Neotropical berries from the Andean region are a rich yet undisclosed source of bioactive phenolic compounds. Consumption of berry fruits is not only limited to fresh or frozen forms, as several processed and derived products are prepared. Moreover, in recent years, berry extracts have been increasingly employed as functional foods and dietary supplements combined with other vegetable and herbal extracts. Berries that belong to the genus Vaccinium have been raising interest for their extremely high content in phenolic compounds, which have been demonstrated to exert a wide range of biological activities. Whereas the composition of phenolic compounds in North American and European blueberries has been widely studied, berries from South America have been scarcely investigated, despite a wide consumption among the local population for their biological properties. The aim of the present project is the deep and detailed characterization of the phenolic compound profile of three neotropical berries, i.e. Vaccinium floribundum, vaccinium meridionale, and disterigma alaternoides. For this purpose, specific extraction procedures, as well as data acquisition approaches, will be compared. Moreover, given the extreme structural variability of phenolic compounds, a specific data analysis workflow on Compound Discoverer software will be employed, keeping in mind the peculiar characteristics of blueberries. Moreover, the transformation of phenolic compounds following fermentation (which is a commonly used practice for sour berry consumption) will be evaluated by specific bioinformatics tools.
At present, few papers have been dealing with the issue of the characterization of phenolic compounds in neotropical species of the genus Vaccinium, and, to the best of our knowledge, there are no papers addressing the characterization of phenolics in berries of the species Disterigma. The few papers focused on V. floribundum and V. meridionale are usually based on low-performance techniques (either targeted MS or LC-UV) and are focused on specific classes (usually anthocyanins). However, it is reported that phenolic acids could be the most abundant class of phenolic compounds in berries. Phenolic acids are poorly ionized in positive ion mode, which is instead the best approach for anthocyanins. Therefore, in most papers, this class of compounds has been neglected to date. In the present project, by acquiring datasets in both polarities, phenolic acids will be effectively addressed.
Untargeted MS analysis is emerging as the foremost technique for phenolic compound identification thanks to the fact that it does not require previous knowledge on phenol composition or analytical standards. However, to date, established and routinary methodologies are still lacking. For achieving the maximum identification, fresh and fermented neotropical berries will be analyzed by HRMS, and raw datasets will be processed by a dedicated workflow with a suspect screening approach. A comprehensive database of phenolic compound derivatives will be implemented into Compound Discoverer software. The database will contain phenolic compounds of different families (e.g., flavonoids, anthocyanins, phenolic acids, and tannins) with various degrees of conjugation (e.g., 1 to 3 sugars, acyl conjugations, acyl-sugar conjugations, dimerizations). Moreover, several compounds that were previously found uniquely into berries from the Ericaceae family (e.g., arbutin, coumaroyl iridoids) will be included in the combinations. The data processing workflow on Compound Discoverer will be the first one specifically aimed at the characterization of phenolic compounds in blueberries with a significant impact on the manual validation and tentative identification of phenolic compounds in fresh and fermented berries.
Finally, the study of the transformation of phenolic compounds in berries that undergo fermentation has been poorly addressed in the literature, and never with high-performance techniques such as HRMS. The study will be first performed on the compounds present in the database used for polyphenol identification with the purpose of evaluating the trends in the annotated compounds before and after fermentation. The different classes will be compared, as well as glycosylated and non-glycosylated species of the same class. In the last part of the study, by setting up a dedicated data processing workflow on Compound Discoverer, polyphenol degradation products will be investigated.