Quelal-Vásconez, MA.; Lerma-García, MJ.; Pérez-Esteve, É.; Arnau-Bonachera, A.; Barat Baviera, JM.; Talens Oliag, P. (2019). Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis. Food Control. 99:68-72. https://doi.org/10.1016/j.foodcont.2018.12.028
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/145563
Title:
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Fast detection of cocoa shell in cocoa powders by near infrared spectroscopy and multivariate analysis
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Author:
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Quelal-Vásconez, Maribel Alexandra
Lerma-García, María Jesús
Pérez-Esteve, Édgar
Arnau-Bonachera, Alberto
Barat Baviera, José Manuel
Talens Oliag, Pau
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UPV Unit:
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Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal
Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments
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Issued date:
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Abstract:
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[EN] Cocoa shell must be removed from the cocoa bean before or after the roasting process. In the case of a low efficient peeling process or the intentional addition of cocoa shell to cocoa products (i.e. cocoa powders) ...[+]
[EN] Cocoa shell must be removed from the cocoa bean before or after the roasting process. In the case of a low efficient peeling process or the intentional addition of cocoa shell to cocoa products (i.e. cocoa powders) to increase the economic benefit, quality of the final product could be unpleasantly affected. In this scenario, the Codex Alimentarius on cocoa and chocolate has established that cocoa cake must not contain more than 5% of cocoa shell and germ (based on fat-free dry matter). Traditional analysis of cocoa shell is very laborious. Thus, the aim of this work is to develop a methodology based on near infrared (NIR) spectroscopy and multivariate analysis for the fast detection of cocoa shell in cocoa powders. For this aim, binary mixtures of cocoa powder and cocoa shell containing increasing proportions of cocoa shell (up to ca. 40% w/w based on fat-free dried matter) have been prepared. After acquiring NIR spectra (1100-2500 nm) of pure samples (cocoa powder and cocoa shell) and mixtures, qualitative and quantitative analysis were done. The qualitative analysis was performed by using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), finding that the model was able to correctly classify all samples containing less than 5% of cocoa shell. The quantitative analysis was performed by using a partial least squares (PLS) regression. The best PLS model was the one constructed using extended multiple signal correction plus orthogonal signal correction pre-treatment using the 6 main wavelengths selected according to the Variable Importance in Projection (VIP) scores. Determination coefficient of prediction and root mean square error of prediction values of 0.967 and 2.43, respectively, confirmed the goodness of the model. According to these results it is possible to conclude that NIR technology in combination with multivariate analysis is a good and fast tool to determine if a cocoa powder contains a cocoa shell content out of Codex Alimentarius specifications.
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Subjects:
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Cocoa powder
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Cocoa shell
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NIR
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PLS
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PLS-DA
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Copyrigths:
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Reserva de todos los derechos
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Source:
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Food Control. (issn:
0956-7135
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DOI:
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10.1016/j.foodcont.2018.12.028
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Publisher:
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Elsevier
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Publisher version:
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https://doi.org/10.1016/j.foodcont.2018.12.028
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Project ID:
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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/
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Thanks:
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The authors wish to acknowledge the financial assistance provided the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). M. A. Quelal thanks the Ministry of Higher Education, Science, ...[+]
The authors wish to acknowledge the financial assistance provided the Spanish Government and European Regional Development Fund (Project RTC-2016-5241-2). M. A. Quelal thanks the Ministry of Higher Education, Science, Technology and Innovation (SENESCYT) of the Republic of Ecuador for her PhD grant.
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Type:
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Artículo
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