مدل‌سازی سینتیکی و رویکرد شبکه عصبی مصنوعی فرایند استخراج به روش ‌حرارت‌دهی مقاومتی عصاره غازیاغی

نویسندگان
زینب حسنلوفرد 1
مهدی قره خانی 2
محسن زندی 3
لیلا روفه گری نژاد 2
علی گنجلو 4
1 دانشجوی دکتری علوم ومهندسی صنایع غذایی، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
2 استادیارگروه علوم و مهندسی صنایع غذایی، واحد تبریز، دانشگاه آزاد اسلامی، تبریز، ایران
3 گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران
4 دانشیار، گروه علوم و مهندسی صنایع غذایی، دانشگاه آزاد واحد تبریز
10.22034/FSCT.19.131.173
چکیده
تجزیه‌وتحلیل مدل‌سازی استخراج از ترکیبات طبیعی در کاربرد صنعتی ضروری است. در مقاله حاضر، استخراج عصاره از گیاه غازیاغیFalcaria vulgaris) ) به روش حرارت‌دهی مقاومتی مورد بررسی قرار گرفت. این مطالعه به منظور بیان تأثیر برخی از متغیرهای مشخص (نظیر گرادیان ولتاژ، نسبت اتانول به آب، زمان و دمای استخراج) بر بازده استخراج و محتوای فنلی کل (TPC) انجام شد. مدل‌های سینتیک (مدل‌های مرتبه اول، مرتبه دوم و پلگ) و شبکه عصبی مصنوعی برای مدل‌سازی فرآیند استخراج به به روش حرارت‌دهی مقاومتی استفاده شد. مطالعه سینتیکی نقش بسیار مهمی در ارزیابی فرآیند استخراج بازی می‌کند، زیرا امکان تخمین مقرون‌به‌صرفه بودن فرآیند از نظر صرفه‌جویی در زمان، هزینه و انرژی را فراهم می‌نماید. نتایج نشان داد که مدل‌های سینتیکی مرتبه دوم و پلگ توانستند به‌ترتیب مقادیر محتوای فنل کل عصاره و راندمان استخراج را با موفقیت پیش‌بینی نمایند. ضریب همبستگی بین بازده استخراج تجربی به‌دست‌آمده و محتوای فنلی کل و مقادیر پیش‌بینی‌شده توسط شبکه عصبی مصنوعی (2-16-4) برای آموزش برابر 995/0، برای اعتبارسنجی برابر 963/0 و برای آزمایش برابر 979/0 بود، که نشان‌دهنده توانایی پیش‌بینی خوب مدل است. مدل شبکه عصبی مصنوعی کارایی پیش‌بینی بالاتری نسبت به مدل‌های جنبشی داشت. شبکه عصبی مصنوعی می‌تواند فرآیند را با به‌طور مطمئن‌تری نسبت به مدل‌های سینتیکی با قابلیت‌های پیش‌بینی و تخمین بهتری مدل کند.
کلیدواژه‌ها
غازیاغی
استخراج
حرارت‌دهی مقاومتی
شبکه عصبی مصنوعی
مدل سینتیکی

موضوعات

عنوان مقاله English

Kinetic modeling and artificial neural network approach for the modelling of Ohmic-assisted extraction of Falcaria vulgaris extract

نویسندگان English

Zeinab Hassanloofard 1
Mehdi Gharekhani 2
Mohsen Zandi 3
Leila Roufegarinejad 2
Ali Ganjloo 4
1 Ph.D. student, Department Food Science and Technology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
2 Assistant professor, Department Food Science and Technology, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
3 Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan 4537-38791 Iran
4 دانشیارگروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران
چکیده English

Analysis of the extraction modelling for natural compounds is essential for industrial application. In the present paper, ohmic-assisted extraction was investigated for the extraction of Falcaria vulgaris extract. This study was performed in order to express the effect of some pecific parameters (as: voltage gradiant, ethanol to water ratio, extraction time and temperature) on the extraction yield and total phenolic content (TPC). Kinetics models (first-order, second's-order and pelleg models) and artificial neural network were used for modeling ohmic-assisted extraction process. Kinetic study plays a very important role in evaluating the extraction process because it allows estimation of the cost-effectiveness of the process in saving time, money and energy. The results showed that the second-order and plleg's kinetic models respectively, were successfully predicted the value of the total phenol content of the extract and extraction yield in all experiments. The correlation coefficient between experimentally obtained extraction yield and total phenolic content and values predicted by artificial neural network (4-16-2) were 0.995 for training, 0.963 for validation, and 0.979 for testing, indicating the good predictive ability of the model. The artificial neural network model had a higher prediction efficiency than the kinetic models. Artificial neural network can reliably model the process with better predictive and estimation capabilities than the kinetic’s models.

کلیدواژه‌ها English

Falcaria vulgaris
Extraction
Ohmic heating
artificial neural network
kinetic’s model
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