ساخت و توسعه یک سامانه‌ی ماشین بویایی در ترکیب با روش‌های شناسایی الگو برای تشخیص تقلب فرمالین در شیر خام

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری

2 استادیار گروه مهندسی مکانیک بیوسیستم دانشگاه شهرکرد

3 عضو هیئت علمی گروه الکترونیک، دانشکده مهندسی برق و کامپیوتر

4 استاد دانشگاه تهران

5 گروه بهداشت مواد غذایی، دانشکده دامپزشکی، دانشگاه شهرکرد

چکیده

تقلب در شیر و دیگر محصولات لبنی نه تنها یک تهدید جدی برای سلامت انسان است بلکه زیان­های اقتصادی متعددی را نیز به دنبال دارد. از جمله تقلبات رایج در شیر خام، استفاده از مواد بازدارنده بار میکروبی است. در این پژوهش، یک سامانه­ی ماشین بویایی (بینی الکترونیکی) بر پایه هشت حسگر نیمه هادی اکسـید فلـزی (MOS) ساخته شد و قابلیت آن در تشخیص مقادیر مختلف فرمالین در شیر خام (0، 05/0، 1/0، 2/0 و 3/0 درصد) مورد بررسی قرار گرفت. بردار ویژگی­ها از سیگنال پاسخ حسگرها استخراج و به عنوان ورودی مدل­های تشخیص الگو استفاده شد. بر اساس نتایج حاصل، آنالیز مؤلفه­های اصلی با دو مولفه­ی PC1 و PC2، % 93 از واریانس داده­ها را پوشش داد. در مجموعه­ی حسگری، حسگرهای MQ4، FIS، TGS822 و TGS2620 بالاترین مقادیر ضریب لودینگ و حسگر TGS2602 کمترین مقدار این ضریب را به خود اختصاص دادند. همچنین استفاده از روش تحلیل تفکیک خطی، دقت طبقه­بندی  1/80% را نشان داد. با کاربرد ماشین بردار پشتیبان با تابع چندجمله­ای درجه سه، دقت آموزش و اعتبارسنجی طبقه­بندی به ترتیب 100 %و  91/90 % به دست آمد. دقت طبقه­بندی کل نیز با به کارگیری تکنیک شبکه­های عصبی مصنوعی  100% به دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Fabrication and development of a machine olfaction system combined with pattern recognition techniques for detecting formalin adulteration in raw milk

نویسنده [English]

  • Mahdi Ghasemi-Varnamkhasti 2
چکیده [English]

Adulteration in milk and other dairy products not only is a serious threat to human health but also leads to the economic losses in the dairy industry. Utilization of the materials reducing microbial load is a common adulteration. In this study, a machine olfaction (electronic nose) based on 8 metal oxide semiconductor (MOS) sensors was fabricated and developed and its capability to formalin detection in the raw milk was investigated. Feature vector was then extracted from the sensors’ response and used as the inputs to pattern recognition models. Based on the obtained results, Principal Component Analysis (PCA) with two first PCs (PC1 and PC2) could describe 93 % of variance within data. In the sensor array, MQ4, FIS, TGS822, and TGS2620 sensors had the highest loading coefficient values whilst TGS2602 devoted the lowest loading value. Linear Discriminant Analysis (LDA) revealed the classification accuracy as 80.1 %. Support Vector Machine (SVM) with three order multinomial kernel function showed the training and validation accuracy values as 100% and 90.91%, respectively. Also, the full success rate was obtained for overall classification using the artificial neural network.

کلیدواژه‌ها [English]

  • electronic nose
  • Semiconductor gas sensors
  • Formalin
  • Principal component analysis
  • Artificial Neural Network
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