Analytical and chemometric strategies for elucidation of yerba mate composition.

Yerba mate, a popular plant consumed mainly as an infusion, possesses nutritional and medicinal properties attributed to its secondary metabolites. This study aimed to develop strategies to elucidate the phenolic composition of yerba mate samples from Brazil, Argentina, Uruguay, and Paraguay. Optimization of ultrasonic-assisted extraction (UAE) was performed, and the extracted compounds were characterized using ultra-high-pressure liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS), molecular fluorescence and high-pressure liquid chromatography with diode-array detection (HPLC-DAD). Chemometric analysis, including parallel factor analysis (PARAFAC) and principal component analysis (PCA) explored metabolite profiles and identify patterns. PARAFAC modelling of the molecular fluorescence results revealed higher pigment content in Brazilian samples, while other countries' samples exhibited higher phenolic content. PCA modeling of HPLC-DAD results indicated that cultivated yerba mate contained higher chlorogenic acids levels, and samples from Argentina, Paraguay, and Uruguay exhibited higher concentrations of chlorogenic acids and flavonoids.

Scores selection via Fisher's discriminant power in PCA-LDA to improve the classification of food data.

This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaça adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling.

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples×Δλ×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.