Enhancing mass spectrometry interpretability by ComDim-ICA multi-block analysis: Geographical and varietal traceability of Brazilian Coffea canephora.

Mass spectrometry can gain analytical interpretability by studying complementarity and synergy between the data obtained by the same technique. To explore its potential in an untargeted metabolomic application, the objective of this work was to obtain organic and aqueous coffee extracts of three coffee Canephora groups produced in Brazil with distinctive aspects: geographical origin and botanical variety. Aqueous and organic extracts of roasted coffee beans were analyzed by direct infusion electrospray ionization mass spectrometry. Due to the large number of samples, the injector of the liquid chromatography system was used to automate the analysis. The column was removed, and a peak tube was added to connect the system directly to the mass spectrometer to inject both polar and nonpolar fractions of the coffee extracts individually. The technique provided characteristic fingerprinting mass spectra that not only allowed for differentiation of geographical origins but also between robusta and conilon botanical varieties. The mass spectra of the organic and water extracts represented two separate data blocks to be analyzed by the ComDim-ICA multi-block data analysis method. While the classical ComDim is based on applying PCA to the iteratively reweighted concatenated matrices, in the ComDim-ICA, the factorization is done using independent components analysis, which promotes specific improvements since it is based on extracting components that are statistically independent of one another. The results highlighted by ComDim-ICA show that both water and organic extracts contributed with important ions to the characterization of the coffee composition. However, the results revealed a high variability of metabolomic composition within each botanical variety (Robusta Amazônico and Conilon Capixaba) and geographical provenance (Rondônia indigenous-1, Rondônia non-indigenous-2 and Espírito Santo-3). Even so, water mass spectra differentiated the botanical variety Conilon from Robusta based on significant ions related to trigonelline, caffeic acid, caffeoylquinic acid, and methylpyridinium; both water and organic mass spectra differentiated Rondônia indigenous from Rondônia non-indigenous and Espírito Santo Conilon based on significant ions related to benzoic acid, pentose, coumaric acid, caffeine in the organic extract and malonic acid, pentose, caffeoylquinic acid, methyl pyridinium, caffeine, and sucrose present in the water extract. With the proposed approach acquiring ion fingerprints of different coffee extracts and their subsequent analysis by ComDim-ICA, new complementary chemical aspects of Brazilian Coffea canephora were put in evidence.

Comparison of different chemometric methods to extract chemical and physical information from Raman images of homogeneous and heterogeneous semi-solid pharmaceutical formulations.

In formulations of nanostructured lipid carriers, lipid solid dispersions and self-emulsifying drug delivery systems, it is common that a solid or semi-solid lipid excipient is mixed with a liquid solvent or liquid lipid. Even when the excipients are visually miscible upon melting, they might have microscopic non-homogeneities which could lead to instability over time and future phase separation. Raman mapping associated with chemometric methods can be useful to evaluate spatial distribution of compounds, however it has not been extensively applied to the formulations mentioned above. The aim of this work was to compare the outcomes of three different chemometric methods - principal components analysis (PCA), multivariate curve resolution with alternating least squares (MCR-ALS) and independent components analysis (ICA) - to study two systems of very different degrees of microscopic miscibility: cetyl palmitate + Transcutol© (heterogeneous) and polyethylene glycol 6000 (PEG 6000) + Tween 80© (homogeneous). These two samples were chosen due to large differences in spatial distribution of the compounds over the pixels which could require different approaches for data treatment. The three methods were compared regarding recovered concentrations (or scores), signals (or loadings) and the need for matrix augmentation to obtain reliable results. Results showed that PCA loadings were the mathematical differences of the spectra of pure compounds for both samples, and therefore only 'contrast images' could be generated. MCR and ICA provided signals that could be related to the chemical components, however MCR presented rotational ambiguities even for the very heterogeneous sample, a situation in which ICA performed better as a blind search method. For the homogeneous sample, both methods showed rank deficiency and therefore the use of a matrix augmentation was necessary. ICA and PCA allowed identifying physical modifications in the homogeneous semi-solid PEG 6000/Tween 80® sample over the time, probably due to the folding/unfolding of the crystalline chains of PEG 6000. Therefore, this work discusses the ability of the three chemometrics methods to extract information from Raman spectra in order to characterize the chemical, spatial and even physical aspects of semi-solid pharmaceutical formulations, which could be of much use for stability studies of different drug delivery systems.

Multi-block data analysis using ComDim for the evaluation of complex samples: Characterization of edible oils.

The ComDim chemometrics method for multi-block analysis was employed to evaluate thirty-two vegetable oil samples analyzed by near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy, and by Gas Chromatography with flame ionization detection (GC-FID) for their fatty acids composition. This unsupervised pattern recognition method was able to extract information from the tables of results that could be presented in informative graphs showing the relationship between the samples through the scores, the predominance of information in particular tables through the saliences and the contribution of the variables in each table which were responsible for the similarities observed in the samples, through the loadings plots. It was possible to infer similarities and differences among the samples studied according to the specific absorption in the UV-Vis and NIR region, as well as their fatty acids composition. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different variables (different techniques) describe the same samples. In this particular study, the ComDim chemometrics method was able to discriminate samples by their characteristics and compositions.