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

Year 2023, Volume: 11 Issue: 2, 312 - 323, 01.06.2023
https://doi.org/10.36306/konjes.1208658

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.

References

  • [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 neural networks 1991; 1: 13-18.
  • [13] Bas E, Grosan C, Egrioglu E, Yolcu U., “High order fuzzy time series method based on pi-sigma neural network”, Engineering Applications of Artificial Intelligence 2018; 72: 350-356.
  • [14] Nayak SC., “Development and performance evaluation of adaptive hybrid higher order neural networks for exchange rate prediction”, International Journal of Intelligent Systems and Applications 2017; 9 (8): 71-85. 10.5815/ijisa.2017.08.08
  • [15] Oguzhan Y, Bas E, Egrioglu E., “The training of pi-sigma artificial neural networks with differential evolution algorithm for forecasting”, Computational Economics 2022; 59: 1699–1711.
  • [16] Ghazali GR, Hussain AJ, Liatsis P, Tawfik H., “The application of ridge polynomial neural network to multi-step ahead financial time series prediction”, Neural Computing and Applications 2008; 17: 311-323.
  • [17] Kang Q, Fan Q, Zurada JM., “Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network”, Information Sciences 2021; 553: 66-82.
  • [18] Ciric A, Krajnc B, Heath D, Ogrinc N., “Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic”, Food and Chemical Toxicology 2020; 135: 110976.
  • [19] Muzolf‑Panek M, Kaczmarek A, Gliszczyńska‑Świgło A., “A predictive approach to the antioxidant capacity assessment of green and black tea infusions”, Journal of Food Measurement and Characterization 2021; 15:1422–1436.
  • [20] Rebollo-Hernanz M, Cañasa S, Taladrid D, Segovia A, Bartolomé B et al., “Extraction of phenolic compounds from cocoa shell: Modeling using response surface methodology and artificial neural networks”, Separation and Purification Technology 2021; 270: 118779.
  • [21] Kadiri O, Gbadamosi SO, Akanbi CT., “Extraction kinetics, modelling and optimization of phenolic antioxidants from sweet potato peel vis‑a‑vis RSM, ANN‑GA and application in functional noodles”, Journal of Food Measurement and Characterization 2019; 13:3267–3284.
  • [22] Pavlic B, Kaplan M, Bera O, Olgun EO, Canli O, et al., “Microwave-assisted extraction of peppermint polyphenols – Artificial neural networks approach”, Food and Bioproducts Processing 2019; 118: 258-269.
  • [23] Curko N, Kelšin K, Dragović-Uzelac V, Valinger D, Tomašević M, et al., “Microwave-assisted extraction of different groups of phenolic compounds from grape skin pomaces: modeling and optimization”, Polish Journal of Food and Nutrition Science. 2019; 69(3): 235–246.
  • [24] Espinosa-Sandoval LA, Cerqueira MA, Ochoa-Martínez CI, Ayala-Aponte AA., “Phenolic compound–loaded nanosystems: artificial neural network modeling to predict particle size, polydispersity index, and encapsulation efficiency”, Food and Bioprocess Technology 2019; 12(8): 1395-1408.
  • [25] Abcha I, Criado P, Salmieri S, Najjaa H, Isoda H, et al., “Edible Rhus tripartita fruit as source of health-promoting compounds: characterization of bioactive components and antioxidant properties”, European Food Research and Technology 2019; 245:2641–2654.
  • [26] Gupta R, Nair S., “Antioxidant flavonoids in common Indian diet.”, South Asian Journal of Preventive Cardiology 1999; 3: 83-94.
  • [27] Esclapez MD, Garcia-Perez JV, Mulet A, Carcel JA., “Ultrasound-assisted extraction of natural products”, Food Engineering Reviews 2011; 3: 108–120.
  • [28] Bukhari SB, Bhanger MI, Memon S., “Antioxidative Activity of Extracts from Fenugreek Seeds (Trigonella foenum-graecum)“, Pakistan Journal of Analytical & Environmental Chemistry 2008; 9(2): 78-83.
  • [29] Mashkor IM., “Phenolic content and antioxidant activity of fenugreek seeds extract”, International Journal of Pharmacognosy and Phytochemical Research 2014; 6(4): 841-844.
  • [30] Dastan S, Türker İ, İşleroğlu H.,” Çemen otu tohumundan fenolik bileşenlerin ekstraksiyonu için optimizasyon çalışması”, Gıda 2021; 46(4): 959-970.
  • [31] Casazza AA, Aliakbarian B, Perego P., “Recovery of phenolic compounds from grape seeds: effect of extraction time and solid–liquid ratio”, Natural Product Research 2011; 25(18): 751–1761
  • [32] Hismath I, Wan Aida WM, Ho CW., “Optimization of extraction conditions for phenolic compounds from neem (Azadirachta indica) leaves”, International Food Research Journal 2011; 18: 931–939.
  • [33] Chaalal M, Touati N, Louaileche H., “Extraction of phenolic compounds and in vitroantioxidant capacity of prickly pear seeds”, Acta Botanica Gallica: Botany Letters 2012: 159(4): 467–475
  • [34] Mitra P, Barman PC, Chang KS., “Coumarin extraction from cuscuta reflexa using supercritical fluid carbon dioxide and development of an artificial neural network model to predict the coumarin yield”, Food Bioprocess Technol 2011; 4: 737–744.
  • [35] Sun Q, Zhang M, Mujumdar AS, Yang P., “Combined LF-NMR and artificial intelligence for continuous real-time monitoring of carrot in microwave vacuum drying”, Food and Bioprocess Technology 2019; 12: 551–562.
  • [36] Wani SA, Bishnoi S, Kumar P., “Ultrasound and microwave assisted extraction of diosgenin from fenugreek seed and fenugreek-supplemented cookies”, Journal of Food Measurement and Characterization 2016; 10(3): 527-532.
  • [37] Singleton VL, Rossi JA., “Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents”, American Journal of Enology and Viticulture 1965; 16(3): 144-158.

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

Year 2023, Volume: 11 Issue: 2, 312 - 323, 01.06.2023
https://doi.org/10.36306/konjes.1208658

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.

References

  • [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 neural networks 1991; 1: 13-18.
  • [13] Bas E, Grosan C, Egrioglu E, Yolcu U., “High order fuzzy time series method based on pi-sigma neural network”, Engineering Applications of Artificial Intelligence 2018; 72: 350-356.
  • [14] Nayak SC., “Development and performance evaluation of adaptive hybrid higher order neural networks for exchange rate prediction”, International Journal of Intelligent Systems and Applications 2017; 9 (8): 71-85. 10.5815/ijisa.2017.08.08
  • [15] Oguzhan Y, Bas E, Egrioglu E., “The training of pi-sigma artificial neural networks with differential evolution algorithm for forecasting”, Computational Economics 2022; 59: 1699–1711.
  • [16] Ghazali GR, Hussain AJ, Liatsis P, Tawfik H., “The application of ridge polynomial neural network to multi-step ahead financial time series prediction”, Neural Computing and Applications 2008; 17: 311-323.
  • [17] Kang Q, Fan Q, Zurada JM., “Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network”, Information Sciences 2021; 553: 66-82.
  • [18] Ciric A, Krajnc B, Heath D, Ogrinc N., “Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic”, Food and Chemical Toxicology 2020; 135: 110976.
  • [19] Muzolf‑Panek M, Kaczmarek A, Gliszczyńska‑Świgło A., “A predictive approach to the antioxidant capacity assessment of green and black tea infusions”, Journal of Food Measurement and Characterization 2021; 15:1422–1436.
  • [20] Rebollo-Hernanz M, Cañasa S, Taladrid D, Segovia A, Bartolomé B et al., “Extraction of phenolic compounds from cocoa shell: Modeling using response surface methodology and artificial neural networks”, Separation and Purification Technology 2021; 270: 118779.
  • [21] Kadiri O, Gbadamosi SO, Akanbi CT., “Extraction kinetics, modelling and optimization of phenolic antioxidants from sweet potato peel vis‑a‑vis RSM, ANN‑GA and application in functional noodles”, Journal of Food Measurement and Characterization 2019; 13:3267–3284.
  • [22] Pavlic B, Kaplan M, Bera O, Olgun EO, Canli O, et al., “Microwave-assisted extraction of peppermint polyphenols – Artificial neural networks approach”, Food and Bioproducts Processing 2019; 118: 258-269.
  • [23] Curko N, Kelšin K, Dragović-Uzelac V, Valinger D, Tomašević M, et al., “Microwave-assisted extraction of different groups of phenolic compounds from grape skin pomaces: modeling and optimization”, Polish Journal of Food and Nutrition Science. 2019; 69(3): 235–246.
  • [24] Espinosa-Sandoval LA, Cerqueira MA, Ochoa-Martínez CI, Ayala-Aponte AA., “Phenolic compound–loaded nanosystems: artificial neural network modeling to predict particle size, polydispersity index, and encapsulation efficiency”, Food and Bioprocess Technology 2019; 12(8): 1395-1408.
  • [25] Abcha I, Criado P, Salmieri S, Najjaa H, Isoda H, et al., “Edible Rhus tripartita fruit as source of health-promoting compounds: characterization of bioactive components and antioxidant properties”, European Food Research and Technology 2019; 245:2641–2654.
  • [26] Gupta R, Nair S., “Antioxidant flavonoids in common Indian diet.”, South Asian Journal of Preventive Cardiology 1999; 3: 83-94.
  • [27] Esclapez MD, Garcia-Perez JV, Mulet A, Carcel JA., “Ultrasound-assisted extraction of natural products”, Food Engineering Reviews 2011; 3: 108–120.
  • [28] Bukhari SB, Bhanger MI, Memon S., “Antioxidative Activity of Extracts from Fenugreek Seeds (Trigonella foenum-graecum)“, Pakistan Journal of Analytical & Environmental Chemistry 2008; 9(2): 78-83.
  • [29] Mashkor IM., “Phenolic content and antioxidant activity of fenugreek seeds extract”, International Journal of Pharmacognosy and Phytochemical Research 2014; 6(4): 841-844.
  • [30] Dastan S, Türker İ, İşleroğlu H.,” Çemen otu tohumundan fenolik bileşenlerin ekstraksiyonu için optimizasyon çalışması”, Gıda 2021; 46(4): 959-970.
  • [31] Casazza AA, Aliakbarian B, Perego P., “Recovery of phenolic compounds from grape seeds: effect of extraction time and solid–liquid ratio”, Natural Product Research 2011; 25(18): 751–1761
  • [32] Hismath I, Wan Aida WM, Ho CW., “Optimization of extraction conditions for phenolic compounds from neem (Azadirachta indica) leaves”, International Food Research Journal 2011; 18: 931–939.
  • [33] Chaalal M, Touati N, Louaileche H., “Extraction of phenolic compounds and in vitroantioxidant capacity of prickly pear seeds”, Acta Botanica Gallica: Botany Letters 2012: 159(4): 467–475
  • [34] Mitra P, Barman PC, Chang KS., “Coumarin extraction from cuscuta reflexa using supercritical fluid carbon dioxide and development of an artificial neural network model to predict the coumarin yield”, Food Bioprocess Technol 2011; 4: 737–744.
  • [35] Sun Q, Zhang M, Mujumdar AS, Yang P., “Combined LF-NMR and artificial intelligence for continuous real-time monitoring of carrot in microwave vacuum drying”, Food and Bioprocess Technology 2019; 12: 551–562.
  • [36] Wani SA, Bishnoi S, Kumar P., “Ultrasound and microwave assisted extraction of diosgenin from fenugreek seed and fenugreek-supplemented cookies”, Journal of Food Measurement and Characterization 2016; 10(3): 527-532.
  • [37] Singleton VL, Rossi JA., “Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents”, American Journal of Enology and Viticulture 1965; 16(3): 144-158.
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Selami Beyhan 0000-0002-9581-2794

Hilal İşleroğlu 0000-0002-4338-9242

Publication Date June 1, 2023
Submission Date November 22, 2022
Acceptance Date January 12, 2023
Published in Issue Year 2023 Volume: 11 Issue: 2

Cite

IEEE S. Beyhan and H. İşleroğlu, “EXTRACTION OF PHENOLIC COMPOUNDS FROM FENUGREEK SEEDS: MODELLING AND ANALYSIS USING ARTIFICIAL NEURAL NETWORKS”, KONJES, vol. 11, no. 2, pp. 312–323, 2023, doi: 10.36306/konjes.1208658.