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.

Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration.

Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

Combining mid infrared spectroscopy and paper spray mass spectrometry in a data fusion model to predict the composition of coffee blends.

This paper describes a robust multivariate model for quantifying and characterizing blends of Robusta and Arabica coffees. At different degrees of roasting, 120 ground coffee blends (0.0-33.0%) were formulated. Spectra were obtained by two different techniques, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and paper spray mass spectrometry (PS-MS). Partial least squares (PLS) models were built individually with the two types of spectra. Nevertheless, better predictions were obtained by low and medium-level data fusion, taking advantage from the synergy between these two data sets. Data fusion models were improved by variable selection, using genetic algorithms (GA) and ordered predictors selection (OPS). The smallest prediction errors were provided by OPS low-level data fusion model. The number of variables used for regression was reduced from 2145 (full spectra) to 230. Model interpretation was performed by assigning some of the selected variables to specific coffee components, such as trigonelline and chlorogenic acids.

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.

Application of multi-block analysis and mixture design with process variable for development of chocolate cake containing yacon (Smallanthus sonchifolius) and maca (Lepidium meyenii).

BACKGROUND: In this study, a chocolate cake formulation was developed with partial substitution of wheat flour by yacon and maca flour. A simplex-centroid design was applied to determine the proportions of the three flours, and the amount of water was included as a process variable at three distinct levels. According to the overall acceptability of the cakes, the tasters were separated into two groups using k-means. RESULTS: After segmentation, regression models were constructed for overall acceptability of each group; R2adjusted values of 92.5% for group 1 and 98.9% for group 2 were obtained. Using the sequential simplex method an optimized formulation was determined for group 1 (0.49 kgwheat kg-1total flour , 0.37 kgyacon kg-1total flour , 0.14 kgmaca kg-1total flour and 140.0 mL of water) and another for group 2 (0.35 kgwheat kg-1total flour , 0.65 kgyacon kg-1total flour and 120.0 mL of water). In addition to these formulations, a third formulation was proposed with a greater maca proportion (0.32 kgmaca kg-1total flour ), which does not significantly alter the overall acceptability of both groups. The three optimized formulations and two control formulations were evaluated through free-choice profiling. The data were evaluated using the multi-block method common components and specific weights analysis (CCSWA). CONCLUSION: It was observed that a greater proportion of maca intensified brownness and burnt aroma and taste, whereas a larger proportion of yacon produced a better appearance, softness, sweetness and chocolate flavor. © 2017 Society of Chemical Industry.

Crystallization and Stereocomplexation of PLA-mb-PBS Multi-Block Copolymers.

The crystallization and morphology of PLA-mb-PBS copolymers and their corresponding stereocomplexes were studied. The effect of flexible blocks (i.e., polybutylene succinate, PBS) on the crystallization of the copolymers and stereocomplex formation were investigated using polarized light optical microscopy (PLOM), differential scanning calorimetry (DSC), infrared spectroscopy (FTIR), and carbon-13 nuclear magnetic resonance spectroscopy (13C-NMR). The PLA and PBS multiple blocks were miscible in the melt and in the glassy state. When the PLA-mb-PBS copolymers are cooled from the melt, the PLA component crystallizes first creating superstructures, such as spherulites or axialites, which constitute a template within which the PBS component has to crystallize when the sample is further cooled down. The Avrami theory was able to fit the overall crystallization kinetics of both semi-crystalline components, and the n values for both blocks in all the samples had a correspondence with the superstructural morphology observed by PLOM. Solution mixtures of PLLA-mb-PBS and PLDA-mb-PBS copolymers were prepared, as well as copolymer/homopolymer blends with the aim to study the stereocomplexation of PLLA and PDLA chain segments. A lower amount of stereocomplex formation was observed in copolymer mixtures as compared to neat L100/D100 stereocomplexes. The results show that PBS chain segments perturb the formation of stereocomplexes and this perturbation increases with the amount of PBS in the samples. However, when relatively low amounts of PBS in the copolymer blends are present, the rate of stereocomplex formation is enhanced. This effect dissappears when higher amounts of PBS are present. The stereocomplexation was confirmed by FTIR and solid state 13C-NMR analyses.