استخراج اسانس بیدمشک (Salix aegyptiaca L.) با روش تقطیر مقاومتی و مدل‌سازی سینتیک استخراج با شبکه عصبی مصنوعی

نویسندگان
محسن زندی
علی گنجلو
ماندانا بی مکر
دانشگاه زنجان، دانشکده کشاورزی، گروه علوم و مهندسی صنایع غذائی
10.52547/fsct.18.120.14
چکیده
در این پژوهش، تاثیر متغیرهای مختلف فرایند تقطیر مقاومتی شامل گرادیان ولتاژ (5، 15 و 25 ولت بر سانتی‌متر)، زمان استخراج (30، 75 و 120 دقیقه) و غلظت نمک کلرید سدیم (5/0، 1 و 5/1 درصد) بر عملکرد استخراج، مصرف انرژی و محتوای فنول کل اسانس بیدمشک مورد بررسی قرار گرفت و با روش تقطیر آبی مقایسه شد. در نهایت از مدل‌سازی شبکه عصبی مصنوعی برای پیش‌بینی سینتیک استخراج اسانس استفاده شد. نتایج نشان داد که بازده استخراج، مصرف انرژی و محتوای فنول کل به‌طور معنی‌داری تحت تاثیر متغیرهای زمان استخراج و گرادیان ولتاژ می‌باشد (05/0>p). بازده اسانس به‌دست آمده توسط روش‌های تقطیر مقاومتی و تقطیر آبی به‌ترتیب برابر 012/0±119/0 و 01/0±081/0 بود. بین وزن مخصوص، ضریب شکست و محتوای فنول کل اسانس حاصل از روش‌های تقطیر مقاومتی و تقطیر آبی تفاوت معنی‌داری وجود نداشت (05/0˃p)، با این‌حال IC50 اسانس استخراج شده توسط روش تقطیر مقاومتی به‌طور معنی‌داری (05/0>p) بالاتر از اسانس بدست آمده با روش تقطیر آبی بود. در طراحی مدل شبکه عصبی مصنوعی گرادیان ولتاژ، زمان استخراج و غلظت نمک به عنوان ورودی در نظر گرفته شد و بازده استخراج اسانس به‌عنوان خروجی مدل پیش‌بینی گردید. نتایج نشان داد که بهترین عملکرد پیش‌بینی مربوط به پیکربندی 3-9-8-1 بود (036/0RMSE= و 99/0R2=). بنابراین، می‌توان نتیجه گرفت که روش تقطیر مقاومتی برای استخراج اسانس قابل استفاده است و مدل شبکه عصبی مصنوعی یک ابزار کمی کارآمد در پیش‌بینی سینتیک استخراج اسانس است.
کلیدواژه‌ها
بیدمشک
تقطیر مقاومتی
شبکه عصبی مصنوعی
بازده استخراج

موضوعات

عنوان مقاله English

Ohmic assisted hydrodistillation extraction of musk willow (Salix aegyptiaca L.) essential oil and artificial neural network modeling of extraction kinetic

نویسندگان English

Mohsen Zandi
Ali Ganjloo
Mandana Bimakr
Department Food Science and Engineering, Faculty of Agriculture, University of Zanjan
چکیده English

In the current research, the effects of different ohmic assisted hydrodistillation (OAHD) parameters including voltages gradient (5, 15 and 25 v/cm), extraction time (30, 75 and 120 min) and NaCl concentrations (0.5, 1 and 1.5%) on the extraction yield, energy consumption and total phenol content (TPC) of Salix aegyptiaca L.essential oil were investigated, and then compared with conventional hydrodistillation (HD). Finally, artificial neural network (ANN) modeling is utilized to predict kinetics of essential oil extraction. Result revealed that extraction time and voltage gradient had significant effect on extraction yield, energy consumption and TPC (p<0.05). Extraction yields of essential oil obtained by OAHD and HD were 0.119 ± 0.012 and 0.081 ± 0.01, respectively. There was no significant difference (p>0.05) between specific gravity, refractive index and TPC of essential oil of OAHD and HD methods, however IC50 of essential oil extracted by OAHD was significantly higher than essential oil obtained with HD method (p<0.05). To design the ANN model, voltages gradient, extraction time and salt concentrations and their interactions were considered as input vectors while the extraction yield of essential oil was considered as the model output. The results showed that the best prediction performance belonged to 3-9-8-1 ANN architecture (RMSE=0.036 and R2=0.99). Therefore, it can be concluded that the OAHD method is applicable for S. aegyptiaca L. essential oil extraction and ANN model is an efficient quantitative tool to predict the kinetics of essential oil extraction.

کلیدواژه‌ها English

Salix aegyptiaca L
Ohmic assisted hydrodistillation
artificial neural network
Extraction yield
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