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

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.

[i] VOCs, volatile organic compounds.

The design of the portable electronic nose.

Photograph of a portable electronic nose.

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

Table 2:

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
KNN	P	[1, 2]
weights	[‘uniform’, ‘distance’]
SVM (type = SVC)	C	[1, 10, 100, 1000]
SVM (type = SVC)	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]

[i] DT, Decision Tree; KNN, K-Nearest Neighbor; NB, Naïve Bayes; RF, Random Forest; SVM, Support Vector Machine.

The architecture of MLP. MLP, multilayer perceptron.

Table 3:

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

[i] MLP, multilayer perceptron; ReLU, Rectified Linear Unit.

Table 4:

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

[i] DT, Decision Tree; KNN, K-Nearest Neighbor; NB, Naïve Bayes; RF, Random Forest; SVM, Support Vector Machine.

Table 5:

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

[i] MLP, multilayer perceptron; ReLU, Rectified Linear Unit.

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DOI: https://doi.org/10.2478/ijssis-2024-0019 | Journal eISSN: 1178-5608

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Language: English

Submitted on: Jun 26, 2023

Published on: Jul 20, 2024

Published by: International Journal on Smart Sensing and Intelligent Systems

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

electronic nose,

tea dregs aroma,

multilayer perceptron,

machine learning

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2024 Adi Djoko Guritno, Agus Harjoko, Megita Ryanjani Tanuputri, Diyah Utami Kusumaning Putri, Nur Achmad Sulistyo Putro, published by International Journal on Smart Sensing and Intelligent Systems
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 17 (2024): Issue 1 (January 2024)

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

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