| Literature DB >> 24152879 |
Shih-Wen Chiu1, Kea-Tiong Tang.
Abstract
Electronic noses have potential applications in daily life, but are restricted by their bulky size and high price. This review focuses on the use of chemiresistive gas sensors, metal-oxide semiconductor gas sensors and conductive polymer gas sensors in an electronic nose for system integration to reduce size and cost. The review covers the system design considerations and the complementary metal-oxide-semiconductor integrated technology for a chemiresistive gas sensor electronic nose, including the integrated sensor array, its readout interface, and pattern recognition hardware. In addition, the state-of-the-art technology integrated in the electronic nose is also presented, such as the sensing front-end chip, electronic nose signal processing chip, and the electronic nose system-on-chip.Entities:
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Year: 2013 PMID: 24152879 PMCID: PMC3859118 DOI: 10.3390/s131014214
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576
Commercial and available electronic nose instruments, modified from [52–54].
| Agilent, | 4440A | Fingerprint of MS | Desktop |
| AIRSENSE Analytics, | i-PEN/MOD | MOX | Laptop |
| Alpha MOS, | FOX 2000, 3000, 4000 | MOX | Desktop |
| AltraSens, | OdourVector | QCM | Desktop |
| AppliedSensor, | Air Quality Module | MOX | Laptop |
| Aromascan PLC, | A32S | CP | Desktop |
| Dr. Foedisch AG, | OMD 98 | MOX | Laptop |
| Draeger, | Multi-IMS | IMS | Palmtop |
| Electronic Sensor Technology, | ZNose 4200, 4300, | GC and SAW | Laptop |
| Environics, | M90-D1-C | IMS | Laptop |
| Forschungszentrum Karlsruhe, | SAGAS | SAW | Laptop |
| GSG Mess- und Analysengeräte, | MOSES II | Modular Gas Sensor Array: | Laptop |
| Owlstone Nanotech, Inc., | Lonestar | IMS | Laptop |
| Rae Systems, | ChemRAE | IMS | Palmtop |
| RST-Rostock, | FF2, FF2D | MOX | Desktop |
| Sacmi, | EOS Ambiente | MOX | Desktop |
| SMart Nose, | SMart Nose 2000 | Fingerprint of MS | Desktop |
| Smith Group, | Cyranose 320 | CP | Palmtop |
| Sysca AG, | Artinose | MOX | N/A |
CP, conductive polymer; MOX, metal-oxide semiconductor; IR, infra red; SAW, surface acoustic wave; QCM, quartz crystal microbalance; QMS, quadrupole mass spectrometry; GC, gas chromatography; IMS, ion mobility spectrometry.
Figure 1.The basic gas identification system blocks: an electronic nose and a mammal olfactory.
Figure 2.The portable electronic nose system consists of the hand-held sensing module and the personal digital apparatus: (a) laptop computer, and (b) PDA; (c) the block diagram of the system. Reprinted with permission from [81].
Architectural alternatives in the design of an electronic nose. Reprinted with permission from [56].
| Sensor Array + μC (PIC) | 8 bit/10 MHz/k bytes | Easy, small, low power, portable, cheaper | ASM/C | Easy algorithms with few data, KNN, easy NN, mostly trained off-system, linear classifiers, quadratic classifiers. |
| SA + high perf. MC | 8–16 bit/ 10–33 MHz/k bytes | Small, low power, portable, cheap | ASM/C | Some small matrix manipulation available, linear (PCA, LDA, PCR), KNN, easy fuzzy interface Systems. |
| SA +μP or DSP | 16–32 bit/ 20–100 MHz/Mb | Very fast, medium size, portable, high power consumption | ASM/C/C++ | Linear (PCA, LDA, PCR, PLS), KNN, easy neural and fuzzy system, standard feature extraction/selection (PCA, LDA). |
| SA + Embedded PC | 32 bit/ 80–233 MHz/Mb | Fast, medium size, portable, huge data capacity, high consume expensive | Any | Linear, complex learning algorithms (GA, NeuroFuzzy Systems, mixture models, APR, FIS Optimization Algorithms), advanced feature extraction/selection (SFS, SFFS). |
| SA + Desktop PC | 32–64 bit/ 700 MHz/Mb | Fast, medium size, portable, huge data capacity, consume not critical, expensive, not portable | Any / Visual | Linear, complex learning algorithms (GA, NeuroFuzzy Systems, mixture models, FIS Optimization Algorithms), advanced feature extraction/selection (SFS, SFFS), |
μC, microcontroller; μP, microprocessor; PIC, peripheral interface controller; SA, sensor array; NN, nearest neighbor algorithm; DSP, digital signal processing; KNN, k-nearest neighbor algorithm; PCA, principal component analysis; LDA, linear discriminant analysis; PCR, price coupling of regions; PLS, projection to latent structures; GA, genetic algorithm; APR, annual percentage rate; FIS, fuzzy inference system; SFS, shape from shading; SFFS, sequential forward floating selection.
Summary of advantages and disadvantages of MOX and CP sensors, modified from [54].
| Metal-Oxide Semiconductor (MOX) | Very high sensitivity Limited sensing range Rapid response and recovery times for low mol. wt. compounds | High temperature operation High power consumption Sulfur & Weak acid poisoning Limited sensor coatings Sensitive to humidity Poor precision |
| Conductive Polymer (CP) | Ambient temperature operation Sensitive to many VOCs Short response time Diverse sensor coatings Inexpensive Resistance to sensor poisoning | Sensitive to humidity and temperature Sensors can be overloaded by certain analytes Sensor life is limited |
Figure 3.The SEM picture of the integrated tin oxide gas sensor array of (a) the single sensor element, and (b) the 4 × 4 gas sensor array. Reprinted with permission from [100].
Figure 4.(a) The well-defined region for depositing sensing material, occupying an area of 100 × 100 μm2. The photograph shows the sensors before and after deposition. (b) The gas sensor array had been integrated into one chip. Reprinted with permission from [139].
Figure 5.(a) The photo shows one corner of the sensor array. The element has the dimension of 220 × 220 μm2 with 20 μm gap between the electrodes. (b) Single transducer element, the sensing material would be deposited to cover A–C. Reprint the photo in [151].
Figure 6.(a) Block diagram of the wide-dynamic-range resistive interface ASIC, and (b) the die photo of the 4-channel interface circuit ASIC. Reprinted with permission from [159].
Figure 7.The PWM-based interface circuit: (a) schematic, and (b) photograph of silicon prototype. Reprinted with permission from [164].
Figure 8.The row–column interface integrated with 128 SnO2-CNT gas sensors, (a) block diagram, and (b) chip photograph. Reprinted with permission from [175].
Figure 9.(a) The differential sensor conditioning circuitry of the read-out circuit and (b) the photo of the integration of SnO2 gas sensors and its differential preprocessing circuits in one chip. Reprinted with permission from [183].
Figure 10.(a) A wide-range programmable sensor conditioning circuitry and (b) the die photo of the CR-array and its readout circuit. Reprinted with permission from [184].
Figure 11.The electronic nose SoC (a) the concept of 3D structure and (b) die photo before coating the sensing materials. Reprinted with permission from [186].
Figure 12.(a) Adaptive neuromorphic olfaction system. (b) The top view of the olfaction chip and chemosensor array, mounting on top microchamber. Reprinted with permission from [191].