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پایش تازگی گوشت قرمز با استفاده از ترکیب طیفنگاری دیالکتریک و پردازش تصویر | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 7، دوره 49، شماره 2، تیر 1397، صفحه 227-236 اصل مقاله (1.02 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2017.237213.664966 | ||
| نویسندگان | ||
| امیر علی صادقپور اصفهانی1؛ مجتبی نادری بلداجی* 2؛ مهدی قاسمی ورنامخواستی3؛ بهرام حسین زاده4 | ||
| 1دانشجوی کارشناسی ارشد، گروه مهندسی مکانیک بیوسیستم، دانشگاه شهرکرد | ||
| 2دانشگاه شهرکرد | ||
| 3استادیار گروه مهندسی مکانیک بیوسیستم دانشگاه شهرکرد | ||
| 4استادیار گروه مکانیک بیوسیستم-دانشگاه شهرکرد | ||
| چکیده | ||
| با توجه به اهمیت کیفیت گوشت و سایر مواد غذایی مورد مصرف روزانه در رشد و سلامت جامعه انسانی، توسعه سامانه‌های تشخیص و پایش کیفیت مواد غذایی بیش از پیش مورد توجه محققین می‌باشد. در این مطالعه 40 نمونه گوشت گوساله در طی پنج روز ماندگاری در دمای پنج درجه سانتیگراد مورد تصویربرداری ماکروسکوپیک و طیفنگاری توان دیالکتریک در 20 فرکانس از بازه MHz 100- 5 قرار گرفت. فرضیه مطالعه بر این اساس بود که با ترکیب دو روش مذکور حجم اطلاعات مفید حاصل از تغییرات فیزیکی و شیمیایی گوشت به واسطه ماندگاری افزایش مییابد. در هر بار آزمایش مجموعا 42 ویژگی (توان دیالکتریک در 20 فرکانس مختلف بین MHz 100-5 و 22 ویژگی بافتی و رنگی تصویر) از هر نمونه استخراج شد. طبقهبندی روز ماندگاری گوشت با استفاده از متغیرهای دیالکتریک و تصویر با اعمال پنج الگوریتم شبکههای عصبی چند لایه پرسپترون (MLP)، رگرسیون منطقی چند جملهای (MRL)، درختهای کاربردی (FT)، درختهای مدل منطقی (LMT) و روش تجمیعی بگینگ (Bagging) انجام گرفت. نتایج نشان داد که توان دیالکتریک در فرکانسهای مختلف با افزایش ماندگاری تا روز پنجم کاهش یافت به طوری که برای مثال از 250 میکرو وات در فرکانس پنج مگاهرتز در روز اول به 100 میکرو وات در همین فرکانس در روز پنجم رسید. همچنین نتایج طبقهبندی نشان داد که متغیرهای تصویر گوشت به تنهایی بیشتر از متغیرهای دیالکتریک گوشت در طبقهبندی روز ماندگاری موثر هستند اما با تجمیع این دو منبع اطلاعات حسگری و اعمال تکنیک کاهش بعد به روش مولفههای اصلی (PCA) بر روی تمام ویژگیها، دقت طبقهبندی 78 % برای الگوریتم درختهای کاربردی (FT) و 77 % برای طبقهبند ترکیبی بگینگ (Bagging) با ردهبند پایه شبکههای عصبی مصنوعی پرسپترون چند لایه (MLP) حاصل شد. | ||
| کلیدواژه‌ها | ||
| تازگی گوشت؛ حسگر ترکیبی؛ پردازش تصویر؛ طیفنگاری دیالکتریک | ||
| عنوان مقاله [English] | ||
| Monitoring the red meat freshness by using combined dielectric spectroscopy and image processing | ||
| نویسندگان [English] | ||
| Amir Ali Sadeghpour Esfahani1؛ Mojtaba Naderi Beldaji2؛ Mahdi Ghasemi-Varnamkhasti3؛ Bahram Hosseinzadeh-Samani4 | ||
| 1Master student, Dept. Biosystems Eng., Shahrekord University. | ||
| 2Shahre Kord University | ||
| 3Assistant Professor, Dept. Biosystems Engineering, Shahrekord University | ||
| 4Assistant Professor, Dept. Biosystems Eng., Shahrekord University | ||
| چکیده [English] | ||
| Regarding the importance of quality of meat and other daily consuming food stuffs in the growth and health of human society, development of quality diagnosing and monitoring systems for food materials are being paid increasing attention by investigators. In this study, 40 beef samples were subjected to macroscopic imaging and dielectric power spectroscopy at 20 frequencies in the range of 5-100 MHz during five days of storage at 5 ° C. It was hypothesized that combination of the two sensing methods would result in more information on physicochemical changes of meat during ageing. For any beef sample, 42 attributes (i.e. 20 dielectric variables including dielectric power at different frequencies and 22 texture and color features of the image) were extracted. Classification analyses for the day of storage were performed with five algorithms of neural networks including multi-layer perceptron (MLP), multinomial logistic regression (MRL), functional trees (FT), logistic model trees (LMT) and Bagging aggregation. The results showed that the dielectric power at different frequencies decreased with the storage day from e.g. 250 µW at 5 MHz on the first day to 100 µW at the same frequency on the fifth day. The results showed that image parameters of beef were more effective in classification than dielectric variables but combining the information of the both sensory techniques, after reduction using PCA, resulted in classification accuracies of %78 for functional tree (FT) algorithm and %77 for Bagging classification with MLP as the base classifier. | ||
| کلیدواژه‌ها [English] | ||
| Meat freshness, Combined sensor, image processing, Dielectric spectroscopy | ||
| مراجع | ||
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