نظارت بر اکسیداسیون روغن زیتون طی دوره نگهداری تسریع شده با استفاده از سامانه ماشین بینایی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، بخش مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه شیراز

2 دانشیار، بخش مهندسی بیوسیستم، دانشکده کشاورزی، دانشگاه شیراز

3 دانشیار، بخش علوم و صنایع غذایی، دانشکده کشاورزی، دانشگاه شیراز

4 استاد، بخش مهندسی مکانیک جامدات، دانشکده مهندسی مکانیک، دانشگاه شیراز

چکیده

در این پژوهش توانایی یک سامانه ماشین بینایی برای نظارت بر سطح اکسیداسیون روغن زیتون ارزیابی شد. به این منظور نمونه‌های روغن زیتون طی دوره نگهداری تسریع شده به مدت 24 روز در آون مورد بررسی قرار گرفته و سپس عملیات پردازش تصویر جهت استخراج پارامترهای مربوط به فضاهای رنگی RGB، HSV و L*a*b*انجام شد. عملکرد تکنیک‌های شبکه عصبی مصنوعی و درخت تصمیم در تعیین میزان اکسیداسیون روغن زیتون مقایسه شد. در هر یک از مدل ها پارامترهای رنگی به عنوان ورودی و مراحل مختلف در دوره اکسیداسیون روغن زیتون به عنوان خروجی تعیین شد. براساس نتایج حاصل شده بیشترین دقت طبقه بندی (44/94%) و کمترین جذر میانگین مربع خطا (0696/0) مربوط به تکنیک درخت تصمیم بوده است. بنابراین سامانه پیشنهادی ماشین بینایی در ترکیب با تکنیک‌های هوش مصنوعی به عنوان ابزاری غیر مخرب و کارآمد برای پایش و کنترل کیفیت در طول نگهداری روغن ارائه می شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Monitoring of olive oil oxidation during accelerated storage using machine vision system

نویسندگان [English]

  • Alireza Sanaeifar 1
  • Abdolabbas Jafari 2
  • S. Mehdi Nassiri 2
  • M. Taghi Golmakani 3
  • Mohammad Eghtesad 4
1 Ph.D. student, Department of Biosystems Engineering, School of Agriculture, Shiraz University
2 Associate Professor, Department of Biosystems Engineering, School of Agriculture, Shiraz University
3 Associate Professor, Department of Food Science and Technology, School of Agriculture, Shiraz University
4 Professor, Department of Solid Mechanics Engineering, School of Mechanical Engineering, Shiraz University
چکیده [English]

In this research, the ability of a machine vision system was evaluated to monitor the oxidation period of olive oil. For this purpose, the olive oil samples studied during accelerated storage for 24 days in an oven and then image processing processes was carried out to extract parameters related to color spaces RGB, HSV and L*a*b*. The performance of artificial neural network and decision tree techniques compared to determine the olive oil oxidation rate. In each of the models color parameters were used as inputs and different stages in the olive oil oxidation period were considered as output. According to the results, the highest classification accuracy (94.44%) and the lowest RMSE (0.0696) are related to the decision tree technique. The proposed machine vision system combined with artificial intelligence techniques as non-destructive and efficient tool will offer for monitoring and quality control during oil storage.

کلیدواژه‌ها [English]

  • Machine vision
  • Olive oil
  • Oxidation
  • Decision tree
  • Artificial neural network

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