Utilizing the response patterns of a temperature modulated chemoresistive gas sensor for gas diagnosis

Amini, Amir; Ghafarinia, Vahid

doi:10.1088/1757-899x/17/1/012048

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Different methods have been utilized for selective gas sensor fabrication [5][6][7][8]: Pallet doping and surface decoration are widely adapted for selectivity enhancement [6] and the addition of microporous barriers [7] and microfluidic channels [8] have been shown to result in single sensor gas analysis devices. It has also been shown that modulating the operating temperature of a chemoresistor can dramatically improve its selectivity [9][10][11][12][13][14]. In optimum conditions, a single chemoresistor can perform as a quasi array; its response at a specific temperature can be assumed as the response obtained from a specified component of the array.…”

Section: Introductionmentioning

confidence: 99%

Fast Diagnosis of Volatile Organic Compounds with a Temperature-Modulated Chemoresistor

Amini

Hosseini-Golgoo

2011

KEM

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Virtual arrays formed by operating temperature modulation of a commercial non selective chemoresistor have been utilized for gas identification. Here, we are reporting the details of a refined system which distinctly classifies methanol, ethanol, 1-butanol, acetone and hydrogen contaminations in a wide concentration range. A staircase voltage waveform of 5 plateaus is applied to the sensor’s microheater and gas recognition is achieved in 25 s. Sensor’s output is modeled by an “autoregressive moving average with exogenous variables” (ARMAX) model. The modeling parameters obtained for an unknown analyte are utilized as the components of its feature vectors which afford its classification in a feature space. Cross-validation in the 5 to 100 ppm concentration range for H2, and 200 to 2000 ppm for the other analytes examined, resulted in an overall classification success rate of 100%.

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Section: Introductionmentioning

confidence: 99%

Fast Diagnosis of Volatile Organic Compounds with a Temperature-Modulated Chemoresistor

Amini

Hosseini-Golgoo

2011

KEM

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Blood glucose prediction by breath analysis system with feature selection and model fusion

Yan

Zhang

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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It has been shown that the concentration of acetone in breath is correlated with the subject's blood glucose level (BGL). Therefore, noninvasive BGL monitoring of diabetics can be achieved by the analysis of components in breath. In this paper, a breath analysis device with 10 gas sensors is designed to measure breath samples. Transient features are extracted from the signals of the sensors. Sequential forward selection is applied on the features to find the most informative ones. In order to reduce the interference brought by the inter-subject variance of breath acetone, global and local BGL prediction models are built and fused. The two models are based on different training strategies and have different advantages. Experiments were conducted using 203 breath samples from 36 diabetic subjects. Results show that the accuracy of the proposed feature is better than other similar features and the model fusion strategy is effective. The mean absolute error and mean relative absolute error of the system are 2.07 mmol/L and 20.69%, respectively.

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Utilizing the response patterns of a temperature modulated chemoresistive gas sensor for gas diagnosis

Cited by 2 publications

References 19 publications

Fast Diagnosis of Volatile Organic Compounds with a Temperature-Modulated Chemoresistor

Fast Diagnosis of Volatile Organic Compounds with a Temperature-Modulated Chemoresistor

Blood glucose prediction by breath analysis system with feature selection and model fusion

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