Alejandro García-Reiriz

The comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed.

Chemometric methods are applied to the analysis and interpretation of large multivariate datasets obtained in environmental monitoring studies. Concentrations of multiple organic compounds were measured in river samples taken from several sampling sites, at various geographical locations, during a number of campaigns and/or sampling time periods. Samples were collected and analyzed as part of an extensive multi-annual monitoring program from a mediterranean river basin (in Catalonia, at the northeast of Spain) by the Water Quality Regional Agency. Due to the great amount of multivariate data stored in environmental databases and to their complexity, chemometric modeling methods such as Principal Component Analysis (PCA) and Multivariate Curve Resolution with Alternating Least-Squares (MCR-ALS) coupled with appropriate mapping representations are proposed for the evaluation of the environmental quality of the studied rivers. Results achieved in this study are intended to be a contribution to water quality assessment and evaluation of contamination of surface waters in river basins, and to support public policies of environmental control and management of the regions under study.

The irradiation dose in tumor and healthy tissue of a boron neutron capture therapy (BNCT) patient depends on the boron concentration in blood. In most treatments, this concentration is experimentally determined before and after irradiation but not while irradiation is being carried out because it is troublesome to take the blood samples when the patient remains isolated in the irradiation room. A few models are used to predict the boron profile during that period, which until now involves a biexponential decay. For the prediction of decay concentration profiles of the p‐boronophenylalanine (BPA) in the human body during BNCT treatment, a Kohonen‐based neural network method is suggested. The results of various (20 × 20 × 40 Kohonen network) models based on different trainings on the data set of 67 concentration sets (profiles) are described and discussed. The prediction ability and robustness of the modeling method were tested by the leave‐one‐out procedure. The results show that the method is very robust and mostly independent of small variations. It can yield predictions, root mean squared prediction error (RMSPE), with a maximum of 3.30 µg g⁻¹ for the present cases. In order to show the abilities and limitations of the method, the best and the few worst results are discussed in detail. It should be emphasized that one of the main advantages of this method is the automatic improvement in the prediction ability and robustness of the model by feeding it with an increasing number of data. Copyright © 2011 John Wiley & Sons, Ltd.