Articles producció científica> Química Analítica i Química Orgànica

Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples

  • Identification data

    Identifier: imarina:5893700
    Authors:
    Mabood, FazalAli, LiaqatBoque, RicardAbbas, GhulamJabeen, FarahHaq, Quazi Mohammad ImranulHussain, JavidHamaed, Ahmed MoahammedNaureen, ZakiraAl-Nabhani, MahmoodKhan, Mohammed ZiauddinKhan, AjmalAl-Harrasi, Ahmed
    Abstract:
    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 criter
  • Others:

    Author, as appears in the article.: 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;
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Boqué Martí, Ricard
    Keywords: Urea Raw-milk Principal components analysis Partial least-squares regressions Partial least-squares discriminant analysis Nir spectroscopy Milk adulteration Melamine Identification Cows Biosensor Authenticity
    Abstract: 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.
    Thematic Areas: Zootecnia / recursos pesqueiros Medicina veterinaria Interdisciplinar Food science & technology Food science Farmacia Ensino Engenharias iv Engenharias ii Ciência de alimentos Biotecnología Biodiversidade
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 20487177
    Author's mail: ricard.boque@urv.cat
    Author identifier: 0000-0001-7311-4824
    Record's date: 2023-02-22
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://onlinelibrary.wiley.com/doi/10.1002/fsn3.987
    Papper original source: Food Science & Nutrition. 8 (10): 5249-5258
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.1002/fsn3.987
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    Food Science,Food Science & Technology
    Urea
    Raw-milk
    Principal components analysis
    Partial least-squares regressions
    Partial least-squares discriminant analysis
    Nir spectroscopy
    Milk adulteration
    Melamine
    Identification
    Cows
    Biosensor
    Authenticity
    Zootecnia / recursos pesqueiros
    Medicina veterinaria
    Interdisciplinar
    Food science & technology
    Food science
    Farmacia
    Ensino
    Engenharias iv
    Engenharias ii
    Ciência de alimentos
    Biotecnología
    Biodiversidade
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