دانلود مقاله ISI انگلیسی شماره 151477
ترجمه فارسی عنوان مقاله

توسعه سیستم بینایی کامپیوتری برای پیش بینی آنزیم های پراکسیداز و پلی فنول اکسیداز برای ارزیابی روند قهوه سازی پوست موز با استفاده از مدل سازی برنامه نویسی ژنتیک

عنوان انگلیسی
Development of computer vision system to predict peroxidase and polyphenol oxidase enzymes to evaluate the process of banana peel browning using genetic programming modeling
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
151477 2018 9 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Scientia Horticulturae, Volume 231, 27 January 2018, Pages 201-209

ترجمه کلمات کلیدی
موز، پلی فنول اکسیداز، پراکسیداز، قهوه ای آنزیمی، پردازش تصویر دیجیتال، برنامه نویسی ژنتیک،
کلمات کلیدی انگلیسی
Banana; Polyphenol oxidase; Peroxidase; Enzymatic browning; Digital image processing; Genetic programming;
پیش نمایش مقاله
پیش نمایش مقاله  توسعه سیستم بینایی کامپیوتری برای پیش بینی آنزیم های پراکسیداز و پلی فنول اکسیداز برای ارزیابی روند قهوه سازی پوست موز با استفاده از مدل سازی برنامه نویسی ژنتیک

چکیده انگلیسی

The process of enzymatic browning is one of the most important chemical reactions, which effects on color, appearance and quality of fruits and vegetables. Polyphenol oxidase (PPO) and peroxidase (POD) enzymes are associated with enzymatic browning in the tissue of agricultural products. Quality of banana as a climacteric fruit is reduced by enzymatic browning during storage. Therefore, to evaluate enzymatic browning in banana, first, images of the fruits were taken at 25 °C for 9 days. Then, these images were investigated using digital image processing in order to predict and study POD and PPO enzymes during the browning process of banana peel. To this end, seventeen color parameters (R¯, G¯, B¯, VR, VG; VB, r, g, b, C1, C2, C3, C4, C5, C6, C7, C8) were extracted from each image as non-destructive parameters. In the following, PPO and POD both were measured through the laboratory methods Finally, using genetic programming (GP) modeling, two equations were obtained which can be used to predict and detect the changes of the activity of the PPO and POD enzymes during the storage period (9 days). The correlation coefficients between the measured values and the predicted values for PPO and POD enzymes were 0.98 and 0.97, respectively. Furthermore, there were no significant differences between predicted values with measured values of PPO and POD enzymes (p > 0.05); these results indicate the proper performance of the designed models.