Multivariate curve resolution modeling of liquid chromatography-mass spectrometry data in a comparative study of the different endogenous metabolites behavior in two tomato cultivars treated with carbofuran pesticide

G.G. SIANO; I. SÁNCHEZ PÉREZ; M.D. GIL GARCÍA; M. MARTÍNEZ GALERA; H.C. GOICOECHEA

TALANTA

ELSEVIER SCIENCE BV

Lugar: Amsterdam; Año: 2011 vol. 85 p. 264 - 275

0039-9140

A metabonomic study based on the application of multivariate curve resolution and alternating least squares (MCR-ALS) to three-way data sets obtained by liquid chromatography coupled to mass spectrometry detection (LC-MS) was carried out for Rambo and Raf tomato cultivars treated with carbofuran pesticide. Samples were picked up during a 21 days period after treatment and analyzed by LC-MS in scan mode, along with the corresponding blank samples. Then, MCR-ALS was applied to the three-way data sets using column wise augmented matrices, and the evolutionary profiles as a function of the time after treatment were estimated for the metabolites present in both cultivars, as well as their corresponding pure spectra estimations. A comparative study using those estimations showed that some of these metabolites followed different behavior for the different cultivars after treatment. Since all treated and untreated Rambo and Raf samples were picked up according to the same sampling protocol and in a similar state of maturation, any difference in the behavior between profiles can be interpreted as an effect due to the presence of pesticide and to the kind of cultivar. Based on this hypothesis, several PLS-DA approaches were tested to check if it would be possible to classify samples by using the metabolites MCR estimations. Results showed that PLS-DA models for classification of treated or non-treated (blank) samples were the best ones obtained (98.44% of correct classifications for the validation set), which supports the stress effects related to carbofuran treatment. In addition, excellent discrimination among the four groups could be attained (89.06% of correct classifications for the validation set).