Prediction of Rice Odor by Using an Electronic Nose and Artificial Neural Network

Abstract

Artificial intelligence (AI) has recently become the most popular technology, being integrated into almost every aspects of life and business. Many industries have already started using AI to gain meaningful insight from large amount of data they already had. These are manufacturing, banking and financial services, retail, energy and utilities, travel, etc. In this study, an example of AI plus artificial sense, in terms of artificial olfaction that mimics the human sense of smell, was demonstrated. A method for prediction of rice odor based on a radial basis function neural networks (RBF) and electronic nose was proposed. Samples of each variety of rice were analyzed with the electronic nose consisting of eight metal oxide semiconductor gas sensors. To imitate neural networks of human brain for pattern recognition of electronic nose, the artificial neural network based on the radial basis function (RBF) will be used in this study. Principal component analysis (PCA) is used to demonstrate the discrimination capability of electronic nose. Test result has shown that RBF neural network and human prediction have nearly equivalent scores of odor level. The PCA result has revealed four classified rice samples: jasmine rice, white rice, sticky rice and brown rice. In conclusion, the designed electronic nose has a detection capability to distinguish between varieties of rice.