable model for reliable cereal bar prediction. The proposed method demonstrates the potential of trace element fingerprints in distinguishing cereal bar samples according to their type (conventional and gluten-free) and principal ingredient (fruit, yogurt, chocolate), thereby contributing to global efforts for food authentication.
format Artículo
author Pérez Rodríguez, Michael
Hidalgo, Melisa Jazmin
Mendoza, Alberto
González, Lucy T.
Longoria Rodríguez, Francisco
Goicoechea, Héctor Casimiro
Pellerano, Roberto Gerardo
author_facet Pérez Rodríguez, Michael
Hidalgo, Melisa Jazmin
Mendoza, Alberto
González, Lucy T.
Longoria Rodríguez, Francisco
Goicoechea, Héctor Casimiro
Pellerano, Roberto Gerardo
author_sort Pérez Rodríguez, Michael
title Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
title_short Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
title_full Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
title_fullStr Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
title_full_unstemmed Measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
title_sort measuring trace element fingerprinting for cereal bar authentication based on type and principal ingredient
publisher Elsevier
publishDate 2025
url http://repositorio.unne.edu.ar/handle/123456789/59136
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