Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring
Abstract
1. Introduction
2. Human Breath for Diagnosis of Diseases
Volatile Organic Compounds in Breath
3. Acetone Metabolism
4. Various Techniques Used in Diabetes Monitoring and Diagnosis
5. Nanomaterial-Based Approaches for Detection of Acetone
6. Limitations of Semiconducting Metal Oxides (SMOs)
- sensitivity, a change of measured signal per analyte unit, that is, the slope of a calibration graph;
- selectivity, a characteristic that determines whether a sensor can respond selectively to a single analyte;
- stability, the ability of a sensor to provide reproducible results for a certain period of time. This includes retaining the sensitivity, selectivity, response and recovery time;
- durability, the ability to withstand damage due to temperature, chemical addition and so on;
- response time, the time required for the sensor to respond to a stepped concentration change from zero to a certain concentration value;
- recovery time, the time it takes for the sensor signal to return to its initial value after a stepped concentration change from a certain value to zero;
- room temperature operation and so on, the ability to detect gases at room temperature.
7. Conclusions and Future Perspectives
Acknowledgments
Author Contributions
Conflicts of Interest
References
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| Technique | Principle | Detection Limit | Advantages | Disadvantages |
|---|---|---|---|---|
| GC–MS | Separate and analyse compounds by MS using chromatographic column (polar or non-polar) | Ppb and ppt levels | Highly selective and sensitive | Preconcentration steps, bulky, long sampling time, need for standards and requires trained operator |
| PTR–MS | Analysis of ionized molecules of target analytes by reaction with H3O+ MS | Low ppb levels | Real-time analysis | Lack of specificity, Narrow range of detectable compounds, bulky and requires trained operator |
| SIFT–MS | Analysis of ions produced by the reaction analytes and precursor ions (H3O+, NO+ or O2+) by quadrupole MS | Low ppb and ppt levels | Real-time, capability of ppt detection, broad range of detection | Cannot identify compounds, bulky and requires trained operator |
| QCL | Electrons are recycled from period to period, containing each time to the gain and the photon emission | Low ppb levels | Real-time analysis, potential for portability and miniaturization | Selectivity required for practical use and currently limited by available technology to reach sufficient specificity |
| LPAS | Analysis of trace gases. It uses the photoacoustic effect, the conversion of light to sound in all materials (solid, liquids and gases) | Ppt–ppb levels | Real-time analysis | Bulky, requires trained operator |
| SMOS-based chemoresistive sensors | Measures resistivity changes based on thinning or thickening the depletion layer of n-type SMOSs and hole accumulation layer of p-type SMOSs around the surface when exposed to oxidizing or reducing ambient gas | Ppm, ppb and ppt levels | Real-time analysis, portable, inexpensive and miniaturization | Relatively low sensitivity and less selectivity |
| Technique | Acetone Concentration | Reference |
|---|---|---|
| GC–MS | 0.049 ppb | [67] |
| 0.22–3.73 ppb | [3] | |
| 06.95–145.99 ppb | [24] | |
| 0.195–0.659 ppm | [54] | |
| PTR–MS | 0.19–1.3 ppm | [67] |
| 50 ppb | [68] | |
| 200–2000 ppb | [69] | |
| SIFT–MS | 1–20 ppm | [8] |
| 293–870 ppb | [69] |
| Material | Sensitivity (Response) (ppm) | Detection Limit (ppm) | Response/Recovery Time (s) | Operating Temperature (°C) | Reference |
|---|---|---|---|---|---|
| ZnO:Pt | 188 | 1000 | 45 | 400 | [89] |
| ZnO:Nb | 224.0 | 1000 | 56 | 400 | [89] |
| PrFeO3 | 234.4 | 500 | 6.1 | 180 | [90] |
| CdNb2O6 | 2 | 10 | 9 | 600 | [91] |
| In/WO3-SnO2 | 66.5 | 50 | 2.12 | 200 | [92] |
| 2D C3N4-SnO2 | 11 | 67 | 7 | 380 | [93] |
| TiO2 | 15.24 | 500 | 9.19 | 270 | [94] |
| 2D ZnOnanosheets | 106.1 | 500 | - | 300 | [95] |
| WO3 decorated with Au and Pd | - | 1000 | 6 | 300 | [96] |
| In2O3 nanoparticle | 21.5 | 1000 | 2 | 250 | [97] |
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Share and Cite
Saasa, V.; Malwela, T.; Beukes, M.; Mokgotho, M.; Liu, C.-P.; Mwakikunga, B. Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring. Diagnostics 2018, 8, 12. https://doi.org/10.3390/diagnostics8010012
Saasa V, Malwela T, Beukes M, Mokgotho M, Liu C-P, Mwakikunga B. Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring. Diagnostics. 2018; 8(1):12. https://doi.org/10.3390/diagnostics8010012
Chicago/Turabian StyleSaasa, Valentine, Thomas Malwela, Mervyn Beukes, Matlou Mokgotho, Chaun-Pu Liu, and Bonex Mwakikunga. 2018. "Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring" Diagnostics 8, no. 1: 12. https://doi.org/10.3390/diagnostics8010012
APA StyleSaasa, V., Malwela, T., Beukes, M., Mokgotho, M., Liu, C.-P., & Mwakikunga, B. (2018). Sensing Technologies for Detection of Acetone in Human Breath for Diabetes Diagnosis and Monitoring. Diagnostics, 8(1), 12. https://doi.org/10.3390/diagnostics8010012
