UHPLC-PAD Protocol for the Simultaneous Identification of Polyphenols and Bitter Acids: Organoleptic and Nutraceutical Fingerprinting of Bergamot-Flavored Craft Beer.

In this study, a UHPLC-PDA method for the simultaneous identification of polyphenols and bitter acids (alpha, beta, and isoalpha) in beer was developed. The resulting chemical profiles were leveraged to distinguish the characteristics of four (IPA, Lager, Blanche, ALE) bergamot-flavored beers, produced on a pilot-scale plant. In a streamlined 29 min analysis, thirty polyphenols and fourteen bitter acids were successfully identified under optimized separation conditions. Validation, encompassing parameters such as LOD (from 0.028 ppm for isorhamnetin to 0.106 for narirutin), LOQ (from 0.077 ppm for naringenin to 0.355 for narirutin), R2 (always more than 0.9992), repeatability (from 0.67% for tangeretin to 6.38% for myricetin), and reproducibility (from 0.99% for sinensetin to 6% for naringin), was conducted for polyphenol quantification using constructed calibration curves with seven levels. Exploring polyphenolic components as potential discriminators among different beer styles, a total of thirty-two polyphenolic compounds were identified and quantified, including characteristic bergamot peel polyphenols like neoeriocitrin (from 7.85 ppm for CBS2 to 11.95 ppm in CBS1); naringin (from 4.56 ppm for CBS4 to 10.96 in CBS1), and neohesperidin (from 5.93 in CBS3 to 15.95 for CBS2). The multivariate analysis provided additional insights into variations among specific beer styles, revealing discrepancies in the presence or relative concentrations of specific compounds linked to brewing ingredients and processes. This research enhances the fingerprinting of the chemistry governing beer quality through a straightforward and cost-effective analytical approach.

A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys.

To better understand the biochemistry of the organoleptic properties of honey influencing its commercial value, a predictive model for correlating amino acid profiles to aromatic compounds was built. Because the amino acid composition of different varieties of honey plays a key role as a precursor of specific aroma bouquets, it is necessary to relate the amino acid typesetting to aromatic molecules. A selection of unifloral honeys produced in Calabria, South Italy, were used, and a new methodology based on the use of HILIC-UHPLC-ESI-MS/MS and HS-SPME-GC-MS combined with multivariate processing has been developed. This study, carried out for the first time on honey, shows its excellent potential as a modern analytical tool for a rapid multicomponent analysis of food-quality indicators. Data obtained showed strong positive linear correlations between aldehydes and isoleucine, valine, leucine, and phenylalanine. Furans are correlated with isoleucine, leucine, and phenylalanine; hydrocarbons with serine, glutamic acid, and aspartic acid; and ketones with serine, alanine, glutamine, histidine, asparagine, and lysine. Alcohols were more associated with tyrosine than esters with arginine. Proline, tryptophan, and threonine showed poor correlations with all the classes of aroma compounds.

High-Performance Anion Exchange Chromatography with Pulsed Amperometric Detection (HPAEC-PAD) and Chemometrics for Geographical and Floral Authentication of Honeys from Southern Italy (Calabria region).

High-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD) combined with chemometric analysis was developed to describe, for the first time, the sugar profile of sixty-one honeys of different botanical origin produced in southern Italy (Calabria Region). The principal component and linear discriminant analysis used to describe the variability of sugar data were able to discriminate the honeys according to their botanical origin with a correlation index higher than 90%. For the purpose of the robustness of the conclusions of this study, the analytical advantages of the HPAEC-PAD method have been statistically demonstrated compared to the official Italian HPLC-RI method (Refractive Index detection). Finally, as the characterization of the floral and geographical origin of honey became an important issue due to high consumer demand, 13 acacia honeys originating from Europe and China were studied by using the same method. By chemometric method it was possible to discriminate the different geographical origin with an index of 100%. All results proved the possibility to identify the sugar profile obtained by HPAEC-PAD combined with a robust statistical analysis, as a tool of authentication.

The potential of e-nose aroma profiling for identifying the geographical origin of licorice (Glycyrrhiza glabra L.) roots.

Licorice roots cultivated commercially in distinct geographical areas such as China, Iran, Italy (Abruzzo, Basilicata, Calabria and Sicily) and Turkey were classified using an artificial olfactive system (e-nose) based on metal oxide semiconductor sensors (MOS). The resultant instrumental data were processed using a multivariate statistical analysis method in order to classify the raw samples according to its origin. The e-nose odourprintings were obtained by a canonical discriminant analysis carried out with the aim of relating the specific data-sets corresponding to whole licorice roots aroma with the e-nose reference dataset. E-nose results were compared to those obtained by SPME/GC-MS. The HS-SPME/GC/MS analysis was used as a control system to check for the actual existence of differences in the chemical composition of sample headspace. These results imply the possibility to use an electronic nose as a tool for a quick, effective and non-destructive authentication of licorice roots.

Non-destructive flavour evaluation of red onion (Allium cepa L.) ecotypes: an electronic-nose-based approach.

This work reports preliminary results on the potential of a metal oxide sensor (MOS)-based electronic nose, as a non-destructive method to discriminate three "Tropea Red Onion" PGI ecotypes (TrT, TrMC and TrA) from each other and the common red onion (RO), which is usually used to counterfeit. The signals from the sensor array were processed using a canonical discriminant function analysis (DFA) pattern recognition technique. The DFA on onion samples showed a clear separation among the four onion groups with an overall correct classification rate (CR) of 97.5%. Onion flavour is closely linked to pungency and thus to the pyruvic acid content. The e-nose analysis results are in good agreement with pyruvic acid analysis. This work demonstrated that artificial olfactory systems have potential for use as an innovative, rapid and specific non-destructive technique, and may provide a method to protect food products against counterfeiting.