Articles producció científica> Química Analítica i Química Orgànica

Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis

  • Datos identificativos

    Identificador: imarina:6112171
    Autores:
    Mabood FBoqué RAlkindi AYAl-Harrasi AAl Amri ISBoukra SJabeen FHussain JAbbas GNaureen ZHaq QMIShah HHKhan AKhalaf SKKadim I
    Resumen:
    © 2020 Elsevier Ltd This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm−1, at a resolution of 2 cm−1 and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R2 value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.
  • Otros:

    Autor según el artículo: Mabood F; Boqué R; Alkindi AY; Al-Harrasi A; Al Amri IS; Boukra S; Jabeen F; Hussain J; Abbas G; Naureen Z; Haq QMI; Shah HH; Khan A; Khalaf SK; Kadim I
    Departamento: Química Analítica i Química Orgànica
    Autor/es de la URV: Boqué Martí, Ricard
    Palabras clave: Quality Prediction Pork meat Plsr Pls-da Pca Near-infrared reflectance Near infrared reflectance spectroscopy Identification Beef Authentication Assay Adulteration plsr pls-da pca near infrared reflectance spectroscopy
    Resumen: © 2020 Elsevier Ltd This study aimed to develop a fast analytical method, combining near infrared reflectance spectroscopy and multivariate analysis, for detection and quantification of pork meat in other meat samples. A total of 5952 mixture samples from 39 types of meat were prepared in triplicate, with the inclusion of pork at 0%, 1%, 5%, 10%, 30%, 50%, 70%, 90% and 100%. Each sample was scanned using an FT-NIR spectrophotometer in the reflection mode. Spectra were collected in the wavenumber range from 10,000 to 4000 cm−1, at a resolution of 2 cm−1 and a total path length of 0.5 mm. Principal Component Analysis (PCA) revealed the similarities and differences among the various types of meat samples and Partial Least-Squares Discriminant Analysis (PLS-DA) showed a good discrimination between pure and pork-spiked meat samples. A Partial Least-Squares Regression (PLSR) model was built to predict the pork meat contents in other meats, which provided the R2 value of 0.9774 and RMSECV value of 1.08%. Additionally, an external validation was carried out using a test set, providing a rather good prediction error, with an RMSEP value of 1.84%.
    Áreas temáticas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Nutrição Medicina veterinaria Medicina ii Medicina i Interdisciplinar Food science & technology Food science Farmacia Engenharias ii Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Biotecnología Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 0309-1740
    Direcció de correo del autor: ricard.boque@urv.cat
    Identificador del autor: 0000-0001-7311-4824
    Fecha de alta del registro: 2023-02-22
    Volumen de revista: 163
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/abs/pii/S0309174019308708?via%3Dihub
    Referencia al articulo segun fuente origial: Meat Science. 163 (108084): 108084-
    Referencia de l'ítem segons les normes APA: Mabood F; Boqué R; Alkindi AY; Al-Harrasi A; Al Amri IS; Boukra S; Jabeen F; Hussain J; Abbas G; Naureen Z; Haq QMI; Shah HH; Khan A; Khalaf SK; Kadim (2020). Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis. Meat Science, 163(108084), 108084-. DOI: 10.1016/j.meatsci.2020.108084
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1016/j.meatsci.2020.108084
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Food Science,Food Science & Technology
    Quality
    Prediction
    Pork meat
    Plsr
    Pls-da
    Pca
    Near-infrared reflectance
    Near infrared reflectance spectroscopy
    Identification
    Beef
    Authentication
    Assay
    Adulteration
    plsr
    pls-da
    pca
    near infrared reflectance spectroscopy
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Química
    Psicología
    Odontología
    Nutrição
    Medicina veterinaria
    Medicina ii
    Medicina i
    Interdisciplinar
    Food science & technology
    Food science
    Farmacia
    Engenharias ii
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências agrárias i
    Ciência de alimentos
    Biotecnología
    Administração, ciências contábeis e turismo
    Administração pública e de empresas, ciências contábeis e turismo
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