Generalized Likelihood Uncertainty Estimation (GLUE) methodology for optimization of extraction in natural products

Maulidiani 1 , Rudiyanto 2 , Abas F 3 , Ismail IS 4 , Lajis NH 5

Affiliations 

  • 1 Laboratory of Natural Products, Institute of Bioscience, University of Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia. Electronic address: maulidiani@upm.edu.my
  • 2 Department of Civil and Environmental Engineering, Bogor Agricultural University, Indonesia
  • 3 Laboratory of Natural Products, Institute of Bioscience, University of Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia; Department of Food Science, Faculty of Food Science and Technology, University of Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia. Electronic address: faridah_abas@upm.edu.my
  • 4 Laboratory of Natural Products, Institute of Bioscience, University of Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia
  • 5 No. 22513, Jalan Melor, Sungai Ramal Dalam, Kajang 43000, Selangor, Malaysia
Food Chem, 2018 Jun 01;250:37-45.
PMID: 29412925 DOI: 10.1016/j.foodchem.2018.01.023

Abstract

Optimization process is an important aspect in the natural product extractions. Herein, an alternative approach is proposed for the optimization in extraction, namely, the Generalized Likelihood Uncertainty Estimation (GLUE). The approach combines the Latin hypercube sampling, the feasible range of independent variables, the Monte Carlo simulation, and the threshold criteria of response variables. The GLUE method is tested in three different techniques including the ultrasound, the microwave, and the supercritical CO2 assisted extractions utilizing the data from previously published reports. The study found that this method can: provide more information on the combined effects of the independent variables on the response variables in the dotty plots; deal with unlimited number of independent and response variables; consider combined multiple threshold criteria, which is subjective depending on the target of the investigation for response variables; and provide a range of values with their distribution for the optimization.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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