Using fluorescence excitation-emission matrices to predict bitterness and pungency of virgin olive oil: A feasibility study.

Unlike other food products, virgin olive oil must undergo an organoleptic assessment that is currently based on a trained human panel, which presents drawbacks that might affect the efficiency and robustness. Therefore, disposing of instrumental methods that could serve as screening tools to support sensory panels is of paramount importance. The present work aimed to explore excitation-emission fluorescence spectroscopy (EEFS) to predict bitterness and pungency, since both attributes are related with fluorophore compounds, such as polar phenols. Bitterness and pungency intensities of 250 samples were provided by an official sensory panel and used to build and compare partial least squares regressions (PLSR) with the excitation-emission matrix. Both PARAFAC scores and two-way unfolded data led to successful PLSR. The most relevant PARAFAC scores agreed with virgin olive oil phenolic spectra, evidencing that EEFS would be the fit-for-purpose screening tool to support the sensory panel.

Forecasting Chronic Diseases Using Data Fusion.

Data fusion, that is, extracting information through the fusion of complementary data sets, is a topic of great interest in metabolomics because analytical platforms such as liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy commonly used for chemical profiling of biofluids provide complementary information. In this study, with a goal of forecasting acute coronary syndrome (ACS), breast cancer, and colon cancer, we jointly analyzed LC-MS, NMR measurements of plasma samples, and the metadata corresponding to the lifestyle of participants. We used supervised data fusion based on multiple kernel learning and exploited the linearity of the models to identify significant metabolites/features for the separation of healthy referents and the cases developing a disease. We demonstrated that (i) fusing LC-MS, NMR, and metadata provided better separation of ACS cases and referents compared with individual data sets, (ii) NMR data performed the best in terms of forecasting breast cancer, while fusion degraded the performance, and (iii) neither the individual data sets nor their fusion performed well for colon cancer. Furthermore, we showed the strengths and limitations of the fusion models by discussing their performance in terms of capturing known biomarkers for smoking and coffee. While fusion may improve performance in terms of separating certain conditions by jointly analyzing metabolomics and metadata sets, it is not necessarily always the best approach as in the case of breast cancer.

Determination of the botanical origin of honey by front-face synchronous fluorescence spectroscopy.

Front-face synchronous fluorescence spectroscopy combined with chemometrics is used to classify honey samples according to their botanical origin. Synchronous fluorescence spectra of three monofloral (linden, sunflower, and acacia), polyfloral (meadow mix), and fake (fake acacia and linden) honey types (109 samples) were collected in an excitation range of 240-500 nm for synchronous wavelength intervals of 30-300 nm. Chemometric analysis of the gathered data included principal component analysis and partial least squares discriminant analysis. Mean cross-validated classification errors of 0.2 and 4.8% were found for a model that accounts only for monofloral samples and for a model that includes both the monofloral and polyfloral groups, respectively. The results demonstrate that single synchronous fluorescence spectra of different honeys differ significantly because of their distinct physical and chemical characteristics and provide sufficient data for the clear differentiation among honey groups. The spectra of fake honey samples showed pronounced differences from those of genuine honey, and these samples are easily recognized on the basis of their synchronous fluorescence spectra. The study demonstrated that this method is a valuable and promising technique for honey authentication.

Application of rotated PCA models to facilitate interpretation of metabolite profiles: commercial preparations of St. John's Wort.

This paper describes the application of orthogonal rotation of models based on principal component analysis (PCA) of (1)H nuclear magnetic resonance (NMR) spectra and high-performance liquid chromatography-photo diode array detection (HPLC-PDA) profiles of natural product mixtures using extracts of antidepressive pharmaceutical preparations of St. John's wort as an example. (1)H-NMR spectroscopy of complex mixtures is often used in metabolomic, metabonomic and metabolite profiling studies for assessment of sample composition. Interpretation of the derived chemometric models may be complicated because several sample properties often contribute to each principal component and because the influence of individual metabolites may be shared by several principal components. Furthermore, extensive signal overlap in (1)H-NMR spectra poses additional challenges to the interpretation of PCA models derived from such data. Orthogonal rotation of PCA models derived from (1)H-NMR spectra and HPLC-PDA profiles of the extracts of St. John's wort preparations facilitate interpretation of the model. Using the varimax criterion, rotation of loadings provides simpler conditions for understanding the influence of individual metabolites on the observed clustering. Alternatively, rotation of scores simplifies the understanding of the influence of whole metabolite profiles on the clustering of individual samples.

Combining PARAFAC analysis of HPLC-PDA profiles and structural characterization using HPLC-PDA-SPE-NMR-MS experiments: commercial preparations of St. John's Wort.

Herbal preparations represent very complex mixtures, potentially containing multiple pharmacologically active entities. Methods for global characterization of the composition of such mixtures are therefore of pertinent interest. In this work, chemometric analysis of high-performance liquid chromatography with photodiode-array detection (HPLC-PDA) data from extracts of commercial preparations of Hypericum perforatum (St. John's wort) that originate from several continents is described. The spectral HPLC profiles were aligned in the elution mode using correlation optimized warping in order to remove peak misalignment caused by retention time shifts due to matrix effects. Furthermore, the warping was assisted by HPLC-PDA-SPE-NMR-MS (SPE = solid-phase extraction) experiments that yielded 1H NMR and 13C NMR data (from 1H-detected heteronuclear correlations), as well as ESI-MS and HRMS data, which enabled the identification of all major mixture constituents. The preprocessed HPLC-PDA data were subjected to parallel factor analysis (PARAFAC), a chemometric method that is a generalization of principal component analysis (PCA) to multi-way data arrays. PCA of the peak areas obtained from the PARAFAC analysis was used to facilitate sample comparison and allowed straightforward interpretation of constituents responsible for the differences in composition between individual preparations. In addition, loadings from the PARAFAC analysis provided pure elution profiles and pure UV spectra even for coeluting peaks, thus enabling the identification of chromatographically unresolved components. In conclusion, PARAFAC analysis of the readily accessible HPLC-PDA data provides the means for unsupervised and unbiased assessment of the composition of herbal preparations, of interest for assessment of their pharmacological activity and clinical efficacy.