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Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.

Miao J, Zhang T, Wang Y, Li G - Sensors (Basel) (2015)

Bottom Line: The relation of the minimum numbers of sensors with number of samples in the sample set was revealed.The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually.Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China. jiacheng@zju.edu.cn.

ABSTRACT
The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

No MeSH data available.


The schematic diagram of the E-nose system.
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sensors-15-16027-f001: The schematic diagram of the E-nose system.

Mentions: The schematic diagram of the homemade E-nose system used is shown in Figure 1. Twelve metal oxide sensors were purchased from Figaro Engineering Inc. (Osaka, Japan) and fixed on a printed circuit board, which was placed in a 200 mL stainless chamber. The response characteristics of the sensors are shown in Table 2. A three-way valve is used to switch between target gas and clean dry air. Two mini vacuum pumps are used for gas washing at a constant flow of 1 L/min and controlled by the computer. A USB6211data acquisition (DAQ) unit, purchased from National Instruments Inc. (Austin, TX, USA), is used to acquire the sensor signals and control the pumps.


Optimal Sensor Selection for Classifying a Set of Ginsengs Using Metal-Oxide Sensors.

Miao J, Zhang T, Wang Y, Li G - Sensors (Basel) (2015)

The schematic diagram of the E-nose system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541866&req=5

sensors-15-16027-f001: The schematic diagram of the E-nose system.
Mentions: The schematic diagram of the homemade E-nose system used is shown in Figure 1. Twelve metal oxide sensors were purchased from Figaro Engineering Inc. (Osaka, Japan) and fixed on a printed circuit board, which was placed in a 200 mL stainless chamber. The response characteristics of the sensors are shown in Table 2. A three-way valve is used to switch between target gas and clean dry air. Two mini vacuum pumps are used for gas washing at a constant flow of 1 L/min and controlled by the computer. A USB6211data acquisition (DAQ) unit, purchased from National Instruments Inc. (Austin, TX, USA), is used to acquire the sensor signals and control the pumps.

Bottom Line: The relation of the minimum numbers of sensors with number of samples in the sample set was revealed.The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually.Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Industrial Control Technology, Institute of Cyber Systems and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China. jiacheng@zju.edu.cn.

ABSTRACT
The sensor selection problem was investigated for the application of classification of a set of ginsengs using a metal-oxide sensor-based homemade electronic nose with linear discriminant analysis. Samples (315) were measured for nine kinds of ginsengs using 12 sensors. We investigated the classification performances of combinations of 12 sensors for the overall discrimination of combinations of nine ginsengs. The minimum numbers of sensors for discriminating each sample set to obtain an optimal classification performance were defined. The relation of the minimum numbers of sensors with number of samples in the sample set was revealed. The results showed that as the number of samples increased, the average minimum number of sensors increased, while the increment decreased gradually and the average optimal classification rate decreased gradually. Moreover, a new approach of sensor selection was proposed to estimate and compare the effective information capacity of each sensor.

No MeSH data available.