Screening of Mangifera indica L. functional content using PCA and neural networks (ANN)

Food Chem. 2019 Feb 1:273:115-123. doi: 10.1016/j.foodchem.2018.01.129. Epub 2018 Feb 1.

Abstract

A method using reversed-phase high-performance liquid chromatography with diode array detection (HPLC-DAD) was applied after extraction with acidified methanol, to determine 12 bioactive phenolic compounds in the peel and pulp of the mango fruit (Mangifera indica L.) cultivated in the Bahia, state of Brazil. The proposed methodology was previously fully validated and proven successful in the analysis of methanolic extracts of lyophilized samples. The limits of quantification ranged between 0.78 and 3.14 mg L-1 and the individual recovery values obtained for the spiked samples ranged from 80% to 120%. The results were evaluated using PCA and ANN. The results indicate that the fruits are rich in polyphenols, mainly: ellagic acid, gallic acid, rutin and catechin, which contribute to their greater use as functional foods, natural antioxidants and in the pharmaceutical industry, as well as other applications.

Keywords: Artificial neural networks; Bioactive phenolic compounds; HPLC-DAD; Mangifera indica L.; Principal component analysis.

MeSH terms

  • Antioxidants / analysis
  • Brazil
  • Catechin / analysis
  • Chromatography, High Pressure Liquid
  • Chromatography, Reverse-Phase
  • Ellagic Acid / analysis
  • Food Analysis / methods*
  • Food Analysis / statistics & numerical data
  • Fruit / chemistry
  • Functional Food / analysis
  • Gallic Acid / analysis
  • Mangifera / chemistry*
  • Neural Networks, Computer*
  • Phenols / analysis
  • Polyphenols / analysis
  • Principal Component Analysis*

Substances

  • Antioxidants
  • Phenols
  • Polyphenols
  • Ellagic Acid
  • Gallic Acid
  • Catechin