Autor según el artículo: M .Isabel López; Cristina Márquez; Itziar Ruisánchez; M. Pilar Callao

Departamento: Química Analítica i Química Orgànica

Autor/es de la URV: LÓPEZ VILARDELL, MARIA ISABEL; Cristina Márquez; RUISANCHEZ CAPELASTEGUI, MARÍA ICIAR; CALLAO LASMARIAS, MARÍA PILAR

Palabras clave: FT-Raman Food adulteration NIR

Resumen: 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.

Grupo de investigación: Grup de Quimiometria, Qualimetria i Nanosensors

Áreas temáticas: Chemistry Química Química

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

ISSN: 0039-9140

Identificador del autor: n/a; n/a; 0000-0002-7097-3583; 0000-0003-2691-329X

Fecha de alta del registro: 2016-09-21

Página final: 86

Volumen de revista: 161

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

Enlace a la fuente original: https://www.sciencedirect.com/science/article/abs/pii/S0039914016305732?via%3Dihub

DOI del artículo: 10.1016/j.talanta.2016.08.003

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2016

Página inicial: 80

Tipo de publicación: Article Artículo Article