Articles producció científica> Química Analítica i Química Orgànica

FT-Raman and NIR spectroscopy data fusion strategy for multivariate qualitative analysis of food fraud

  • Dades identificatives

    Identificador: PC:1902
    Autors:
    M .Isabel LópezCristina MárquezItziar RuisánchezM. Pilar Callao
    Resum:
    Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96–100% and 88–100% for the mid- and high-level data fusion strategies, respectively.
  • Altres:

    Autor segons l'article: M .Isabel López; Cristina Márquez; Itziar Ruisánchez; M. Pilar Callao
    Departament: Química Analítica i Química Orgànica
    Autor/s de la URV: LÓPEZ VILARDELL, MARIA ISABEL; Cristina Márquez; RUISANCHEZ CAPELASTEGUI, MARÍA ICIAR; CALLAO LASMARIAS, MARÍA PILAR
    Paraules clau: FT-Raman Food adulteration NIR
    Resum: Two data fusion strategies (high- and mid-level) combined with a multivariate classification approach (Soft Independent Modelling of Class Analogy, SIMCA) have been applied to take advantage of the synergistic effect of the information obtained from two spectroscopic techniques: FT-Raman and NIR. Mid-level data fusion consists of merging some of the previous selected variables from the spectra obtained from each spectroscopic technique and then applying the classification technique. High-level data fusion combines the SIMCA classification results obtained individually from each spectroscopic technique. Of the possible ways to make the necessary combinations, we decided to use fuzzy aggregation connective operators. As a case study, we considered the possible adulteration of hazelnut paste with almond. Using the two-class SIMCA approach, class 1 consisted of unadulterated hazelnut samples and class 2 of samples adulterated with almond. Models performance was also studied with samples adulterated with chickpea. The results show that data fusion is an effective strategy since the performance parameters are better than the individual ones: sensitivity and specificity values between 75% and 100% for the individual techniques and between 96–100% and 88–100% for the mid- and high-level data fusion strategies, respectively.
    Grup de recerca: Grup de Quimiometria, Qualimetria i Nanosensors
    Àrees temàtiques: Chemistry Química Química
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 0039-9140
    Identificador de l'autor: n/a; n/a; 0000-0002-7097-3583; 0000-0003-2691-329X
    Data d'alta del registre: 2016-09-21
    Pàgina final: 86
    Volum de revista: 161
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/abs/pii/S0039914016305732?via%3Dihub
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1016/j.talanta.2016.08.003
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2016
    Pàgina inicial: 80
    Tipus de publicació: Article Artículo Article
  • Paraules clau:

    Avellanes -- Adulteració i inspecció
    Espectroscòpia infraroja pròxima
    Espectroscòpia Raman
    FT-Raman
    Food adulteration
    NIR
    Chemistry
    Química
    Química
    0039-9140
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