Comparison of Difference, Relative and Fractional Methods for Classification of The Black Tea Based on Electronic Nose

Lelono, Danang; Nuradi, Hanif; Satriyo, Muhammad Rangga; Widodo, Triyogatama Wahyu; Dharmawan, Andi; Istiyanto, Jazi Eko

doi:10.1109/cenim48368.2019.8973308

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…In addition to the extraction and classification processes, other efforts to optimize the baseline reduction approaches were carried out by Wirawan et al (2021). This led to the examination of three baseline reduction methods, namely the Difference, Relative, and Fractional Difference methods [4,12,31]. In the Difference method, the baseline reduction process involves subtracting the value of the experiment EEG signal feature by the average feature value of the baseline EEG signal feature.…”

Section: Figure 1 the Architecture Of The Cnn Methods For Four Emotio...mentioning

confidence: 99%

Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition

et al. 2022

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Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it determines an appropriate baseline reduction method to characterize emotional reactions after the smoothing process. Data collected from four scenarios conducted on three datasets was used to reduce the interference and amplitude of the electroencephalogram signals. The result showed that the smoothing process can eliminate interference and lower the signal's amplitude. Based on the three baseline reduction methods, the Relative Difference method is appropriate for characterizing emotional reactions in different electroencephalogram signal patterns and has higher accuracy. Based on testing on the DEAP dataset, these proposed methods achieved accuracies of 97.14, 99.70, and 96.70% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 89.71, 97.63, and 96.58% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 99.59, 98.20, and 99.96% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively.

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Section: Figure 1 the Architecture Of The Cnn Methods For Four Emotio...mentioning

confidence: 99%

Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition

et al. 2022

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“…Several baseline reduction methods are applicable to characterize signals data, such as the difference, relative difference, and fractional difference methods. However, they have been observed to be effective with only black tea aroma data [43]. However, the tea aroma has similar characteristics to the EEG signals data, such as containing a lot of noise and weak frequency intensity.…”

Section: Rq 2: How Can An Eeg Signal Be Generated With Consideration Of Differences In Participant Characteristics?mentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

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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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“…The GUI functions as a display program and hardware controller in measuring samples, the data displayed is the response of the gas sensor, the response of the temperature and humidity sensors. Sample measurement consisted of one sniffing cycle, namely flushing, collecting, and purging as shown in Figure 2, this process has also been cried out in Bulletin of Electr Eng & Inf ISSN: 2302-9285  Implementation of an electronic nose for classification of synthetic flavors (Radi) 1285 [21], [22]. Flushing is the process of reading the sensor response without a test sample.…”

Section: Softwarementioning

confidence: 99%

Implementation of an electronic nose for classification of synthetic flavors

et al. 2021

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Classification and identification of synthetic flavor become routine activities in the flavor and food industry due to its application. As a modern olfactory technology, electronic nose (e-nose) has the possibility to be applied in these activities. This study aimed to evaluate an e-nose for classifying synthetic flavors. In this study, an e-nose was designed with an array of gases sensors as the main sensing component and principal component analysis (PCA) for the pattern recognition software. This research was started with preparation of the hardware, continued with preparation of sample, data collection, and analysis. There were nine samples of synthetic flavors with different aroma, namely: grapes, strawberry, mocha, pandanus, mango, jackfruit, orange, melon, and durian. The data collection process includes three stages, i.e. flushing, collecting, and purging of 2 min, 3 min, 2 min respectively. These sensor responses were then analyzed for forming aroma patterns. Four pre-treatment methods were applied for the aroma pattern formation: absolute data, normalize of absolute data, relative data, and normalize of relative data. With the PCA for evaluation, the results showed that the absolute data treatment provided the best results, indicated from the distribution of aroma patterns that were grouped according to the type of samples.

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Comparison of Difference, Relative and Fractional Methods for Classification of The Black Tea Based on Electronic Nose

Cited by 7 publications

References 16 publications

Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition

Modified Weighted Mean Filter to Improve the Baseline Reduction Approach for Emotion Recognition

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Implementation of an electronic nose for classification of synthetic flavors

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