  It is crucial for the wine industry to have methods like electronic nose
systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines,
preventing its spoilage or determining its quality. In this paper, we prove
that the portable and compact self-developed E-Nose, based on thin film
semiconductor (SnO2) sensors and trained with an approach that uses deep
Multilayer Perceptron (MLP) neural network, can perform early detection of wine
spoilage thresholds in routine tasks of wine quality control. To obtain rapid
and online detection, we propose a method of rising-window focused on raw data
processing to find an early portion of the sensor signals with the best
recognition performance. Our approach was compared with the conventional
approach employed in E-Noses for gas recognition that involves feature
extraction and selection techniques for preprocessing data, succeeded by a
Support Vector Machine (SVM) classifier. The results evidence that is possible
to classify three wine spoilage levels in 2.7 seconds after the gas injection
point, implying in a methodology 63 times faster than the results obtained with
the conventional approach in our experimental setup.
