An electronic nose (E-nose), comprising eight metal oxide semiconductor (MOS) gas sensors, was used in situ for real-time classification of black tea according to its quality level. Principal component analysis (PCA) coupled with signal preprocessing techniques (i.e., time set value preprocessing, F1; area under curve preprocessing, F2; and maximum value preprocessing, F3), allowed grouping the samples from seven brands according to the quality level. The E-nose performance was further checked using multivariate supervised statistical methods, namely, the linear and quadratic discriminant analysis, support vector machine together with linear or radial kernels (SVM-linear and SVM-radial, respectively). For this purpose, the experimental dataset was split into two subsets, one used for model training and internal validation using a repeated K-fold cross-validation procedure (containing the samples collected during the first three days of tea production); and the other, for external validation purpose (i.e., test dataset, containing the samples collected during the 4th and 5th production days). The results pointed out that the E-nose-SVM-linear model together with the F3 signal preprocessing method was the most accurate, allowing 100% of correct predictive classifications (external-validation data subset) of the samples according to their quality levels. So, the E-nose-chemometric approach could be foreseen has a practical and feasible classification tool for assessing the black tea quality level, even when applied in-situ, at the harsh industrial environment, requiring a minimum and simple sample preparation. The proposed approach is a cost-effective and fast, green procedure that could be implemented in the near future by the tea industry. Author Contributions: Conceptualization, K.T. and A.M.P.; Data curation, I.F., D.L., and N.N.; Formal analysis, S.N.H., K.T., A.C.A.V. and A.M.P.; Funding acquisition, K.T.; Investigation, I.F.; Methodology, K.T., T.J., A.C.A.V.; Project administration, T.J.; Resources, Y.Y. and N.N.; Software, S.N.H., A.C.A.V. and A.M.P.; Supervision, K.T. and N.N.; Validation, K.T., T.J., Y.Y.; Visualization, D.L. and A.M.P.; Writing-original draft, S.N.H., D.L.; Writing-review & editing, K.T., A.C.A.V., A.M.P.

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

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an electronic nose (e-nose) based on a gas sensor array equipped with a stable temperature controller has been successfully developed by applying proportional-integral-derivative (PID) controller. This study was motivated due to the dependence of sensor response on the temperature of a sample. Here, the temperature influences the volatile organic compounds (VOC) of the sample. The performance of the e-nose was then evaluated to classify the quality levels of black tea (Q1, Q2, and Q3). The black tea samples were purchased from Tambi Tea Industry in Central Java, Indonesia. Here, the quality is based on the information from the factory. For each quality level, the measurement was repeated 31 times. Therefore, the total of measurements is 93, and only six sensors, i.e. MQ-7, TGS 813, TGS 2602, TGS 826, TGS 2620 and TGS 825 have the high influence on the patterns differentiation. It is found that the e-nose is not able to distinguish the three quality levels of black tea correctly when applying without heating treatment. In this condition, only 88.9% of cross-validated grouped are correctly classified. On the other hand, for the black tea sample with unstable heating, the performance of the e-nose has been improved. In this case, 92.5% of crossvalidated grouped are correctly classified. Moreover, for the black tea sample with stable heating treatment, all samples are separated where 97.8% of cross-validated grouped are correctly classified. The results indicated that the e-nose with the highly stable heater was capable of detecting the quality of black tea.