Gas chromatography–mass spectrometry (GC–MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC–MS can be resolved by taking advantage of the multivariate nature of GC–MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis–orthogonal signal deconvolution (ICA–OSD) and multivariate curve resolution–alternating least squares (MCR–ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC–qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA–OSD and MCR–ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R2 coefficients of determination exhibited a high correlation (R2 > 0.90) in both ICA–OSD and MCR–ALS moving window-based approaches.
Domingo, X.; Perera, A.; Ramirez, N.; Canellas, N.; Correig, X.; Brezmes, J. Journal of chromatography A Vol. 1409, p. 226-233 DOI: 10.1016/j.chroma.2015.07.044 Data de publicació: 2015-08-28 Article en revista
Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices. (C) 2015 Elsevier B.V. All rights reserved.
Obiols, J.; Rosell, M. G.; Farran, A.; Serra, C.; Guardino, X. Journal of chromatography A Vol. 823, num. 1-2, p. 91-96 DOI: 10.1016/S0021-9673(98)00272-6 Data de publicació: 1998-10 Article en revista
A capillary electrophoresis (CE) method to determine metal-cyano complexes from leaching solutions of automobile catalytic converters has been developed. The separation and detection conditions have been optimized and analysis times up to 20 min and metal detection limits in the ppb range have been obtained. The CE analysis of leaching solutions from different converters allowed the determination of Fe(II)-, Cu(I)-, Pd(II)-complexes and NO3-. On the other hand, adsorption onto activated carbon is used as a concentration process for precious metal-cyano complexes and as a process of pollutant removal. The adsorption kinetics of the compounds of interest have been studied by means of the developed CE method. The results obtained by CE have been compared with inductively coupled plasma in order to validate this newly developed method.
Aguilar, M.; Farran, A.; Serra, M.; Sepaniak, M.; Whitaker, K. Journal of chromatography A Vol. 778, num. 1-2, p. 201-205 DOI: 10.1016/S0021-9673(97)00220-3 Data de publicació: 1997-08-22 Article en revista
High-performance liquid chromatography (HPLC) and micellar electrokinetic capillary chromatography (MECC) methods for the determination of different pesticides mixtures have been developed. These mixtures include triazine, phenylurea, phenoxyalkyl acid, carbamate and organophosphorous pesticides that have been selected because their use in different combinations in commercial formulations. The best results have been obtained by using mobile phase gradients in HPLC and by working with n-alcohols as mobile phase modifiers in MECC. When the quality parameters of both methods have been compared, it was observed that they are not sensitive enough for environmental analysis. In this sense two different concentration methodologies, off-line solid extraction by using Carbopack columns and a special injection MECC method, have been studied to enhance sensitivity. Off-line solid extraction can be used for both HPLC and MECC methodologies and permits to detect low ppb levels of individual pesticides. Special injection methods applied to the MECC system provides a 200-fold sensitivity improvement for the ionic pesticides.