Articles producció científica> Química Analítica i Química Orgànica

Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars

  • Datos identificativos

    Identificador: imarina:5132618
    Autores:
    Whei Miaw, Carolina ShengSena, Marcelo MartinsCarvalho de Souza, Scheilla VitorinoRuisanchez, ItziarPilar Callao, Maria
    Resumen:
    During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.
  • Otros:

    Autor según el artículo: Whei Miaw, Carolina Sheng; Sena, Marcelo Martins; Carvalho de Souza, Scheilla Vitorino; Ruisanchez, Itziar; Pilar Callao, Maria
    Departamento: Química Analítica i Química Orgànica
    Autor/es de la URV: Callao Lasmarias, María Pilar / Ruisánchez Capelastegui, María Iciar
    Palabras clave: Vitis Variable selection Statistics as topic Simca Pls-da Plant nectar Multivariate analysis Multi-class methods Least-squares analysis Grape nectar Fraud Food fraud Food analysis Discriminant analysis simca pls-da multi-class methods grape nectar food fraud
    Resumen: During the quality inspection control of fruit beverages, some types of adulterations can be detected, such as the addition or substitution with less expensive fruits. To determine whether grape nectars were adulterated by substitution with apple or cashew juice or by a mixture of both, a methodology based on attenuated total reflectance Fourier transform mid infrared spectroscopy (ATR-FTIR) and multivariate classification methods was proposed. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) models were developed as multi-class methods (classes unadulterated, adulterated with cashew and adulterated with apple) with the full-spectra. PLS-DA presented better performance parameters than SIMCA in the classification of samples with just one adulterant, while poor results were achieved for samples with blends of two adulterants when using both classification methods. Three variable selection methods were tested in order to improve the effectiveness of the classification models: interval partial least squares (iPLS), variable importance in projection scores (VIP scores) and a genetic algorithm (GA). Variable selection methods improved the performance parameters for the SIMCA and PLS-DA methods when they were used to predict samples with only one adulterant. Only PLS-DA coupled with iPLS was able to classify samples with blends of two adulterants, providing sensitivity values between 100% and 83% at 100% specificity for the three studied classes.
    Áreas temáticas: Zootecnia / recursos pesqueiros Spectroscopy Saúde coletiva Química Nutrição Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Interdisciplinar Geociências General medicine General chemistry Farmacia Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Chemistry, analytical Chemistry (miscellaneous) Biotecnología Biodiversidade Biochemistry Astronomia / física Analytical chemistry Administração pública e de empresas, ciências contábeis e turismo
    ISSN: 00399140
    Direcció de correo del autor: mariapilar.callao@urv.cat itziar.ruisanchez@urv.cat
    Identificador del autor: 0000-0003-2691-329X 0000-0002-7097-3583
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://www.sciencedirect.com/science/article/abs/pii/S0039914018307859
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Talanta. 190 55-61
    Referencia de l'ítem segons les normes APA: Whei Miaw, Carolina Sheng; Sena, Marcelo Martins; Carvalho de Souza, Scheilla Vitorino; Ruisanchez, Itziar; Pilar Callao, Maria (2018). Variable selection for multivariate classification aiming to detect individual adulterants and their blends in grape nectars. Talanta, 190(), 55-61. DOI: 10.1016/j.talanta.2018.07.078
    DOI del artículo: 10.1016/j.talanta.2018.07.078
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2018
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Analytical Chemistry,Biochemistry,Chemistry (Miscellaneous),Chemistry, Analytical,Spectroscopy
    Vitis
    Variable selection
    Statistics as topic
    Simca
    Pls-da
    Plant nectar
    Multivariate analysis
    Multi-class methods
    Least-squares analysis
    Grape nectar
    Fraud
    Food fraud
    Food analysis
    Discriminant analysis
    simca
    pls-da
    multi-class methods
    grape nectar
    food fraud
    Zootecnia / recursos pesqueiros
    Spectroscopy
    Saúde coletiva
    Química
    Nutrição
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Interdisciplinar
    Geociências
    General medicine
    General chemistry
    Farmacia
    Ensino
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Engenharias i
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Chemistry, analytical
    Chemistry (miscellaneous)
    Biotecnología
    Biodiversidade
    Biochemistry
    Astronomia / física
    Analytical chemistry
    Administração pública e de empresas, ciências contábeis e turismo
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