E-nose: A low-cost fruit ripeness monitoring system

Abstract

All fruits emit some specific volatile organic compounds (VOCs) during their life cycle. These VOCs have specific characteristics, by using these characteristics fruit ripening stage can be identified without destructing the fruit. In this study, an application-specific electronic nose device was designed for monitoring fruit ripeness. The proposed electronic nose is cost-efficient and does not require any modern or costly laboratory instruments. Metal oxide semiconductor (MOS) sensors were used for designing the proposed electronic nose. These MOS sensors were integrated with a microcontroller board to detect and extract the meaningful features of VOCs, and an artificial neural network (ANN) algorithm was used for pattern recognition. Measurements were done with apples, bananas, oranges, grapes, and pomegranates. The designed electronic nose proved to be reliable in classifying fruit samples into three different fruit ripening stage (unripe, ripe, and over-ripe) with high precision and recall. The proposed electronic nose performed uniformly on all three fruit ripening stages with an average accuracy of ≥ 95%.

Keywords

• Artificial neural network
• electronic nose
• fruit quality control
• MOS sensors
• volatile organic compounds.