Optimization of kidney bean antioxidants using RSM & ANN and characterization of antioxidant profile by UPLC-QTOF-MS

Qiong Qiong Yang, Ren You Gan, Dan Zhang, Ying Ying Ge, Li Zeng Cheng, Harold Corke*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

In this study, response surface methodology (RSM) and two artificial neural networks (ANN), multi-layer perceptron (MLP) and radial basis function (RBF), were used for the first time to model and optimize the extraction conditions of phenolic compounds in kidney beans in order to compare and establish effective prediction models. A total of 40 experiments were performed to screen for variables. The highest amount of polyphenols (917.2 mg GAE/100 g DW) and the strongest antioxidant activity (56.03 μmol Trolox/g DW) were obtained under optimal extraction conditions (70 min extraction time, 53% acetone, and 37 mL/g solvent-to-solid ratio). Response values were close to the predicted values with prediction accuracy as RBF > MLP > RSM, indicating that ANN model has higher prediction accuracy than RSM. Furthermore, UPLC-QTOF-MS analysis of extracts under the optimal extraction conditions showed that there were 25 antioxidant compounds detected, 13 of which were proposed from kidney beans for the first time.

Original languageEnglish
Article number108321
JournalLWT - Food Science and Technology
Volume114
DOIs
StatePublished - Nov 2019
Externally publishedYes

Keywords

  • Artificial neural network
  • Mathematical model
  • Phaseolus vulgaris
  • Response surface methodology
  • Total polyphenol content

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