Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods

این مقاله توسط مترجمان گروه مکانیک از فارسی به انگلیسی ترجمه شده و در سال 2020 به چاپ رسیده است.
نویسنده اصلی
حامد کرمی
نام مجله
J Food Process Preserv
سال انتشار
دانلود فایل مقاله
حامد کرمی


Foodstuff adulteration involves addition of any lowcost substances to the highprice materials to reduce the content of the expensive components, and hence decrease the production cost and reach to the maximum profit. An electronic nose was used in this study to detect the adulterations in mixed edible oils. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the official American Oil Chemist Society (AOCS) standard. The results were analyzed by Cluster analysis (CA), principle component analysis (PCA), principal component regression (PCR), linear discriminant analysis (LDA), and artificial neural network (ANN) methods with accuracy of 95, 98, 98, 88, and 97.3%, respectively. According to the results, the ANN method with structure of 875 showed the highest accuracy in classification of oil adulteration. Its correct classification ratio, mean square errors, and correlation (r ) were 97.3%, .117211, and .0963, respectively. The results also indicated that the proposed method can be used as an alternative of the official AOCS methods to innovatively detect the edible oil adulteration with high accuracy and speed

نظرتان در مورد این صفحه چیست؟

Skip Navigation Links
ثبت سفارش جدید