کنترل مراحل رسیدگی گوجه‌فرنگی گیلاسی پوشش‌دهی شده با ژل آلوئه‌ورا با استفاده از سامانه بینایی مصنوعی

نویسندگان
علی گنجلو 1
محسن زندی 1
ماندانا بی مکر 1
سمانه منجم 2
1 گروه علوم و مهندسی صنایع غذایی، دانشگاه زنجان
2 دانشجوی کارشناسی ارشد فناوری مواد غذایی، گروه علوم و مهندسی صنایع غذایی، دانشکده کشاورزی، دانشگاه زنجان، زنجان، ایران
10.52547/fsct.17.105.135
چکیده
کنترل مراحل رسیدگی فرآورده‌های کشاورزی طی نگهداری و درجه‌بندی کیفی آنها بر اساس مرحله رسیدگی از اهمیت بالایی برخوردار است. پوشش‌های خوراکی می‌توانند عمر انبارمانی فرآورده‌های کشاورزی را افزایش دهند و از آنها طی عملیات جابجایی، نگهداری، فرآوری و عرضه به بازار مصرف محافظت نمایند. هدف از پژوهش حاضر ایجاد سامانه‌ای برای کنترل و جداسازی کیفی گوجه‌فرنگی گیلاسی در دو حالت با و بدون پوشش ژل آلوئه‌ورا بر مبنای سامانه بینایی مصنوعی می‌باشد. برای این منظور نخست خصوصیات فیزیکی‌وشیمیایی شامل اسیدیته قابل تیتر (TA)، مواد جامد محلول کل (TSS) و سفتی بافت (F) گوجه‌فرنگی‌های گیلاسی در هر دو حالت اندازه‌گیری گردید. براساس این خصوصیات شاخص رسیدگی (RPI) تعیین گردید و نمونه‌‌ها بر اساس مرحله رسیدگی به دو درجه کیفی MS1 و MS2 طبقه‌بندی شدند. در ادامه با کمک سامانه بینایی مصنوعی با استفاده از دو سیستم تجزیه و تحلیل مولفه‌های اصلی (PCA)، شبکه عصبی مصنوعی پس انتشار (BPNN) و با کمک خصیصه‌های رنگی و بافتی حاصل از تصویر به‌صورت مجزا و با هم، نمونه‌ها طبقه‌بندی گردید. نتایج طبقه‌بندی نشان داد که استفاده از خصیصه‌های رنگی و بافتی باهم سبب طبقه‌بندی با صحت بیشتر می‌گردد. در این میان با کمک 21 خصیصه رنگی و بافتی روش‌های PCA و BPNN قادر به جداسازی نمونه‌‌ها به ترتیب با دقت 72/85 و 21/98 بودند. صحت بالاتر روش BPNN به سبب عملکرد غیر خطی آن است. نتایج به‌دست آمده از این پژوهش حاکی از آن است که ژل آلوئه‌ورا در به تاخیر انداختن فرایند رسیدن گوجه‌های گیلاسی به‌طور موفقیت آمیزی عمل می‌نماید و می‌توان از سامانه بینایی مصنوعی به‌عنوان یک روش غیرمخرب برای ارزیابی میزان رسیدگی گوجه‌فرنگی گیلاسی براساس خصیصه‌های رنگی و بافتی به‌طور کارآمد استفاده کرد.
کلیدواژه‌ها
گوجه‌فرنگی گیلاسی
درجه رسیدگی
پردازش تصویر
تجزیه و تحلیل چند متغیره
شبکه عصبی مصنوعی پس‌انتشار

موضوعات

عنوان مقاله English

Ripening Stages Control of Cherry Tomato Coated with Aloe Vera Gel using Artificial Vision System

نویسندگان English

Ali Ganjloo 1
Mohsen Zandi 1
Mandana Bimakr 1
Samaneh Monajem 2
1 Department of Food Science and Engineering, University of Zanjan
2 MSc Student of Food Technology,Department of Food Science and Engineering, Faculty of Agriculture, University of Zanjan, Zanjan, Iran
چکیده English

It is important to control the ripening stages of agricultural products during storage and their quality grading based on their ripening stage. Edible coatings can prolong the storage life of agricultural products and protect them through the handling, storage, processing and marketing. The purpose of the current study was to develop an artificial vision system for quality control and segregation of cherry tomatoes in two different conditions including with and without Aloe vera gel coating. For this purpose, physicochemical properties including titrable acidity, total soluble solids and firmness of cherry tomatoes were measured in both conditions. Based on these properties, the ripening index (RPI) was determined and the samples were classified to MS1 and MS2 according to the ripening stage. Subsequently, the samples were classified using color features, color texture features separately and their combination through principal component analysis (PCA) and back propagation neural network (BPNN). Classification results showed that the use of color and color texture features combination made the classification more accurate; PCA and BPNN methods were able to segregate the samples with high accuracy (85.72 and 98.21, respectively) using the 21 color and color texture features. The higher accuracy of the BPNN method is due to its nonlinear performance. The results of this study indicate that Aloe vera gel is promising in delaying the ripening process of cherry tomatoes and artificial vision system can be used as a non-destructive method for evaluation of cherry tomato ripening level based on the color and color texture features.

کلیدواژه‌ها English

Cherry tomato
Degree of maturity
Image processing
Multivariate analysis
Back propagation artificial neural network
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