INVESTIGADORES
GOICOECHEA Hector Casimiro
artículos
Título:
Multivariate curve resolution modelling of liquid chromatography-mass spectrometry data in a comparative study of the different endogenous metabolites behaviour in two tomato cultivars treated with carbofuran pesticide
Autor/es:
SIANO G G; SANCHEZ PEREZ I; GIL GARCÍA, MARÍA; MARTINEZ GALERA, M; GOICOECHEA, HÉCTOR
Revista:
TALANTA
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Año: 2011 vol. 85 p. 264 - 275
ISSN:
0039-9140
Resumen:
squares (MCR-ALS) to three-way data sets obtained by liquid chromatography coupled to mass spectrometrydetection (LCMS) was carried out for Rambo and Raf tomato cultivars treated with carbofuranpesticide. Samples were picked up during a 21 days period after treatment and analyzed by LCMS inscan mode, along with the corresponding blank samples. Then, MCR-ALS was applied to the three-waydata sets using column wise augmented matrices, and the evolutionary profiles as a function of the timeafter treatment were estimated for the metabolites present in both cultivars, as well as their correspondingpure spectra estimations. A comparative study using those estimations showed that some of thesemetabolites followed different behavior for the different cultivars after treatment. Since all treated anduntreated Rambo and Raf samples were picked up according to the same sampling protocol and in a similarstate of maturation, any difference in the behavior between profiles can be interpreted as an effectdue to the presence of pesticide and to the kind of cultivar. Based on this hypothesis, several PLS-DAapproaches were tested to check if it would be possible to classify samples by using the metabolites MCRestimations. Results showed that PLS-DA models for classification of treated or non-treated (blank) sampleswere the best ones obtained (98.44% of correct classifications for the validation set), which supportsthe stress effects related to carbofuran treatment. In addition, excellent discrimination among the fourgroups could be attained (89.06% of correct classifications for the validation set).