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Development of a Dual MOS Electronic Nose/Camera System for Improving Fruit Ripeness Classification.

Li-Ying ChenCheng-Chun WuTing-I ChouShih-Wen ChiuKea-Tiong Tang
Published in: Sensors (Basel, Switzerland) (2018)
Electronic nose (E-nose) systems have become popular in food and fruit quality evaluation because of their rapid and repeatable availability and robustness. In this paper, we propose an E-nose system that has potential as a non-destructive system for monitoring variation in the volatile organic compounds produced by fruit during the maturing process. In addition to the E-nose system, we also propose a camera system to monitor the peel color of fruit as another feature for identification. By incorporating E-nose and camera systems together, we propose a non-destructive solution for fruit maturity monitoring. The dual E-nose/camera system presents the best Fisher class separability measure and shows a perfect classification of the four maturity stages of a banana: Unripe, half-ripe, fully ripe, and overripe.
Keyphrases
  • machine learning
  • deep learning
  • convolutional neural network
  • risk assessment
  • human health
  • climate change