CN106295575A - A kind of Electronic Nose pre-method of calibration of sampled data based on response curve derivative characteristic - Google Patents
A kind of Electronic Nose pre-method of calibration of sampled data based on response curve derivative characteristic Download PDFInfo
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Abstract
The present invention relates to a kind of Electronic Nose pre-method of calibration of sampled data based on response curve derivative characteristic, being applicable for use with three circulations is the bionical sample mode of Electronic Nose of a complete cycle, including: the array sampled data for using the bionical sample mode of Electronic Nose to be gathered carries out pretreatment successively, if StFor the Relative electro-conductivity rate of change after normalization；Utilize sampled gradients method to StDifferentiate；Extract maximum and the maximum of 3 decline stage differential curves of 3 ascent stage differential curves of each sensor curve；Calculate each sensor differential curve 3 average differential in the time period and absolute value of 3 average differential in coordinate axes underlying time section above coordinate axes respectively；The maximum of the differential curve of each sensor obtained is compared judgement；The average differential of each sensor obtained is compared judgement；When differential extremum operator and average differential all meet the derivative characteristic of bionical breathing sampling, it is determined that the sampled data of this sensor is verified as accurately in advance.
Description
Technical field
The invention belongs to instrument and fields of measurement, be specifically related to the sampling of a kind of Electronic Nose based on response curve derivative characteristic
The pre-method of calibration of data.
Background technology
Electronic Nose, also known as Artificial Olfactory, be a kind of modern bionical detecting instrument, and it can simulating human and suckling
The structure of animal olfactory system and function, it is achieved the detection identification to simple or complicated abnormal smells from the patient.Electronic Nose generally includes gas
Sensor array, information pre-processing and pattern recognition three parts.When detection by electronic nose sample, volatile flavor need to be with multiple
Gas sensor composition array reaction, the chemical signal of sample is converted into the signal of telecommunication, be then passed through a series of signal conditioning,
The preprocessing process such as normalization, obtain comprehensive " fingerprint " information corresponding to this sample, more therefrom extract suitable feature (feature
Generate, feature selection, feature extraction) it is input to specific algorithm for pattern recognition, it is finally completed qualitatively or quantitatively distinguishing sample
Know.For Electronic Nose Technology, Electronic Nose sampled data reliable, be accurately the crucial guarantee analyzed of Electronic Nose, to Electronic Nose
Analysis result impact the biggest.Therefore to improve the accuracy that Electronic Nose is analyzed, also for the precision of raising Electronic Nose instrument,
It is necessary that Electronic Nose is gathered data carries out pre-verification.Here pre-verification refers to that the sampled data to Electronic Nose is according to certain standard
Or rule detects and proofreads, to ensure reliability and the accuracy of sampled data.Especially for portable electric nose, by
More complicated in application scenarios and environment, it is easier to made its sampled data inaccurate by the interference of environment.
The time sequence that in Electronic Nose gathered data really array, each gas sensor obtains after reacting with gas sample
Row, sampling when being interfered the sampled data in this moment be also interfered and inaccurate.If the more moment in whole time series
Sample the most inaccurate, then this sampling time sequence is the most inaccurate.If this mistake sample sequence is as Electronic Nose training sample just
Electronic Nose identification model can be made to set up inaccurate, and then affect the accuracy of identification of Electronic Nose.And if this mistake sample sequence is made
Will make a mistake identification situation for Electronic Nose test sample or detection sample, can be because this error sample reduces Electronic Nose
Recognition accuracy, makes people take for Electronic Nose instrument itself unreliable.Sample is the most either still detected as training sample,
The sample sequence of mistake all may bring serious consequence to Electronic Nose.
QiPei Feng propose a kind of Electronic Nose data preprocessing method (QiPei Feng. for the electric nasus system of Chinese liquor identification
Design is studied with data analysis. Tianjin: University Of Tianjin, and 2013.), the method uses by First-order Rc Circuit and voltage follower group
The hardware low pass ripple become and holding circuit, can preferably filter the high-frequency noise in sample circuit, and the method is also by ADC
The self-checking function of (Analog-digital Converter) sampling A/D chip eliminates Hz noise, ensure that sampled data will not be deposited to a certain extent
In bad point and event of data loss.
Simon M.Scott propose multiple Electronic Nose data processing method (S.M.Scott, D.James,
Z.Ali.Data analysis for electronic nose systems,MicrochimicaActa,156(3-4)
(2006) 183-207.), by Electronic Nose sampled data being carried out the operations such as difference, Relative Difference or normalization, remove electronics
The interference of nose data.
And the research the most sampling time sequence not verified in Electronic Nose research at present.
In sum, there is following defect and weak point at present in Electronic Nose sampled data verification field:
(1) Electronic Nose data preprocessing method is only sampled data to carry out some filter, smooth and at conversion at present
Reason, can not verify sampled data entirety.
(2) effective ways that Electronic Nose sampled data overall accuracy is verified in real time are lacked at present.
Summary of the invention
For the problems referred to above, it is an object of the invention to overcome the deficiencies in the prior art, bionical in conjunction with a kind of new Electronic Nose
The feature of method of sampling respiration, proposes a kind of pre-method of calibration of sampled data.Present invention breathing bionical to Electronic Nose sampling curve
Carry out pretreatment and differential transform, obtain the differential curve of response curve, by extracting Local Extremum and the meter of differential curve
Calculating the average differential value of curve, there is the time in differential extremum operator and average differential value according to bionical breathing sampling curve three circulation
Successively decrease relation, it is judged that this sampled data is the most accurate, solve the pre-check problem to Electronic Nose sampled data, it is achieved Electronic Nose is adopted
The reliable inspection of sample data.Technical scheme is as follows:
A kind of Electronic Nose pre-method of calibration of sampled data based on response curve derivative characteristic, it is adaptable to use and follow with three times
Ring is the bionical sample mode of Electronic Nose of a complete cycle, and circulation is divided into suction sampling element, waits link and exhalation every time
Link, the pre-method of calibration of described sampled data comprises the following steps:
1) for using the array sampled data that gathered of the bionical sample mode of Electronic Nose to carry out following pretreatment successively:
Smothing filtering eliminates noise and Relative electro-conductivity rate of change calculates and normalized, if StChange for the Relative electro-conductivity after normalization
Rate；
2) utilize sampled gradients method to StDifferentiate:Wherein i represents ith sample point, dt=1/f, f
It it is sample frequency.Sampling curve ascent stage differential value is just, decline stage differential value is negative；
3) maximum M of 3 ascent stage differential curves of each sensor curve is extracted1、M2、M3With each sensor
Maximum M of 3 decline stage differential curves of curve4、M5、M6；
4) average differential is definedWherein N represents this section of time total sampling number, a and b
Represent starting sample point and the finish time sampled point of this period respectively, calculate each sensor differential curve respectively at coordinate axes
3 average differential K in the time period of topder1、Kder2、Kder3, each sensor differential curve is in coordinate axes underlying time section
The absolute value K of 3 average differentialder4、Kder5、Kder6；
5) maximum of the differential curve of each sensor obtained is compared judgement, if certain sensor M1>M2>M3And
M4>M5>M6, then judge that the differential extremum operator of sensor meets rule；
6) the average differential of each sensor obtained is compared judgement, if Kder1>Kder2>Kder3And Kder4>Kder5>
Kder6, then judge that the average differential of this sensor meets rule；
7) when and if only if (5) and in (6), differential extremum operator and average differential all meet the derivative characteristic of bionical breathing sampling,
Just judge that the sampled data of this sensor is verified as accurately in advance.
Compared with existing Electronic Nose data preprocessing method, the pre-method of calibration of sampled data using the present invention to propose can
Obvious advantage is brought to Electronic Nose:
(1) improve sampling precision, the accuracy of sampled result can be judged by verification in time, and proposition mistake is adopted in time
Sample data.
(2) improve the reliability of Electronic Nose, eliminate wrong data by verification, Electronic Nose trains the grader obtained
More accurate, also make the discrimination of Electronic Nose get a promotion.
(3) enhance the practicality of Electronic Nose, decrease the objective discrimination that improve Electronic Nose of error sample so that electricity
The range of application of sub-nose is wider, and practicality is the most higher.
Accompanying drawing explanation
Fig. 1 is used Electronic Nose structured flowchart and workflow diagram by the present invention
Fig. 2 is Electronic Nose of the present invention bionical method of sampling respiration sampling control strategy
Fig. 3 is Electronic Nose of the present invention bionical breathing sampling single-sensor result curve
Fig. 4 is pretreated Electronic Nose of the present invention bionical breathing sampling array curve
Fig. 5 is the differential curve area graph of the bionical sampling curve of Electronic Nose of the present invention
Fig. 6 is that single-sensor of the present invention responds differential curve schematic diagram
Detailed description of the invention
The present invention will be described with embodiment below in conjunction with the accompanying drawings.
The Electronic Nose structure that the present invention relates to as it is shown in figure 1, this Electronic Nose not only can direct detected gas sample, can also be used with
In liquid samples such as detection Chinese liquor.This Electronic Nose mainly includes evaporation and sampling apparatus, sensor gas chamber reaction device, and control
System and data acquisition pretreatment system three parts.
The device of the Electronic Nose that the present invention relates to includes adjustable speed air pump, electromagnetic valve, evaporation air chamber, sensor air chamber, AD
Acquisition chip and master controller.The method of sampling that the present invention relates to by detection Chinese liquor sample as a example by, the groundwork stream of Electronic Nose
First journey as it is shown in figure 1, made the Chinese liquor sample in evaporation air chamber fully evaporate by the heating of silicon heating tape, then by adjustable speed pump
Squeezing into pure air is carrier gas, and electromagnetic valve for adjusting controls gas circuit, makes Chinese liquor sample gas enter sensor gas with certain rule
Room is reacted with sensor array therein, fully after reaction sampling, gathers electricity by AD (analog-digital conversion controller)
Collection signal is uploaded preservation and analyzes further by road and signal condition, and whole work process is completed by main controller controls, uses
Family also can complete interactive controlling by touch screen interface.
The speed governing air pump that adjustable speed air pump is Chengdu Qihai E & M Manufacturing Co., Ltd. that the present invention relates to, model is
FAY6003, running voltage 12V, peak flow is 3000mL/min, relative vacuum Du Yue-36kPa, maximum output pressure
60kPa, the rotating speed of this air pump can be adjusted by the dutycycle of the PWM ripple that blue line end inputs, and the rotating speed of air pump is at yellow line
End feeds back with square wave frequency.
The electromagnetic valve that the present invention relates to is the OKD-1306 model of Shenzhen Ou Kada, running voltage 12V, watt level
5.2W, D.C. resistance 220 Ω.
The bionical method of sampling respiration that the present invention relates to is by adjustable speed air pump and electromagnetic valve 1,2,3,4, and cooperate realization
(as shown in Figure 1), air channels sampling controls to be automatically performed according to sampling control strategy set in advance by master controller.
Bionical the used control strategy of breathing sampling method that the present invention relates to is as shown in Figure 2.Whole control strategy can be drawn
Being divided into 3 cyclic processes (as Fig. 2 dotted line divides), each cycle stage is completed by same policy again, the most first opens air pump and is passed through
A bit of time samples gas, is then shut off air pump, waits for a period of time and makes sensor response reach stable state, then opens air pump one
Short time, is passed through pure air and is carried out air chamber, then turns off air pump, and sensor response of waiting for a period of time reaches
To new stable state.
The sensor response curve that the bionical method of sampling respiration that the present invention relates to obtains is as shown in Figure 3.As it is shown on figure 3,
Whole sampling curve can be divided into 3 corresponding with sampling policy the cycle stage, and the most each cycle stage have employed phase
Same control strategy.As a example by Fig. 3 second circulation B, it includes 1, sucks, and 2, suspend, 3, exhalation, 4, suspend totally 4 links.
The bionical method of sampling respiration of Electronic Nose, a complete sampling period comprises 3 sample cyclic, and concrete steps are such as
Under:
(1) electric nasus system powers on, and during preheated one-section, chien shih boil-off gas room constant temperature is to 70 degrees Celsius, and makes gas sample
Evaporation air chamber fully evaporates.
(2) first circulation of bionical breathing sampling starts, and first electromagnetic valve for adjusting realizes sucking sampling gas circuit: make electromagnetic valve
1 connects with evaporation air inlet of air chamber, makes evaporation air chamber gas outlet connect with electromagnetic valve 2, then makes electromagnetic valve 3 enter with sensor air chamber
QI KOU is connected, thus connects suction sampling gas circuit.
(3) the suction sampling element that adjustable speed air pump starts to circulate for the first time is opened, will by the pure air after filtering
Gas sample brings sensor air chamber into along sampling gas circuit, and duration of ventilation is 1 second, controls the PWM ripple duty of adjustable speed air pump rotating speed
Ratio is set to 55000/125000.
(4) close adjustable speed air pump, and simultaneously close off electromagnetic valve 1 and 3, make evaporation air chamber and sensor air chamber separate
And airtight, wait that sensor reacts with sample and reach stable state, 15 seconds waiting time.
(5) start the exhalation link of circulation for the first time, first electromagnetic valve for adjusting and realize exhalation gas circuit, make electromagnetic valve 1 and electricity
Magnet valve 3 is connected, then connects with sensor air inlet of air chamber, and the electromagnetic valve 4 being connected with the gas outlet of sensor air chamber is as cleaning
Mouth 2 is connected with extraneous.
(6) open adjustable speed air pump to start to breathe out sample for the first time, by the pure air after filtering by sensor air chamber
In sample gaseous mixture breathed out by Butterworth Hatch 2, duration of ventilation is 4 seconds, and PWM duty cycle is 125000/12500.
(7) it is then switched off adjustable speed air pump, and simultaneously closes off path between electromagnetic valve, make evaporation air chamber and sensor air chamber
Separate and airtight, wait that sensor reaches new stable state, 12 seconds waiting time, so far first breath sample cyclic knot
Bundle.
(8) second and third time circulating sampling process substantially with circulate for the first time identical, difference is as follows: for the second time
It is 70000/125000 that circulation sucks sampling element PWM ripple dutycycle, and the time is still 1 second；Leading to of second time circulation exhalation link
The gas time is 2 seconds.
(9) third time circulation sucks sampling element PWM ripple dutycycle is 125000/125000, and the time is still 1 second；3rd
The duration of ventilation of secondary circulation exhalation link is 1 second.
Electronic Nose shown in Fig. 1 uses bionical method of sampling respiration to sample, smoothed filtering, Relative electro-conductivity rate of change meter
After calculation and normalization pretreatment, its array sampled result curve is as shown in Figure 4, and this sensor array comprises 7 gas sensors.
Sampling curve pretreated to Fig. 4 carries out differential transform can obtain the differential curve of Electronic Nose sampling curve.
Fig. 5 show the differential curve area graph of sensor array, it can be seen that each sensor differential extremum operator successively decreases in time (such as Fig. 6
Shown in), and the area that surrounds of differential curve and coordinate axes also tapers off trend, namely average differential tapers off trend.Differential
The extreme value of curve and average differential embody dynamic characteristic and the prevailing characteristics information of sensor response jointly, it is possible to reflect and adopt
The Global Information of sample data, in conjunction with the feature of bionical breathing sampling, it is accurate that its response derivative characteristic can be used to sampled data
Property verifies.
The present invention responds the extreme value of differential curve by extracting sensor and calculates average differential, and according to above-mentioned rule pair
Electronic Nose sampled data verifies.
Concrete method of calibration step (referring to the drawings 4,5,6) as described below:
(1), after waiting that primary electron nose bionical breathing sampling terminates, array sampled data is gathered to be analyzed to master controller.
(2) array sampled data being carried out pretreatment, pretreatment includes that smothing filtering eliminates noise and Relative electro-conductivity change
Rate calculates and normalization.
(3) smothing filtering employing local regression weighting algorithm:Wherein x is currently to be smoothed a little
Data, xiFor its next-door neighbour's point value in smoothing windows, d (x) is the x ultimate range to Neighbor Points all in window.
(4) due to the short term drift characteristic of sensor itself, adding the impact of environmental factors, its baseline value is the most unstable,
It is usually present a certain degree of fluctuation.In order to suppress the fluctuation of baseline, the method for Relative Difference can be used to obtain a kind of new
The sensitivity method for expressing of sensor, referred to as Relative electro-conductivity rate of change, symbol is S.
(5) Relative electro-conductivity rate of change computing formula is:Wherein,For
T sensor conductance,For sensor electric conductivity value near baseline,For t sensor response value, VrefFor with reference to electricity
Pressure value.
(6) normalization computing formula be:Wherein, StRelative for after normalization
Conductance rate of change, and min (S)=0.
(7) sampled gradients method is to StDifferentiate:Wherein i represents ith sample point, and dt=1/f represents
In the sampling period, f is sample frequency.Sampling curve ascent stage differential value is just, decline stage differential value is negative.
(8) maximum M of 3 ascent stage differential curves of each sensor curve as shown in Figure 6, is extracted1、M2、M3With
Maximum M of 3 decline stage differential curves of each sensor curve4、M5、M6。
(9) average differential embodies the main flow characteristic of sensor response, and its computing formula is:
Wherein N represents this section of time total sampling number, a and b represents starting sample point and the finish time sampled point of this period respectively.
Each sensor differential curve average differential K of 3 above coordinate axes in the time period is calculated respectively according to formulader1、Kder2、
Kder3, each sensor differential curve 3 average differential (taking absolute value) K in coordinate axes underlying time sectionder4、Kder5、
Kder6。
(10) differential extremum operator of each sensor obtained in (8) is compared judgement, if M1>M2>M3And M4>M5>M6, then
Judge that the differential extremum operator of this sensor meets rule.
(11) the average differential of each sensor obtained in (9) is compared judgement, if Kder1>Kder2>Kder3And
Kder4>Kder5>Kder6, then judge that the average differential of this sensor meets rule.
(12) differential that and if only if (10) and in (11), differential extremum operator and average differential all meet bionical breathing sampling is special
During property, just judge that the sampled data of this sensor is verified as accurately in advance.
(13) to a sampled data, when in array, the sampled result of all the sensors is verified as accurate the most in advance, ability
Judging that this Electronic Nose sampled data is verified by pre-, this group sampled data just can be for further processing, such as training, test
Deng.
(14) if a sampled data, in its array, all by pre-verification, each sensor can not then judge that this group is sampled
Data are inaccurate, should give rejecting.
Claims (1)
1. the Electronic Nose pre-method of calibration of sampled data based on response curve derivative characteristic, it is adaptable to use with three circulations
Being the bionical sample mode of Electronic Nose of a complete cycle, circulation is divided into suction sampling element, waits link and exhalation ring every time
Joint.The pre-method of calibration of described sampled data comprises the following steps:
1) the array sampled data for using the bionical sample mode of Electronic Nose to be gathered carries out following pretreatment successively: smooth
Filtering eliminates noise and Relative electro-conductivity rate of change calculates and normalized, if StFor the Relative electro-conductivity rate of change after normalization；
2) utilize sampled gradients method to StDifferentiate:Wherein i represents ith sample point, and dt=1/f, f are to adopt
Sample frequency.Sampling curve ascent stage differential value is just, decline stage differential value is negative；
3) maximum M of 3 ascent stage differential curves of each sensor curve is extracted1、M2、M3With each sensor curve
Maximum M of 3 decline stage differential curves4、M5、M6；
4) average differential is definedWherein N represents this section of time total sampling number, a and b is respectively
Represent starting sample point and the finish time sampled point of this period, calculate each sensor differential curve respectively above coordinate axes
3 average differential K in time periodder1、Kder2、Kder3, in coordinate axes underlying time section 3 of each sensor differential curve
The absolute value K of average differentialder4、Kder5、Kder6；
5) maximum of the differential curve of each sensor obtained is compared judgement, if certain sensor M1>M2>M3And M4>M5>
M6, then judge that the differential extremum operator of sensor meets rule；
6) the average differential of each sensor obtained is compared judgement, if Kder1>Kder2>Kder3And Kder4>Kder5>
Kder6, then judge that the average differential of this sensor meets rule；
7), when and if only if (5) and in (6), differential extremum operator and average differential all meet the derivative characteristic of bionical breathing sampling, just sentence
The sampled data of this sensor fixed is verified as accurately in advance.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107015023A (en) * | 2017-03-22 | 2017-08-04 | 天津大学 | A kind of smell source three-dimensional detection method |
| CN112036482A (en) * | 2020-08-31 | 2020-12-04 | 重庆大学 | Traditional Chinese medicine classification method based on electronic nose sensor data |
| CN115236135A (en) * | 2021-04-23 | 2022-10-25 | 中国石油化工股份有限公司 | Base line calibration method for gas sensor, control device and gas sensor |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103499609A (en) * | 2013-07-30 | 2014-01-08 | 中国标准化研究院 | Method for researching on dynamic characterization of intelligent olfaction dynamic responding characteristics and differentiate information on honey fragrance |
| CN104792826A (en) * | 2015-03-23 | 2015-07-22 | 中国农业大学 | System and method for detecting milk freshness based on electronic nose |
-
2016
- 2016-08-10 CN CN201610663135.5A patent/CN106295575B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103499609A (en) * | 2013-07-30 | 2014-01-08 | 中国标准化研究院 | Method for researching on dynamic characterization of intelligent olfaction dynamic responding characteristics and differentiate information on honey fragrance |
| CN104792826A (en) * | 2015-03-23 | 2015-07-22 | 中国农业大学 | System and method for detecting milk freshness based on electronic nose |
Non-Patent Citations (3)
| Title |
|---|
| PEI-FENG QI等: "A Portable E-nose System for Classification of Chinese Liquor", 《2015 IEEE SENSORS》 * |
| 亓培锋等: "用于白酒识别的电子鼻数据分析与参数优化", 《天津大学学报(自然科学与工程技术版)》 * |
| 亓培锋等: "电子鼻中预处理算法选择及阵列优化", 《数据采集与处理》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107015023A (en) * | 2017-03-22 | 2017-08-04 | 天津大学 | A kind of smell source three-dimensional detection method |
| CN107015023B (en) * | 2017-03-22 | 2019-06-14 | 天津大学 | A kind of smell source three-dimensional detection method |
| CN112036482A (en) * | 2020-08-31 | 2020-12-04 | 重庆大学 | Traditional Chinese medicine classification method based on electronic nose sensor data |
| CN112036482B (en) * | 2020-08-31 | 2023-10-24 | 重庆大学 | Traditional Chinese medicine classification method based on electronic nose sensor data |
| CN115236135A (en) * | 2021-04-23 | 2022-10-25 | 中国石油化工股份有限公司 | Base line calibration method for gas sensor, control device and gas sensor |
| CN115236135B (en) * | 2021-04-23 | 2023-08-22 | 中国石油化工股份有限公司 | Baseline calibration method for gas sensor, control device and gas sensor |
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