The response of the said sensors is read-out in the form of the resistance across the active layer of each sensor; hence each measurement produced a 16-channel time series, each of which represented by an aggregate of features reflecting all the dynamic processes occurring at the sensor surface in reaction to the chemical substance being evaluated. In particular, two distinct types of features were considered in the creation of this dataset: (i) The so-called steady-state feature (ΔR), defined as the difference of the maximal resistance change and the baseline and its normalized version expressed by the ratio of the maximal resistance and the baseline values when the chemical vapor is present in the test chamber. And (ii), an aggregate of features reflecting the sensor dynamics of the increasing/decaying transient portion of the sensor response during the entire measurement procedure under controlled conditions, namely the exponential moving average (emaα). These aggregate of features is a transform, borrowed from the field of econometrics originally introduced to the chemo-sensing community by Muezzinoglu et al. (2009), that converts the said transient portion into a real scalar, by estimating the maximum value —minimum for the decaying portion of the sensor response— of its exponential moving average (emaα), with an initial condition set to zero and a scalar smoothing parameter of the operator, α, that defines both the quality of the feature and the time of its occurrence along the time series the scalar, set to range between 0 and 1. In particular, three different values for α were set to obtain three different feature values from the pre-recorded rising portion of the sensor response and three additional features with the same α values but for the decaying portion of the sensor response, covering thus the entire sensor response dynamics. For a more detailed analysis and discussion on these features as well as a graphical illustration of them please refer to Section 2.3 and Figure 2, respectively of the annotated manuscript. Once the abovementioned features are calculated, one is to form a feature vector containing the 8 features extracted from each particular sensor multiplied by the 16 sensors considered here. In the end, the resulting 128-dimensional feature vector containing all the features indicated above (8 features × 16 sensors) is organized as follows: ΔR_1, |ΔR|_1, EMAi0.001_1, EMAi0.01_1, EMAi0.1_1, EMAd0.001_1, EMAd0.01_1, EMAd0.1_1, ΔR_2, |ΔR|_2, EMAi0.001_2, EMAi0.01_2, EMAi0.1_2, EMAd0.001_2, EMAd0.01_2, EMAd0.1_2,..., ΔR_16, |ΔR|_16, EMAi0.001_16, EMAi0.01_16, EMAi0.1_16, EMAd0.001_16, EMAd0.01_16, EMAd0.1_16, where: “ΔR_1” and “|ΔR|_1” is the ΔR and the normalized ΔR feature, respectively, “EMAi0.001_1”, “EMAi0.01_1”, and “EMAi0.1_1”, the emaα of the rising transient portion of the sensor response for α equals to 0.001, 0.01, and 0.1, respectively, and “EMAd0.001_1”, “EMAd0.01_1”, and “EMAd0.1_1”, the emaα of the decaying transient portion of the sensor response for α equals to 0.001, 0.01, and 0.1, respectively, all corresponding to sensor # 1; “ΔR_2” and “|ΔR|_2” is the ΔR and the normalized ΔR feature, respectively, “EMAi0.001_2”, “EMAi0.01_2”, and “EMAi0.1_2”, the emaα of the rising transient portion of the sensor response for α equals to 0.001, 0.01, and 0.1, respectively, and “EMAd0.001_2”, “EMAd0.01_2”, and “EMAd0.1_2”, the emaα of the decaying transient portion of the sensor response for α equals to 0.001, 0.01, and 0.1, respectively, all corresponding to sensor # 2; and so forth up until sensor # 16, forming thus the 128-dimensional feature vector that is to be fetched to the classifiers for training. For processing purposes, the data is organized into ten batches, each containing the number of measurements per class and month indicated in the table below. This reorganization of data was done to ensure having a sufficient and as uniformly distributed as possible number of experiments in each class and month when training the classifier. Dataset organization details. Each row corresponds to months that were combined to form a batch: Batch ID Month IDs Batch 1 Months 1 and 2 Batch 2 Months 3, 4, 8, 9 and 10 Batch 3 Months 11, 12, and 13 Batch 4 Months 14 and 15 Batch 5 Month 16 Batch 6 Months 17, 18, 19, and 20 Batch 7 Month 21 Batch 8 Months 22 and 23 Batch 9 Months 24 and 30 Batch 10 Month 36 The data format follows the same coding style as in libsvm, in which one indicates the class each data point belongs to (1: Ethanol; 2: Ethylene; 3:Ammonia; 4: Acetaldehyde; 5: Acetone; 6: Toluene), and, then, the collection of features in a format x:v, where x stands for the feature number and v for the actual value of the feature. For example, in 1 1:15596.162100 2:1.868245 3:2.371604 4:2.803678 5:7.512213 … 128:-2.654529 The number “1” stands for the class number (in this case Ethanol), whereas the remaining 128 columns list the actual feature values for each measurement recording organized as described above. Finally, to make the results presented in the associated article reproducible for the reader, please use the following parameter values in the training task: • folds: 10 • log2c = -5, 10, 1 • log2g = -10, 5, 1 • Scale the features in the training set appropriately to lie between -1 and +1. • And use the following cross validation parameters: Batch C Gamma (ɤ) Rate 1 256.0 0.03125 98.8764 2 64.0 0.00390625 99.7588 3 128.0 0.03125 100.0 4 1.0 0.25 100.0 5 2.0 0.015625 99.4924 6 256.0 0.0009765625 99.5217 7 64.0 0.0625 99.9723 8 1024.0 0.0078125 99.6599 9 2.0 0.00390625 100.0