1H NMR combined with chemometrics tools for rapid characterization of edible oils and their biological properties

Jânio Sousa Santos, Graziela Bragueto Escher, Jean Marcos da Silva Pereira, Marina Tolentino Marinho, Leonardo do Prado-Silva, Anderson S. Sant'Ana, Lívia Macedo Dutra, Andersson Barison, Daniel Granato

Research output: Contribution to journalArticlepeer-review

Abstract

1H NMR spectroscopy was used to characterize seventeen Brazilian plant oils from different botanical origins and to determine the total phenolic content, as well as the antioxidant and antimicrobial activity. A profiling approach based on NMR and PCA revealed that the phenolic compounds signals (tangeretin, nobiletin and demethylnobiletin), as well as limonene, play an important role in the discrimination of the edible oils of tangerine (conventional and organic). Ricinolenic acid was responsible for the grouping of castor oil and the distinguishing other oils that can be characterized by the presence of fatty acids. In addition, polyaltic acid and kaurenoic acid were detected in the copaíba oil but were not observed in the other oils. The organic tangerine oil showed the highest content of total phenolic compounds and the highest level of antioxidant activity in relation to DPPH and ABTS. Most of the tested oils showed antimicrobial activity in relation to S. Typhimurium and S. Enteritidis, in a way that the buriti and the golden flaxseed organic oils displayed the highest activity, respectively. However, S. aureus, L. monocytogenes, and B. cereus demonstrated resistance to most of the tested oils. These findings may help in the understanding of edible oils and also how to check their quality based on analytical and biological approaches.

Original languageEnglish
Pages (from-to)191-200
Number of pages10
JournalIndustrial Crops and Products
Volume116
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes

Keywords

  • Antimicrobial agents
  • Antioxidants
  • Chemometrics
  • Edible oils
  • Nuclear magnetic resonance
  • Principal component analysis

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