Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques
Abstract
:1. Introduction
- Proposal of a new system for essential oil characterisation based on a low-cost sensor network and ML techniques;
- Comparison of results includes a larger dataset of essential value data, including five sorts of essential oils and an adulterated sample;
- Evaluate the suitability of using individual data or the mean data for classification;
- Assess the best classification algorithm for the obtained data;
- Calculate metrics, precision, accuracy, recall, and F1-score to allow fair comparison of results with existing, ongoing, and future studies;
- Inclusion of the sensor in a sensor network allowing cloud computing.
2. Related Work
2.1. Sensors of the MQ for Detection of Adulteration in Essential Oils and Applications in Food
2.2. Other Applications of MQ Family Sensors
3. Test Bench
3.1. Prototype Description
3.2. Oil Samples
3.3. Measurement Methodology
3.4. Data Preprocessing
3.5. Classification
4. Results
4.1. Data Classification with All Measured Values
4.2. Data Classification with Calculated Mean Values
5. Discussion
5.1. Discussion of Obtained Results and Selection of the Most Suitable Configuration
5.2. Comparison with Existing eNoses for Similar Applications
5.3. Relevance of Proposed Sensor for Essential Oil Characterisation
5.4. Limitations of Performed Tests
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Source | Acronym | Nº of Individual Measurements |
---|---|---|
Cistus ladanifer | Cla | 1813 |
Pinus pinaster | Pp | 1213 |
Cistus ladanifer + Pinus pinaster | CP | 1241 |
Melaleuca alternifolia | Ma | 410 |
Citrus limonum | Cli | 2062 |
Red fruits | Rf | 375 |
Parameter | Maximum Value | Minimum Value | Mean Value | Standard Deviation |
---|---|---|---|---|
MQ2–1 | 116,856.0 | 0.0 | 434.8 | 3010.0 |
MQ2–2 | 933.0 | 31.0 | 160.1 | 146.8 |
MQ2–3 | 11,741.0 | 1.0 | 1164.1 | 1693.3 |
MQ2–4 | 2591.0 | 1.0 | 326.8 | 384.8 |
MQ2–6 | 1157.0 | 41.0 | 210.2 | 186.0 |
MQ3–2 | 266.0 | 0.0 | 9.3 | 13.1 |
MQ3–3 | 788.0 | 0.0 | 8.8 | 10.8 |
MQ3–8 | 36,118.0 | 0.0 | 15.0 | 441.0 |
MQ4–2 | 153,780.0 | 24.0 | 1308.9 | 2059.9 |
MQ4–3 | 878,916.0 | 0.0 | 763.0 | 11,344.1 |
MQ4–4 | 85,719.0 | 0.0 | 456.9 | 1602.7 |
MQ4–5 | 3012.0 | 79.0 | 607.2 | 422.1 |
MQ4–9 | 100,708.0 | 0.0 | 1861.9 | 3164.3 |
MQ5–1 | 332.0 | 19.0 | 96.8 | 56.0 |
MQ5–2 | 1183.0 | 32.0 | 160.4 | 148.0 |
MQ5–3 | 7033.0 | 81.0 | 1223.1 | 1153.1 |
MQ5–4 | 20,971.0 | 1.0 | 1220.1 | 1014.9 |
MQ5–5 | 356.0 | 59.0 | 155.8 | 56.4 |
MQ6–1 | 34,088.0 | 664.0 | 2540.4 | 1659.9 |
MQ6–2 | 2811.0 | 212.0 | 475.0 | 173.1 |
MQ6–3 | 153,519,925.0 | 44.0 | 92,054.9 | 3,167,065.0 |
MQ6–4 | 2,501,354.0 | 1.0 | 7710.1 | 35,559.1 |
MQ6–5 | 3939.0 | 288.0 | 670.6 | 265.1 |
MQ7–1 | 280.0 | 14.0 | 94.3 | 34.3 |
MQ7–2 | 903.0 | 0.0 | 13.3 | 33.0 |
MQ7–3 | 297.0 | 51.0 | 87.7 | 37.1 |
MQ7–4 | 1,891,349.0 | 1.0 | 3331.7 | 27,593.2 |
MQ7–5 | 25,483,977.0 | 1.0 | 4139.3 | 302,144.1 |
MQ8–1 | 10,450.0 | 3298.0 | 6410.1 | 1894.5 |
MQ8–2 | 7005.0 | 17.0 | 1554.7 | 1023.7 |
MQ8–3 | 7,913,118.0 | 1.0 | 232,761.3 | 293,845.6 |
MQ8–4 | 6031.0 | 134.0 | 2269.8 | 970.0 |
MQ8–5 | 70,742.0 | 0.0 | 3622.5 | 3862.6 |
ML Technique | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|
DA | 0.91 | 0.91 | 0.82 | 0.97 |
NB | 0.94 | 0.97 | 0.95 | 0.99 |
SVM | 0.89 | 0.85 | 0.85 | 0.97 |
KNN | 1.00 | 0.99 | 0.99 | 1.00 |
NN | 0.97 | 0.96 | 0.96 | 0.99 |
Year | Nº of Tech. | Used ML | Type | Applications | Products | Nº of Sensors | For | Accuracy | Precision | Recall | F1-Score | Ref. |
---|---|---|---|---|---|---|---|---|---|---|---|---|
2020 | 5 | ANN and LDA | Multiclass | Adulterated products | Edible oils | 8 | ANN | 0.9893 | 0.975 | - | - | [28] |
LDA | 0.942 | 0.897 | - | - | ||||||||
2021 | 3 | ANN | Multiclass | Adulterated products | Essential oils | 9 | Mult. | 0.989 | - | - | - | [21] |
Binary | Bin. | 1 | - | - | - | |||||||
2021 | 7 | GBC, SVM, NB, KNN, NN, and AB | Multiclass | Adulterated products | Olive oil | 8 | GBC | - | - | - | - | [39] |
SVM | - | - | - | - | ||||||||
NB | - | - | - | - | ||||||||
KNN | - | - | - | - | ||||||||
ANN | - | - | - | - | ||||||||
AB | - | - | - | - | ||||||||
2022 | 4 | SVM, DTC, and NN | Multiclass | Adulterated products | Beef and pork meat | 9 | SVM | 0.8742 | - | - | - | [30] |
DTC | 0.8541 | - | - | - | ||||||||
ANN | 0.8885 | - | - | - | ||||||||
2023 | 2 | ANN | Multiclass | Adulterated products | Essential oil | 7 | ANN | - | - | - | - | [20] |
2023 | 6 | ANN, SVM, KNN, NB, and AB | Multiclass | Adulterated products | Olive oil | 8 | ANN | 0.6752 | - | - | - | [40] |
SVM | 0.8652 | - | - | - | ||||||||
KNN | 0.8989 | - | - | - | ||||||||
BN | 0.8202 | - | - | - | ||||||||
AB | 0.3483 | - | - | - | ||||||||
2019 | 2 | KNN and f-RF | Multiclass | Identify products | General food | 16 | KNN | 0.69 | - | - | - | [43] |
RF | 0.78 | - | - | - | ||||||||
2019 | 7 | KNN, CART, NB, SVM, LSVM, and RF | Binary | Identify products | Banana | 10 | KNN | 0.72 | - | - | - | [33] |
CART | 0.654 | - | - | - | ||||||||
NB | 0.602 | - | - | - | ||||||||
SVM | 0.494 | - | - | - | ||||||||
LSVM | 0.628 | - | - | - | ||||||||
RF | 0.718 | - | - | - | ||||||||
2021 | 4 | QDA, QSVM, CSVM, and KNN | Multiclass | Identify products | Edible oils | 4 | QDA | 0.589 | - | 0.69 | - | [44] |
QSVM | 0.953 | - | 0.966 | - | ||||||||
CSVM | 0.802 | - | 0.851 | - | ||||||||
KNN | 0.811 | - | 0.858 | - | ||||||||
2022 | 3 | DA and RF | Multiclass | Identify products | Fish and meat | 8 | RF | 0.95 | - | - | - | [41] |
DA | - | - | - | - | ||||||||
2023 | 1 | KNN | Binary | Identify products | Potatoes | 5 | KNN | 0.90 | - | - | - | [42] |
2023 | 5 | DA, SVM, BN, KNN, and NN | Multiclass | Identify product | Essential oils | 7 | DA | 0.9112 | 0.9092 | 0.4105 | 97.46 | Proposed |
NB | 0.9401 | 0.9651 | 0.4734 | 98.83 | ||||||||
SVM | 0.8874 | 0.8519 | 0.4234 | 96.82 | ||||||||
KNN | 0.9982 | 0.9893 | 0.4960 | 99.89 | ||||||||
NN | 0.9671 | 0.9609 | 0.4816 | 99.38 |
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Viciano-Tudela, S.; Parra, L.; Navarro-Garcia, P.; Sendra, S.; Lloret, J. Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques. Sensors 2023, 23, 5812. https://doi.org/10.3390/s23135812
Viciano-Tudela S, Parra L, Navarro-Garcia P, Sendra S, Lloret J. Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques. Sensors. 2023; 23(13):5812. https://doi.org/10.3390/s23135812
Chicago/Turabian StyleViciano-Tudela, Sandra, Lorena Parra, Paula Navarro-Garcia, Sandra Sendra, and Jaime Lloret. 2023. "Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques" Sensors 23, no. 13: 5812. https://doi.org/10.3390/s23135812
APA StyleViciano-Tudela, S., Parra, L., Navarro-Garcia, P., Sendra, S., & Lloret, J. (2023). Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques. Sensors, 23(13), 5812. https://doi.org/10.3390/s23135812