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<title>Tese (PPGB)</title>
<link href="https://repositorio.ufba.br/handle/ri/9461" rel="alternate"/>
<subtitle/>
<id>https://repositorio.ufba.br/handle/ri/9461</id>
<updated>2026-05-17T06:10:05Z</updated>
<dc:date>2026-05-17T06:10:05Z</dc:date>
<entry>
<title>Espectrometria de Massas e Molecular Networking como  ferramentas para o estudo da diversidade química de espécies  nativas de Passiflora L. (Passifloraceae).</title>
<link href="https://repositorio.ufba.br/handle/ri/43953" rel="alternate"/>
<author>
<name>Garcia, Laryana Borges</name>
</author>
<id>https://repositorio.ufba.br/handle/ri/43953</id>
<updated>2026-01-30T17:25:54Z</updated>
<published>0007-07-11T00:00:00Z</published>
<summary type="text">Espectrometria de Massas e Molecular Networking como  ferramentas para o estudo da diversidade química de espécies  nativas de Passiflora L. (Passifloraceae).
Garcia, Laryana Borges
Amaral, Juliano Geraldo
The Passifloraceae family, with more than 630 species, is predominant in tropical &#13;
and subtropical regions, with Passiflora being the most extensive and diverse genus. This &#13;
study aims to deepen the knowledge about the Passifloraceae family and the metabolic &#13;
profiles of Passiflora subspecies, known for their rich phytochemical composition, &#13;
including glycosylated flavonoids, carotenoids, cyanogenic glycosides, alkaloids, &#13;
steroids, lignans, fatty acids, amino acids, chlorogenic acid derivatives and &#13;
proanthocyanidins. Initially, we performed a comprehensive review covering studies &#13;
from 1983 to 2023, offering a detailed overview of the scientific discoveries on the &#13;
chemical compounds present in the genus Passiflora. Then, we combined mass &#13;
spectrometry and molecular networking to explore the chemical diversity of native &#13;
species of the genus Passiflora from Brazil. We developed a comprehensive database &#13;
using liquid chromatography coupled to mass spectrometry (HPLC-MS/MS) to analyze &#13;
the metabolic profile of several subspecies. The generated data were submitted to the &#13;
GNPS platform, which generated a Molecular Networking, in which the same substances &#13;
were grouped in a single node and the similar ones in clusters. The data generated by the &#13;
network were dereplicated and also analyzed by multivariate statistical methods, and thus &#13;
this work revealed metabolic similarities between species, such as P. incarnata, &#13;
suggesting shared pharmacological potential. We documented 25 species not previously &#13;
studied, expanding the knowledge about their medicinal properties and opening paths for &#13;
the development of new products.
Universidade Federal da Bahia
Dissertação
</summary>
<dc:date>0007-07-11T00:00:00Z</dc:date>
</entry>
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