پایش تازگی گوشت قرمز با استفاده از ترکیب طیف‌نگاری دی‌الکتریک و پردازش تصویر

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی مکانیک بیوسیستم، دانشگاه شهرکرد

2 دانشگاه شهرکرد

3 استادیار گروه مهندسی مکانیک بیوسیستم دانشگاه شهرکرد

4 استادیار 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

موضوعات


عنوان مقاله [English]

Monitoring the red meat freshness by using combined dielectric spectroscopy and image processing

نویسندگان [English]

  • Amir Ali Sadeghpour Esfahani 1
  • Mojtaba Naderi Beldaji 2
  • Mahdi Ghasemi-Varnamkhasti 3
  • Bahram Hosseinzadeh-Samani 4
1 Master student, Dept. Biosystems Eng., Shahrekord University.
2 Shahre Kord University
3 Assistant Professor, Dept. Biosystems Engineering, Shahrekord University
4 Assistant 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
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