مدل‌سازی ریاضی، منطق فازی و شبکه عصبی مصنوعی سینتیک استخراج اسانس از اندام هوایی بومادران (Achillea millefolium L.) با روش تقطیر مقاومتی

نویسندگان
پروانه کرمی 1
محسن زندی 2
علی گنجلو 3
1 کارشناس ارشد، گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران
2 استادیار، گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران
3 دانشیار، گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران.
10.52547/fsct.19.123.341
چکیده
هدف پژوهش حاضر، پیش‌بینی سینتیک استخراج اسانس طی تقطیر مقاومتی با سه مدل مختلف (روش‌های رگرسیون غیر خطی (ریاضی)، شبکه عصبی مصنوعی (ANN) و منطق فازی) برای مقایسه دقت این مدل‌ها بود. بر اساس نتایج به دست آمده شبکه عصبی مصنوعی بهترین روش در بین همه مدل‌های اجرا شده برای پیش‌بینی عملکرد استخراج بود. چهار مدل ریاضی (مدل‌های مرتبه اول، مرتبه دوم، جذب و سیگموئید) بر داده‌های تجربی عملکرد استخراج برازش گردید. نتایج نشان داد که مدل مرتبه اول می‌تواند عملکرد استخراج اسانس را با ضریب همبستگی (R2) برابر 988/0 و ریشه میانگین مربعات خطا (RMSE) برابر 00014/0 به‌طور رضایت‌بخشی توصیف کند. شبکه عصبی با یک و دو لایه پنهان و 4 تا 30 نورون به‌طور تصادفی انتخاب شد و قدرت شبکه برای پیش‌بینی عملکرد استخراج برآورد شد. شبکه عصبی با ساختار پس‌انتشار پیش‌خور، الگوریتم آموزش لونبرگ-مارکوآرت و پیکربندی 3-11-11-1 دارای حداکثر R2 (999/0) و حداقل RMSE (0004/0) هستند. ابزار منطق فازی در متلب با مدل ممدانی در قالب قوانین اگر-آنگاه همراه با تابع عضویت مثلثی برای پیش‌بینی عملکرد استخراج استفاده گردید. علی‌رغم این واقعیت که منطق فازی نرخ برازش کمتری (997/0= R2) نسبت به ANNرا تضمین می‌کند، این یک تکنیک قدرتمند برای برازش داده‌های تجربی عملکرد استخراج بود.
کلیدواژه‌ها
بومادران
مدل ریاضی
شبکه عصبی مصنوعی
منطق فازی

موضوعات

عنوان مقاله English

Mathematical, fuzzy logic and artificial neural network modeling of extraction kinetics of essential oil from aerial parts of yarrow (Achillea millefolium L.) using ohmic-assisted hydrodistillation

نویسندگان English

Parvaneh Karami 1
Mohsen Zandi 2
Ali Ganjloo 3
1 MSc degree, Department of Food Science and Engineering
2 Assistant Professor, Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
3 Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
چکیده English

The aim of present research was to predict the kinetics of essential oil extraction during ohmic-assisted hydrodistillation by three different modeling (nonlinear regression techniques (mathematical), artificial neural networks (ANN), and fuzzy logic) techniques to compare the accuracy of those models. Based on the results obtained the ANN was the best technique among all implemented models in predicting of extraction yield. Four mathematical models (first order, second order, adsorption and sigmoid models) describing essential oil extraction has been fitted to the extraction yield experimental data. Results indicated that first order model could satisfactorily describe the extraction kinetics of essential oil with correlation coefficient (R2) equal 0.988 and root mean square error (RMSE) equal 0.00014. Neural network with one and two hidden layers and 4–30 neurons were randomly selected and network power was estimated for predicting the extraction yield. ANNs with Feedforward–backpropagation structure, Levenberg–Marquardt training algorithm and 3-11-11-1 topology deserved the maximum R2 (0.999) and minimum RMSE (0.0004). Fuzzy logic tool in MATLAB with Mamdani model in the form of If–Then rules along with triangular membership function was used for predict the extraction yield. Despite the fact that fuzzy logic warranted lower fitting rates (R2 = 0.997) than that of ANN, it was a powerful technique for fitting of extraction yield experimental data.

کلیدواژه‌ها English

Achillea millefolium L
mathematical modeling
artificial neural network
Fuzzy Logic
1. Ahmadi DA, Gholami AM, Faghani M, Ansari CS, Saafizadeh Z. Comparison of Essential Oil of Achillea millefolium with Chemical Antioxidants and Preservatives. 2019.
2. Ijaz F, Nawaz H, Hanif MA, Ferreira PMP. Yarrow. Medicinal Plants of South Asia: Elsevier; 2020. p. 685-97.
3. Salvagnini LE, Migliato KF, Isaac VL, Correa MA, Salgado H, Pietro RC. Evaluation of efficacy of preservatives associated with Achillea millefolium L. extract against Bacillus subtilis. Brazilian Journal of Microbiology. 2006;37(1):75-7.
4. Azmir J, Zaidul I, Rahman M, Sharif K, Mohamed A, Sahena F, et al. Techniques for extraction of bioactive compounds from plant materials: A review. Journal of food engineering. 2013;117(4):426-36.
5. Hashemi SMB, Kamani MH, Amani H, Khaneghah AM. Voltage and NaCl concentration on extraction of essential oil from Vitex pseudonegundo using ohmic-hydrodistillation. Industrial Crops and Products. 2019;141:111734.
6. Gavahian M, Farahnaky A. Ohmic-assisted hydrodistillation technology: A review. Trends in Food Science & Technology. 2018;72:153-61.
7. Meziane IAA, Maizi N, Abatzoglou N, Benyoussef E-H. Modelling and optimization of energy consumption in essential oil extraction processes. Food and Bioproducts Processing. 2020;119:373-89.
8. Zandi M, Dardmeh N, Pirsa S, Almasi H. Migration of ohmic heating electrode components into a food. Iranian Journal Food Science and Technology Research. 2015;11(3):273-8.
9. Gavahian M, Farahnaky A, Javidnia K, Majzoobi M. Comparison of ohmic-assisted hydrodistillation with traditional hydrodistillation for the extraction of essential oils from Thymus vulgaris L. Innovative Food Science & Emerging Technologies. 2012;14:85-91.
10. Ramaswamy HS, Marcotte M, Sastry S, Abdelrahim K. Ohmic heating in food processing: CRC press; 2014.
11. Zandi M, Niakousari M. Design, manufacture and performance evaluation of a batch ohmic heating system. 2012.
12. Hashemi SMB, Roohi R. Ohmic heating of blended citrus juice: Numerical modeling of process and bacterial inactivation kinetics. Innovative Food Science & Emerging Technologies. 2019;52:313-24.
13. Kusuma H, Mahfud M. Comparison of kinetic models of oil extraction from sandalwood by microwave-assisted hydrodistillation. International Food Research Journal. 2017;24(4).
14. Radivojac A, Bera O, Micić D, Đurović S, Zeković Z, Blagojević S, et al. Conventional versus microwave-assisted hydrodistillation of sage herbal dust: Kinetics modeling and physico-chemical properties of essential oil. Food and Bioproducts Processing. 2020;123:90-101.
15. Milojević SŽ, Radosavljević DB, Pavićević VP, Pejanović S, Veljković VB. Modeling the kinetics of essential oil hydrodistillation from plant materials. Hemijska industrija. 2013;67(5):843-59.
16. Okeleye AA, Betiku E. Kariya (Hildegardia barteri) seed oil extraction: comparative evaluation of solvents, modeling, and optimization techniques. Chemical Engineering Communications. 2019;206(9):1181-98.
17. Bahmani L, Aboonajmi M, Arabhosseini A, Mirsaeedghazi H. ANN modeling of extraction kinetics of essential oil from tarragon using ultrasound pre-treatment. Engineering in agriculture, environment and food. 2018;11(1):25-9.
18. Pavlić B, Kaplan M, Bera O, Olgun EO, Canli O, Milosavljević N, et al. Microwave-assisted extraction of peppermint polyphenols–Artificial neural networks approach. Food and Bioproducts Processing. 2019;118:258-69.
19. Akbar A, Kuanar A, Patnaik J, Mishra A, Nayak S. Application of artificial neural network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.). Computers and Electronics in Agriculture. 2018;148:160-78.
20. Shojaeimehr T, Rahimpour F, Khadivi MA, Sadeghi M. A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA). Journal of Industrial and Engineering Chemistry. 2014;20(3):870-80.
21. Xi J, Xue Y, Xu Y, Shen Y. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols. Food chemistry. 2013;141(1):320-6.
22. Khajeh M, Moghaddam MG, Shakeri M. Application of artificial neural network in predicting the extraction yield of essential oils of Diplotaenia cachrydifolia by supercritical fluid extraction. The Journal of Supercritical Fluids. 2012;69:91-6.
23. Kuvendziev S, Lisichkov K, Zeković Z, Marinkovski M. Artificial neural network modelling of supercritical fluid CO2 extraction of polyunsaturated fatty acids from common carp (Cyprinus carpio L.) viscera. The Journal of Supercritical Fluids. 2014;92:242-8.
24. Mjalli FS, Al-Asheh S, Alfadala H. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance. Journal of Environmental Management. 2007;83(3):329-38.
25. Zadeh LA. Fuzzy sets. Information and control. 1965;8(3):338-53.
26. Ligus M, Peternek P. Determination of most suitable low-emission energy technologies development in Poland using integrated fuzzy AHP-TOPSIS method. Energy Procedia. 2018;153:101-6.
27. Sakthivel G, Saravanakumar D, Muthuramalingam T. Application of failure mode and effect analysis in manufacturing industry-an integrated approach with FAHP-fuzzy TOPSIS and FAHP-fuzzy VIKOR. International Journal of Productivity and Quality Management. 2018;24(3):398-423.
28. Birle S, Hussein M, Becker T. Fuzzy logic control and soft sensing applications in food and beverage processes. Food Control. 2013;29(1):254-69.
29. Fashi M, Naderloo L, Javadikia H. The relationship between the appearance of pomegranate fruit and color and size of arils based on image processing. Postharvest Biology and Technology. 2019;154:52-7.
30. Jafari SM, Ganje M, Dehnad D, Ghanbari V. Mathematical, fuzzy logic and artificial neural network modeling techniques to predict drying kinetics of onion. Journal of Food Processing and Preservation. 2016;40(2):329-39.
31. Gharibi H, Mahvi AH, Nabizadeh R, Arabalibeik H, Yunesian M, Sowlat MH. A novel approach in water quality assessment based on fuzzy logic. Journal of Environmental Management. 2012;112:87-95.
32. Abbaspour‐Gilandeh Y, Jahanbakhshi A, Kaveh M. Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS. Food Science & Nutrition. 2020;8(1):594-611.
33. Rad SJ, Kaveh M, Sharabiani VR, Taghinezhad E. Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer. 2018;54(11):3361-74.
34. Iraji MS, Tosinia A. Classification tomatoes on machine vision with fuzzy the mamdani inference, adaptive neuro fuzzy inference system based (anfis-sugeno). Australian Journal of Basic and Applied Sciences. 2011;5(11):846-53.
35. Goel N, Sehgal P. Fuzzy classification of pre-harvest tomatoes for ripeness estimation–An approach based on automatic rule learning using decision tree. Applied Soft Computing. 2015;36:45-56.
36. Papageorgiou EI, Aggelopoulou K, Gemtos TA, Nanos G. Development and evaluation of a fuzzy inference system and a neuro-fuzzy inference system for grading apple quality. Applied Artificial Intelligence. 2018;32(3):253-80.
37. Zandi M, Ganjlo A, Bimakr M, Nikoomanesh N, Moradi N. Application of fuzzy logic and neural-fuzzy inference system (ANFIS) for prediction of physicochemical changes and quality classification of coated sweet lemon during storage. Iranian Food Science and Technology Research Journal. 2021;17(2):339-51.
38. Zandi M, Ganjloo A, Bimakr M. Applying Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network to the Prediction of Quality changes of Hawthorn Fruit (Crataegus pinnatifida) during Various Storage Conditions. Journal of Agricultural Machinery. 2021;11(2):343-57.
39. Tunç MT, Koca İ. Ohmic heating assisted hydrodistillation of clove essential oil. Industrial Crops and Products. 2019;141:111763.
40. Franco-Vega A, Ramírez-Corona N, López-Malo A, Palou E. Studying microwave assisted extraction of Laurus nobilis essential oil: Static and dynamic modeling. Journal of Food Engineering. 2019;247:1-8.
41. Marković MS, Milojević SŽ, Bošković-Vragolović NM, Pavićević VP, Babincev LМ, Veljković VB. A new kinetic model for the common juniper essenstial oil extraction by microwave hydrodistillation. Chinese Journal of Chemical Engineering. 2019;27(3):605-12.
42. Lagergren SK. About the theory of so-called adsorption of soluble substances. Sven Vetenskapsakad Handingarl. 1898;24:1-39.
43. Babu GDK, Singh B. Simulation of Eucalyptus cinerea oil distillation: A study on optimization of 1, 8-cineole production. Biochemical Engineering Journal. 2009;44(2-3):226-31.
44. Ho Y-S, Harouna-Oumarou HA, Fauduet H, Porte C. Kinetics and model building of leaching of water-soluble compounds of Tilia sapwood. Separation and Purification Technology. 2005;45(3):169-73.
45. Stanković MZ, Nikolić NČ, Stanojević L, Cakić MD. The effect of hydrodistillation technique on the yield and composition of essential oil from the seed of Petroselinum crispum (Mill.) Nym. ex. AW Hill. Hemijska industrija. 2004;58(9):409-12.
46. Mandal BB, Mann JK, Kundu S. Silk fibroin/gelatin multilayered films as a model system for controlled drug release. European Journal of Pharmaceutical Sciences. 2009;37(2):160-71.
47. Arrieta MP, Castro-Lopez MdM, Rayón E, Barral-Losada LF, López-Vilariño JM, López J, et al. Plasticized poly (lactic acid)–poly (hydroxybutyrate)(PLA–PHB) blends incorporated with catechin intended for active food-packaging applications. Journal of agricultural and food chemistry. 2014;62(41):10170-80.
48. Chen X, Lee DS, Zhu X, Yam KL. Release kinetics of tocopherol and quercetin from binary antioxidant controlled-release packaging films. Journal of Agricultural and Food Chemistry. 2012;60(13):3492-7.
49. Crank J. The mathematics of diffusion: Oxford university press; 1979.
50. Hashemi A, Asefpour Vakilian K, Khazaei J, Massah J. An artificial neural network modeling for force control system of a robotic pruning machine. Journal of Information and Organizational Sciences. 2014;38(1):35-41.
51. Nadian MH, Abbaspour-Fard MH, Martynenko A, Golzarian MR. An intelligent integrated control of hybrid hot air-infrared dryer based on fuzzy logic and computer vision system. Computers and Electronics in Agriculture. 2017;137:138-49.
52. Banakar A, Zareiforoush H, Baigvand M, Montazeri M, Khodaei J, Behroozi‐Khazaei N. Combined application of decision tree and fuzzy logic techniques for intelligent grading of dried figs. Journal of food process engineering. 2017;40(3):e12456.
مجله علوم و صنایع غذایی ایران
دوره 19، شماره 123 - شماره پیاپی 123
اردیبهشت 1401
صفحه 341-354