Regression model on electronic nose data from aromatic rice samples

As of today, aroma of rice is measured by an expert sensory panel and they assign scores like `+', `++', `+++' and `NA' for mild, medium, strong and non aromatic varieties of rice respectively. This method of human panel testing is very subjective with numerous problems like inaccuracy, non-repeatability and it is laborious and time consuming also. On the other hand, the analytical instruments, which are used for this purpose are prohibitively expensive and are available in the laboratories only. It is in this pursuit, an electronic nose with an array of gas sensors has been developed for aroma measurement of rice. This user friendly and low cost electronic nose may be extremely useful for rice scientists, researchers and exporters to determine the aroma of aromatic rice. In this paper, we describe the experimental setup and the regression model for classification of rice samples. With unknown rice samples, aroma based classification accuracy by multi-sensor electronic nose using the regression model, has been found to be more than 80%.