مدل‌سازی اثر پیش‌تیمار مایکروویو براستخراج روغن از دانه‌های گوجه‌فرنگی به روش شبکه عصبی مصنوعی

نویسندگان
زهرا ممیوند
آریو امامی فر
فخرالدین صالحی
مصطفی کرمی
گروه علوم و صنایع غذایی، دانشکده صنایع غذایی، دانشگاه بوعلی سینا، همدان، ایران
10.22034/FSCT.21.152.181
چکیده
افزایش تقاضای مصرف‌کنندگان برای استفاده از مواد غذایی طبیعی و بدون افزودنی و همچنین افزایش ضایعات صنایع غذایی، محرک استفاده از محصولات جانبی کارخانه­های مواد غذایی در دیگر صنایع غذایی است. تفاله گوجه‌فرنگی ازجمله ضایعات کارخانه­های مواد غذایی است که در کارخانه­های تولید رب و سس از گوجه­فرنگی تولید می‌شود. هدف از این پژوهش بررسی اثر پیش­تیمار مایکروویو و روش استخراج بر ویژگی­های فیزیکی و شیمیایی روغن دانه ­گوجه­فرنگی بود. پیش­تیمار دانه­ها با امواج مایکرویوو (0، 200 و 500 وات) طی زمان­های مختلف (0، 1، 3 و 5 دقیقه) انجام و روغن دانه­ها با روش سوکسله و پرس استخراج گردید. برخی ویژگی­های فیزیکی و شیمیایی روغن استحصالی شامل بازده استخراج، ویسکوزیته، عدد اسیدی، عدد پراکسید، و مؤلفه‌های رنگی شامل روشنایی، قرمزی و زردی ارزیابی گردید. تجزیه و تحلیل داده‌ها بر اساس آزمایش فاکتوریل در قالب طرح آماری کاملاً تصادفی در سه تکرار انجام شد. داده‌های آزمایشگاهی این پژوهش توسط روش الگوریتم ژنتیک- شبکه عصبی مصنوعی با 3 ورودی (روش استخراج، توان مایکروویو و زمان تیماردهی) و 7 خروجی (درصد استخراج، عدد اسیدی، عدد پراکسید، ویسکوزیته، روشنایی، قرمزی و زردی) مدل‌سازی شد. نتایج مدل‌سازی به روش الگوریتم ژنتیک- شبکه عصبی مصنوعی نشان داد شبکه‌ای با ساختار 7-8-3 در یک لایه پنهان و با استفاده از تابع فعال‌سازی تانژانت هیپربولیک می‌تواند درصد استخراج، عدد اسیدی، عدد پراکسید، ویسکوزیته، روشنایی، قرمزی و زردی روغن تهیه‌شده از دانه‌های گوجه‌فرنگی را با ضریب همبستگی بالا و مقدار خطا پایین پیش‌بینی نماید. بر اساس نتایج آزمون آنالیز حساسیت، روش استخراج در مقایسه توان و زمان پیش‌تیمار دانه­ها با مایکرویوو، به‌عنوان عامل اصلی تعیین گردید
کلیدواژه‌ها
آنالیز حساسیت
شبکه عصبی مصنوعی
روغن دانه گوجه‌فرنگی
مایکروویو

موضوعات

عنوان مقاله English

Modeling of microwave pretreatment effect on the oil extraction from tomato seeds by artificial neural network method

نویسندگان English

Zahra Mamivand
Aryou Emamifar
Fakhreddin Salehi
Mostafa Karami
Department of Food Science and Technology, Faculty of Food Industry, Bu-Ali Sina University, Hamedan, 65178-38695, Iran
چکیده English

Increasing consumers demand for natural and additive-free foods and high volumes of food industry wastes, are stimulating the use of these resources in other food industries. Tomato pomace is one of the food factory wastes is the resulting by-product of tomato paste and sauce factories. The aim of this study was to evaluate the effect extraction method and microwave pretreatment of tomato seeds on the physicochemical characteristics of their extracted oil. The seeds were treated with microwaves using various power levels (0, 200 and 500 W) and different process times (0, 1, 3 and 5 min) and their oil was extracted by Soxhlet and press methods. Fatty acids composition of oils was determined by gas chromatography. Some physicochemical characteristics of extracted seed oil including oil yield, viscosity, acid value, peroxide value, and color index (L, b, a values) were evaluated. Data was analyzed with factorial treatment structure in a Completely Randomized Design in three replications. The experimental data was modeled by artificial neural network with 3 inputs (extraction method, microwave power and pretreatment time) and 7 outputs (oil yield, acid value, peroxide value, viscosity, L value, b value and a value). The results of artificial neural network modeling showed that the network with a 3-8-7 structure and using the Hyperbolic tangent activation function can predict the oil yield, acid value, peroxide value, viscosity, L value, b value and a value of tomato seed oil with high correlation coefficient and low error. Based on the results of the sensitivity analysis, the extraction method compared to the power and time of microwave assisted pretreatment of seeds was determined as the main factor.

کلیدواژه‌ها English

artificial neural network
Sensitivity Analysis
Tomato seed oil
Microwave
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