Heat-reflux processing of black peppercorn into bioactive antioxidant oleoresins: a three-functioned Taguchi-based grey relational grading

Olusegun Olalere, Chee-Yuen Gan, Ablola Ezekiel Taiwo, Abdurahman Hamid Nour, Bernardo Castro Dominguez, Oladayo Adeyi, Gbemisola J. Fadimu, Olumide Samson Fawale, Oladlpupo Q. Aldamo

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The focus of this research is to identify the best set of factors that influence the heat-reflux recovery of total phenolic content and antioxidant activities under multiple quality characteristics. Parametric Taguchi L9 orthogonal design and grey relational analysis technique were used to investigate the effect of three variables—reflux duration, particle size, and feed-to-solvent ratio on the multiple responses of total phenolic contents, DPPH, and H2O2 activities. According to the grey relational grades response table, the ideal number of criteria for the heat reflux results were 120 min of reflux duration, 0.2 mm of particle size, and a feed-solvent ratio of 1:16. The total phenolic content, DPPH, and H2O2 scavenging activities were measured as 35.23 ± 0.004 mgGAE/g d.w, 107.57 ± 0.04 g/mL, and 87.78 ± 0.32 g/mL, respectively. Moreover, with the Levenberg–Marquardt (LM) neural network architecture, the trained network has a mean square error (MSE) of 3.7646E−07 and an R2 of 0.9500 as the training function outcome, indicating a significant predicted endpoint. The confirmatory experimental results show a 41.9 per cent improvement in relation to the predicted values. The results of this study indicated that, optimising the heat reflux process would be an innovative and beneficial approach for preparing bioactive compounds from functional plants, resulting in cost savings while increasing antioxidant capacity and overall phenolic recovery.
Original languageEnglish
Pages (from-to)4140-4148
Number of pages9
JournalJournal of Food Measurement and Characterization
Issue number4
Early online date26 Apr 2023
Publication statusPublished - 31 Aug 2023


  • Antioxidants
  • Artificial neural network
  • Black pepper
  • Grey relational analysis (GRA)
  • Optimization
  • Total phenolic content

ASJC Scopus subject areas

  • Food Science
  • General Chemical Engineering
  • Safety, Risk, Reliability and Quality
  • Industrial and Manufacturing Engineering


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