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In this work, an electronic nose is used to assess the ripeness state of pinklady apples through their shelf-life. In order to evaluate the electronic nose performance, fruit quality indicators are also obtained to compare results from both techniques. Pinklady apples were harvested at their optimal date so that electronic nose measurements and fruit quality measurements could be performed on the fruit samples during their ripening process. A PCA analysis, a well-known linear classification technique, does not show any clustering behaviour that might be attributed to ripening. On the other hand, Fuzzy art, an unsupervised neural network classification algorithm, shows a tendency to classify measurements regarding to their shelf-life period. Finally, electronic nose signals are correlated with classical fruit quality parameters such as firmness, starch index and acidity. Good correlation coefficients are obtained, a clear …