Çemen tohumlarından fenolik bileşiklerin ekstraksiyonu: yapay sinir ağları ile modelleme ve analiz

Abstract

Bu çalışma, farklı katı-çözücü oranlarında (0,5-60 g/L) çemen tohumlarından biyoaktif bileşiklerin ekstraksiyon işleminin ve ekstraksiyon sürelerinin modellenmesini ve analizini tanıtmaktadır. Ekstraksiyon işlemleri için çalkalamalı maserasyon ve ekstraktların toplam fenolik bileşikleri, toplam flavonoid içeriği ve antioksidan aktivitesi deneysel veri olarak ölçülmüştür. Antioksidan etkiye sahip ekstrakte edilebilir fenolik bileşiklerin miktarı, katı-çözücü oranı değiştirilerek arttırılmıştır. Elde edilen sonuçlara göre sırasıyla toplam fenolik bileşikler, toplam flavonoid içeriği ve toplam antioksidan aktivite için en yüksek değerler 12564,08±376,88 mg gallik asit/100 gr kuru numune, 7540,44±39,67 mg kuersetin/100 gr kuru numune ve 1904,80±17,43 mM Trolox/100 gr kuru numune olarak belirlenmiştir. Çıkarma işlemi, sırasıyla standart Yapay Sinir Ağları (YSA) ve Pi-Sigma Sinir Ağları (PSNN) kullanılarak modellenmiştir. PSNN modeli, hem eğitim hem de test için %0,94 ile %1,30 arasında değişen daha düşük RMSE (%) değerleri ile daha yüksek bir tahmin verimliliğine sahip olduğu gösterilmiştir.

Keywords

• Çemen otu tohumu
• fenolik bileşikler
• antioksidan aktivitesi
• modelleme
• yapay sinir ağları

EXTRACTION OF PHENOLIC COMPOUNDS FROM FENUGREEK SEEDS: MODELLING AND ANALYSIS USING ARTIFICIAL NEURAL NETWORKS

Abstract

This study introduces the modeling and analysis of the extraction process of bioactive compounds from fenugreek seeds in different solid-to-solvent ratios (0.5-60 g/L) and extraction times. Maceration was applied with agitation for the extraction processes and total phenolic compounds, total flavonoid content and antioxidant activity of the extracts were measured as experimental data. The amount of extractable phenolic compounds having antioxidant effect was increased by adjusting the solid-to-solvent ratio. According to obtained results, the highest values were determined as 12564.08±376.88 mg gallic acid/100 g dry sample, 7540.44±39.67 mg quercetin/100 g dry sample and 1904.80±17.43 mM Trolox/100 g dry sample for total phenolic compounds, total flavonoid content, and antioxidant activity, respectively. The extraction process was modeled using standard Artificial Neural Networks (ANN) and Pi-Sigma Neural-Networks (PSNN). The PSNN model had a higher prediction efficiency with lower RMSE (%) values varied between 0.94% and 1.30% for both training and testing.

Keywords

• Fenugreek seed
• Phenolic compounds
• Antioxidant activity
• Modelling
• Artificial neural network

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