  Real-time gas classification is an essential issue and challenge in
applications such as food and beverage quality control, accident prevention in
industrial environments, for instance. In recent years, the Deep Learning (DL)
models have shown great potential to classify and forecast data in diverse
problems, even in the electronic nose (E-Nose) field. In this work, we used a
Support Vector Machines (SVM) algorithm and three different DL models to
validate the rapid detection approach (based on processing an early portion of
raw signals and a rising window protocol) over different measurement
conditions. We performed a set of trials with five different E-Nose databases
that include fifteen datasets. Based on the results, we concluded that the
proposed approach has a high potential, and it can be suitable to be used for
E-nose technologies, reducing the necessary time for making forecasts and
accelerating the response time. Because in most cases, it achieved reliable
estimates using only the first 30% or fewer of measurement data (counted after
the gas injection starts.) The findings suggest that the rapid detection
approach generates reliable forecasting models using different classification
methods. Still, SVM seems to obtain the best accuracy, right window size, and
better training time.
