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Gas sensor baseline drift compensation method for VOC detection

A gas sensor, baseline drift technology, applied in instruments, measuring devices, scientific instruments, etc., can solve problems such as insufficient generalization, inability to apply nonlinear baseline drift, etc., and achieve the effects of low cost, high efficiency, and easy use.

Active Publication Date: 2020-06-19
宝狮子科技(苏州)有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, the product drift correction method and the multivariate component correction method have good effects on linear baseline drift, and cannot be applied to nonlinear baseline drift; neural network methods require a large number of labeled data samples for training, and there are "local minimum" and general inadequacy problem

Method used

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  • Gas sensor baseline drift compensation method for VOC detection
  • Gas sensor baseline drift compensation method for VOC detection
  • Gas sensor baseline drift compensation method for VOC detection

Examples

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Embodiment

[0056] In conjunction with the present invention, a specific application example is provided: as figure 2 Shown is a typical sensor resistance value output for VOC detection. The sensor is a semiconductor gas sensor modified by gold nanoparticles, which can detect many gases containing toluene, ethylbenzene, xylene, methanol, ethanol, acetone, isopropanol, etc. A variety of different VOC gases are detected, preferably ethylbenzene, ethanol, acetone and isopropanol are the detected VOC gases. In a period of 8 consecutive detection sample signals, the high-purity air is used as the background gas to clean and desorb the VOC after each ventilation, and the initial value (or baseline) of the detection resistance of the sensor has continuously shifted, from the initial value of 294.2 It was reduced to 291.6 at the 8th time, and the drift resistance value reached 120% of the maximum measurement (4th time), which seriously affected the actual use effect.

[0057] Studies have shown...

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Abstract

The invention discloses a gas sensor baseline drift compensation method for VOC detection. The method comprises a signal decomposition part and a data reconstruction part. According to low-frequency slow-varying signal features of a VOC gas sensor, a measurement signal of the gas sensor is decomposed into a sum of a series of frequency high-low arrangement type intrinsic mode functions IMF and a remainder by adopting IEMD; a low-frequency slowly-varying baseline drift signal is decomposed into the last few IMFs and remainders, then a baseline drift part is screened out through an appropriate rule and is removed, and an accurate and reliable measurement signal can be reconstructed by using the remaining IMFs. Compared with the prior art, the method provided by the invention decomposes the response signal of the sensor to remove the component causing drift, is a non-pattern recognition method, can be applied to linear and non-linear baseline drift, does not need a large amount of data sample training, and has the advantages of low cost, high efficiency and simple and convenient use.

Description

technical field [0001] The invention relates to a gas sensor baseline drift compensation method for VOC detection, belonging to the field of gas sensor signal processing. Background technique [0002] Since the exhaled breath of the human body contains some endogenous metabolites, the detection and analysis of exhaled volatile organic compounds (Volatile Organic Compounds, VOC) gas has gradually become a new type of auxiliary diagnostic means, in clinical diagnosis and health The field of monitoring has broad application prospects. Selecting endogenous metabolites as biomarkers can reflect the health status of the human body, and has received extensive attention in the early screening and diagnosis of major diseases such as lung cancer, gastric cancer, and senile dementia. At present, the commonly used exhaled gas detection methods include GC-MS, SIFT-MS, FAIMS and gas sensor, etc. However, the three methods of GC-MS, SIFT-MS and FAIMS are high in cost, large in size and co...

Claims

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Application Information

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IPC IPC(8): G01N27/12G01N27/416
CPCG01N27/12G01N27/127G01N27/4162G01N27/4163
Inventor 冯李航陈伟陈铭
Owner 宝狮子科技(苏州)有限公司
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