نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه مهندسی مواد و طراحی صنایع غذایی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.
2 بخش تحقیقات فنی و مهندسی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، گرگان، ایران.
چکیده
در این تحقیق به‌منظور مدل‌سازی شبکه‌های عصبی مصنوعی فرایند استخراج روغن از دانه‌های سیاه‌دانه به کمک پیش‌تیمار مایکروویو از زمان‌های مختلف فرایند (90، 180 و 270 ثانیه) و توان‌های مختلف (180، 540 و 900 وات) استفاده گردید. بعد از اعمال این پیش‌تیمارها، روغن دانه‌ها با پرس مارپیچی و با سرعت‌های متفاوت (11، 34 و 57 دور در دقیقه) استخراج شد و میزان راندمان استخراج، اندیس اسیدی و اسیدیته، رنگ و پایداری اکسیداتیو نمونه‌ها مورد بررسی قرار گرفت. جهت پیش‌بینی روند تغییرات از ابزارشبکه‌های عصبی مصنوعی در نرم‌افزار Matlab R2013a استفاده شد. با بررسی توپولوژی‌های مختلف شبکه عصبی، شبکه پس‌انتشار پیشخور با توپولوژی‌های 3-10-5 با ضریب همبستگی بیشتر از 995/0 و میانگین مربعات خطای کمتر از 0005/0 و با بکارگیری تابع فعال‌سازی تانژانت سیگموئید هیپربولیکی، الگوی یادگیری لونبرگ– مارکوات و چرخه یادگیری 1000 به‌عنوان بهترین مدل عصبی مشخص گردید. نتایج حاصل از مدل‌های بهینه انتخاب شده نیز ارزیابی گردید و این مدل‌ها با ضرایب همبستگی بالا )بیش از 949/0( قادر به پیش‌بینی روند تغییرات بودند.
کلیدواژه‌ها
عنوان مقاله [English]
Modeling of the some selected chemical properties of black Cumin seeds’ oil influenced by microwave pretreatment and screw rotational speed
نویسندگان [English]
- Hamid Bakhshabadi 1
- Habibollah Mirzaee 1
- Alireza Ghodsvali 2
- Seyed Mahdi Jafari 1
- Aman Mohammad Ziaiifar 1
1 Department of Food Materials and Processing Design Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Iran.
2 Agricultural Engineering Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran.
چکیده [English]
Introduction: Black Cumin seed (Nigella sativa L.) as one of the novel edible oil resources used commonly nowadays as seasoning in food product industries due to considerable medicinal properties and high nutritional impacts. Oil extraction by pressing method as an approach compared to other methods including solvent extraction is faster, safer and cheaper. In the oil extraction process, the preparation of the seeds is a substantial stage for obtaining oil with high quality and efficiency. Microwaves are electromagnetic waves that have a frequency ranged from 300 MHz to 300 GHz with corresponding wave lengths ranged from 1 mm to 1 m. On the other hand the artificial neural network as a powerful predictive tool in a wide scale of process parameters has been studied on an industrial scale in this research in order to achieve a simple, rapid, precise as well as effective model in the oil extraction of Nigella sativa L seed.
Materials and Methods: In the present study Black Cumin seeds after preparation including cleaning and passing resistance of the samples in front of air and moisture were stored in a plastic bag until the day of experiments. Then, they have been pre-treated with microwave within different processing times (90, 180 and 270 S) and powers (180, 540, and 900 W). Afterwards, seeds’ oil was extracted by screw rotational speed levels approach (11, 34 and 57 rpm). Different selected parameters including extraction efficiency, oil acidity value, color and oxidative stability were determined. To predict the alterations trend, the artificial neural network (ANN) design in MATLAB R2013a software was used.
Results and Discussion: According to MSE and R2 values obtained in this study, feed forward neural network with transfer function sigmoid hyperbolic tangent and Levenberg- Marquardt learning algorithm with topology of 3-10-5 (input layer with 3 neurons– a hidden layer with 10 neurons – output layer with 5 neurons) were selected as the optimal neural network with R2 more than 0.995 and MSE equal to 0.0005. Also, the results of the optimized and selected models were evaluated and these models with high correlation coefficients (over 0.949), were able to predict the changes' trend. According to the complexity and multiplicity of the effective factors in food industry processes and the results of this research, the neural network can be introduced as an acceptable model for modeling these processes. By determining the activation function in neural networks which was a function of sigmoid hyperbolic tangent in this study and also, with having the amounts of weight and bias, the connections created by the neuro-fuzzy model can be extracted. By defining this simple created mathematical equation, in computer software such as Excel, we can have a useful, simple and accurate program for predicting the desired parameters in the process of oil extraction by using microwave pre-treatment. Due to high accuracy of neural model we can trust the prediction of these models with high confidence, and this model can be used to optimize and control the process, which can lead to saving in energy and time, and on the other hand, can create a better final product.
کلیدواژه‌ها [English]
- oil extraction
- Black
- Cumin seed
- Screw rotational speed
- Microwave
- Modeling
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