Characterization, Classification and Authentication of Turmeric and Curry Samples by Targeted LC-HRMS Polyphenolic and Curcuminoid Profiling and Chemometrics.

The importance of monitoring bioactive substances as food features to address sample classification and authentication is increasing. In this work, targeted liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) polyphenolic and curcuminoid profiles were evaluated as chemical descriptors to deal with the characterization and classification of turmeric and curry samples. The profiles corresponding to bioactive substances were obtained by TraceFinderTM software using accurate mass databases with 53 and 24 polyphenolic and curcuminoid related compounds, respectively. For that purpose, 21 turmeric and 9 curry samples commercially available were analyzed in triplicate by a simple liquid-solid extraction procedure using dimethyl sulfoxide as extracting solvent. The obtained results demonstrate that the proposed profiles were excellent chemical descriptors for sample characterization and classification by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), achieving 100% classification rates. Curcuminoids and some specific phenolic acids such as trans-cinnamic, ferulic and sinapic acids, helped on the discrimination of turmeric samples; polyphenols, in general, were responsible for the curry sample distinction. Besides, the combination of both polyphenolic and curcuminoid profiles was necessary for the simultaneous characterization and classification of turmeric and curry samples. Discrimination among turmeric species such as Curcuma longa vs. Curcuma zedoaria, as well as among different Curcuma longa varieties (Alleppey, Madras and Erode) was also accomplished.

Total Polyphenol Content in Food Samples and Nutraceuticals: Antioxidant Indices versus High Performance Liquid Chromatography.

Total polyphenol content and antioxidant capacity were estimated in various food and nutraceutical samples, including cranberries, raspberries, artichokes, grapevines, green tea, coffee, turmeric, and other medicinal plant extracts. Samples were analyzed by using two antioxidant assays-ferric reducing antioxidant power (FRAP) and Folin-Ciocalteu (FC)-and a reversed-phase high-performance liquid chromatography (HPLC), with a focus on providing compositional fingerprints dealing with polyphenolic compounds. A preliminary data exploration via principal component analysis (PCA) revealed that HPLC fingerprints were suitable chemical descriptors to classify the analyzed samples according to their nature. Moreover, chromatographic data were correlated with antioxidant data using partial least squares (PLS) regression. Regression models have shown good prediction capacities in estimating the antioxidant activity from chromatographic data, with determination coefficients (R2) of 0.971 and 0.983 for FRAP and FC assays, respectively.