CN105466885B - Based on the near infrared online measuring method without measuring point temperature-compensating mechanism - Google Patents
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Abstract
The present invention relates to based on the near-infrared spectrometers On-line sampling system method without measuring point temperature-compensating mechanism, spectra collection is carried out including the horizontal experimental program of design multi-temperature, outlier processing is pre-processed and counted respectively as target using temperature and physical parameter to be measured to the spectrum of collection, then establishes temperature prediction model, low temperature point geophysical parameter prediction model and high temperature dot geophysical parameter prediction model respectively with offset minimum binary；Then calculating is modified to forecast model of the different temperatures under horizontal from low-temperature zone or high temperature section；Online recursive algorithm is finally constructed, completion has the near-infrared real-time online measuring without measuring point temperature compensation function.The present invention covers compensating effect in near-infrared modeling process, and forms recursive algorithm based on physical parameter to be measured, so as to rely on model in itself to the physical measurement under the adaptability completion different temperatures of temperature.Simultaneously to the recursive algorithm of physical parameter, it ensure that temperature compensation function can influence intensity of the adaptive temperature near infrared online measured value automatically.
Description
Technical field
The present invention relates to the near-infrared spectrometers On-line sampling system method without measuring point temperature-compensating mechanism, it is applied to
Physical parameter influenced by ambient temperature, such as fluid viscosity, material density, constituent concentration, food quality, agricultural product composition, medicine
The on-line real-time measuremen of product active constituent content, oil product of gasoline quality etc..
Background technology
Traditional analysis detection method is mostly the off-line test technology that uses, and measure has hysteresis quality, on the one hand can not make a living
Production and quality testing department provide more comprehensive, real-time sample message, and another aspect off-line measurement can not possibly realize that computer is supervised online
Survey the purpose with controlling in real time.And near-infrared spectrum technique because its analyze speed it is fast, it is small to sample broke, without chemical contamination,
The features such as being almost adapted to all kinds of sample analysis, multicomponent multichannel to determine simultaneously, turn into a bright spot in on-line analysis instrument.
In recent years, with the development of Chemical Measurement, optical fiber and computer technology, the positive extensive use of On-line NIR analytical technology
In industries such as agricultural, food, petrochemical industry, weaving, medicine, very wide use space is provided for production process control,
Also bring considerable economic benefit and social benefit for enterprise simultaneously.
But when near-infrared spectrometers are used for on-line measurement, measurement result can be influenceed by environmental factor.Research
Show, for the near infrared spectrum of one-component, temperature affecting laws are more obvious.For complex system, temperature is to biological group
Knitting optical characteristics has considerable influence；Especially when measuring fluid sample, the rise of temperature can cause the hydroxyl value of stretching vibration
Mesh is reduced and the increase of the number of free vibration, so as to produce the skew of vibrational spectrum so that the near-infrared established under specified temp
Spectral model may be only available for sample quality analysis at this temperature, and the on-line analysis for the sample quality of different temperatures is imitated
Fruit is undesirable, and this shortcoming greatly limit the application of near-infrared spectrometers real-time online measuring technology.Therefore, temperature is studied
The real-time online measuring method that strong adaptability, precision are high, robustness is good, turn near infrared technology can effective application on site pass
Key.
The content of the invention
Method proposed by the present invention, be when being measured in real time for near-infrared, the temperature change immeasurability of measurand or
Do not measure, and the situation that the change of temperature in itself can have a significant effect to the measurement result of near-infrared.One kind is provided to temperature
Spend insensitive, and the less On-line Measuring Method with temperature-compensating mechanism of error.
The present invention to achieve the above object, adopts the following technical scheme that：
Step of the present invention is divided into three parts.Part I, the experimental design of modeling data and spectral collection；Second
Point, the pretreatment of near infrared spectrum and the foundation of calibration model；Part III, constructs online recursive algorithm, and completion has nothing
The near infrared online measurement of measuring point temperature compensation function.
The experimental facilities of modeling data includes, and the sample cell (2) that (1) sample temperature can be adjusted can displays temperature change
Temperature meter (3) the near infrared spectrum collection instrument (4) of change does not produce the optic probe significantly affected to sample temperature.(5)
The computer tape deck connected near infrared spectrum collection instrument.Package unit is as shown in Figure 1.
Present invention experiment and data collection step are as follows：
Experimental procedure one：Minimum and maximum temperature value under the conditions of confirmatory sample is online.Temperature range is divided into multiple levels
Value.Each temperature levels are typically greater than 5 times of thermometric instruments resolution ratio, and precision is effectively distinguished to reach.
Experimental procedure two：Determine the temperature range under sample real-time conditions.Under defined normal temperature, to all samples
Physical parameter obtains primary standard analyze data.
Experimental procedure three：Spectroscopic data is collected respectively under different temperatures level to same sample.Record simultaneously relative
The sample temperature value answered.This temperature value is used for the foundation of temperature correction model.
Temperature is as follows as explicit variable factors modeling procedure：
Modeling procedure one：Pretreatment using temperature model as target is carried out to spectrum：By original spectrum do first derivative or
Second derivative operator, produce first derivative spectrum or second derivative spectra.The determination of derivative order is with physical parameter herein
Characteristic and it is different, be preferable using second dervative to macromolecule high viscosity samples；To low viscosity sample using first derivative as compared with
It is good.
Modeling procedure two：Pivot analysis (PCA) is done to caused derivative spectrum above, rejects statistics exceptional value so that whole
The pivot pattern of individual derivative spectrum data is all within a statistical certainty.
Modeling procedure three：Using temperature as predictive variable, derivative spectrum wave number is as independent variable.Use partial least squares algorithm
(PLS) the temperature correction model of following form is established：
Tc=A1x1+A2x2+…Anxn
Herein, Ai, i=1,2 ... n are regression coefficients, xiIt is derivative spectrum in wave number i=1,2 ... the numerical value at n.
Modeling procedure four：Pretreatment using physical property parameter mode to be measured as target is carried out to original spectrum.These pretreatments
Include the superposition of one or more of following algorithms：First derivative, second dervative, maximum-minimum sandards, basic bottom line school
Just, scatter correction, constant bias correction, etc..The determination of Preprocessing Algorithm is different with physical parameter to be measured herein.
Modeling procedure five：Pivot analysis (PCA) is done to spectrum after caused pretreatment above, statistics exceptional value is rejected, makes
Entirely pretreated spectroscopic data pivot pattern all within a statistical certainty.
Modeling procedure six：The spectral data groups corresponding to minimum experimental temperature are chosen, prediction is used as using physical parameter to be measured
Variable, spectrum wave number is as independent variable after pretreatment.The Physical Properties of Low Temperature that following form is established with partial least squares algorithm (PLS) is joined
Number calibration model：
Pl=C1z1+C2z2+…Cnzn
Herein, Ci, i=1,2 ... n are regression coefficients, ziBe after pretreatment spectrum in wave number i=1,2 ... the numerical value at n.
Modeling procedure seven：The spectral data groups corresponding to highest experimental temperature are chosen, prediction is used as using physical parameter to be measured
Variable, spectrum wave number is as independent variable after pretreatment.The high temperature physical property that following form is established with partial least squares algorithm (PLS) is joined
Number calibration model：
Ph=B1y1+B2y2+…Bnyn
Herein, Bi, i=1,2 ... n are regression coefficients, yiBe after pretreatment spectrum in wave number i=1,2 ... the numerical value at n.
Modeling procedure eight：Construct the following physical parameter formula based on low-temperature model predicted value at any temperature：
Pc=Pl+{(Pl0-Ph0)/(Tl-Th)}×(Tc-Tl)
P hereinl0, Ph0It is same sample respectively in low-temperature model and the minimum warm spot and highest warm spot of high temperature model
Model predication value.Th, TlRespectively be experiment highest and minimum temperature point temperature model predicted value, PcIt is in temperature TcUnder
Physical measurement value.
The following physical parameter formula at any temperature based on high temperature model predication value can similarly be constructed：
Pc=Ph-{(Pl0-Ph0)/(Tl-Th)}×(Th-Tc)
Modeling procedure nine：New spectroscopic data collection is obtained online, and recurrence correction algorithm is formed using following methods：
(1) it is used as currency P (k) using above-mentioned step 6 acquired results
(2) calculate and measure in next step：Pr(k+1)=P (k)+K [L (k-1)-P (k-1)]
(3) will current revised predicted value Pr(k) the measured value P (k-1) of last moment is assigned to, repeats to walk above
Suddenly, recursive operation is done.
P hereinr(k) it is the current near-infrared physical measurement correction value with temperature-compensating, P (k-1) is that previous step does not have
There is the near-infrared physical measurement value of amendment, L (k-1) is the actual physical parameter value used in last computation, and K is modifying factor or number
Word wave filter.
Modifying factor or lower order filter, can be more compared with general Statistic analysis and logic in above-mentioned modeling procedure nine
Judge, or both combinations.
In above-mentioned modeling procedure nine, when each step calculates, physical parameter calibration model used can be the light by updating
Modal data regenerates.Whole computational algorithm forms recursive form.
The method invented, temperature compensation effect is covered in near-infrared modeling process, and be based on physical parameter to be measured
Form recursive algorithm.When thus carrying out real-time online measuring using near-infrared, model can be relied in itself to the adaptability of temperature
Complete the physical measurement under different temperatures, it is not necessary to direct temperature metrical information and correlation computations.Meanwhile physical parameter is passed
Reduction method, it ensure that temperature compensation function can influence intensity of the adaptive temperature near infrared online measured value automatically.
Brief description of the drawings
Fig. 1 is without measuring point temperature-compensating experimental provision
A kind of second dervative part spectrum of high polymer materials of Fig. 2
Host element ideograph caused by Fig. 3 second derivative spectras
The temperature prediction model of Fig. 4 high molecular polymers
A kind of first derivative of high molecular polymers of Fig. 5 pre-processes local spectrum
A kind of pre-processed spectrum host element ideograph of high molecular polymers of Fig. 6
Fig. 7 high molecular polymer viscous low temperature point prediction models
The modeling wave number of Fig. 8 high molecular polymer viscous low temperature point prediction models
Fig. 9 high molecular polymer viscosity high temperature dot forecast models
Modeling wave number used in Figure 10 high molecular polymer viscosity high temperature dot forecast models
Figure 11 on-line implement block diagrams
Figure 12 temperature is surveyed and the comparison of model predication value
A kind of effect of the real-time measurement temperature compensation of high molecular polymer viscosity of Figure 13
Embodiment
Below by taking a kind of viscosity measurement of high-molecular compound as an example, illustrate specific implementation method.This example is not formed
The scope of the inventive method is limited.
Implementation steps block diagram is as shown in figure 11.
Step 1：Sample is gathered under the conditions of difference is online, it is ensured that the physical parameter to be measured of sample can cover measurement will
The scope asked.Total number of samples is at 40-60.
Step 2：Using the laboratory equipment shown in Fig. 1, respectively in 24 DEG C, 35 DEG C, 50 DEG C, 60 DEG C, 70 DEG C of five differences
The near infrared spectrum of each sample is gathered under temperature levels, while records experimental temperature.
Step 3：The spectrum gathered is pre-processed and pivot analysis.Different pretreatments is carried out to spectrum and does ratio
Compared with to determine finally applicable preprocess method.In example, second dervative processing has been carried out to macromolecule high viscosity sample.Place
It is as shown in Figure 2 to manage effect.The second dervative of original spectrum is pre-processed, eliminated by near-infrared light source aging, on-line sample and
It is spectrally lower caused by the order of contact of probe or probe vibrations to drift about, at the same can be changed with keeping temperature the spectrum peak that bring with
The change of shape.Host element pattern caused by second derivative spectra allows whole as shown in figure 3, reject one of singular point
Spectroscopic data pivot pattern is within statistical certainty.
Step 4：Establish the near-infrared forecast model of sample temperature.This model will obtain the temperature of sample directly from spectrum
Angle value.Fig. 4 is temperature model example, uses modeling wave band as 7397-6880cm-1And 5299-4558cm-1.Figure 12 is temperature reality
The comparison with model predication value is surveyed, as can be seen from the figure temperature model predicted value and the correlation of measured value are 0.99, model
Precision R2For 0.98.
Step 5：The single order using physical property parameter mode to be measured as target is carried out to original spectrum and pre-processes and do pivot point
Analyse (PCA), reject statistics exceptional value so that whole pretreated spectroscopic data pivot pattern all a statistical certainty it
It is interior.Fig. 5 is that a kind of high molecular polymer first derivative pre-processes local spectrum.Fig. 6 is a kind of pretreatment of high molecular polymer
Spectrum PCA ideographs.
Step 6：The near-infrared forecast model of low temperature and high temperature dot is established respectively.If Fig. 7 and Fig. 9 are low temperature and high temperature respectively
Model result.Figure 8 below and Figure 10 are modeling spectrum wave-number range examples used.Select the wavelength band shown in Fig. 8
8900-4497cm-1Modeling obtains low-temperature model Fig. 7, selects the wavelength band 8955-4497cm shown in Figure 10-1Modeling obtains
High temperature illustraton of model 9.As can be seen from Figure 7 low-temperature model predicted value and the correlation of measured value are 0.991, model accuracy R2For
0.98.As can be seen from Figure 9 the correlation of high temperature model predication value and measured value is 0.988, model accuracy R2For 0.9772.
Step 7：Pay attention to established low temperature near-infrared parameter models of physical, be accurate in low-temperature zone.And high temperature is near
Infrared parameter models of physical is accurate in high temperature section.Calculating can be modified from low-temperature zone or high temperature section different directions.
It is as follows from physical parameter formula of the low-temperature zone based on low-temperature model predicted value at any temperature：
Pc=Pl+{(Pl0-Ph0)/(Tl-Th)}×(Tc-Tl)
P hereinl0, Ph0It is same sample respectively in low-temperature model and the minimum warm spot and highest warm spot of high temperature model
Model predication value.Th, TlRespectively be experiment highest and minimum temperature point temperature model predicted value, PcIt is in temperature TcUnder
Physical measurement value.
Step 8：10 new spectroscopic data collection, and laboratory initial data corresponding to acquisition simultaneously are obtained online.
Step 9：Error E (k)=L (k)-P (k) of 10 samples in the past is calculated, and forms an error time sequence
E(k-1),E(k-2),…E(k-10)。
Step 10：Low pass dynamic filter computing is done to above-mentioned error time sequence, one-step prediction value is obtained, is designated as B.
Step 11：Calculate viscosity correction measured value：Pr=P+B
P is the current near-infrared physical measurement value with temperature-compensating herein.
Step 12：By current correction value Pr(k) the measured value P (k-1) of last moment is assigned to, does recursive operation.
Repeat above step 8-12.
Legend shown in Figure 13, is the effect example of a high molecular polymer viscosity real-time measurement temperature compensation, sample temperature
Excursion is spent in 20-70 degrees centigrades, and required measured value is the viscosity number of sample at 50 c.Fixed temperature
Model is that have stronger sensitiveness to temperature based on the physical measurement model established at a temperature of 50 degree, its measured value.Present invention side
Method acquired results, it is insensitive to temperature change.More because employing recursive algorithm so that measurement is overall to better conform to sample
True assay value.
Claims (6)
1. a kind of existed based on the near-infrared spectrometers On-line sampling system method without measuring point temperature-compensating mechanism, its feature
In comprising the following steps：
Step 1：The physical parameter measured value of testing sample is obtained, and near infrared spectrum is gathered under different temperatures level；
Step 2：The near infrared spectrum gathered in step 1 is carried out using temperature as the pretreatment of target and counted at exceptional value
Reason, produce derivative spectrum；
Step 3：Using temperature as predictive variable, derivative spectrum wave number caused by step 2 establishes temperature mould as independent variable
Type；
Step 4：The near infrared spectrum gathered in step 1 is carried out abnormal as the pretreatment of target and statistics using physical parameter
Value processing, produces pre-processed spectrum；
Step 5：The data group corresponding to minimum experimental temperature is chosen, using physical parameter measured value as predictive variable, step 4
Caused pre-processed spectrum wave number is independent variable, establishes the geophysical parameter prediction model of low temperature point；
Step 6：Choose the data group corresponding to highest experimental temperature, using physical parameter measured value as predictive variable, step 4
Caused pre-processed spectrum wave number is independent variable, establishes the geophysical parameter prediction model of high temperature dot；
Step 7：Calculating is modified to forecast model of the different temperatures under horizontal from low temperature point or high temperature dot, amendment is any
At a temperature of geophysical parameter prediction model；
Step 8：New near infrared spectrum collection is obtained online, and physical parameter measured value is updated using online recursive algorithm,
Online recursive algorithm is described in step 8：
Pr (k+1)=Pr (k)+K [L (k-1)-P (k-1)]
Wherein Pr (k+1) is the measurement correction value that subsequent time has temperature-compensating, and Pr (k) is that have temperature-compensating at current time
Measurement correction value, P (k-1) is the physical parameter measured value of last moment, and L (k-1) is the actual physical property used in last computation
Reference value, K are modifying factor or lower order filter.
It is 2. according to claim 1 based on the near-infrared spectrometers On-line sampling system without measuring point temperature-compensating mechanism
Method, it is characterised in that：The foundation of temperature model carries out linear regression using offset minimum binary：
Tc=A1x1+A2x2+ ... Anxn
Herein, Ai, i=1,2 ... n are regression coefficients, and xi is the numerical value at the wave number i=1,2 ... n of derivative spectrum.
It is 3. according to claim 1 based on the near-infrared spectrometers On-line sampling system without measuring point temperature-compensating mechanism
Method, it is characterised in that：The foundation of low temperature point geophysical parameter prediction model is entered using partial least squares algorithm in the step 5
The following linear regression of row：
Pl=C1z1+C2z2+ ... Cnzn
Herein, Ci, i=1,2 ... n are regression coefficients, and zi is numerical value of the pre-processed spectrum at wave number i=1,2 ... n.
It is 4. according to claim 1 based on the near-infrared spectrometers On-line sampling system without measuring point temperature-compensating mechanism
Method, it is characterised in that：The foundation of the model of high temperature dot geophysical parameter prediction described in step 6 is entered using partial least squares algorithm
The following linear regression of row：
Ph=B1y1+B2y2+ ... Bnyn
Herein, Bi, i=1,2 ... n are regression coefficients, and yi is numerical value of the pre-processed spectrum at wave number i=1,2 ... n.
It is 5. according to claim 1 based on the near-infrared spectrometers On-line sampling system without measuring point temperature-compensating mechanism
Method, it is characterised in that：Model described in step 7 is from low temperature point correction algorithm：
Pc=Pl+ { (Pl0-Ph0)/(Tl-Th) } × (Tc-Tl)
Pl0 herein, Ph0 are model of the same sample in the minimum warm spot and highest warm spot of low-temperature model and high temperature model respectively
Predicted value, Th, Tl are the highest of experiment and the temperature model predicted value of minimum temperature point respectively, and Pc is the physical property under temperature Tc
Measured value, Pl are the physical parameter measured values of low temperature point.
It is 6. according to claim 1 based on the near-infrared spectrometers On-line sampling system without measuring point temperature-compensating mechanism
Method, it is characterised in that：Model described in step 7 is from high temperature dot correction algorithm：
Pc=Ph- { (Pl0-Ph0)/(Tl-Th) } × (Th-Tc)
Pl0 herein, Ph0 are model of the same sample in the minimum warm spot and highest warm spot of low-temperature model and high temperature model respectively
Predicted value, Th, Tl are the highest of experiment and the temperature model predicted value of minimum temperature point respectively, and Pc is the physical property under temperature Tc
Measured value, Ph are the physical parameter measured values of high temperature dot.
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| CN109902972A (en) * | 2019-03-31 | 2019-06-18 | 深圳广田集团股份有限公司 | The pre-evaluation method of content of formaldehyde in a kind of indoor environment |
| CN111713746B (en) * | 2020-06-08 | 2024-03-15 | 深圳市康泓威科技有限公司 | Method for detecting and controlling solution temperature of electronic atomization device and electronic atomization device |
| CN111579528B (en) * | 2020-06-30 | 2022-06-21 | 四川长虹电器股份有限公司 | Calibration method of micro near-infrared spectrometer |
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