电子鼻
芳香
主成分分析
食品科学
模式识别(心理学)
数学
人工智能
化学
计算机科学
统计
作者
Fardin Ayari,Esmaeil Mirzaee‐Ghaleh,Hekmat Rabbani,Kobra Heidarbeigi
摘要
Abstract Diagnosis of adulteration in cow ghee is one of the key concerns of recent years. In this study, the aroma fingerprints of cow ghee were detected. For this purpose, an electronic nose system was developed and its ability for detection of different amounts of margarine mixed with pure cow ghee (10, 20, 30, 40, and 50% levels) was investigated. The system was equipped with eight sensors (MOS type), that each of them reacts to specific volatile compounds in the samples. The features of the signals were considered for data analysis. In this research, principal components analysis (PCA) and artificial neural networks (ANN) methods were used for data analyzing. According to the results, the PCA analysis explained 98% of the variance in the data set. Also, ANN analysis identified 85.6 and 97.2% of pure cow ghee from the adulteration ones when samples were classified in 7 and 2 classes, respectively. Practical applications Diagnosis and estimation of adulteration in cow ghee is one of the key concerns for consumers. Electronic nose is a new method that can be used for detection of products quality. We show that electronic nose system is a valuable tool for detection of margarine as a one of common adulteration in cow ghee.
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