نوع مقاله : مقاله پژوهشی

نویسندگان

دانشگاه کشاورزی و منابع طبیعی رامین خوزستان

چکیده

در این پژوهش به‌منظور ارزیابی کیفیت آب توت سیاه طی مراحل مختلف رسیدگی، پارامترهای فرکتالی استخراج شده توسط آنالیز فرکتال و پارامترهای بیوشیمیایی (TSS، ویتامین ث، اسیدیته، فنول، آنتوسیانین، مواد ایجاد کننده رنگ قهوه‏ای و (pHبه‌ترتیب به‌عنوان ویژگی‏های غیرمخرب و مخرب مورد استفاده قرار گرفتند؛ سپس از شبکه عصبی مصنوعی (ANN) و نزدیکترین همسایه (k-NN) به‌منظور توسعه مدل پیشگو و طبقه‌بندی داده‌ها استفاده گردید. از میان پنچ ویژگی استخراج شده از آنالیز فرکتال؛ Y و S که به‌ترتیب مربوط به بیشیه فرکتال و مساحت منحنی فرکتال می‏باشند، به‌عنوان موثرترین ویژگی در فرآیند آموزش شبکه عصبی و طبقه‌بند k-NN مورد استفاده قرار گرفتند. الگوریتم طبقه‌بند k-NN تغییرات رنگ در هر چهار مرحله رسیدگی را با دقت 08/97 طبقه‏بندی نمود. همچنین شبکه عصبی آنتوسیانین را با مجذور میانگین مربعات خطا (RMSE) 141/0، ضریب همبستگی 99/0، مواد ایجادکننده رنگ قهوه‏ای را با 0016/0= RMSE، ضریب همبستگی 97/0، فنول را با 879/1590=RMSE، ضریب همبستگی 8057/0، TSSرا با 0040/0=RMSE، ضریب همبستگی 907/0، اسیدیته را با 50/3=RMSE، ضریب همبستگی 986/0، ویتامین ث را با 285/0=RMSE، ضریب همبستگی 878/0 و pH را با 00017/0=RMSE و ضریب همبستگی 99/0 پیش‌بینی نمود. بنابراین، نتایج این بررسی نشان داد که شبکه عصبی مصنوعی و طبقه‌بند k-NN با آنالیز فرکتال می‌تواند به‌عنوان یک روش مناسب در ارزیابی برخط پارامترهای کیفی آب توت سیاه طی مراحل رسیدگی مورد استفاده قرار گیرد.

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