Characterization of dark chocolates based on polyphenolic profiles and antioxidant activity.

Dark chocolates were characterized according to geographical origin, cocoa variety, and cocoa content using the methylxanthine and polyphenolic composition and antioxidant activity as the data. The main study objective was to uncover sample patterns and identify possible markers of quality, variety, or origin to deal with authentication or fraud detection issues. In the study, a set of 26 dark chocolates from different varieties (e.

Characterization of the Polyphenolic Profile in Tomato ( P. Mill) Peel and Seeds by LC-HRMS/MS.

Peel and seeds are the main byproducts from tomato ( P. Mill) processing with high concentrations of polyphenols that have been underexploited. Herein, polyphenolic profiles in tomato peel and seeds were elucidated by untargeted liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) with an LTQ Orbitrap analyzer.

Olive tree leaves as a great source of phenolic compounds: Comprehensive profiling of NaDES extracts.

Waste from the olive industry is a noticeable source of antioxidant compounds that can be extracted and reused to produce raw materials related to the chemical, cosmetic, food and pharmaceutical sectors. This work studies the phenolic composition of olive leaf samples using liquid chromatography with ultraviolet detection coupled to mass spectrometry (LC-UV-MS). Olive leaf waste samples have been crushed, homogenized, and subjected to a solid-liquid extraction treatment with mechanical shaking at 80 °C for 2 h using Natural Deep Eutectic Solvents (NaDES).

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions.

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain.