Autor según el artículo: Mabood, Fazal; Ali, Liaqat; Boque, Ricard; Abbas, Ghulam; Jabeen, Farah; Haq, Quazi Mohammad Imranul; Hussain, Javid; Hamaed, Ahmed Moahammed; Naureen, Zakira; Al-Nabhani, Mahmood; Khan, Mohammed Ziauddin; Khan, Ajmal; Al-Harrasi, Ahmed;

Departamento: Química Analítica i Química Orgànica

Autor/es de la URV: Boqué Martí, Ricard

Palabras clave: Urea Raw-milk Principal components analysis Partial least-squares regressions Partial least-squares discriminant analysis Nir spectroscopy Milk adulteration Melamine Identification Cows Biosensor Authenticity

Resumen: Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near-infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825-1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000-4,000 cm(-1), using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm(-1). Principal components analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and partial least-squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS-DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R-square and root mean square error (RMSE) values obtained for the PLS-DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave-one-out cross-validation procedure was used as an internal cross-validation criteria and the R-square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.

Áreas temáticas: Zootecnia / recursos pesqueiros Medicina veterinaria Interdisciplinar Food science & technology Food science Farmacia Ensino Engenharias iv Engenharias ii Ciência de alimentos Biotecnología Biodiversidade

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 20487177

Direcció de correo del autor: ricard.boque@urv.cat

Identificador del autor: 0000-0001-7311-4824

Fecha de alta del registro: 2023-02-22

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://onlinelibrary.wiley.com/doi/10.1002/fsn3.987

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Food Science & Nutrition. 8 (10): 5249-5258

Referencia de l'ítem segons les normes APA: Mabood, Fazal; Ali, Liaqat; Boque, Ricard; Abbas, Ghulam; Jabeen, Farah; Haq, Quazi Mohammad Imranul; Hussain, Javid; Hamaed, Ahmed Moahammed; Naureen (2020). Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples. Food Science & Nutrition, 8(10), 5249-5258. DOI: 10.1002/fsn3.987

DOI del artículo: 10.1002/fsn3.987

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications