Transferring results from NIR-hyperspectral to NIR-multispectral imaging systems: A filter-based simulation applied to the classification of Arabica and Robusta green coffee.

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

Relationship between levels of polycyclic aromatic hydrocarbons in pine needles and socio-geographic parameters.

The ability of pine needles to capture polycyclic aromatic hydrocarbons (PAHs) from the surrounding air is well known. In this work the current knowledge of this affinity will be enhanced, investigating the plausible links between the concentrations of PAHs found in pine needles collected in different sites in Portugal, and several socio-geographic variables with environmental relevance. Canonical correlation analysis (CCA) has proven to be a suitable and innovative technique to look for relationships within environmental datasets. In the current work, CCA will simultaneously include chemical information (concentration of PAHs found in pine needles) and socio-geographic information associated to the sampling areas. In order to be more robust in these conclusions, Pinus pinea and Pinus pinaster species were considered separately, allowing an accurate direct comparison between them. The information concerning the different seasons and land occupation was also taken into account. Our results demonstrate how CCA can be a useful tool in environmental impact assessment, and highlight the importance of pine needles as trustful biomonitors of the influence of socio-geographic parameters on the levels of PAHs in a given area.

Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer.

Plant metabolomics: resolution and quantification of elusive peaks in liquid chromatography-mass spectrometry profiles of complex plant extracts using multi-way decomposition methods.

Previous studies on LC-MS metabolomic profiling of 127 F2 Barbarea vulgaris plants derived from a cross of parental glabrous (G) and pubescent (P) type, revealed four triterpenoid saponins (hederagenin cellobioside, oleanolic acid cellobioside, epihederagenin cellobioside, and gypsogenin cellobioside) that correlated with resistance of plants against the insect herbivore, Phyllotreta nemorum. In this study, for the first time, we demonstrate the efficiency of the multi-way decomposition method PARAllel FACtor analysis 2 (PARAFAC2) for exploring complex LC-MS data. PARAFAC2 enabled automated resolution and quantification of several elusive chromatographic peaks (e.g. overlapped, elution time shifted and low s/n ratio), which could not be detected and quantified by conventional chromatographic data analysis. Raw LC-MS data of 127 F2 B. vulgaris plants were arranged in a three-way array (elution time point×mass spectra×samples), divided into 17 different chromatographic intervals and each interval were individually modeled by PARAFAC2. Three main outputs of the PARAFAC2 models described: (1) elution time profile, (2) relative abundance, and (3) pure mass spectra of the resolved peaks modeled from each interval of the chromatographic data. PARAFAC2 scores corresponding to relative abundances of the resolved peaks were extracted and further used for correlation and partial least squares (PLS) analysis. A total of 71 PARAFAC2 components (which correspond to actual peaks, baselines and tails of neighboring peaks) were modeled from 17 different chromatographic retention time intervals of the LC-MS data. In addition to four previously known saponins, correlation- and PLS-analysis resolved five unknown saponin-like compounds that were significantly correlated with insect resistance. The method also enabled a good separation between resistant and susceptible F2 plants. PARAFAC2 spectral loadings corresponding to the pure mass spectra of chromatographic peaks matched well with experimentally recorded mass spectra (correlation based similarity >95%). This enabled to extract pure mass spectra of highly overlapped and low s/n ratio peaks.

Implementation of enhanced correlation maps in near infrared chemical images: application in pharmaceutical research.

Recent developments in Hyperspectral Imaging equipment have made possible the use of this analytical technique for fast scanning of sample surfaces. This technique has turned out to be especially useful in Pharmacy, where information about the distribution of the components in the surface of a tablet can be obtained. One particular application of Hyperspectral Chemical Imaging is the search for singularities inside pharmaceutical tablets, e.g. coating defects. Nevertheless, one problem has to be faced: how to analyze a sample without any previous knowledge about it, or having only the minimum information about the tablet. In this work a new methodology, based on correlation coefficients, is introduced to obtain valuable information about one Hyperspectral Image (detection of defects, punctual contaminants, etc.) without any previous knowledge. The methodology combines Principal Component Analysis (PCA), correlation coefficient between one specific pixel included in the image and the rest of the image; and a new enhanced contrast function to obtain more selective chemical and spatial information about the image. To illustrate the applicability of the proposed methodology, real tablets of ibuprofen have been studied. The proposed methodology is presented as a control technique to detect batch variability, defects in final tablets and punctual contaminants, being a potential supplementary tool for quality controls. In addition, the usefulness of the proposed methodology is not exclusive to NIR-CI devices, but to any hyperspectral and multivariate image system.