Potential of electronic nose based on temperature-modulated metal oxide gas sensors for detection of geographical origin of spices

Spices in addition to effect on the taste and quality of food, it also increases the shelf-life of foods because of its antimicrobial or antioxidant properties. Different types of spices have various quality and economic value based on their geographical origin. Therefore, classification and separation based on geographic origin are of great interest to consumers and sellers and is particular importance. In this research, the ability of an electronic nose based on metal oxide semiconductor sensors as a non-destructive tool for detecting the geographical origin (India, China and Pakistan) of three spices of black pepper, cinnamon and turmeric was studied. Temperature modulation was performed as a sinusoidal voltage pattern and transient responses of sensors were analyzed for data analysis. Principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN) were the methods used to achieve this goal. The results showed that the data visualization using the PCA method, created a completely distinct cluster on the PC's deformed space. By using the LDA, SVM and ANN methods, the classification accuracy was 100% based on the geographical origin for all three spices. Also, verification of these models was carried out and accuracy of 100% was achieved. Therefore, we can conclude that the electronic nose based on metal oxide semiconductor sensors under temperature modulation and in combination with the chemometrics methods as an effective and efficient tool can be used for fast and non-destructive classification of black pepper, cinnamon and turmeric samples based on geographical origin.