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
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
Repositori URV