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Journals / Konya Mühendislik Bilimleri Dergisi / 2023 / Cilt: 11 Sayı: 2

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

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

Selami BEYHAN
Hilal İŞLEROĞLU
Pages
312–323
Publication Date
2023-06-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The PSNN model had a higher prediction efficiency with lower RMSE (%) values varied between 0.94% and 1.30% for both training and testing.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:The amount of extractable phenolic compounds having antioxidant effect was increased by adjusting the solid-to-solvent ratio.
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Conclusion & Impact:The PSNN model had a higher prediction efficiency with lower RMSE (%) values varied between 0.94% and 1.30% for both training and testing.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Çemen otu tohumu · fenolik bileşikler · antioksidan aktivitesi

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.

Özet

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.

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Cite This Article

BEYHAN, S., İŞLEROĞLU, H. (2023). EXTRACTION OF PHENOLIC COMPOUNDS FROM FENUGREEK SEEDS: MODELLING AND ANALYSIS USING ARTIFICIAL NEURAL NETWORKS. Konya Mühendislik Bilimleri Dergisi, Cilt: 11 Sayı: 2, 312–323. https://ajindex.com/articles/938450

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References (12)

  1. Baba WN, Tabasum Q, Muzzaffar S, Masoodi FA, Wani I et al., “Some nutraceutical properties of fenugreek seeds and shoots (Trigonella foenum-graecum L.) from the high Himalayan region”, Food Bioscience 2018; 23: 31–37
  2. Yao D, Zhang B, Zhu J, Zhang Q, Hu Y et al., “Advances on application of fenugreek seeds as functional foods: Pharmacology, clinical application, products, patents and market”, Critical Reviews in Food Science and Nutrition 2020; 60 (14): 2342-2352
  3. Naidu MM, Shyamala BN, Naik JP, Sulochanamma G, Srinivas P., “Chemical composition and antioxidant activity of the husk and endosperm of fenugreek seeds”, LWT-Food Science and Technology 2011; 44, 451–456
  4. Dixit P, Ghaskadbi S, Mohan H, Devasagayam TP., “Antioxidant properties of germinated fenugreek seeds”, Phytotherapy Research 2005; 19 (11): 977-983
  5. Srinivasan K. Fenugreek (Trigonella foenum-graecum): A review of health beneficial physiological effects. Food Reviews International 2006; 22 (2): 203-224
  6. Belguith-Hadriche O, Bouaziz M, Jamoussi K, Simmonds MSJ, El Feki A. et al., “Comparative study on hypocholesterolemic and antioxidant activities of various extracts of fenugreek seeds”, Food Chemistry 2013; 138: 1448–1453
  7. Mukthamba P, Srinivasan K. “Hypolipidemic and antioxidant effects of dietary fenugreek (Trigonella foenum-graecum) seeds and garlic (Allium sativum) in high-fat fed rats”, Food Bioscience 2016; 14: 1–9
  8. Kenny O, Smyth TJ, Hewage CM, Brunton NP., “Antioxidant properties and quantitative UPLC-MS analysis of phenolic compounds from extracts of fenugreek (Trigonella foenum-graecum) seeds and bitter melon (Momordica charantia) fruit”, Food Chemistry 2013; 141 (4): 4295-4302
  9. Khorshidian N, Yousefi Asli M, Arab M, Adeli Mirzaie A, Mortazavian AM., “Fenugreek: potential applications as a functional food and nutraceutical”, Nutrition and Food Sciences Research 2016; 3(1): 5-16
  10. Yadav R, Chowdhury P., “Screening the Antioxidant activity of Trigonella Foenum graecum seeds”, International Journal of Pharmaceutical Research and Applications 2017; 2(1): 65-70
  11. Nayak J, Vakula K, Dinesh P, Naik B, Pelusi D., “Intelligent food processing: Journey from artificial neural network to deep learning”, Computer Science Review 2020; 38: 100297
  12. Shin Y, Ghosh J., “The pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation”, In: Proceedings IJCNN-91-Seattle international joint conference on neur