بکارگیری بینی‌الکترونیک برای تشخیص تقلب رب‌ انار با روش‌های شناسایی الگو و شبکه عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه فنی ، مهندسی و ماشین‌های کشاورزی، موسسه آموزش و ترویج کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

2 مربی، موسسه آموزش و ترویج کشاورزی، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

چکیده

تقلب در محصولات غذایی یک چالش جدی در صنعت غذا است که کیفیت و سلامت آن را تحت تاثیر قرار می‌دهد. رب انار به دلیل داشتن طعم دلپذیر و خواص آنتی‌اکسیدانی، یکی از مواد خوراکی ارزشمند است که در برنامه غذایی مردمِ برخی از کشورها مورد توجه قرار گرفته است. این محصول به علت قیمت بالای آن در معرض ایجاد تقلب توسط برخی تولیدکنندگان یا توزیع‌کنندگان برای دستیابی به سود بیشتر قرار دارد. در این پژوهش استفاده از سامانه ماشین بویایی به منظور تشخیص تقلب رب انار با شیره خرما با بکارگیری آرایه حسگرهای گازی و شناسایی ترکیبات فرار مواد آلی مورد نظر قرار گرفت. برای تحلیل پاسخ آرایه حسگرها از روش‌های تحلیل مولفه اصلی، تحلیل تفکیک خطی و شبکه عصبی مصنوعی استفاده شد. بر اساس نتایج بدست آمده، تحلیل مولفه‌های اصلی با دو مولفه PC1 و PC2، 94 درصد واریانس کل داده‌ها را توصیف کرد. در روش LDA دقت طبقه‌بندی نمونه‌های رب انار 65/97 درصد به دست آمد که در مقایسه با روش PCA از دقت بالاتری برخوردار بود. بر اساس نتایج بدست آمده در روش ANN مقادیر ضریب همبستگی و میانگین مربعات خطا در شبکه عصبی با ساختار 6-9-7 به ترتیب 984/0 و 0018/0 به دست آمد. نتایج این پژوهش نشان می‌دهد که دستگاه بینی‌الکترونیک می‌تواند به عنوان یک ابزار غیرمخرب برای طبقه‌بندی و تشخیص تقلب در رب انار بکار گرفته شود.

چکیده تصویری

بکارگیری بینی‌الکترونیک برای تشخیص تقلب رب‌ انار با روش‌های شناسایی الگو و شبکه عصبی مصنوعی

تازه های تحقیق

  • توسعه سامانه ماشین‌بویایی برای تشخیص تقلب رب انار
  • تحلیل پاسخ حسگرها به منظور تعیین الگوی شناسایی با استفاده از روش شیمی‌سنجی
  • طبقه‌بندی و تشخیص رب انار تقلبی با شبکه عصبی مصنوعی با بیشترین دقت

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Application of Electronic Nose to Detect Pomegranate Paste Adulteration Using Pattern Recognition Methods and Artificial Neural Network

نویسندگان [English]

  • AHMAD SADEGHI 1
  • Hadi Hosseini 2
1 Institute of Agricultural Education and Extension, Agricultural Research, Education and Extension Organization (AREEO),Tehran, Iran
2 Lecturer, Institute of Agricultural Education and Extension, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
چکیده [English]

Adulteration in food products is regarded as a main challenge in food industry, which adversely affects food quality and health. Owing to pleasant taste and antioxidant properties, pomegranate paste is one of the most valuable and desirable foods in the people diets in some countries. As a luxury and expensive food, it is likely to be adulterated by some producers or distributors for the more profits. In this study development and application of machine olfaction system using array of gas sensors to detect adulteration in pomegranate paste was aimed. Principal Component Analysis (PCA), Linear Discrimination analysis (LDA) and Artificial Neural Network (ANN) methods were used to analyze response of the sensor arrays. Based on the results, PCA with two components PC1 and PC2 described 94% of total data variance. In LDA method, the classification accuracy of pomegranate paste samples was 97.65% which higher than PCA method. The values of correlation coefficient (R2) and root mean squared error (RSME) of neural network in ANN method using the structure of 6-9-7 were 0.984 and 0.0018 respectively. This study reveals that the electronic nose device can be used as a non-destructive tool to classify and detect adulteration of different classes of pomegranate paste.

کلیدواژه‌ها [English]

  • Adulteration
  • Gas Sensor
  • Olfaction Machine System
  • Pomegranate Molasses
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