مدل‌سازی اثر فراصوت بر ویسکوزیته، ضریب قوام و شاخص رفتار جریان غلظت‌های مختلف صمغ گزانتان

نویسندگان
فخرالدین صالحی 1
معین اینانلودوقوز 2
1 دانشیار، گروه علوم و صنایع غذایی، دانشگاه بوعلی سینا، همدان، ایران
2 دانشجوی کارشناسی ارشد، گروه علوم و صنایع غذایی، دانشگاه بوعلی سینا، همدان، ایران
10.22034/FSCT.21.146.158
چکیده
استفاده از امواج فراصوت برای تغییر ساختار صمغ‌ها، منجر به اصلاح و بهبود ویژگی‌های عملکردی و خواص رئولوژیکی آنها می‌شود. در این پژوهش اثرات شدت فراصوت و زمان تیماردهی بر ویسکوزیته ظاهری، ضریب قوام و شاخص رفتار جریان غلظت‌های مختلف صمغ گزانتان بررسی و مدل‌سازی شد. برای مدل‌سازی فرآیند نیز از روش الگوریتم ژنتیک- شبکه عصبی مصنوعی با سه ورودی (توان فراصوت، زمان تیماردهی و غلظت صمغ) و سه خروجی (ویسکوزیته، ضریب قوام و شاخص رفتار جریان) استفاده گردید. ویسکوزیته ظاهری نمونه شاهد صمغ گزانتان (تیمار نشده) با غلظت‌های 1/0، 15/0 و 2/0 درصد به ترتیب برابر 0/21، 9/39 و 5/66 میلی‌پاسکال ثانیه بود. نتایج این پژوهش نشان داد که با افزایش شدت و زمان اعمال فراصوت، ویسکوزیته صمغ کاهش می‌یابد. تیماردهی با امواج فراصوت به مدت 20 دقیقه باعث کاهش معنی‌دار ویسکوزیته ظاهری صمغ گزانتان از 9/39 به 2/23 میلی‌پاسکال ثانیه گردید (05/0>p). نتایج مدل‌سازی به روش الگوریتم ژنتیک- شبکه عصبی مصنوعی نشان داد شبکه‌ای با ساختار 3-5-3 در یک لایه پنهان و با استفاده از تابع فعال‌سازی تانژانت هیپربولیک می‌تواند پارامترهای رئولوژیکی صمغ گزانتان را با ضریب همبستگی بالا و مقدار خطا پایین پیش‌بینی نماید. مقادیر میانگین مربعات خطا (MSE)، میانگین مربعات خطا نرمالیزه شده (NMSE)، میانگین خطا مطلق (MAE) و ضریب همبستگی (r) برای پیش‌بینی ویسکوزیته ظاهری صمغ گزانتان به ترتیب برابر 17/73، 20/0، 48/6 و 90/0 بود. بر اساس نتایج آزمون آنالیز حساسیت، شدت ‌تیماردهی با فراصوت به‌عنوان مؤثرترین عوامل در تغییر ویسکوزیته ظاهری، ضریب قوام و شاخص رفتار جریان صمغ گزانتان بود.
کلیدواژه‌ها
آنالیز حساسیت
الگوریتم ژنتیک- شبکه عصبی مصنوعی
تانژانت هیپربولیک
ویسکوزیته

موضوعات

عنوان مقاله English

Modeling the effect of ultrasound on viscosity, consistency coefficient, and flow behavior index of different concentrations of xanthan gum

نویسندگان English

Fakhreddin Salehi 1
Moein Inanloodoghouz 2
1 Associate Professor, Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran
2 MSc Student, Department of Food Science and Technology, Bu-Ali Sina University, Hamedan, Iran
چکیده English

The use of ultrasonic waves to change the structure of the gums leads to the modification and improvement of their functional characteristics and rheological properties. In this research, the effects of ultrasonic intensity and treatment time on apparent viscosity, consistency coefficient, and flow behavior index of different concentrations of xanthan gum were investigated and modeled. Genetic algorithm-artificial neural network method with three inputs (ultrasonic power, treatment time and gum concentration) and three outputs (viscosity, consistency coefficient, and flow behavior index) was used to model the process. The apparent viscosities of the xanthan gum control sample (untreated) at concentrations of 0.1, 0.15, and 0.2% were 21.0, 39.9, and 66.5 mPa.s, respectively. The results of this research showed that gum viscosity decreased with increasing intensity and duration of ultrasound application. Ultrasonic treatment for 20 min significantly reduced the apparent viscosity of xanthan gum from 39.9 to 23.2 mPa.s (p< 0.05). The genetic algorithm-artificial neural network modeling results showed that the network with 3-5-3 structure in a hidden layer and using the hyperbolic tangent activation function can predict the rheological parameters of xanthan gum with high correlation coefficient and low error value. Values of mean squared error (MSE), normalized mean squared error (NMSE), mean absolute error (MAE), and correlation coefficient (r) to predict the apparent viscosity of xanthan gum were 73.17, 0.20, 6.48, and 0.90, respectively. Based on the results of the sensitivity analysis test, ultrasonic treatment intensity was the most effective factor in changing the apparent viscosity, consistency coefficient, and flow behavior index of xanthan gum.

کلیدواژه‌ها English

Apparent viscosity
Genetic algorithm-artificial neural network
Hyperbolic tangent
Sensitivity Analysis
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