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It is crucial for the wine industry to have methods like electronic nosesystems (E-Noses) for real-time monitoring thresholds of acetic acid in wines,preventing its spoilage or determining its quality. In this paper, we provethat the portable and compact self-developed E-Nose, based on thin filmsemiconductor (SnO2) sensors and trained with an approach that uses deepMultilayer Perceptron (MLP) neural network, can perform early detection of winespoilage thresholds in routine tasks of wine quality control. To obtain rapidand online detection, we propose a method of rising-window focused on raw dataprocessing to find an early portion of the sensor signals with the bestrecognition performance. Our approach was compared with the conventionalapproach employed in E-Noses for gas recognition that involves featureextraction and selection techniques for preprocessing data, succeeded by aSupport Vector Machine (SVM) classifier. The results evidence that is possibleto classify three wine spoilage levels in 2.7 seconds after the gas injectionpoint, implying in a methodology 63 times faster than the results obtained withthe conventional approach in our experimental setup.

Wine quality rapid detection using a compact electronic nose system: application focused on spoilage thresholds by acetic acid