ارزیابی غیرمخرب کیفیت کیوی رقم ابوت با استفاده از بینی الکترونیکی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران

2 استاد دانشکدۀ مهندسی و فناوری کشاورزی، دانشگاه تهران

3 استادیار گروه مهندسی مکانیک ماشین‏های کشاورزی، دانشکدۀ کشاورزی، دانشگاه شهرکرد

چکیده

امروزه روش‏های گوناگونی برای ارزیابی غیرمخرب کیفیت محصولات کشاورزی ابداع شده است و از آنها استفاده می‏شود. در تحقیق حاضر از سامانۀ بینی الکترونیکی به‏منظور ارزیابی غیرمخرب کیفیت کیوی رقم ابوت استفاده شده است. سامانۀ بینی الکترونیکی به کمک تکنیک شبکۀ عصبی مصنوعی و آنالیز مؤلفه‏های اصلی (PCA) قادر به طبقه‏بندی نارس، نیمه‌رسیده، رسیده، بیش از حد رسیده، و فساد کیوی رقم ابوت است. آنالیز مؤلفه‌های اصلی با دو مؤلفۀ  و ، 99 درصد از واریانس داده‏ها را پوشش داد و مراحل رسیدگی کیوی رقم ابوت را بدون تداخل طبقه‏بندی کرد. دقت طبقه‏بندی کل به کمک تکنیک شبکۀ عصبی مصنوعی 100 درصد محاسبه شد. کمترین و بیشترین میزان میانگین مربعات خطا به‌ترتیب در مرحلۀ نیمه‌رسیده 02523/0 و فساد کیوی رقم ابوت 00198/0 به‌دست آمد. همچنین در این تحقیق، سفتی به‌عنوان یکی از روش‏های مخرب ارزیابی رسیدگی کیفیت کیوی رقم ابوت اندازه‏گیری شد. نتایج آنالیز سفتی کیوی رقم ابوت نشان داد که بین سفتی مراحل پس از برداشت (نارس، نیمه‌رسیده، رسیده و بیش از حد رسیده) در سطح 5 درصد تفاوت معنی‏داری وجود دارد. پیش‏بینی سفتی کیوی رقم ابوت از روی بوی مراحل رسیدگی با استفاده از شبکۀ عصبی مصنوعی با ضریب 995/0  تعیین شد. سامانۀ بینی الکترونیکی مطالعه‌شده می‏تواند به‌عنوان ابزاری مطمئن برای پایش رسیدگی میوۀ کیوی در سردخانه‏ها استفاده شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Nondestructive quality evaluation of Abbot Kiwifruit using electronic nose

نویسندگان [English]

  • Amin Foroughi Rad 1
  • Seyed Saeed Mohtasebi 2
  • Mahdi Ghasemi 3
  • Mahmoud Omid 2
چکیده [English]

Currently, nondestructive quality evaluation of various agricultural products are being developed and used. In this study, application of an electronic nose system for the quality assessment of kiwifruit (Abbot variety) is used. Electronic nose system coupled with artificial neural network (ANN) and principal component analysis (PCA) techniques was able to classify unripe, half-ripe, ripe, over-ripe and spoiled kiwifruit cultivars successfully. The two main components & , contains about 99% of variance without overlapping. The success rate for ANN was found to be 100%. The minimum and maximum mean square error was obtained for the Half-ripe and spoiled samples as 0.02523 & 0.00198, respectively. In this paper, stiffness as a quality indicator for kiwi was measured and them predicted by electronic nose data. Analysis of the results showed that the kiwifruit firmness after harvest (unripe, half-ripe, ripe and overripe) has significant difference at 5%. Using artificial neural network, the firmness prediction of Abbot variety of kiwifruit through ripening stages aroma determined with the coefficient Electronic nose system can be considered as a reliable tool for the monitoring of kiwifruits in storage conditions.

کلیدواژه‌ها [English]

  • Gas Sensors
  • electronic nose
  • Nondestructive evaluation
  • artificial neural network (ANN)
  • Principal Component Analysis (PCA)
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