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Development of a portable electronic nose for the classification of tea quality based on tea dregs aroma

Adi Djoko Guritno
Adi Djoko Guritno
, 
Agus Harjoko
Agus Harjoko
, 
Megita Ryanjani Tanuputri
Megita Ryanjani Tanuputri
, 
Diyah Utami Kusumaning Putri
Diyah Utami Kusumaning Putri
 e 
Nur Achmad Sulistyo Putro
Nur Achmad Sulistyo Putro
   | 20 lug 2024
International Journal on Smart Sensing and Intelligent Systems's Cover Image
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Article Category: Article
Pubblicato online: 20 lug 2024
Pagine: -
Ricevuto: 26 giu 2023
DOI: https://doi.org/10.2478/ijssis-2024-0019
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© 2024 Adi Djoko Guritno, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Electronic diagram of portable electronic nose.
Electronic diagram of portable electronic nose.

Figure 2:

The design of the portable electronic nose.
The design of the portable electronic nose.

Figure 3:

Photograph of a portable electronic nose.
Photograph of a portable electronic nose.

Figure 4:

Typical response of sensor array to tea aroma (A) good, (B) strange, and (C) burnt.
Typical response of sensor array to tea aroma (A) good, (B) strange, and (C) burnt.

Figure 5:

The architecture of MLP. MLP, multilayer perceptron.
The architecture of MLP. MLP, multilayer perceptron.

The evaluation performance of MLP

Activation function Hidden layer Hidden node Accuracy Precision Recall F1-score
ReLU 2 50 0.8472 ± 0.0461 0.8714 ± 0.0545 0.8472 ± 0.0461 0.8394 ± 0.0489
100 0.8750 ± 0.0461 0.8921 ± 0.0464 0.8750 ± 0.0461 0.8683 ± 0.0511
3 50 0.8611 ± 0.0278 0.8831 ± 0.0362 0.8611 ± 0.0278 0.8537 ± 0.0323
100 0.8750 ± 0.0241 0.8921 ± 0.0361 0.8750 ± 0.0241 0.8703 ± 0.0246
4 50 0.8472 ± 0.0461 0.8564 ± 0.0581 0.8472 ± 0.0461 0.8399 ± 0.0494
100 0.8472 ± 0.0241 0.8712 ± 0.0307 0.8472 ± 0.0241 0.8362 ± 0.0290
Sigmoid 2 50 0.7361 ± 0.0241 0.6907 ± 0.0486 0.7361 ± 0.0241 0.6816 ± 0.0385
100 0.7500 ± 0.0278 0.7738 ± 0.0636 0.7500 ± 0.0278 0.7149 ± 0.0247
3 50 0.7083 ± 0.0241 0.7401 ± 0.0736 0.7083 ± 0.0241 0.6486 ± 0.0305
100 0.7222 ± 0.0000 0.7817 ± 0.0413 0.7222 ± 0.0000 0.6583 ± 0.0185
4 50 0.6806 ± 0.0461 0.5978 ± 0.1663 0.6806 ± 0.0461 0.5978 ± 0.0888
100 0.7083 ± 0.0241 0.7153 ± 0.1564 0.7083 ± 0.0241 0.6191 ± 0.0495

Variation of the number of hidden layers, the number of hidden nodes, and the activation function of MLP architecture

Activation Function Hidden Layer Hidden Node
ReLU 2 50, 50
100, 100
3 50, 50
100, 100
4 50, 50
100, 100
Sigmoid 2 50, 50
100, 100
3 50, 50
100, 100
4 50, 50
100, 100

Sensor array used in the portable electronic nose and its specifications.

Name Specification
TGS 2602 High sensitivity to gaseous air contaminants and VOCs such as ammonia, H2S, ethanol, and toluene.
TGS 2620 High sensitivity to alcohol and organic solvent vapors and other volatile vapors, such as hydrogen, carbon monoxide, methane, and iso-butane.
MQ-7 High sensitivity to carbon monoxide.
MQ-9 High sensitivity to carbon monoxide, methane, and LPG.
TGS 2600 High sensitivity to low concentrations of gaseous air contaminants such as hydrogen, carbon monoxide, methane, iso-butane, and ethanol.
TGS 2611 High sensitivity and selectivity to methane gas, response to ethanol, hydrogen, and iso-butane.

Hyperparameter settings for training using machine learning classifiers

Classifier Hyperparameter Value
NB (type = multinomial NB) alpha [0, 0.1, 0.01, 0.001, 1.0]
fit_prior [True, False]
KNN n_neighbors 1–10
P [1, 2]
weights [‘uniform’, ‘distance’]
SVM (type = SVC) C [1, 10, 100, 1000]
gamma [0.1, 0.01, 0.001, 0.0001]
kernel [‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’]
DT criterion [‘gini’, ‘entropy’]
max_depth [5, 10, 15, 20, 25, 30]
max_features [‘sqrt’, ‘log2’, None]
min_samples_leaf [2, 3, 4, 5]
min_samples_split [2, 3, 4, 5]
splitter [‘best’, ‘random’]
RF n_estimators [10, 25, 50, 100]
criterion [‘gini’, ‘entropy’, ‘log_loss’]
max_depth [5, 10, 15, 20, 25, 30]
max_features [‘sqrt’, ‘log2’, None]
min_samples_leaf [2, 3, 4, 5]
min_samples_split [2, 3, 4, 5]

The evaluation performance of machine learning classifiers

Classifier Accuracy Precision Recall F1-score
NB 0.5972 ± 0.0241 0.6513 ± 0.0877 0.5972 ± 0.0241 0.5432 ± 0.0181
KNN 0.7778 ± 0.0000 0.7897 ± 0.0253 0.7778 ± 0.0000 0.7575 ± 0.0112
SVM 0.7778 ± 0.0393 0.7792 ± 0.0473 0.7778 ± 0.0393 0.7702 ± 0.0433
DT 0.5972 ± 0.0992 0.6196 ± 0.0894 0.5972 ± 0.0992 0.6002 ± 0.0932
RF 0.6667 ± 0.0556 0.6649 ± 0.0443 0.6667 ± 0.0556 0.6572 ± 0.0444
eISSN:
1178-5608
Lingua:
Inglese
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