Detection of Unintended Stress Effects Based on a Metabonomic Study in Tomato Fruits after Treatment with Carbofuran Pesticide. Capabilities of MCR-ALS Applied to LC-MS Three-Way Data Arrays

A chemometric strategy based on multivariate curve resolution and alternating least-squares (MCR-ALS) applied to LC-MS three-way data arrays has been developed to perform a metabonomic study in tomato (Lycopersicon esculentum) fruits (cultivar Rambo) following treatment with carbofuran. This methodology has proved to be adequate for the detection of unintended stress effects due to the previous treatment with this pesticide. MCR-ALS was performed on augmented matrices built with the LC-MS three-way data obtained from treated and nontreated samples through the sampling time. The strategy allowed us to obtain the concentration and spectra profiles of the main components (previously estimated with the SVD algorithm) from samples treated with pesticide as well as from blank samples, showing how they vary with time after plants treatment with the pesticide. In addition, a simple resolved mass spectrum was obtained corresponding to the peaks of a particular component in all matrices, thus avoiding ambiguity in the compound identity assignment. Different time profiles were found for some metabolites in treated and nontreated samples, which demonstrate that the presence of pesticide causes changes thorough time in the behavior of certain endogenous tomato metabolites as a result of physiological stress.