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Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy

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Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy

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dc.contributor.author Quelal-Vásconez, Maribel Alexandra es_ES
dc.contributor.author Pérez-Esteve, Édgar es_ES
dc.contributor.author Arnau-Bonachera, Alberto es_ES
dc.contributor.author Barat Baviera, José Manuel es_ES
dc.contributor.author Talens Oliag, Pau es_ES
dc.date.accessioned 2020-07-08T03:31:59Z
dc.date.available 2020-07-08T03:31:59Z
dc.date.issued 2018-10 es_ES
dc.identifier.issn 0956-7135 es_ES
dc.identifier.uri http://hdl.handle.net/10251/147619
dc.description.abstract [EN] Cocoa powder is a highly valuable global product that can be adulterated with low-cost raw materials like carob flour as small amounts of this flour would not change the color, aroma and taste characteristics of the final product. Rapid methods, like NIR technology combined with multivariate analysis, are interesting for such detection. In this work, unaltered cocoa powders with different alkalization levels, carob flours with three different roasting degrees, and adulterated samples, prepared by blending cocoa powders with carob flour at several proportions, were analyzed. The diffuse reflectance spectra of the samples of 1100¿2500¿nm were acquired in a Foss NIR spectrophotometer. A qualitative and a quantitative analysis were done. For the qualitative analysis, a principal component analysis (PCA) and a partial least squares discriminant analysis (PLS-DA) were performed. Good results (100% classification accuracy) were obtained, which indicates the possibility of distinguishing pure cocoa powders from adulterated samples. For the quantitative analysis, a partial least squares (PLS) regression analysis was performed. The most robust PLS prediction model was obtained with one factor (LV), a coefficient of determination for prediction (RP2) of 0.974 and a root mean square error of prediction (RMSEP) of 3.2% for the external set. These data allowed us to conclude that NIR technology combined with multivariate analysis enables the identification and determination of the amount of natural cocoa powder present in a mixture adulterated with carob flour. es_ES
dc.description.sponsorship The authors wish to acknowledge the financial assistance provided by the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). Maribel Quelal Vásconez thanks the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant. The Olam Food Ingredients Company is acknowledged for proving part of the cocoa samples used herein es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Food Control es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Cocoa powder es_ES
dc.subject Adulteration es_ES
dc.subject Carob flour es_ES
dc.subject NIR es_ES
dc.subject PCA es_ES
dc.subject PLS es_ES
dc.subject.classification TECNOLOGIA DE ALIMENTOS es_ES
dc.subject.classification PRODUCCION ANIMAL es_ES
dc.title Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.foodcont.2018.05.001 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RTC-2016-5241-2/ES/Estudio de la relación entre variables de procesado y cambios en la composición nutricional y perfil funcional del cacao en polvo. Desarrollo de una metodología predictiva aplicada al procesamiento/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.description.bibliographicCitation Quelal-Vásconez, MA.; Pérez-Esteve, É.; Arnau-Bonachera, A.; Barat Baviera, JM.; Talens Oliag, P. (2018). Rapid fraud detection of cocoa powder with carob flour using near infrared spectroscopy. Food Control. 92:183-189. https://doi.org/10.1016/j.foodcont.2018.05.001 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.foodcont.2018.05.001 es_ES
dc.description.upvformatpinicio 183 es_ES
dc.description.upvformatpfin 189 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 92 es_ES
dc.relation.pasarela S\362139 es_ES
dc.contributor.funder Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Ecuador es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES


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