A systematic review on phenolic compounds in Passiflora plants: Exploring biodiversity for food, nutrition, and popular medicine.
Izabel Lucena GadioliMarcela de Sá Barreto da CunhaMariana Veras Oliveira de CarvalhoAna Maria CostaLívia de Lacerda de Oliveira PineliPublished in: Critical reviews in food science and nutrition (2017)
Passiflora plants are strategic in the context of biodiversity for food and nutrition. We applied the procedures of a systematic review protocol to study the state of the art on identification of phenolic compounds from Passiflora plants. An automated literature search was conducted using six databases and a combination of seven keywords. All the analytical, chromatographic, and spectroscopic methods were included. The studies were classified according to their method of identification, phenolic classes, and method of extraction. In total, 8,592 abstracts were found, from which 122 studies were selected for complete reading and 82 were selected for further analysis. Techniques of extraction, evaluated parts of the plant and methods of identification were systematized. Studies with leaves were most conspicuous (54.4%), 34 species of Passiflora were evaluated and orientin, isoorientin, vitexin, isovitexin were commonly found structures. A High Performance Liquid Chromatography-diode array detector was the technique most applied, with which the same structures were identified all through the studies, although other unknown structures were detected, but not elucidated. The use of Nuclear Magnetic Resonance and Mass Spectrometry, which are more sensitive techniques, needs to be intensified, to identify other unconventional compounds detected in Passiflora, to enhance the comprehension of the bioactive compounds in these plants.
Keyphrases
- high performance liquid chromatography
- mass spectrometry
- high resolution
- magnetic resonance
- case control
- liquid chromatography
- tandem mass spectrometry
- simultaneous determination
- physical activity
- systematic review
- working memory
- machine learning
- molecular dynamics simulations
- gas chromatography
- ms ms
- human health
- plant growth