Rice, the most leading food crop in the world, involves inevitable physiochemical and physiological changes during its aging. Despite many proposed theories regarding rice aging, the complexity of this process has made it difficult to be fully comprehended. In this study, we traced the aroma change of stored aromatic and non-aromatic rice with a metal-oxide semiconductor based electronic nose to characterize their aging process. For doing so, software part of the system was analyzed. Various steady-state and transient features related to adsorption and desorption phases were derived to evaluate and compare the considered pattern recognition algorithms. Principle component analysis was utilized to analyze the aging process in terms of seven Keywords : Electronic nose, Rice aging, Feature extraction, Pattern recognition, Artificial neural networks, ltr"
