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J Ethnopharmacol ; 259: 112968, 2020 Sep 15.
Artículo en Inglés | MEDLINE | ID: mdl-32417426

RESUMEN

ETHNOPHARMACOLOGICAL RELEVANCE: In the quilombola communities of the municipality of Oriximiná (Pará State, Brazil), Protium spp. (Burseraceae) oleoresins are distinguished in black and white pitch. White pitch oleoresins may be superior to black pitch in terms of quality, but the criteria used for their differentiation are fairly subjective. AIM OF THE STUDY: This study was designed to provide a scientific rationale for the traditional differentiation of black and white pitch oleoresins based on their non-volatile fraction. MATERIALS AND METHODS: Black and white pitch oleoresin samples collected in quilombola territories in Oriximiná were analysed by GC-EI-MS and UPLC-APCI-MS. The feasibility of EI and APCI mass spectrometry-based pattern recognition methods PLS-DA and Random Forest Analysis (RFA) for black and white pitch oleoresins differentiation was demonstrated. RESULTS: The UPLC-APCI-MS method allowed the separation of 43 triterpenoids. Assessment of the triterpenoid fingerprints by GC-EI-MS led to the tentative identification of ursa-9(11),12-dien-3-ol as a potential marker for black pitch oleoresins. PLS-DA and RFA applied to the APCI-MS and EI-MS data gave good models for black and white pitch oleoresins classification. The most important ions for the classifications of black pitch oleoresins by APCI-MS/PLS-DA and APCI-MS/RFA likely represented triterpenoid acids. CONCLUSIONS: The triterpenoid pattern differs between black and white pitch oleoresins. The characteristic presence of ursa-9(11),12-dien-3-ol and triterpenoids acids in black pitch oleoresins, along with other field observations, suggest that black pitch oleoresins are actually aged white pitch oleoresins.


Asunto(s)
Burseraceae/química , Color , Extractos Vegetales/aislamiento & purificación , Espectrometría de Masa por Ionización de Electrospray , Triterpenos/aislamiento & purificación , Inteligencia Artificial , Burseraceae/clasificación , Análisis de los Mínimos Cuadrados , Reconocimiento de Normas Patrones Automatizadas , Extractos Vegetales/clasificación , Triterpenos/clasificación
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