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ساخت و توسعه یک سامانهی ماشین بویایی در ترکیب با روش‌های شناسایی الگو برای تشخیص تقلب فرمالین در شیر خام | ||
| مهندسی بیوسیستم ایران | ||
| مقاله 18، دوره 47، شماره 4، بهمن 1395، صفحه 761-770 اصل مقاله (719.26 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijbse.2017.60273 | ||
| نویسندگان | ||
| مجتبی توحیدی1؛ مهدی قاسمی ورنامخواستی* 2؛ وحید غفاری نیا3؛ سید سعید محتسبی4؛ مجتبی بنیادیان5 | ||
| 1دانشجوی دکتری | ||
| 2استادیار گروه مهندسی مکانیک بیوسیستم دانشگاه شهرکرد | ||
| 3عضو هیئت علمی گروه الکترونیک، دانشکده مهندسی برق و کامپیوتر | ||
| 4استاد دانشگاه تهران | ||
| 5گروه 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 | ||
| بینی الکترونیکی؛ حسگرهای نیمه هادی؛ فرمالین؛ تحلیل مؤلفههای اصلی؛ شبکههای عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Fabrication and development of a machine olfaction system combined with pattern recognition techniques for detecting formalin adulteration in raw milk | ||
| نویسندگان [English] | ||
| Mahdi Ghasemi-Varnamkhasti2؛ | ||
| 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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