CN105259136B - Near infrared correction without measuring point temperature correction - Google Patents
Near infrared correction without measuring point temperature correction Download PDFInfo
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- CN105259136B CN105259136B CN201510827828.9A CN201510827828A CN105259136B CN 105259136 B CN105259136 B CN 105259136B CN 201510827828 A CN201510827828 A CN 201510827828A CN 105259136 B CN105259136 B CN 105259136B
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Abstract
The present invention relates in a kind of near-infrared spectral measurement without measuring point temperature adjustmemt modeling method, including the sample that collection is representative, it is ensured that the physical parameter to be measured of sample can cover the scope of measurement request；The near infrared spectrum of each sample is gathered under different temperatures level；Temperature is directed to the spectrum of collection and physical parameter to be measured pre-processes respectively and pivot analysis；The spectrum that two different pretreatments obtain is merged into generation and merges spectroscopic data；Finally, pivot analysis is done to merging spectrum, and physical parameter calibration model is established with offset minimum binary.The present invention is participated in temperature as the implicit variable factors of separation in near-infrared modeling process, thus when using near-infrared measuring, model can be relied in itself to the physical measurement under the adaptability completion different temperatures of temperature, direct temperature metrical information and correlation computations are not needed so that the model established has more preferable versatility and thermal adaptability.
Description
Technical field
The present invention relates in near-infrared spectral measurement without measuring point temperature adjustmemt modeling method, suitable for easily by environment temperature
The substance viscosity of influence, fermentation process alanine concentration, food quality, quality of agricultural product, medicine quality, oil product of gasoline etc. it is fast
Speed detection, it may also be used for the measurement of human body Woundless blood sugar concentration, soil constituent and mineralogical composition etc..
Background technology
In recent years, near-infrared spectral analysis technology with its quick detection, Non-Destructive Testing, without chemical contamination, easy to operate, sample
Product prepare the advantages that simple, have been widely used for the industries such as petrochemical industry, food, agricultural, medicine, turn into fastest developing speed
One of qualitative and quantitative analysis technology.Absorption near infrared spectrum mostlys come from state caused by molecular vibration or rotation
Change, the vibration of its each group is easily influenceed by external conditions such as temperature, especially when measuring fluid sample, temperature
Rise can cause the hydroxy number of stretching vibration to reduce and the increase of the number of free vibration, so as to produce the skew of vibrational spectrum,
So that the near-infrared spectroscopy established under specified temp may be only available for sample quality analysis at this temperature, and for difference
The sample quality analytical effect of temperature is undesirable, and this shortcoming strongly limit answering for near-infrared spectrometers modeling technique
With.
In order to obtain preferable accuracy of analysis, measurement at a constant temperature can effectively reduce the influence of temperature change, but
Temperature can not be controlled accurately in practical application, therefore solve temperature and the certain methods that near infrared spectrum influences are suggested successively,
As preprocessing procedures reject the influence of temperature in spectrum；Choose influences insensitive wave band to temperature establishes analysis model；
Using the method for interior correction, temperature information is included in mathematical modeling；The temperature of sample is measured while gathering spectrum,
Establish Temperature correction model etc..These methods can be used for overcoming testing sample temperature change to do Quantitative Analysis Model
Disturb, still, there is presently no general rule to judge which kind of uses which kind of method in the case of, and to be selected according to particular problem
Select.Therefore, near infrared detection calibration model more generally applicable, that thermal adaptability is stronger is established under temperature change, near red
Can outer technology effectively using very crucial.
The content of the invention
Method proposed by the present invention, sample spectra is obtained under different temperatures level, using temperature as separation it is implicit because
Plain variable is participated in near-infrared modeling process, thus when using near-infrared measuring, can rely on model in itself to temperature
Adaptability completes the physical measurement under different temperatures, it is not necessary to direct temperature metrical information and correlation computations so that established
Model has more preferable versatility and thermal adaptability.
The present invention to achieve the above object, adopts the following technical scheme that：
Step of the present invention is divided into two 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.
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.
Present invention experiment and data collection step are as follows：
Experimental procedure one：The minimum and maximum temperature value of confirmatory sample.Temperature range is divided into multiple level values.Each temperature
Level is typically greater than 5 times of thermometric instruments resolution ratio, and precision is effectively distinguished to reach.
Experimental procedure two：In the range of highest and minimum temperature, at a temperature of all samples physical parameter acquirement defined
Primary standard analyze data.
Experimental procedure three：The spectroscopic data of sample is collected under different temperatures level.Record corresponding sample temperature simultaneously
Angle value.This temperature value is participated in modeling as an implicit factor, and the exact numerical for recording temperature is not required to this method
's.
Temperature is as follows as the implicit variable factors modeling method step of separation：
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 sample and physical property herein
The characteristic of parameter and it is different.It is preferable using second dervative for example, to macromolecule high viscosity samples.To low viscosity sample with
First derivative is preferable.
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：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 four：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 five：By it is formed above using temperature as the derivative spectrum of target and using physical property parameter to be measured as target
Spectrum merges after pretreatment.
Modeling procedure six：Pivot analysis (PCA) is done to caused merging spectrum above, rejects statistics exceptional value so that whole
The individual pivot pattern for merging spectroscopic data is all within a statistical certainty.
Using physical parameter to be measured a set point of temperature original analysis value as predictive variable, to merge spectrum wave number work
For independent variable.Geophysical parameter prediction model is established with partial least squares algorithm (PLS)：
P=B1y1+B2y2+…Bnyn+A1x1+A2x2+…Anxn
Herein, P is the measured value under physical property variable set point of temperature, Bi,Ai, i=1,2 ... n are regression coefficients, yi, xiPoint
Be not after pretreatment spectrum and derivative spectrum in wave number i=1,2 ... the numerical value at n.
The present invention is participated in near-infrared modeling process using temperature as the implicit variable factors of separation, thus using near
During infrared survey, model can be relied in itself to the physical measurement under the adaptability completion different temperatures of temperature, it is not necessary to directly
Temperature measurement information and correlation computations so that the model established has preferable compensation effect to temperature, thus with more preferable
Versatility.
Brief description of the drawings
Fig. 1 is without measuring point temperature-compensating experimental provision
A kind of second dervative pre-processed spectrum of high-molecular compounds of Fig. 2
PCA ideographs of the Fig. 3 based on second dervative pre-processed spectrum
Fig. 4 first derivative pre-processed spectrums
Fig. 5 first derivative spectrum host element patterns
Fig. 6 implementation steps block diagrams
The merging spectrum of Fig. 7 different temperatures
Fig. 8 merges the PCA ideographs of spectrum
Fig. 9 merges Viscosity Model caused by spectrum.
The wave number that Figure 10 Viscosity Models use.
Adaptability of Figure 11 the inventive method result to temperature
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.
Near infrared correction without measuring point temperature correction implementation steps block diagram as shown in fig. 6, specifically including following
Step：
Step 1：Gather representative sample, it is ensured that the physical parameter to be measured of sample can cover measurement request
Scope.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 experiment condition such as temperature etc..
Step 3：The pretreatment using temperature as target and pivot analysis are done to the spectrum gathered, produces derivative spectrum number
According to.In example, second dervative processing and pivot analysis have been carried out to macromolecule high viscosity sample.Treatment effect is as shown in Fig. 2 main
Element patterns are as shown in Figure 3.Second dervative pretreatment is on the basis of first derivative, and the sensitive spectrum of temperature information is carried out
Extract again, efficiently reduce temperature and physical parameter in the overlapping of modeling wave number.In the PCA ideographs shown in Fig. 3, there are two
Point has a little very big distance with other, and this point is singular point, is rejected in modeling so that whole pretreated light
Modal data pivot pattern is all within a statistical certainty.
Step 4：Pretreatment using physical property parameter mode to be measured as target and pivot analysis (PCA) are carried out to original spectrum.
Produce pre-processed spectrum data.In example, first derivative pretreatment and pivot analysis have been carried out to macromolecule sample.By processing
Spectrum afterwards is eliminated due to light source ages, the spectrally lower drift that the factor such as vibrations and probe and sample order of contact of popping one's head in is brought
Move, while remain the effective information that temperature influences on spectrum peak and shape again.Pre-processed spectrum is as shown in Figure 4.Fig. 5 is pre-
Handle the pivot ideograph of spectrum.There are two singular points in Fig. 5, should give rejecting, be no longer participate in modeling.
Step 5：It caused derivative spectrum and pre-processed spectrum will merge above, and produce and merge spectroscopic data.Fig. 7 be
The merging spectrum of different temperatures.In merging spectrum in the figure 7, left-half is first derivative part, there is provided effective thing
Property modeling spectral information；Right half part is second dervative part, there is provided the spectral information of temperature compensation function.
Step 6：Pivot analysis (PCA) is done to caused merging spectrum above, rejects statistics exceptional value so that whole to close
And the pivot pattern of spectroscopic data is all within a statistical certainty.Fig. 8 is the PCA ideographs for merging spectrum.Have three in Fig. 8
Individual singular point, rejecting is should give, be no longer participate in modeling.
Step 7：Using physical parameter original analysis value to be measured as predictive variable, to merge spectrum wave number as independent variable,
Geophysical parameter prediction model is established with partial least squares algorithm (PLS)：
P=B1y1+B2y2+…Bnyn+A1x1+A2x2+…Anxn
Herein, P is the measured value under physical property variable set point of temperature, Bi,Ai, i=1,2 ... n are regression coefficients, yi, xiPoint
Be not after pretreatment spectrum and derivative spectrum in wave number i=1,2 ... the numerical value at n.
Fig. 9 is to merge Viscosity Model caused by spectrum, and Figure 10 is the wave number that Viscosity Model uses, and Figure 11 is result to temperature
The comparison of adaptability.The correlation of Fig. 9 complex spectrums model predication value and measured value is 0.98, model accuracy R2For 0.97.Figure
Wavelength band shown in 10, first derivative spectrum section are 9056-4765cm-1, the selection of second derivative spectra section is 6024-
4528cm-1.Figure 11 is comparison of the no measuring point temperature compensation algorithm proposed by the present invention with being fixed on modeling algorithm at a temperature of 50 degree,
As can be seen from the figure the measurement result of fixed temperature model has larger sensitiveness to temperature change, and the inventive method is established
Model, have preferable compensation effect to temperature.
Claims (5)
1. a kind of near infrared correction without measuring point temperature correction, it is characterised in that this method comprises the following steps：
Step 1：The physical parameter primary standard data of multiple samples at the specified temperature are obtained, and under different temperatures level
Gather near infrared spectrum；
Step 2：Pretreatment using temperature as target and pivot point are carried out to the original near infrared spectrum gathered in step 1
Analysis, produce derivative spectrum data；The original near infrared spectrum that is gathered in step 1 is carried out using physical property parameter mode to be measured as
The pretreatment of target and pivot analysis, produce pre-processed spectrum；
Step 3：By caused by step 2 using temperature as the derivative spectrum of target and using physical property parameter mode to be measured as target
Pre-processed spectrum carries out spectrum merging, produces and merges spectroscopic data；
Step 4：Pivot analysis is done to the merging spectroscopic data described in step 3, rejects statistics exceptional value so that whole to merge
The pivot pattern of spectroscopic data is all within a statistical certainty；
Step 5：Using physical parameter original analysis value to be measured as predictive variable, to merge spectrum wave number as independent variable, establish
Geophysical parameter prediction model.
2. near infrared correction according to claim 1 without measuring point temperature correction, it is characterised in that：The step
The temperature range that sample near infrared spectrum is gathered in rapid one covers real-time physical parameter measurement temperature scope；Multiple sample determinands
The scope of property parameters distribution measurement request and have and enough distinguish interval.
3. near infrared correction according to claim 1 without measuring point temperature correction, it is characterised in that：The step
It is second dervative that the Pretreated spectra that temperature is target is directed in rapid two.
4. near infrared correction according to claim 1 without measuring point temperature correction, it is characterised in that：The step
Include the superposition of one or more of following algorithms in rapid two for the Pretreated spectra of target for physical parameter：Single order is led
Number, second dervative, maximum-minimum sandards, basic bottom line correction, scatter correction.
5. near infrared correction according to claim 1 without measuring point temperature correction, it is characterised in that：The step
The foundation of geophysical parameter prediction model uses partial least squares algorithm to enter trip temperature to imply the linear regression of variable in rapid five：
P=B1y1+B2y2+…Bnyn+A1x1+A2x2+…Anxn
Herein, P is the measured value under physical property variable set point of temperature, Bi,Ai, i=1,2 ... n are regression coefficients, yi, xiIt is pre- respectively
Spectrum and derivative spectrum be in wave number i=1,2 after processing ... the numerical value at n.
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| CN105866062A (en) * | 2016-04-01 | 2016-08-17 | 南京富岛信息工程有限公司 | Temperature correction method for gasoline near-infrared spectrum |
| CN106404177B (en) * | 2016-08-23 | 2018-10-16 | 合肥金星机电科技发展有限公司 | Infrared scanning thermometric modification method |
| CN108205432B (en) * | 2016-12-16 | 2020-08-21 | 中国航天科工飞航技术研究院 | Real-time elimination method for observation experiment data abnormal value |
| CN112432917B (en) * | 2019-08-08 | 2023-02-28 | 北京蓝星清洗有限公司 | Spectrum difference correction method and system |
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| CN101299022A (en) * | 2008-06-20 | 2008-11-05 | 河南中医学院 | Method for evaluating Chinese medicine comprehensive quality using near infrared spectra technique |
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| CN101299022A (en) * | 2008-06-20 | 2008-11-05 | 河南中医学院 | Method for evaluating Chinese medicine comprehensive quality using near infrared spectra technique |
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