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مقایسه شبکه عصبی مصنوعی و روش شناسی سطح پاسخ در پیشبینی تغییرات برخی از ویژگیهای میوه کندس خشک شده به روش اسمز با پیش تیمار مایکروویو | ||
| تحقیقات مهندسی صنایع غذایی | ||
| دوره 25، شماره 2، شهریور 1405، صفحه 1-18 اصل مقاله (927.08 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/fooder.2026.371675.1441 | ||
| نویسندگان | ||
| علیرضا قدس ولی* 1؛ محمد گنجه2؛ معصومه مقیمی3؛ حمید بخش آبادی2 | ||
| 1بخش تحقیقات فنی ومهندسی کشاورزی- مرکز گلستان | ||
| 2گروه کشاورزی، مرکز آموزش عالی میناب، دانشگاه هرمزگان، بندرعباس، ایران. | ||
| 3گروه شیمی، واحد گنبد کاووس، دانشگاه آزاد اسلامی، گنبد کاووس، ایران | ||
| چکیده | ||
| امروزه استفاده از روشهای غیر حرارتی، مانند خشک کردن اسمزی و آبگیری با استفاده از فراصوت و مایکروویو برای تولید محصولات غذایی افزایش یافته است، در این راستا این مطالعه با هدف پیشبینی روند تغییرات برخی از ویژگیهای میوة کُندس خشک شده به روش اسمز با پیشتیمار مایکروویو تحت تاثیر زمانهای مختلف مایکروویو (0 تا 4 دقیقه)، دماهای (40 تا 60 درجه سلسیوس) و زمان فرآیند اسمز (60 تا 240 دقیقه) اجرا شد. برای پیشبینی روند تغییرات این ویژگیها از روششناسی سطح پاسخ با یک طرح مرکب مرکزی و شبکه عصب مصنوعی استفاده شد. بهمنظور کمینه کردن میزان جذب مواد جامد و بیشینه کردن درصد کاهش آب و نسبت دفع آب به جذب مواد جامد مشخص شد که باید زمان مایکروویو، دما و زمان فرآیند اسمز بهترتیب 1.69 دقیقه، 60 درجه سانتیگراد و 240 دقیقه باشد. تحت این شرایط میزان جذب مواد جامد (15.17 درصد)، کاهش وزن آب (24.21 درصد)، نسبت دفع آب به جذب مواد جامد (1.63) و کاهش وزن (10.00 درصد) است. با بررسی شبکههای مختلف، شبکه پسانتشار پیشخور با توپولوژیهای 4-5-3 با ضریب همبستگی بیشتر از 0.996 و میانگین مربعات خطای کمتر از 001/0 و با بکارگیری تابع فعالسازی تانژانت هیپربولیکی، الگوی یادگیری جهنده و چرخة یادگیری 1000 به عنوان بهترین مدل عصبی مشخص گردید. از طرفی با مقایسة ضریبهای همبستگی مدلهای حاصل از روش سطح پاسخ و شبکة عصب مصنوعی مشخص شد که روش شبکة عصب مصنوعی کارایی بیشتری برای پیشبینی روند تغییرات ویژگیهای میوة کُندس خشک شده با فرآیند اسمز با پیش تیمار مایکروویو دارد. | ||
| کلیدواژهها | ||
| اسمز؛ خشککردن؛ شبکه عصب مصنوعی؛ کُندس؛ مایکروویو | ||
| عنوان مقاله [English] | ||
| Comparison of Artificial Neural Network and Response Surface Methodology in Predicting Changes in Some Characteristics of Osmotic Dried Candied Fruit with Microwave Pretreatment | ||
| نویسندگان [English] | ||
| Mohammad Ganje2؛ Masumeh Moghimi3؛ Hamid Bakhshabadi2؛ | ||
| 2Department of Agriculture, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran | ||
| 3Department of Chemistry,Gonbad Kavoos Branch, Islamic Azad University, Gonbad Kavoos,Iran. | ||
| چکیده [English] | ||
| Today, the use of non-thermal methods such as osmotic drying, dehydration using ultrasound, and microwave are increasing for the production of food products. This study aimed to predict the trend of changes in some characteristics of osmotic dried medlar fruit with microwave pretreatment under the influence of different microwave times (0 to 4 min), temperatures (40 to 60°C), and osmotic process times (60 to 240 min). To predict the trend of changes in these characteristics, response surface methodology with a central composite design and artificial neural network was utilized. It was determined that the optimal microwave time, temperature, and osmotic process time should be 1.69 min, 60°C, and 240 min, respectively, to minimize the solid gain and to maximize the percentage of water loss and the of water loss/solid gain. Under these conditions, the solid gain was 15.17%, water loss was 24.21%, the water loss/solid gain was 1.63, and weight reduction was 10.00%. Through the examination of different networks, the feedforward back-diffusion network with 4-5-3 topologies was found to be the best neural model, with a correlation coefficient greater than 0.996 and a mean square error less than 0.001. The hyperbolic tangent activation function, hopping learning model, and 1000 learning cycles were also determined to be optimal. Comparing the correlation coefficients of the models obtained from the response surface method and the artificial neural network, it was concluded that the artificial neural network method was more effective in predicting the trend of changes in the characteristics of dried medlar fruit through the osmotic process with microwave pretreatment. | ||
| کلیدواژهها [English] | ||
| Artificial neural network, Medlar, Drying, Microwave, Osmotic | ||
| مراجع | ||
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