نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار مهندسی مکانیک بیوسیستم،دانشکده کشاورزی سنقر، دانشگاه رازی، کرمانشاه، ایران
2 دانشجوی دکترای مهندسی مکانیک بیوسیستم، دانشگاه بوعلی سینا، همدان ،ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Wheat is an important grain product that constitutes about 20% of the calories consumed by the human population around the world. Due to the strong dependence of wheat growth and yield on its variety, the choosing the right variety for cultivation is very important. In this research, the feasibility of using an e-nose system along with artificial intelligence based on metal oxide semiconductor sensors (MOS) as a non-destructive tool for the separation and identification of three varieties of wheat with the names: Salari dry wheat, Quds blue wheat and local red wheat is evaluated. Support vector machine (SVM), artificial neural network (ANN) and principal component analysis (PCA) were the methods used to achieve this goal. The obtained results showed that TGS822 and TGS2620 sensors play the most role and TGS813 and TGS2610 sensors play the least role in wheat variety detection. ANN analysis method with 91.7% accuracy showed better result than SVM method (75% accuracy) in identifying and classifying wheat varieties. In the meantime, the PCA method showed a relatively good performance in separating and identifying wheat varieties with 77% of the total variance of the total data. Also, the results showed that wheat of Salari dry wheat variety had different aromatic compounds with other two varieties of Quds blue wheat and local red wheat. The proper performance of the e-nose in the separation of wheat varieties can indicate the promising application of this technology in the separation and identification of wheat varieties.
کلیدواژه‌ها [English]
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