Comparison of an Ultrasound-Assisted Aqueous Two-Phase System Extraction of Anthocyanins from Pomegranate Pomaces by Utilizing the Artificial Neural Network–Genetic Algorithm and Response Surface Methodology Models

Author:

Yue Qisheng1234,Tian Jun1234,Dong Ling5,Zhou Linyan1234ORCID

Affiliation:

1. Faculty of Food Science and Engineering, Kunming University of Science and Technology, Kunming 650500, China

2. Yunnan Engineering Research Center for Fruit & Vegetable Products, Kunming 650500, China

3. Yunnan Key Laboratory for Food Advanced Manufacturing, Kunming 650500, China

4. International Green Food Processing Research and Development Center of Kunming City, Kunming 650500, China

5. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Abstract

As a by-product of pomegranate processing, the recycling and reuse of pomegranate pomaces (PPs) were crucial to environmentally sustainable development. Ultrasound-assisted aqueous two-phase extraction (UA-ATPE) was applied to extract the anthocyanins (ACNs) from PPs in this study, and the central composite design response surface methodology (CCD-RSM) and artificial neural network–genetic algorithm (ANN-GA) models were utilized to optimize the extraction parameters and achieve the best yield. The results indicated that the ANN-GA model built for the ACN yield had a greater degree of fit and accuracy than the RSM model. The ideal model process parameters were optimized to have a liquid–solid ratio of 49.0 mL/g, an ethanol concentration of 28 g/100 g, an ultrasonic time of 27 min, and an ultrasonic power of 330 W, with a maximum value of 86.98% for the anticipated ACN yield. The experimental maximum value was 87.82%, which was within the 95% confidence interval. A total of six ACNs from PPs were identified by utilizing UHPLC-ESI-HRMS/MS, with the maximum content of cyanidin-3-O-glucoside being 57.01 ± 1.36 mg/g DW. Therefore, this study has positive significance for exploring the potential value of more by-products and obtaining good ecological and economic benefits in the future.

Funder

National Natural Science Foundation of China

Science and Technology Project of Yunnan Province

Special Foundation for Excellent Youth Scholars of Yunnan Province, China

Kunming International (Foreign) Science and Technology Cooperation Project of the ‘Green Food Processing International Science and Technology R & D Center’

Publisher

MDPI AG

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