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توسعه و ارزیابی یک سامانه بینایی ماشین هوشمند بهمنظور تشخیص تقلب سنگدان مرغ در گوشت قرمز چرخکرده | ||
| مهندسی بیوسیستم ایران | ||
| دوره 56، شماره 2، شهریور 1404، صفحه 90-105 اصل مقاله (3.56 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2025.398182.665600 | ||
| نویسندگان | ||
| مبین رضازاده1؛ سجاد کیانی* 2؛ مهدی قاسمی ورنامخواستی1؛ زهرا ایزدی1 | ||
| 1گروه مهندسی مکانیک بیوسیستم، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران. | ||
| 2گروه مهندسی مکانیک بیوسیستم، دانشکده مهندسی زراعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران. | ||
| چکیده | ||
| در این پژوهش سامانهای هوشمند مبتنی بر بینایی ماشین با بهرهگیری از تحلیل رنگی تصاویر دیجیتال گرفته شده با تلفن همراه برای تشخیص تقلب افزودن سنگدان مرغ به گوشت قرمز گوسفندی-گوساله چرخ شده توسعه داده و ارزیابی شد. بدین منظور، نمونههایی مرسوم گوشت قرمز چرخ شده (%55 گوسفند و %45 گوساله) با درصدهای مختلف سنگدان (۰ تا ۱۰۰ درصد) تهیه و در شرایط نورپردازی محیطی آزمایشگاه مستقیم از روی نمونه و از روی بسته سلفونی تصویربرداری شدند. ویژگیهای رنگی (R، G، و B) تصاویر نمونهها در محیط متلب استخراج و با روشهای یادگیری ماشین شامل تحلیل مؤلفههای اصلی (PCA)، رگرسیون حداقل مربعات جزئی (PLSR) و شبکه عصبی مصنوعی پرسپترون چندلایه (MLP) مدلسازی انجام شد. بهترین نتایج مدل PLSR برای تخمین درصد تقلب با ضریب تعیین R² و شاخص RMSR در حالت بدون پوشش سلفون برابر با 7/0 و 51/16 حاصل شد. این شاخصها برای مدل MLP برابر با 97/0 و 673/6 بدست آمد در صورتیکه نتایج برای حالت با پوشش سلفون به دلیل انعکاسهای نوری پوشش پایینتر (90/0 و 54/12) بدست آمد. مدل طبقهبند MLP با دقتهای %85، %4/96، %6/92، %7/73، %2/76 و %7/96 به ترتیب نمونههای تقلبی %10-0، %20-10، %30-20، %40-30، %50-40 و بیش از %50 را طبقهبندی کرد. میانگینهای صحت، حساسیت و F1 score برای مدل ایجاد شده به ترتیب 975/0، 974/0 و 975/0 بدست آمد. نتایج نشان داد این روش غیرمخرب، سریع و قابل اعتماد برای شناسایی تقلب سنگدان در گوشت چرخکرده است و میتواند بهعنوان مبنایی برای توسعه سامانههای کنترل کیفیت گوشت مورد استفاده قرار گیرد. | ||
| کلیدواژهها | ||
| پردازش تصویر؛ تقلب غذایی؛ شبکههای عصبی مصنوعی؛ مدلسازی | ||
| عنوان مقاله [English] | ||
| Development and Evaluation of an Intelligent Machine Vision System for Detecting Chicken Gizzard Adulteration in Minced Red Meat | ||
| نویسندگان [English] | ||
| Mobin Rezazadeh1؛ sajad Kiani2؛ Mahdi Ghasemi Varnamkhasti1؛ Zahra Izadi1 | ||
| 1Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Shahrekord University, Shahrekord, I.R.Iran | ||
| 2Biosystems Engineering Department,, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| چکیده [English] | ||
| In this study, an intelligent machine-vision system was developed and evaluated for detecting chicken gizzard adulteration in ground red mutton-veal meat, using digital images captured by a mobile phone. To this end, standard samples of minced red meat (55% mutton and 45% beef) with varying proportions of chicken gizzard (0 to 100%) were prepared, and images were captured in a laboratory environment both directly from the sample surface and through plastic wrap packaging. The color features (RGB) of the images were extracted using the MATLAB image processing toolbox, and modeling was performed employing statistical and machine learning methods, including Principal Component Analysis (PCA), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP) neural networks. The best performance of the linear PLSR model for estimating the adulteration percentage yielded an R²V=0.7 and RMSEV=16.51 under conditions without plastic wrap, whereas the nonlinear MLP model achieved an R²V=0.97 and RMSEV=6.673. These results were lower (0.9 and 12.54) for the data acquisition through plastic wrap due to the light reflections caused by the covering. Furthermore, the MLP classifier achieved classification accuracies of 85%, 96.4%, 92.6%, 73.7%, 76.2%, and 96.7% for adulteration levels 0-10%, 10–20%, 20–30%, 30–40%, 40–50%, and above 50%, respectively. The average precision, sensitivity, and F1 score for the developed model were obtained as 0.975, 0.974, and 0.975, respectively. The results showed that this non-destructive method is both fast and reliable for identifying gizzard fraud in ground meat, and can serve as a basis for developing meat quality control systems. | ||
| کلیدواژهها [English] | ||
| Artificial neural network, Image processing, food fraud, Modeling | ||
| مراجع | ||
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