CN111307881A - Gas sensor baseline drift compensation method for VOC detection - Google Patents
Gas sensor baseline drift compensation method for VOC detection Download PDFInfo
- Publication number
- CN111307881A CN111307881A CN202010162441.7A CN202010162441A CN111307881A CN 111307881 A CN111307881 A CN 111307881A CN 202010162441 A CN202010162441 A CN 202010162441A CN 111307881 A CN111307881 A CN 111307881A
- Authority
- CN
- China
- Prior art keywords
- signal
- gas sensor
- baseline drift
- imf
- zero
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000001514 detection method Methods 0.000 title claims abstract description 33
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 30
- 238000005259 measurement Methods 0.000 claims abstract description 21
- 238000012216 screening Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 108010076504 Protein Sorting Signals Proteins 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 239000004065 semiconductor Substances 0.000 claims description 2
- 230000008030 elimination Effects 0.000 claims 1
- 238000003379 elimination reaction Methods 0.000 claims 1
- 230000004044 response Effects 0.000 abstract description 4
- 238000012549 training Methods 0.000 abstract description 4
- 238000012567 pattern recognition method Methods 0.000 abstract description 2
- 239000007789 gas Substances 0.000 description 49
- 239000012855 volatile organic compound Substances 0.000 description 28
- 239000000463 material Substances 0.000 description 7
- 231100000572 poisoning Toxicity 0.000 description 7
- 230000000607 poisoning effect Effects 0.000 description 7
- CSCPPACGZOOCGX-UHFFFAOYSA-N Acetone Chemical compound CC(C)=O CSCPPACGZOOCGX-UHFFFAOYSA-N 0.000 description 6
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 5
- 230000002452 interceptive effect Effects 0.000 description 5
- YNQLUTRBYVCPMQ-UHFFFAOYSA-N Ethylbenzene Chemical compound CCC1=CC=CC=C1 YNQLUTRBYVCPMQ-UHFFFAOYSA-N 0.000 description 4
- KFZMGEQAYNKOFK-UHFFFAOYSA-N Isopropanol Chemical compound CC(C)O KFZMGEQAYNKOFK-UHFFFAOYSA-N 0.000 description 4
- 230000032683 aging Effects 0.000 description 4
- 238000012937 correction Methods 0.000 description 4
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 description 3
- YXFVVABEGXRONW-UHFFFAOYSA-N Toluene Chemical compound CC1=CC=CC=C1 YXFVVABEGXRONW-UHFFFAOYSA-N 0.000 description 3
- 238000004643 material aging Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 238000000766 differential mobility spectroscopy Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000002290 gas chromatography-mass spectrometry Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 239000002207 metabolite Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000000717 retained effect Effects 0.000 description 2
- 238000000824 selected ion flow tube mass spectrometry Methods 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 208000024827 Alzheimer disease Diseases 0.000 description 1
- 206010058467 Lung neoplasm malignant Diseases 0.000 description 1
- CTQNGGLPUBDAKN-UHFFFAOYSA-N O-Xylene Chemical compound CC1=CC=CC=C1C CTQNGGLPUBDAKN-UHFFFAOYSA-N 0.000 description 1
- 206010039966 Senile dementia Diseases 0.000 description 1
- 208000005718 Stomach Neoplasms Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000000090 biomarker Substances 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 206010017758 gastric cancer Diseases 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical group [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 201000005202 lung cancer Diseases 0.000 description 1
- 208000020816 lung neoplasm Diseases 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000002105 nanoparticle Substances 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 201000011549 stomach cancer Diseases 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
- 239000008096 xylene Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/12—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon absorption of a fluid; of a solid body in dependence upon reaction with a fluid, for detecting components in the fluid
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
- G01N27/12—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon absorption of a fluid; of a solid body in dependence upon reaction with a fluid, for detecting components in the fluid
- G01N27/125—Composition of the body, e.g. the composition of its sensitive layer
- G01N27/127—Composition of the body, e.g. the composition of its sensitive layer comprising nanoparticles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/416—Systems
- G01N27/4162—Systems investigating the composition of gases, by the influence exerted on ionic conductivity in a liquid
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/416—Systems
- G01N27/4163—Systems checking the operation of, or calibrating, the measuring apparatus
Landscapes
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Electrochemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Chemical Kinetics & Catalysis (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Molecular Biology (AREA)
- Engineering & Computer Science (AREA)
- Nanotechnology (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention discloses a gas sensor baseline drift compensation method for VOC detection, which comprises two parts of signal decomposition and data reconstruction, wherein according to the low-frequency slowly-varying signal characteristics of a VOC gas sensor, a measurement signal of the gas sensor is decomposed into the sum of a series of frequency high-low arrangement eigenmode functions IMF and a remainder by adopting IEMD, the low-frequency slowly-varying baseline drift signal can be decomposed into the last IMF and the remainder, then the baseline drift part is screened out and discarded by proper rules, and the accurate and reliable measurement signal can be reconstructed by using the rest IMF. Compared with the prior art, the method removes the component causing the drift by decomposing the response signal of the sensor, is a non-pattern recognition method, can be applied to linear and nonlinear 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
The invention relates to a gas sensor baseline drift compensation method for VOC detection, and belongs to the field of gas sensor signal processing.
Background
Because the exhaled gas of a human body contains part of endogenous metabolites, the detection and analysis of the exhaled Volatile Organic Compound (VOC) gas gradually becomes a novel auxiliary diagnosis means, and has wide application prospects in the fields of clinical diagnosis and health monitoring. Endogenous metabolites are selected as biomarkers, can reflect the health condition of human bodies, and are widely concerned in early screening and diagnosis of serious diseases such as lung cancer, gastric cancer, senile dementia and the like. At present, commonly used exhaled gas detection methods comprise GC-MS, SIFT-MS, FAIMS, a gas sensor and the like, however, the GC-MS, SIFT-MS and FAIMS methods are high in cost, large in size and complex in structure, large-scale use of the methods is limited, and meanwhile real-time online monitoring is difficult to achieve, so that the gas sensor with the advantages of small size, low power consumption, low price and the like has great advantages and has huge market potential.
However, due to the influence of aging of the gas-sensitive material, changes in ambient temperature and humidity, sensor poisoning or interfering gases in the environment, etc., the output of the gas sensor often generates a large baseline shift (BaselineShift), which seriously affects the accuracy of the measurement. In order to obtain stable and reliable measurement results, the following three solutions for solving the drift of the gas sensor are mainly used: (1) the gas sensitive material with higher stability and reliability is developed, and the generation of drift is avoided from the source; (2) modulating or calibrating the sensor by adopting a dynamic temperature modulation heating mode; (3) drift in the sensor response data is suppressed and removed using advanced signal processing and pattern recognition techniques. The scheme (1) relates to the improvement of physical and chemical properties and manufacturing process of the gas-sensitive material, and has the disadvantages of high difficulty and high cost; the scheme (2) needs to introduce a complicated temperature modulation hardware part, so that the cost and the volume of the system are increased; the scheme (3) is simple to operate and low in cost, is a convenient and quick baseline drift inhibition method, and has good research and application prospects. Based on the scheme (3), the scholars successively put forward methods such as product drift correction, multivariate component correction, neural network and the like to correct the baseline drift of the gas sensor, and certain positive effects are achieved. However, the product drift correction method and the multivariate component correction method have good effects on the linear baseline drift and cannot be applied to the nonlinear baseline drift; the neural network method requires a large number of labeled data samples for training, and has the problems of local minimum and insufficient generalization.
Disclosure of Invention
The invention mainly aims to overcome the defects of the prior art, and provides a gas sensor baseline drift compensation method for VOC detection, which is suitable for linear and nonlinear baseline drift, does not need a large amount of data sample training, and has the advantages of low cost, high efficiency and simplicity and convenience in use.
In order to achieve the purpose, the technical solution of the invention is as follows:
a gas sensor baseline drift compensation method for VOC detection, comprising two parts of signal decomposition and data reconstruction, wherein:
the signal decomposition part adopts an Improved Empirical Mode Decomposition (IEMD) algorithm to decompose the original measurement signal, and the specific steps are as follows:
the algorithm decomposes an original measurement signal, and comprises the following specific steps:
(1) the resistance value output by the VOC gas sensor is regarded as a time sequence signal x (t), and Gaussian white noise W with the number of L is addedk(t) noise variance and standard deviation are respectively
And βkDefining k as an Intrinsic Mode Function (IMF) order obtained by decomposition, and initializing k to be 1;
(2) defining j as the number of times noise is added, initially making j equal to 0,
namely, it is
An intermediate variable representing the jth order of the signal x (t);
(3) let j equal j +1 at
Adding random white noise W into the signalk(t) that is
White Gaussian noise representing the jth IMF of the kth order;
(4) find out
All maximum and minimum values of (a) are constructed using a cubic spline difference method
Upper and lower envelopes of the signal sequence, the upper envelope being defined as
Fitting by using a maximum value; the lower envelope is defined as
Fitting by a minimum value to obtain;
(5) calculate the mean of the upper and lower envelopes
And is arranged at
By subtracting this mean value, i.e.
(6) Judgment of
Whether the IMF stop condition is satisfied: if not, returning to the step (4) to continue screening, and if the condition is met, adding the noise at the jth time into the k-th order eigenmode function of the noise to be the
(7) Judging whether j is equal to L: if equal, a final k-th order eigenmode function can be obtained
Otherwise make
And returning to the step (3);
(8) let x (t) be x (t) -IMFk(t), judging: if the maximum value of the absolute value of x (t) is less than or equal to 0.1 or x (t) is a monotonic function, EMD decomposition is completed, and the remainder r isn(t) ═ x (t), n is the number of layers of IEMD decomposition; otherwise, k is k +1, and the step (2) is returned.
The data reconstruction part screens out the components of which the baseline drift is dominant in the original signal and reconstructs the components, and the specific steps are as follows:
(9) after the steps (1) to (8), the original signal x (t) of the gas sensor is decomposed into eigenmode functions IMF of n layers in totalkAnd a remainder rn(t) the set of component functions formed, i.e., x (t), can be expressed as:
(10) the IMF of each eigenmode function in equation (1) is evaluated using the zero crossing rate detection as a criterionk(t) screening to remove the component of the original signal x (t) where baseline drift is dominant, i.e. to remove IMFk(t) and rn(t) components less than a zero-crossing rate threshold;
(11) recombining the screened eigenmode function components to obtain a new signal:
wherein m is less than n, and the intrinsic mode function IMF of the m layers left after screening is representedk，rm(t) is the corresponding remainder. In the recombined sensor baseline measurement signal, the intrinsic drift characteristics of the device generated by the problems of gas sensitive material aging, environmental influence, sensor poisoning or interfering gas and the like are removed, and the original information of baseline measurement is retained.
Further, the sensor for detecting VOC is a semiconductor-type or electrochemical gas sensor, the baseline signal of the sensor is an unloaded measurement signal obtained by desorbing VOC with high-purity air or standard gas when the gas sensor does not perform VOC detection or after the VOC detection, and the baseline drift mostly comes from the influence of factors such as aging of gas-sensitive materials, environmental temperature and humidity changes, sensor poisoning or interfering gas in the environment, which determines that the baseline drift signal is a low-frequency slowly-varying signal, and the frequency is generally less than 1 Hz.
Further, in the signal decomposition part, an improved empirical Mode decomposition algorithm (IEMD) used for baseline cancellation of VOC gas sensors, in order to solve the problem of Mode aliasing in the original emd (empirical Mode decomposition) method, a noise-assisted analysis-based method is adopted, and each IMF obtained by decomposing the signal must satisfy the following stopping conditions: (1) in the whole signal sequence, the number of the extreme points is equal to the number of the zero-crossing points or the difference between the extreme points and the zero-crossing points is not more than 1 at most; (2) at any point, the mean of the upper and lower envelopes is determined to be 0 by the maximum and minimum of the signal.
Further, in the data reconstruction part, the zero crossing rate is defined as the number of zero crossing points of the signal in unit time, and the specific way of judging by using the zero crossing rate in the step (10) is as follows: according to the low-frequency characteristic of baseline drift of the gas sensor, the frequency of a signal in unit time is in direct proportion to the number of zero-crossing points, the higher the proportion of high-frequency components in the signal is, the more the number of zero-crossing points is, and the larger the zero-crossing rate is; on the contrary, the higher the low-frequency component occupation ratio is, the lower the zero crossing point number is, and the smaller the zero crossing rate is correspondingly. Therefore, the principle of zero crossing rate detection is to obtain the time domain characteristics of the signal by judging the time change of the number of zero crossings of the signal in the time domain.
Further, in the above step, βkTypical values of (A) are between 0.1 and 0.4, typical values of L are 50, 100 and 200, a zero crossing rate threshold value is set to be 0.01, and an eigenmode function IMF obtained after decomposition is usedk(t) and remainder rnAnd (t) removing the component with the zero crossing rate smaller than the threshold value from the output signal of the gas sensor to obtain the output signal of the gas sensor after baseline drift compensation.
The invention has the beneficial effects that:
the invention provides a gas sensor baseline drift compensation method for VOC detection, which adopts IEMD to decompose a measurement signal of a gas sensor into the sum of a series of frequency high-low arrangement eigenmode functions IMF and a remainder according to the low-frequency slowly-varying signal characteristic of a VOC gas sensor. Wherein, the high frequency transient signal is separated out firstly and exists in the first IMFs; the low frequency slowly varying signal is then separated out and is present in the following IMFs and residuals. Therefore, the low-frequency slowly-changed baseline drift signal can be decomposed into the last IMFs and the rest items, the baseline drift part is screened out and discarded through a proper rule, and an accurate and reliable measurement signal can be reconstructed by using the rest IMFs.
Most of the baseline drift of the gas sensor comes from the aging of the gas-sensitive material, the poisoning of the sensor and the like caused by long-term measurement, at the moment, the intrinsic characteristics of the device are changed and the device is difficult to use, and the gas sensor needs to be recalibrated or prepared for the second time, even replaced. Compared with the prior art, the method removes the component causing the drift by decomposing the response signal of the sensor, is a non-pattern recognition method, can be applied to linear and nonlinear 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.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention.
Fig. 2 is a graph of the output raw resistance values of the VOC sensor of the present invention.
Fig. 3 is a decomposition result diagram of raw resistance data IEMD output by the VOC sensor of the present invention.
Fig. 4 is a graph of the results of baseline drift compensation of the VOC sensor output raw resistance values.
Detailed Description
The invention is further illustrated in the following description with reference to the figures and the detailed description, without limiting the scope of the invention to the embodiments.
The invention provides a low-cost and high-efficiency gas sensor baseline drift compensation method for VOC detection, which specifically comprises the following steps:
the baseline drift of the gas sensor for VOC detection is mostly influenced by factors such as gas sensitive material aging, environmental temperature and humidity change, sensor poisoning or interfering gas in the environment, and the like, so that the baseline drift signal is determined to be a low-frequency slowly-varying signal, and the frequency is generally less than 1 Hz. Based on this, the present invention adopts an Empirical Mode Decomposition (EMD) method to eliminate the baseline drift. EMD is a data-driven time-domain data analysis method that can decompose a signal into the sum of a series of frequency high-low permutation Intrinsic Mode Functions (IMFs) and 1 remainder. Wherein, the high frequency transient signal is separated out firstly and exists in the first IMFs; the low frequency slowly varying signal is then separated out and is present in the following IMFs and residuals. Therefore, the low-frequency slowly-changed baseline drift signal can be decomposed into the last IMFs and the rest items, the baseline drift part is screened out and discarded through a proper rule, and an accurate and reliable measurement signal can be reconstructed by using the rest IMFs.
In combination with the above-mentioned ideas, the flow of the method for compensating the baseline drift of the gas sensor for VOC detection according to the present invention is shown in fig. 1, and mainly includes two parts, namely signal decomposition and data reconstruction, wherein:
the signal decomposition part adopts an Improved Empirical Mode Decomposition (IEMD) algorithm to decompose the original measurement signal, and the specific steps are as follows:
(1) when the resistance value output by the VOC gas sensor is regarded as oneAdding L Gaussian white noise W into the intermediate sequence signal x (t)k(t) noise variance and standard deviation are respectively
And βkDefining k as an Intrinsic Mode Function (IMF) order obtained by decomposition, and initializing k to be 1;
(2) defining j as the number of times noise is added, initially making j equal to 0,
namely, it is
An intermediate variable representing the jth order of the signal x (t);
(3) let j equal j +1 at
Adding random white noise W into the signalk(t) that is
White Gaussian noise representing the jth IMF of the kth order;
(4) find out
All maximum and minimum values of (a) are constructed using a cubic spline difference method
Upper and lower envelopes of the signal sequence, the upper envelope defining the sound
Fitting by using a maximum value; the lower envelope is defined as
Fitting by a minimum value to obtain;
(5) calculate the mean of the upper and lower envelopes
And is arranged at
By subtracting this mean value, i.e.
(6) Judgment of
Whether the IMF stop condition is satisfied: if not, returning to the step (4) to continue screening, and if the condition is met, adding the noise at the jth time into the k-th order eigenmode function of the noise to be the
(7) Judging whether j is equal to L: if equal, a final k-th order eigenmode function can be obtained
Otherwise make
And returning to the step (3);
(8) let x (t) be x (t) -IMFk(t), judging: if the maximum value of the absolute value of x (t) is less than or equal to 0.1 or x (t) is a monotonic function, EMD decomposition is completed, and the remainder r isn(t) ═ x (t), n is the number of layers of IEMD decomposition; otherwise, k is k +1, and the step (2) is returned.
The data reconstruction part screens out the components of which the baseline drift is dominant in the original signal and reconstructs the components, and the specific steps are as follows:
(9) through the steps (1) to (8),the raw signal x (t) of the gas sensor is decomposed into eigenmode functions IMF of a total of n layerskAnd a remainder rn(t) the set of component functions formed, i.e., x (t), can be expressed as:
(10) the IMF of each eigenmode function in equation (1) is evaluated using the zero crossing rate detection as a criterionk(t) screening to remove the component of the original signal x (t) where baseline drift is dominant, i.e. to remove IMFk(t) and rn(t) components less than a zero-crossing rate threshold;
(11) recombining the screened eigenmode function components to obtain a new signal:
wherein m is less than n, and the intrinsic mode function IMF of the m layers left after screening is representedk，rm(t) is the corresponding remainder. In the recombined sensor baseline measurement signal, the intrinsic drift characteristics of the device generated by the problems of gas sensitive material aging, environmental influence, sensor poisoning or interfering gas and the like are removed, and the original information of baseline measurement is retained.
Implementation example:
in combination with the present invention, a specific application example is given: fig. 2 shows a typical resistance value output of a sensor for VOC detection, which is a semiconductor gas sensor modified with gold nanoparticles and capable of detecting a plurality of different VOC gases including toluene, ethylbenzene, xylene, methanol, ethanol, acetone, isopropanol, etc., preferably ethylbenzene, ethanol, acetone, and isopropanol. In a section of continuous 8 detection sample signals, the initial value (or baseline) of the detection resistance of the sensor is continuously shifted and is reduced from the initial 294.2 to 291.6 of the 8 th time after each ventilation of the VOC is cleaned and desorbed by using high-purity air as background air, and the drift resistance value reaches 120% of the maximum measurement (4 th time), which seriously influences the actual use effect.
Research shows that the gas sensor for VOC detection has more or less baseline drift problems, and the baseline drift is mostly caused by aging of the gas sensitive material, sensor poisoning or environmental interference and the like caused by long-term measurement, so that the intrinsic characteristics of the sensor or the sensitive material are changed. The method removes the drift influence by decomposing the base line response of the sensor, is suitable for linear and nonlinear drift, and has low cost, high efficiency and simple and convenient use.
FIG. 3 shows the IEMD decomposition result of the VOC sensor outputting the raw resistance data, and the VOC detection curve of FIG. 2 is subjected to IEMD decomposition by the method of the present invention to obtain 9 eigenmode functions [ IMF ]1(t)，IMF2(t)，...，IMF9(t)]And 1 remainder rn(t) of (d). It can be observed that the information of the original signal is distributed from high frequency to low frequency [ IMF ]1，IMF9]And the baseline wander is contained as a low frequency signal in the latter few eigenmode functions.
Fig. 4 shows the final resistance data result, which is the result of fig. 4 obtained by removing the last 3 eigenmode functions IMF in fig. 3 and performing reconstruction according to the zero-crossing rate detection principle described in the present invention. It can be observed that the baseline shift at VOC detection is no longer present, 8 different detections maintain the same baseline value, compared to the original detection signal of fig. 2.
The above description is one embodiment of the present invention and is not intended to limit the present invention. All equivalents which come within the spirit of the invention are therefore intended to be embraced therein. Details not described herein are well within the skill of those in the art.
Claims (6)
1. A gas sensor baseline drift compensation method for VOC detection is characterized in that: the method comprises two parts of signal decomposition and data reconstruction, and the specific compensation method comprises the following steps:
the signal decomposition part adopts an improved empirical mode decomposition algorithm to decompose the original measurement signal, and the specific steps are as follows:
(1) the resistance value output by the VOC gas sensor is regarded as a time sequence x (t), Gaussian white noise Wk (t) with the number of L is added, and the noise variance and the standard deviation are respectively
And βkDefining k as the order of an eigenmode function obtained by decomposition, and initializing k to be 1;
(2) defining j as the number of times noise is added, initially making j equal to 0,
namely, it is
An intermediate variable representing the jth order of the signal x (t);
(3) let j equal j +1 at
Adding random white noise W into the signalk(t) that is
White Gaussian noise representing the jth order of eigenmode function of kth order;
(4) find out
All maximum and minimum values of (a) are constructed using a cubic spline difference method
Upper and lower envelope lines of the signal sequence, the upper envelope line being
Fitting by using a maximum value; a lower envelope of
Fitting by a minimum value to obtain;
(5) calculate the mean of the upper and lower envelopes
And is arranged at
By subtracting this mean value, i.e.
(6) Judgment of
If the stopping condition of the eigenmode function is met, returning to the step (4) for continuous screening if the stopping condition of the eigenmode function is not met, and if the stopping condition of the eigenmode function is met, adding noise at the jth order into the eigenmode function of the kth order of the jth time into the eigenmode function of the noise to be the
(7) Judging whether j is equal to L or not, if so, obtaining the final k-th order eigenmode function
Otherwise make
And returning to the step (3);
(8) let x (t) be x (t) -IMFk(t) intoAnd (4) line judgment: if the maximum value of the absolute value of x (t) is less than or equal to 0.1 or x (t) is a monotonic function, EMD decomposition is completed, and the remainder r isn(t) ═ x (t), n is the number of layers decomposed by the improved empirical mode decomposition algorithm; otherwise, k is k +1, and the step (2) is returned;
the data reconstruction part screens out the components of which the baseline drift is dominant in the original signal and reconstructs the components, and the specific steps are as follows:
(9) after the steps (1) to (8), the raw signal x (t) of the gas sensor is decomposed into an eigenmode function IMF of n layers in totalkAnd a remainder rn(t) the set of component functions formed, i.e., x (t), can be expressed as:
(10) the IMF of each eigenmode function in equation (1) is evaluated using the zero crossing rate detection as a criterionk(t) screening to remove the component of the original signal x (t) where baseline drift is dominant, i.e. to remove IMFk(t) and rn(t) components less than a zero-crossing rate threshold;
(11) recombining the screened eigenmode function components to obtain a new signal:
wherein m is less than n, and the intrinsic mode function IMF of the m layers left after screening is representedk，rm(t) is the corresponding remainder; in the recombined sensor baseline measurement signal, the intrinsic drift characteristics of the device caused by the problems are removed, and the original information of the baseline measurement is kept.
2. A gas sensor baseline drift compensation method for VOC detection according to claim 1, wherein: the gas sensor for VOC detection is a semiconductor type or electrochemical type gas sensor.
3. A gas sensor baseline drift compensation method for VOC detection according to claim 1, wherein: when the signal decomposition part is used for baseline elimination of the VOC gas sensor, in order to solve the problem of mode confusion existing in the original empirical mode decomposition algorithm method, a noise-assisted analysis mode is adopted, and each eigenmode function obtained by decomposing a signal must meet the following stop conditions: (1) in the whole signal sequence, the number of the extreme points is equal to the number of the zero-crossing points or the difference between the extreme points and the zero-crossing points is not more than 1 at most; (2) at any point, the mean of the upper and lower envelopes is determined to be 0 by the maximum and minimum of the signal.
4. A gas sensor baseline drift compensation method for VOC detection according to claim 1, wherein: in the data reconstruction part, the zero crossing rate is defined as the number of zero crossing points of the signal in unit time, and the specific mode of judging by adopting the zero crossing rate in the step (10) is as follows: according to the low-frequency characteristic of baseline drift of the gas sensor, the frequency of a signal in unit time is in direct proportion to the number of zero-crossing points, the higher the proportion of high-frequency components in the signal is, the more the number of zero-crossing points is, and the larger the zero-crossing rate is; on the contrary, the higher the low-frequency component occupation ratio is, the lower the zero crossing point number is, and the smaller the zero crossing rate is correspondingly.
5. The method of claim 1, wherein the step (1) to the step (11) are βkTypical values of (A) are between 0.1 and 0.4, typical values of L are 50, 100 and 200, and a threshold value of the zero crossing rate is set to be 0.01.
6. The gas sensor baseline drift compensation method for VOC detection according to claim 5, wherein: the screening method in the step (10) comprises the following steps: IMF obtained after decompositionk(t) and remainder rn(t) removing the component with the zero crossing rate smaller than the set zero crossing threshold value from the output signal of the gas sensor to obtain the gas sensor with baseline drift compensationThe sensor outputs a signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010162441.7A CN111307881B (en) | 2020-03-10 | 2020-03-10 | Gas sensor baseline drift compensation method for VOC detection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010162441.7A CN111307881B (en) | 2020-03-10 | 2020-03-10 | Gas sensor baseline drift compensation method for VOC detection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111307881A true CN111307881A (en) | 2020-06-19 |
| CN111307881B CN111307881B (en) | 2022-03-08 |
Family
ID=71145432
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010162441.7A Active CN111307881B (en) | 2020-03-10 | 2020-03-10 | Gas sensor baseline drift compensation method for VOC detection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111307881B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090090626A1 (en) * | 2006-10-12 | 2009-04-09 | Holt Christopher T | Hydrogen sensitive composite material, hydrogen gas sensor, and sensor for detecting hydrogen and other gases with improved baseline resistance |
| CN101482531A (en) * | 2009-01-10 | 2009-07-15 | 大连理工大学 | Baseline shift adaptive compensation detecting method used for combustible gas detector |
| US20100179409A1 (en) * | 2002-02-12 | 2010-07-15 | Dexcom, Inc. | Systems and methods for replacing signal artifacts in a glucose sensor data stream |
| CN105030232A (en) * | 2015-06-30 | 2015-11-11 | 广东工业大学 | Baseline drift correction method for electrocardiosignal |
| CN106344005A (en) * | 2016-10-28 | 2017-01-25 | 张珈绮 | Mobile ECG (electrocardiogram) monitoring system and monitoring method |
| CN107361762A (en) * | 2017-08-04 | 2017-11-21 | 山东理工大学 | ECG baseline drift bearing calibration based on variation mode decomposition |
| CN107607144A (en) * | 2017-09-15 | 2018-01-19 | 深圳市卡普瑞环境科技有限公司 | A kind of sensor base line drift correction method and detection device |
| CN107607143A (en) * | 2017-09-15 | 2018-01-19 | 深圳市卡普瑞环境科技有限公司 | A kind of method and detection device of sensor base line drift correction |
| US20190257803A1 (en) * | 2018-02-22 | 2019-08-22 | Apple Inc. | Gas sensor baseline correction using multiple co-located gas sensors |
-
2020
- 2020-03-10 CN CN202010162441.7A patent/CN111307881B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100179409A1 (en) * | 2002-02-12 | 2010-07-15 | Dexcom, Inc. | Systems and methods for replacing signal artifacts in a glucose sensor data stream |
| US20090090626A1 (en) * | 2006-10-12 | 2009-04-09 | Holt Christopher T | Hydrogen sensitive composite material, hydrogen gas sensor, and sensor for detecting hydrogen and other gases with improved baseline resistance |
| CN101482531A (en) * | 2009-01-10 | 2009-07-15 | 大连理工大学 | Baseline shift adaptive compensation detecting method used for combustible gas detector |
| CN105030232A (en) * | 2015-06-30 | 2015-11-11 | 广东工业大学 | Baseline drift correction method for electrocardiosignal |
| CN106344005A (en) * | 2016-10-28 | 2017-01-25 | 张珈绮 | Mobile ECG (electrocardiogram) monitoring system and monitoring method |
| CN107361762A (en) * | 2017-08-04 | 2017-11-21 | 山东理工大学 | ECG baseline drift bearing calibration based on variation mode decomposition |
| CN107607144A (en) * | 2017-09-15 | 2018-01-19 | 深圳市卡普瑞环境科技有限公司 | A kind of sensor base line drift correction method and detection device |
| CN107607143A (en) * | 2017-09-15 | 2018-01-19 | 深圳市卡普瑞环境科技有限公司 | A kind of method and detection device of sensor base line drift correction |
| US20190257803A1 (en) * | 2018-02-22 | 2019-08-22 | Apple Inc. | Gas sensor baseline correction using multiple co-located gas sensors |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111307881B (en) | 2022-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Winquist et al. | A hybrid electronic tongue | |
| Sohn et al. | Characterisation of humidity dependence of a metal oxide semiconductor sensor array using partial least squares | |
| AU777114B2 (en) | Highly time resolved impedance spectroscopy | |
| Akhter et al. | Graphene oxide (GO) coated impedimetric gas sensor for selective detection of carbon dioxide (CO 2) with temperature and humidity compensation | |
| Omatu et al. | E-nose system by using neural networks | |
| KR102095959B1 (en) | Artificial Neural Network Model-Based Methods for Analyte Analysis | |
| Kim et al. | Electronic-nose for detecting environmental pollutants: Signal processing and analog front-end design | |
| CN106093134B (en) | The compensation method of metal oxide sensor array response drift | |
| CN111307881B (en) | Gas sensor baseline drift compensation method for VOC detection | |
| Hierold et al. | Quantitative analysis of gas mixtures with non-selective gas sensors | |
| Artursson et al. | Wavelet transform of electronic tongue data | |
| D’Amico et al. | Ingredients for sensors science | |
| CN111458395A (en) | Kalman filtering method and device for changing Q value and readable storage medium | |
| Sisk et al. | Comparison of analytical methods and calibration methods for correction of detector response drift in arrays of carbon black-polymer composite vapor detectors | |
| Yang et al. | Recognition algorithms in E-nose: A Review | |
| Zhang et al. | Fast measurement with chemical sensors based on sliding window sampling and mixed-feature extraction | |
| CN116933084A (en) | Pollutant emission prediction method and device | |
| Głowacz et al. | Comparison of various data analysis techniques applied for the classification of oligopeptides and amino acids by voltammetric electronic tongue | |
| CN112198207B (en) | Formaldehyde detection method and formaldehyde detection device | |
| CN113959979B (en) | Near infrared spectrum model migration method based on deep Bi-LSTM network | |
| Majzoub et al. | Time-frequency design of a multi-sine excitation with random phase and controllable amplitude for (bio) impedance measurements | |
| Ghoreishizadeh et al. | Empirical study of noise dependence in electrochemical sensors | |
| Martinelli et al. | Chemical sensors clustering with the dynamic moments approach | |
| Chou et al. | Ga2O3 nanorods synthesized by hydrothermal method for dual-functional sensing of pH value and glucose | |
| CN113011321A (en) | Spectral signal denoising method, system, terminal and readable storage medium based on joint dictionary |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230413 Address after: Room A10, 1st Floor, Building 3, Lingyu Business Plaza Podium, No. 66 Qinglonggang Road, High Speed Rail New City, Xiangcheng District, Suzhou City, Jiangsu Province, 215100 Patentee after: Treasure Lion Technology (Suzhou) Co.,Ltd. Address before: 220000 No. 5, Xinfan Road, Nanjing, Jiangsu Province Patentee before: NANJING University OF TECHNOLOGY |