olfactory machine system,an effective solution for detection of adulteration in rosewater

Rosewater is one of the main products of rosa damascena and is a traditional long-lasting product in the Kashan region that has a global reputation. Regarding the use of rosewater in the treatment of rheumatic, cardiovascular, and also in the baking of different types of sweets and the preparation of ice creams, the main detection or adulteration of the produced rosewater is of particular importance. In this research, the ability to use an olfactory machine system (electronic nose) based on metal oxide semiconductor sensors as a non-destructive tool for detecting different levels of adulteration in rosewater and its authenticity assessment was studied. Principal component analysis (PCA), linear discriminant analysis (LDA), loading analysis, support vector machine (SVM) and decision tree (DT) were the methods used to achieve this goal. Based on the results, the PCA with the two main components of PC1 and PC2 described 92% of the variance of the data set for the used samples. In the sensor array, MQ4, TGS2620 and FIS sensors revealed the highest loading coefficient values and TGS822 and MQ8 sensors devoted the lowest ones. Based on the results of LDA method, the accuracy of the classification was 94%. By use of support vector machine with linear kernel function, in the C-SVM method, training and validation accuracy were obtained 98.75% and 87.5%, respectively. Also, the accuracy of the decision tree method in the classification of samples of rosewater was 80%. Based on the results, the olfactory machine system based on MOS sensors in combination with the pattern recognition methods has the ability to detect adulteration in rosewater and the LDA method has the highest classification accuracy. The research team also suggests using the system ability to detect adulteration in other products with potential for adulteration.