ارزیابی تأثیر پرتو گاما بر خصوصیت‌های نانوایی گندم رقم‌های امید، روشن و طبسی با استفاده از شبکه‌ی عصبی مصنوعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 1. پژوهشکده‌ی کشاورزی هسته‌ای، پژوهشگاه علوم و فنون هسته‌ای، سازمان انرژی اتمی ایران، صندوق پستی: 498-31485، کرج ـ ایران

2 2. گروه اصلاح نباتات و بیوتکنولوژی، دانشکده‌ی تولید گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، صندوق پستی: 15739-49138، گرگان ـ ایران

3 3. گروه بیوشیمی و بیولوژی مولکولی، دانشگاه دانمارک جنوبی، صندوق پستی: 5230DK-، اودنس- دانمارک

چکیده

مهم­ترین عامل کنترل­ کننده­ ی صفت کیفیت نانوایی گندم، ژنوم گیاه است که با ایجاد تنوع ژنتیکی از طریق جهش ­زاها در گیاه این صفت قابل ­بهبود و ارتقا است. در این پژوهش آزمایش ­های شیمیایی و فارینوگراف در ژن­مانه­ های جهش ­یافته­ ی 60-58-66-T، 10-1-64-O، 1RO-، 3RO- و 5RO- به ­ترتیب حاصل از پرتودهی رقم­ های گندم طبسی، امید و روشن مورد بررسی قرار گرفت. هم­چنین تابع انتقال اِس شکل برای برآورد ضریب­ ها از طریق مدل شبکه­ ی عصبی پیش­خور با روش آموزش الگوریتم لونبرگ- مارکوارت مورد استفاده قرار گرفت. در ژن­مانه­ ی 3RO- خصوصیت شیمیایی عدد زلنی، مقدار سختی دانه، گلوتن مرطوب و درصد پروتئین نسبت به شاهد افزایش معنی­دار داشت.
هم­چنین در ژن­مانه­­ ی 10-1-64-O، صفت­ های درصد جذب آب و عدد والوریمتری افزایش معنی ­دار و صفت درجه­ی سست ­شدن خمیر بعد از 10 و 20 دقیقه کاهش معنی­ داری نسبت به شاهد داشت. خصوصیت ­های مقدار درصد پروتئین، حجم نان، عدد کیفی فارینوگراف و درجه­ی
سست­ شدن خمیر بیش­ترین تأثیر را در مدل شبکه ­ی عصبی داشتند. نتیجه­ ها نشان­دهنده­ ی تأثیر مثبت پرتودهی بر بهبود خواص کیفی خمیر نان هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of gamma ray effect on wheat bakery properties in Omid, Roshan and Tabasai cultivars by artificial neural network

نویسندگان [English]

  • M.R Rahemi 1
  • A Yamchi 2
  • S Navabpour 2
  • H Soltanloo 2
  • P Roepstorff 3
چکیده [English]

The genome of a plant is the most critical factor to control bakery- quality trait in wheat, where it can bemade by applying genetic variation upon using mutagens for its improvement. In this study, chemical and Farinograph experiments were investigated in T-66-58-60, O-64-1-10, RO-1, RO-3 and RO-5 lines from Tabassi, Omid, and Roshan cultivar, respectively. Also, the sigmoid transfer function was used for the assessment of factors by the model of feed-forward neural network with training method of levenberg-Marquardt algorithm. The chemical traits of Zeleny number, the hardness, wet gluten and protein content in the RO-3 line increased significantly compared with the control. Also, water absorption percentage and valorimeter value increased substantially in the O-64-1-10, whereas it was shown that the dough softening after 10 and 20 minutes decreased significantly compared with the control. The protein content, bread volume, Farinograph quality number and E10 properties had the most significant impact on the neural network model. The results show a positive effect of the irradiation on the improvement of dough quality properties.

کلیدواژه‌ها [English]

  • Wheat
  • Gamma Ray
  • Mutant
  • Artificial Neural Network
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