Cow ghee is very used in some regions of Iran, such as Kermanshah province. Cow ghee is a natural source that contains high-quality nutrients which are needed for the human body. Adulteration in dairy products is not only a serious threat to human health but also it causes economic losses. Diagnosis of foodstuff cheating and its estimation is one of the key concerns in recent years. The aim of this study was the detection of the adulteration in cow ghee by olfactory machine system. Therefore, an electronic nose system was used for the different levels of sunflower oil and cow body fat mixed with pure cow ghee (10%, 20%, 30%, 40%, and 50%). The principal components analysis (PCA) and artificial neural networks (ANNs) methods were used to achieve this goal. Based on the results, the accuracy of the principal components analysis of sunflower oil and cow body fat were 96% and 97% of the data variance, respectively. According to the results, artificial neural networks identified the adulteration with sunflower oil and cow body fat with an accuracy of 91.3% and 82.5%, respectively.

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.

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization.

In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in MF285 Tractor was studied in the second and third gears from two positions: driver and bystander, at 1000 and 1600 r/min, and running on 10 different fuel levels. For data analysis, the ANFIS network, neural network, and response surface methodology were applied. Comparing the means of noise pollution at different levels demonstrated that the B 25 E 6 D 69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 r/ min. Although the noise pollution emitted in the third gear was a little more than that emitted in the second gear. All the resultant models, laid by response surface methodology, neural network, and ANFIS had excellent results. Considering the statistical criteria, the best models with high correlation coefficients and low mean square errors were ANFIS, response surface methodology, and artificial neural network models, respectively.

No abstract