CN105300924A - Measuring point-free compensation modeling method of near infrared calibration model under effect of temperature - Google Patents
Measuring point-free compensation modeling method of near infrared calibration model under effect of temperature Download PDFInfo
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- CN105300924A CN105300924A CN201510827829.3A CN201510827829A CN105300924A CN 105300924 A CN105300924 A CN 105300924A CN 201510827829 A CN201510827829 A CN 201510827829A CN 105300924 A CN105300924 A CN 105300924A
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Abstract
The invention discloses a measuring point-free compensation modeling method of a near infrared calibration model under the effect of temperature. The method comprises the following steps: collecting a sample, acquiring laboratory data of the sample at a prescribed temperature, and respectively collecting near infrared spectral data of the same sample at different temperatures; carrying out different pretreatments and statistical abnormal value treatment on acquired spectra to make the principal component modes of the pretreated spectral data be within a statistical reliability; and establishing a physical property parameter calibration model with the temperature as a latent variable by adopting a partial least squares algorithm. The temperature participates in the near infrared modeling process as a latent factor variable in the invention, so physical property measurement at different temperatures can be completed on the dependence of the adaptability of the model to the temperature in the near infrared measurement process without directly measuring temperature information or correlated calculation, so the application range of the model and the robustness of the model to the temperature are improved.
Description
Technical field
The present invention relates under sample temperature impact, there is the near-infrared spectrometers model tuning method without measuring point temperature compensation function.Be applicable to the quick detection of physical parameter influenced by ambient temperature as substance viscosity, sweat alanine concentration, food quality, quality of agricultural product, medicine quality, oil product of gasoline etc., be also applicable to the measurement of human body Woundless blood sugar concentration, soil constituent and mineralogical composition.
Background technology
In recent years, near-infrared spectral analysis technology with its fast detection, Non-Destructive Testing, without chemical contamination, the advantage such as easy and simple to handle, sample preparation is simple, the industries such as petrochemical complex, food, agricultural, medicine are widely used in, it not only can be applicable to lab analysis, and is applicable to field quick detection and on-line analysis.The laboratory accurately controlled from temperature when detecting instrument moves on to production scene, and measurement result can be subject to such environmental effects.Research shows, no matter for the near infrared spectrum of one-component or for complex system, the optical characteristics of temperature on biological tissue has larger impact, if therefore sample temperature changes, Near-Infrared Spectroscopy has obvious change.
But because near infrared light spectrum signature not isoplastic in molecule is different by the impact of temperature, the impact of temperature on sample spectra is different from the impact on reference.Therefore near-infrared spectrum analysis can not utilize reference to eliminate the impact of temperature on sample characteristic.Conventional minimizing temperature variation has the method that near infrared spectrum affect: rejected by the spectrum of temperature influence, choose and affect insensitive wave band to temperature and set up analytical model, add a correction temperature to influence value of spectrum etc. in a model.These methods may be used for overcoming the interference that testing sample temperature variation is brought Quantitative Analysis Model, but also do not have general rule to use which kind of method under judging which kind of situation at present, and will select according to particular problem.Therefore, under temperature variation, set up more general, that thermal adaptability is stronger near infrared detection calibration model, can effectively apply very crucial near infrared technology.
Summary of the invention
The method that the present invention proposes, temperature is implied variable factors as non-separation to be participated near infrared modeling process, thus when using near-infrared measuring, can rely on the adaptability of model to temperature itself complete different temperatures under physical measurement, do not need direct temperature metrical information and correlation computations, improve the scope of application of model and the robustness to temperature.
The present invention for achieving the above object, adopts following technical scheme:
Step of the present invention is divided into two parts.Part I, the experimental design of modeling data and spectral collection; Part II, the pre-service of near infrared spectrum and the foundation of calibration model.
The experimental facilities of modeling data comprises, and the sample cell (2) that (1) can regulate sample temperature temperature meter (3) the near infrared spectrum collection instrument (4) of displays temperature change can not produce the optic probe of obviously impact on sample temperature.(5) the computer recording device be connected with near infrared spectrum collection instrument.The duty of package unit as shown in Figure 1.
Experiment of the present invention and data collection step as follows:
Experimental procedure one: the minimum and maximum temperature value of confirmatory sample.Temperature range is divided into multiple level.Each temperature levels is generally greater than thermometric instruments resolution 5 times, effectively distinguishes precision to reach.
Experimental procedure two: under the standard temperature of defined, obtains primary standard to all samples physical parameter and analyzes data.
Experimental procedure three: respectively spectroscopic data is collected to same sample under different temperatures level.Record corresponding sample temperature value simultaneously.This temperature value may be used for the foundation of temperature correction model, also can use as an implicit variable factors, directly set up the parameter models of physical with better Acclimation temperature.For when temperature is only implied variable as a non-separation, the record of temperature value itself not necessarily.
It is as follows that temperature implies variable factors modeling procedure as non-separation:
Modeling procedure one: form target optical spectral data set with the spectrum under different temperatures level, to the target optical spectrum set pre-service that to carry out with physical property parameter mode to be measured be target.These pre-service comprise the superposition of one or more following algorithms: first order derivative, second derivative, maximum-minimum sandards, and basic bottom line corrects, scatter correction, constant bias correction, etc.The determination of Preprocessing Algorithm is herein different with the state of physical parameter to be measured and sample.
Modeling procedure two: do pivot analysis (PCA) to spectrum after the pre-service produced above, rejects statistics exceptional value, makes the pivot pattern of whole pre-processed spectrum data all within a statistical certainty.
Modeling procedure three: based on spectrum after above pre-service, using physical parameter to be measured in the original analysis value of defined temperature as predictive variable, after pre-service, spectrum wave number is as independent variable.Physical parameter calibration model is set up with partial least squares algorithm (PLS):
P＝D
1y
1+D
2y
2+…D
ny
n
Herein, P is the measured value of physical property variable at required standard temperature, D
i, i=1,2 ... n is regression coefficient, y
ibe after pre-service spectrum at wave number i=1,2 ... the numerical value at n place.
Temperature is implied variable factors as non-separation by the inventive method sets up temperature correction model, thus when using near-infrared measuring, the response of spectrum to temperature itself can be relied on, complete the physical measurement under different temperatures, thus do not need direct temperature metrical information and correlation computations just can measure the physical parameter of sample.Proposed by the invention has preferably robustness without measuring point temperature compensation to temperature variation.
Accompanying drawing explanation
Fig. 1 is without measuring point temperature compensation experimental provision
A kind of macromolecular material of Fig. 2 is at the original spectrum of different temperatures
The local spectrum of Fig. 3 under pretreated different temperatures
Fig. 4 spectrum pivot analysis and pattern abnormity point
The impure moisture near-infrared model of a kind of macromolecular material of Fig. 5
Fig. 6 modeling spectrum wave-number range used
Fig. 7 has the forecast model result of temperature compensation
The lower near infrared correction of Fig. 8 temperature impact without measuring point compensating Modeling method implementation step block diagram
Embodiment
Be measured as example with a kind of impurity moisture of macromolecular compound below, specific implementation method is described.This example does not form and limits the scope of the inventive method.
The lower near infrared correction of temperature impact without measuring point compensating Modeling method implementation step block diagram as shown in Figure 8, specifically comprise the following steps:
Step one: gather representative sample, ensure that the physical property parameters distribution to be measured of sample measures the scope required.Total number of samples is at 40-60.
Step 2: utilize the laboratory equipment shown in Fig. 1, gathers the near infrared spectrum of each sample under 24 DEG C, 35 DEG C, 50 DEG C, 60 DEG C, 70 DEG C five different temperatures levels.If Fig. 2 is the original spectrum of a kind of macromolecular compound in different temperatures.
Step 3: carry out different pre-service to spectrum and compare, to determine the last preprocess method be suitable for.In example, carried out first order derivative process to macromolecule high viscosity sample, treatment effect as shown in Figure 3.Spectrum after treatment eliminates due to light source ages, and what probe vibrations and probe and the factor such as sample contacts degree were brought drift about spectrally down, while remain again the effective information that temperature affects spectrum peak and shape.
Step 4: do pivot analysis (PCA) to spectrum after the pre-service produced above, rejects statistics exceptional value, makes the pivot pattern of whole pre-processed spectrum data all within a statistical certainty.As shown in Figure 4 in pivot mode chart, there are four singular points, rejected.
Step 5: based on spectrum after above pre-service, using physical parameter to be measured in the original analysis value of defined temperature as predictive variable, after pre-service, spectrum wave number is as independent variable.Physical parameter calibration model is set up with partial least squares algorithm (PLS):
P＝D
1y
1+D
2y
2+…D
ny
n
Herein, P is the measured value of physical property variable at required standard temperature, D
i, i=1,2 ... n is regression coefficient, y
ibe after pre-service spectrum at wave number i=1,2 ... the numerical value at n place.
Fig. 5 is a kind of impure moisture near-infrared model of macromolecular material, and the correlativity of model predication value and measured value is 0.98, model accuracy R
2be 0.95.Fig. 6 is the wave number that model uses, and shown modeling spectrum wave-number range used is 9700-9210cm
-1; 8600-8423cm
-1and 7600-4497cm
-1.Fig. 7 is the forecast model result with temperature compensation.In this example, sample temperature is from the change of 24-70 degree, and the measurement result without measuring point model of temperature compensation set up has good robustness to temperature variation.
Claims (5)
1. in near-infrared spectral measurement temperature influence without a measuring point temperature adjustmemt modeling method, it is characterized in that the method comprises the steps:
Step one: the physical parameter laboratory standard data gathering multiple sample, and carry out the near infrared spectra collection under different temperatures level for same sample;
Step 2: the near infrared spectrum gathered in step one is carried out different pre-service and compared;
Step 3: statistics outlier processing is carried out to the near infrared spectrum produced in step 2;
Step 4: with physical parameter to be measured in the raw data of defined temperature for predictive variable, with the spectrum wave number under different temperatures level after pre-service for independent variable, set up physical parameter near infrared correction.
2. the lower near infrared correction of temperature according to claim 1 impact without measuring point compensating Modeling method, it is characterized in that: in described step one, multiple sample physical parameter to be measured will cover the scope measuring requirement; Temperature range covers the temperature range that testing sample physical parameter is measured.
3. the lower near infrared correction of temperature according to claim 1 impact without measuring point compensating Modeling method, it is characterized in that: the preprocessing procedures in described step 2 comprises the superposition of one or more following algorithms: first order derivative, second derivative, maximum-minimum sandards, basis bottom line corrects, scatter correction, constant bias correction etc.
4. the lower near infrared correction of temperature according to claim 1 impact without measuring point compensating Modeling method, it is characterized in that: adding up outlier processing in described step 3 is pca method, make the pivot pattern of whole pre-processed spectrum data all within a statistical certainty.
5. the lower near infrared correction of temperature according to claim 1 impact without measuring point compensating Modeling method, it is characterized in that: in described step 4, the method for building up employing partial least squares algorithm of physical parameter calibration model carries out temperature is the linear regression that variable is implied in non-separation.
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| CN109682968A (en) * | 2018-11-08 | 2019-04-26 | 上海艾瑞德生物科技有限公司 | A kind of fluorescence immunoassay strip quantitative detection test signal temperature correction method |
| CN112432917A (en) * | 2019-08-08 | 2021-03-02 | 北京蓝星清洗有限公司 | Spectrum difference correction method and system |
| CN114813615A (en) * | 2022-06-23 | 2022-07-29 | 广东省计量科学研究院（华南国家计量测试中心） | Alcohol concentration range detection method based on infrared light absorption characteristics |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109682968A (en) * | 2018-11-08 | 2019-04-26 | 上海艾瑞德生物科技有限公司 | A kind of fluorescence immunoassay strip quantitative detection test signal temperature correction method |
| CN109682968B (en) * | 2018-11-08 | 2022-03-11 | 上海艾瑞德生物科技有限公司 | Temperature correction method for quantitative detection test signal of fluorescence immunoassay strip |
| CN112432917A (en) * | 2019-08-08 | 2021-03-02 | 北京蓝星清洗有限公司 | Spectrum difference correction method and system |
| CN112432917B (en) * | 2019-08-08 | 2023-02-28 | 北京蓝星清洗有限公司 | Spectrum difference correction method and system |
| CN114813615A (en) * | 2022-06-23 | 2022-07-29 | 广东省计量科学研究院（华南国家计量测试中心） | Alcohol concentration range detection method based on infrared light absorption characteristics |
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Application publication date: 20160203 |