Optimization of accelerated solvent extraction of ellagitannins in black raspberry seeds using artificial neural network coupled with genetic algorithm

Ga Eun Lee; Ryun Hee Kim; Taehwan Lim; Jaecheol Kim; Suna Kim; Hyoung-Geun Kim; Keum Taek Hwang

doi:10.1016/j.foodchem.2022.133712

Optimization of accelerated solvent extraction of ellagitannins in black raspberry seeds using artificial neural network coupled with genetic algorithm

Food Chem. 2022 Dec 1:396:133712. doi: 10.1016/j.foodchem.2022.133712. Epub 2022 Jul 14.

Authors

Ga Eun Lee¹, Ryun Hee Kim¹, Taehwan Lim², Jaecheol Kim¹, Suna Kim³, Hyoung-Geun Kim⁴, Keum Taek Hwang⁵

Affiliations

¹ Department of Food and Nutrition, and Research Institute of Human Ecology, Seoul National University, Seoul 08826, Republic of Korea; BK21 FOUR Education and Research Team for Sustainable Food & Nutrition, Seoul National University, Seoul 08826, Republic of Korea.
² Department of Biomedical Engineering, Tufts University, 4 Colby St., Medford 02155, MA, USA.
³ Division of Human Ecology, College of Natural Science, Korea National Open University, Seoul 03078, Republic of Korea.
⁴ Graduate School of Biotechnology and Department of Oriental Medicinal Biotechnology, Kyung Hee University, Yongin 17104, Republic of Korea.
⁵ Department of Food and Nutrition, and Research Institute of Human Ecology, Seoul National University, Seoul 08826, Republic of Korea; BK21 FOUR Education and Research Team for Sustainable Food & Nutrition, Seoul National University, Seoul 08826, Republic of Korea. Electronic address: keum@snu.ac.kr.

PMID: 35863176
DOI: 10.1016/j.foodchem.2022.133712

Abstract

This study aimed to identify ellagitannins in black raspberry seeds (BRS) and to optimize accelerated solvent extraction of ellagitannins using an artificial neural network (ANN) coupled with genetic algorithm. Fifteen monomeric and dimeric ellagitannins were identified in BRS. For ANN modeling, extraction time, extraction temperature, and solvent concentration were set as input variables, and total ellagitannin content was set as output variable. The trained ANN had a mean squared error value of 0.0102 and a regression correlation coefficient of 0.9988. The predicted optimal extraction conditions for maximum total ellagitannin content were 63.7% acetone, 4.21 min, and 43.9 °C. The actual total ellagitannin content under the optimal extraction conditions was 13.4 ± 0.0 mg/g dry weight, and the prediction error was 0.75 ± 0.27%. This study is the first attempt to analyze the composition of ellagitannins in BRS and to determine optimal extraction conditions for maximum total ellagitannin content from BRS.

Keywords: Accelerated solvent extraction; Artificial neural network; Black raspberry seeds; Ellagitannin; Genetic algorithm; Optimization.

MeSH terms

Hydrolyzable Tannins
Neural Networks, Computer
Nigella sativa*
Rubus*
Seeds
Solvents

Substances

Hydrolyzable Tannins
Solvents