Neural network based electronic nose for apple ripeness determination

1 January 1999

journal article
Published by Institution of Engineering and Technology (IET) in Electronics Letters

Vol. 35 (10), 821-823
https://doi.org/10.1049/el:19990547

Abstract

It is possible to non-destructively determine apple ripeness using a simple electronic nose. The instrument employs tin oxide resistive gas sensors and neural networks (fuzzy ARTMAP, LVQ and MLP) to classify the samples into three states of ripeness with 100% accuracy. Fuzzy ARTMAP was found to be the best classifier in the presence of simulated Gaussian noise.

Keywords

APPLE RIPENESS DETERMINATION
CLASSIFIER
FUZZY ARTMAP
SIMULATED GAUSSIAN NOISE
NEURAL NETWORK BASED ELECTRONIC NOSE
LVQ
MLP
RESISTIVE GAS SENSORS

This publication has 1 reference indexed in Scilit:

Electronic Noses
Published by Oxford University Press (OUP) ,1999

Cited by 47 articles