Process Optimization for the Extraction of Phenolic Compounds from Pomegranate Peels: Response Surface Methodology-Desirability Function and Artificial Neural Network-Genetic Algorithm

Year 2024, Volume: 22 Issue: 1, 23 - 33, 29.03.2024

Esra Uca Hacı Ali Güleç

https://doi.org/10.24323/akademik-gida.1460968

Cited By: 1

Abstract

Valorization of agricultural wastes is ongoing topic in industry. Determining the best conditions by artificial neural networks based optimization techniques is the key step to extract valuable compounds efficiently and to obtain high quality extracts. In this study, the response surface methodology (RSM)-desirability function (DF) and artificial neural network (ANN)-genetic algorithm (GA) approaches were compared in modeling and optimization the extraction parameters (temperature, time and ethanol concentration (ratio of ethanol to water, % v/v)) of phenolic compounds in pomegranate peels. The ANN-GA approach providing higher coefficient of determination and lower root mean square deviation showed better predictive capability than the RSM. The optimum time (81.4 min) and ethanol concentration (15.7%) of RSM-DF approach shifted to the lower levels (78.8 min and 15.3%) with the ANN-GA approach while the optimum temperature (54.0°C) shifted to a higher level (59.3°C). The use of these values provided total phenolic content of >1000 mg GAE L-1 and the corresponding antioxidant activity was 11 mmol TE L-1. As a result, increasing temperature up to a critical level decreased the extraction time and ethanol concentration, and it was determined that higher time-temperature combinations must be used for the complete water-based extraction of phenolic compounds from plant wastes in comparison to ethanol-water based extraction.

Keywords

Valorization pomegranate peel, Extraction, Response surface methodology, Artificial neural network, Genetic algorithm

Project Number

2017/37

Nar Kabuklarından Fenolik Bileşiklerin Ekstraksiyonunda Proses Optimizasyonu: Yanıt Yüzeyi Yöntemi-İstenebilirlik Fonksiyonu ve Yapay Sinir Ağı-Genetik Algoritma

Abstract

Tarımsal atıkların değerlendirilmesi endüstride güncelliğini sürdüren bir konudur. Değerli bileşikleri verimli bir şekilde ekstrakte etmek ve yüksek kaliteli ekstraktlar elde etmek için en iyi koşulların yapay sinir ağları tabanlı optimizasyon teknikleri ile belirlenmesi önemli bir adımdır. Bu çalışmada, nar kabuklarındaki fenolik bileşiklerin ekstraksiyon parametrelerinin (sıcaklık, süre ve etanol konsantrasyonu (etanol/su oranı, % v/v)) modellenmesinde ve optimizasyonunda yanıt yüzeyi yöntemi (RSM)-istenebilirlik fonksiyonu (DF) ve yapay sinir ağı (YSA)-genetik algoritma (GA) yaklaşımları karşılaştırılmıştır. ANN-GA yaklaşımı daha yüksek determinasyon katsayısı ve daha düşük ortalama karekök sapması sağlayarak RSM'den daha iyi bir tahmin yeteneği göstermiştir. RSM-DF yaklaşımının optimum süresi (81.4 dakika) ve etanol konsantrasyonu (%15.7) ANN-GA yaklaşımı ile daha düşük seviyelere (78.8 dakika ve %15.3) kayarken optimum sıcaklık (54.0°C) ise daha yüksek bir seviyeye kaymıştır (59.3°C). Bu değerlerin kullanımı >1000 mg GAE L-1 toplam fenolik içerik ve 11 mmol TE L-1 antioksidan aktivite sağlamıştır. Sonuç olarak, sıcaklığın kritik bir seviyeye çıkarılması ekstraksiyon süresini ve etanol konsantrasyonunu azaltmıştır ve bitki atıklarından fenolik bileşiklerin tamamen su bazlı ekstraksiyonunda etanol-su bazlı ekstraksiyonuna göre daha yüksek zaman-sıcaklık kombinasyonlarının kullanılması gerektiği belirlenmiştir.

Keywords

Nar kabuklarının değerlendirilmesi, Ekstraksiyon, Yanıt yüzey yöntemi, Yapay sinir ağı, Genetik algoritma

Supporting Institution

Trakya University Scientific Research Projects Coordination Unit

Project Number

2017/37

Thanks

This study was supported by Trakya University Scientific Research Projects Coordination Unit. Project Number: 2017/37

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