CA2323878A1 - Automatic calibration method - Google Patents
Automatic calibration method Download PDFInfo
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- CA2323878A1 CA2323878A1 CA002323878A CA2323878A CA2323878A1 CA 2323878 A1 CA2323878 A1 CA 2323878A1 CA 002323878 A CA002323878 A CA 002323878A CA 2323878 A CA2323878 A CA 2323878A CA 2323878 A1 CA2323878 A1 CA 2323878A1
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- Prior art keywords
- calibration
- spectra
- data
- components
- computation
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
- G01N21/274—Calibration, base line adjustment, drift correction
Abstract
The invention relates to an automatic calibration method related to spectra of a spectrometer for examining substances in the pharmaceutical, chemical, cosmetic, dye, plastics, rubber, and foodstuff industries.
Description
PBUll~/11.09.00 1 An automatic calibration method The invention relates to a calibration method for evaluating spectra of a spectrometer for examining solid, liquid or gaseous substances.
This calibration method serves the analysis of spectra produced by way of a spectrometer. Such analysis methods and devices function according to the principle whereby a transmitter emits electromagnetic radiation onto the substance to be examined and these rays which are reflected by or penetrate through the sub-stance are recorded by a receiver. For these examinations all wavelength region of the electromagnetic spectrum may be used.
Particularly suitable is the wave region in the near.infra-red (NIR). Such methods and devices are amongst others mentioned in CH 685807 A5 or CH 683'713 A5.
This analysis method is applied in particular in the pharmaceu-tical, chemical, cosmetic, colouring, plastics, rubber and food-stuffs industries.
Since with this spectral method there are various instrument types, sensors, substance tyes, aggregate conditions, mathemati-cal methods and calibration behaviour and for each substance to be examined there are nominal spectra, before the actual evalua-tion a calibration is required.
Until now the calibration was effected by time-consuming, em-pirical, intuitive or ~~guessing~~ determination of the'required variables cited above.
The object of the present invention then lies in specifying a calibration method which automatically in as short a time as PBU112/11.09.00 - possible ascertains the optimal boundary conditions of the ac-tual evaluation method.
According to the invention this object is achieved by the fol-lowing method steps:
- defining and determining the number of components of a vec-tor-equivalent data set - producing the data sets on account of experience values, measuring methods, computation methods and/or specific sub-stance properties - acquiring nominal spectra - calibration computation - selecting primary and/or secondary factors - determining and subsequent sorting of the calibration qual-ity, in~particular Q-value - selecting the calibration on account of the best Q-values wherein the calibration is effected automatically.
Various selection criteria (primary and secondary factors) are computed independently from the used calibration method (MLR/PCR/PLS/Cluster?, wherein the data necessazy for evaluation is processed.
Finally a quality factor optimal for the calibration is deter-mined which results with the computation of the calibration method (MLR/PCR/PLS/Cluster).
Furthermore with the calibration method according to the inven-tion the structure of the knowledge base which contains all data expert knowledge and their weightings may be developed.
psoiis/ii.os.oo Further advantageous method steps are deduced from the dependent claims. In particular as components of a data set the measuring device, in particular the sensor type, specifically optical and/or mechanical as well as the number of measurement run-throughs may be ascertained. As further components of the data sets necessary for the calibration the specific data of the sub-stance to be examined is determined. Furthermore likewise in the form of components of a data set the computation method and the behaviour of the calibration may be laved down. As computation methods for the calibration in particular the methods: MLR, PCR, PLS and/or CLU known in chemvmetry are used.
For calibration further there serve the nominal spectra known for a substance to be examined.
In order to obtain an optimal calibration, the components of the data sets completely and/or partly may be permutated within the calibration method.
The measured spectra are generally characterised by a multitude of oscillation superpositions. A visual evaluation is therefore practically not possible. Specific differences in the spectra of similiar substances often consist only of a slight displacement or small shape changes of the mostly wide absorption bands. It is therefore necessary to evaluate these spectra with mathemati-cal methods. These mathematical methods are based on chemometric software. Chemometry is to be understood as the application of mathematical methods in chemistry. The chemometric software in spectroscopy has the exercise of finding a statistical relation-ship between spectral data and the known property values of the samples used for calibration.
pHOiia/ii.o9.ao As an example, in the following, the invention is represented in the form of flow diagrams. There are shown in:
Figure 1 schematic representation of a possible measuring de-vice Figure 2 a flow diagram of the automatic calibration Figure 3 initialisation of the knowledge base Figure 4 input Fzgure 5 permutation Figure 6 method computation Figure 7 factor selection Figure 8 qualitative and quantitative Q-value determination In Figure Z there is represented a possible measuring arrange-ment. The samples 6 are acquired by a measuring head 5 contain-ing a sensor 5, wherein a spectrometer ~ records the spectra.
The digitalised data are via a lead 3 led into a computer Z
which then carries out the calibration according to the inven-tion. The spectra and the evaluation is usually displayed on a monitor 2.
In Figure 2 the basic course of the automatic calibration ac-cording to the invention is shown. The first step comprises the initialisation of the knowledge base. There follows the actual input of the data with the formation of data sets. The compo-nents of the data sets are completely or partly permutated and PBfT112/11.09.00 are subjected to a computing method. The used permutations are initialised, wherein for each permutation a special factor se-lection, a Q-value is computed and thus a specific calibration is allocated and stored. There results therefrom a multitude of quality values from which the optimal Q-value may be selected and thus the most suitable calibration may be determined.
Figure 3 represents the initialisation of the knowledge base.
The knowledge base forms the basis for the components containing the data sets. To the initial data there belong for example the instrument type/sensor, the substance type, the calibration type, the calibration behaviour and/or spectral data. From these five initial data, on account of expert knowledge the parameter group is determined and weighted. As a sensor type an optical or mechanical sensor may be considered. The substance type is laved down by way of a prior treatment of data and by way of the se-lection of the measuring procedures. To the calibration type there belongs a calibration data set with a possible computing method. The calibration behaviour is detern~ined from the se-lected wavelength region, a pre-treatment of data, the primary, the secondary factors and/or the number of measuring procedures.
The spectra data are composed of the calibration data set, the validation data set, the wavelength selection, the pretreatment of data and/or the number of measuring procedures.
After the initialisation there follows the actual input accord-ing to Figure 4. From the spectra data there is selected in par-ticular the instzument type/sensor with which the respective spectrum is recorded, Furthermore the substance type, the cali-bration type and the calibration behaviour are inputted.
After the input there follows according to Figure 5 the complete and/or part permutation of the components of the data'sets. The PBU111/11.09.00 f>
respective parameter group is subjected to a computation for the calibration. With this, the method itself forms a component of the data set. The method computation is broken down in Figure 6.
The computation course begins with the main component analysis,-amongst others published in S. Wood, K. Esbensen, P. Geladi, ~~Principle Component Analysis~~, Chemometrics and Tntelligent Laboratory Systems 2 (1987) 37- 52. A spectrum measured with the device according to Figure 1 may for example be composed of for example 500 measuring points. This individual measuring data corresponds to the intensity values in dependence on 500 wave numbers in the near infra-red. In order to obtain a good cali-bration, one in turn requires a large number of spectra. For ex-ample 100 substance spectra thus result in 50,000 data points which entails a great computing effort. In order to obtain ac-ceptable computing times the spectral data is condensed With the help of the main component analysis. With this no important in-formation is lost.
With a calibration, to the samples there are allocated two dif-ferent data sets independent of one another, specifically the calibration data set on the one hand and the validation data set on the other hand. with the calibration data set the main compo-nent analysis is carried out. With the validation data set the results of the calibration are evaluated.
Far the quantitative calibration there are available a multitude of computation methods. In particular there are cited here the three most common computation methods: Multiple Linear Regres-sion (MLR), Principle Component Regression (PCR) and the Partial Least Squares Regression (PLS), for example published in Hruce R
Kow~alski, °Chemometrics, Mathematics & Statistics in Chemistry~~
P8LT112/11 .09. 00 7 NATO AIS Series, Series C: Math. & Phys. Sciences Vol. 138 (1984) .
MLR is an extension of the linear regression to several dimen-sions. This evaluation on account of a few selective wavelengths requires no main component analysis. In this method the proper-ties are computed by intensity values and correlation coeffi-cients.
The PCR is composed of two steps. In the first step the intensi-ties to be loaded are computed by way of the main component analysis. The second step produces the correlation coefficients by way of MLR.
According to the principle of recursion with PLS the data to be loaded is computed. The quantitative reference values are how-ever taken account of already at the beginning of the computa-tion. Whilst the PCR reduces the spectral data to the most domi-nant dimensions, the PLS targets the most relative dimensions, i.e. the best agreement between prognosed and actual values.
The actual calibration is made up on the one hand of the quali-tative and on the other hand of the quantitative calibration.
The course of the quantitative calibration begins with the meas-uring of the calibration spectra. In order to set up a represen-tative calibration a multitude of various charges of the same class should be measured. With an increasing number of measuring run-throughs the signal/optical noise ratio may be improved and inhomogenities compensated.
After the measuring of the samples, the actual setting up of the calibration begins.
psuii2/ii.os.oo g 1. The spectra are divided into a calibration data set and into a validation daCa set, wherein the selection is effected purely randomly. As mentioned above, the calibration data set must be independent from the validation data set. zn this first set one works without a wavelength selection and without data pretreatment.
This calibration method serves the analysis of spectra produced by way of a spectrometer. Such analysis methods and devices function according to the principle whereby a transmitter emits electromagnetic radiation onto the substance to be examined and these rays which are reflected by or penetrate through the sub-stance are recorded by a receiver. For these examinations all wavelength region of the electromagnetic spectrum may be used.
Particularly suitable is the wave region in the near.infra-red (NIR). Such methods and devices are amongst others mentioned in CH 685807 A5 or CH 683'713 A5.
This analysis method is applied in particular in the pharmaceu-tical, chemical, cosmetic, colouring, plastics, rubber and food-stuffs industries.
Since with this spectral method there are various instrument types, sensors, substance tyes, aggregate conditions, mathemati-cal methods and calibration behaviour and for each substance to be examined there are nominal spectra, before the actual evalua-tion a calibration is required.
Until now the calibration was effected by time-consuming, em-pirical, intuitive or ~~guessing~~ determination of the'required variables cited above.
The object of the present invention then lies in specifying a calibration method which automatically in as short a time as PBU112/11.09.00 - possible ascertains the optimal boundary conditions of the ac-tual evaluation method.
According to the invention this object is achieved by the fol-lowing method steps:
- defining and determining the number of components of a vec-tor-equivalent data set - producing the data sets on account of experience values, measuring methods, computation methods and/or specific sub-stance properties - acquiring nominal spectra - calibration computation - selecting primary and/or secondary factors - determining and subsequent sorting of the calibration qual-ity, in~particular Q-value - selecting the calibration on account of the best Q-values wherein the calibration is effected automatically.
Various selection criteria (primary and secondary factors) are computed independently from the used calibration method (MLR/PCR/PLS/Cluster?, wherein the data necessazy for evaluation is processed.
Finally a quality factor optimal for the calibration is deter-mined which results with the computation of the calibration method (MLR/PCR/PLS/Cluster).
Furthermore with the calibration method according to the inven-tion the structure of the knowledge base which contains all data expert knowledge and their weightings may be developed.
psoiis/ii.os.oo Further advantageous method steps are deduced from the dependent claims. In particular as components of a data set the measuring device, in particular the sensor type, specifically optical and/or mechanical as well as the number of measurement run-throughs may be ascertained. As further components of the data sets necessary for the calibration the specific data of the sub-stance to be examined is determined. Furthermore likewise in the form of components of a data set the computation method and the behaviour of the calibration may be laved down. As computation methods for the calibration in particular the methods: MLR, PCR, PLS and/or CLU known in chemvmetry are used.
For calibration further there serve the nominal spectra known for a substance to be examined.
In order to obtain an optimal calibration, the components of the data sets completely and/or partly may be permutated within the calibration method.
The measured spectra are generally characterised by a multitude of oscillation superpositions. A visual evaluation is therefore practically not possible. Specific differences in the spectra of similiar substances often consist only of a slight displacement or small shape changes of the mostly wide absorption bands. It is therefore necessary to evaluate these spectra with mathemati-cal methods. These mathematical methods are based on chemometric software. Chemometry is to be understood as the application of mathematical methods in chemistry. The chemometric software in spectroscopy has the exercise of finding a statistical relation-ship between spectral data and the known property values of the samples used for calibration.
pHOiia/ii.o9.ao As an example, in the following, the invention is represented in the form of flow diagrams. There are shown in:
Figure 1 schematic representation of a possible measuring de-vice Figure 2 a flow diagram of the automatic calibration Figure 3 initialisation of the knowledge base Figure 4 input Fzgure 5 permutation Figure 6 method computation Figure 7 factor selection Figure 8 qualitative and quantitative Q-value determination In Figure Z there is represented a possible measuring arrange-ment. The samples 6 are acquired by a measuring head 5 contain-ing a sensor 5, wherein a spectrometer ~ records the spectra.
The digitalised data are via a lead 3 led into a computer Z
which then carries out the calibration according to the inven-tion. The spectra and the evaluation is usually displayed on a monitor 2.
In Figure 2 the basic course of the automatic calibration ac-cording to the invention is shown. The first step comprises the initialisation of the knowledge base. There follows the actual input of the data with the formation of data sets. The compo-nents of the data sets are completely or partly permutated and PBfT112/11.09.00 are subjected to a computing method. The used permutations are initialised, wherein for each permutation a special factor se-lection, a Q-value is computed and thus a specific calibration is allocated and stored. There results therefrom a multitude of quality values from which the optimal Q-value may be selected and thus the most suitable calibration may be determined.
Figure 3 represents the initialisation of the knowledge base.
The knowledge base forms the basis for the components containing the data sets. To the initial data there belong for example the instrument type/sensor, the substance type, the calibration type, the calibration behaviour and/or spectral data. From these five initial data, on account of expert knowledge the parameter group is determined and weighted. As a sensor type an optical or mechanical sensor may be considered. The substance type is laved down by way of a prior treatment of data and by way of the se-lection of the measuring procedures. To the calibration type there belongs a calibration data set with a possible computing method. The calibration behaviour is detern~ined from the se-lected wavelength region, a pre-treatment of data, the primary, the secondary factors and/or the number of measuring procedures.
The spectra data are composed of the calibration data set, the validation data set, the wavelength selection, the pretreatment of data and/or the number of measuring procedures.
After the initialisation there follows the actual input accord-ing to Figure 4. From the spectra data there is selected in par-ticular the instzument type/sensor with which the respective spectrum is recorded, Furthermore the substance type, the cali-bration type and the calibration behaviour are inputted.
After the input there follows according to Figure 5 the complete and/or part permutation of the components of the data'sets. The PBU111/11.09.00 f>
respective parameter group is subjected to a computation for the calibration. With this, the method itself forms a component of the data set. The method computation is broken down in Figure 6.
The computation course begins with the main component analysis,-amongst others published in S. Wood, K. Esbensen, P. Geladi, ~~Principle Component Analysis~~, Chemometrics and Tntelligent Laboratory Systems 2 (1987) 37- 52. A spectrum measured with the device according to Figure 1 may for example be composed of for example 500 measuring points. This individual measuring data corresponds to the intensity values in dependence on 500 wave numbers in the near infra-red. In order to obtain a good cali-bration, one in turn requires a large number of spectra. For ex-ample 100 substance spectra thus result in 50,000 data points which entails a great computing effort. In order to obtain ac-ceptable computing times the spectral data is condensed With the help of the main component analysis. With this no important in-formation is lost.
With a calibration, to the samples there are allocated two dif-ferent data sets independent of one another, specifically the calibration data set on the one hand and the validation data set on the other hand. with the calibration data set the main compo-nent analysis is carried out. With the validation data set the results of the calibration are evaluated.
Far the quantitative calibration there are available a multitude of computation methods. In particular there are cited here the three most common computation methods: Multiple Linear Regres-sion (MLR), Principle Component Regression (PCR) and the Partial Least Squares Regression (PLS), for example published in Hruce R
Kow~alski, °Chemometrics, Mathematics & Statistics in Chemistry~~
P8LT112/11 .09. 00 7 NATO AIS Series, Series C: Math. & Phys. Sciences Vol. 138 (1984) .
MLR is an extension of the linear regression to several dimen-sions. This evaluation on account of a few selective wavelengths requires no main component analysis. In this method the proper-ties are computed by intensity values and correlation coeffi-cients.
The PCR is composed of two steps. In the first step the intensi-ties to be loaded are computed by way of the main component analysis. The second step produces the correlation coefficients by way of MLR.
According to the principle of recursion with PLS the data to be loaded is computed. The quantitative reference values are how-ever taken account of already at the beginning of the computa-tion. Whilst the PCR reduces the spectral data to the most domi-nant dimensions, the PLS targets the most relative dimensions, i.e. the best agreement between prognosed and actual values.
The actual calibration is made up on the one hand of the quali-tative and on the other hand of the quantitative calibration.
The course of the quantitative calibration begins with the meas-uring of the calibration spectra. In order to set up a represen-tative calibration a multitude of various charges of the same class should be measured. With an increasing number of measuring run-throughs the signal/optical noise ratio may be improved and inhomogenities compensated.
After the measuring of the samples, the actual setting up of the calibration begins.
psuii2/ii.os.oo g 1. The spectra are divided into a calibration data set and into a validation daCa set, wherein the selection is effected purely randomly. As mentioned above, the calibration data set must be independent from the validation data set. zn this first set one works without a wavelength selection and without data pretreatment.
2. The main component analysis is carried out.
3. The total number of the primary factors is selected. Primary factors are to be understood as the factors which are mean-ingful for the description of the spectra up to the optical noise limit of the applied measuring method.
4. Following the, detemination of the primary factors, the se-lection factors, called secondary factors are selected.
These selection factors are the factors which allocate to the associated spectra an unequivocal separation of the various calibration qualities. The factor selection is shown in the form of a flow diagram in Figure 7, 5. There follows an optimisation of the calibration. If no se-lection is achieved the various data pretreatments should be carried out. Those wavelength regions are rejected which do not contain any significant information.
These selection factors are the factors which allocate to the associated spectra an unequivocal separation of the various calibration qualities. The factor selection is shown in the form of a flow diagram in Figure 7, 5. There follows an optimisation of the calibration. If no se-lection is achieved the various data pretreatments should be carried out. Those wavelength regions are rejected which do not contain any significant information.
6. The best calibration is then stored and tested.
The qualitative calibration is used for measuring property values (e. g. water content, mixing consituent parts, hydroxy number, etc . ) .
PHU111/11.09.00 After measuring the samples the calibration is set up. Tt is ef-fected essentially according to the same course as with the qualitative calibration.
According to the type of calibration, qualitative or quantita-tive, according to the flaw diagram in Figure 8, the quality of the calibratio~l, the so-called Q-value, is laved down.
At the end of the calibration computations subsequently a table of the various Q-values is set up from which one may then select the optimal calibration.
The qualitative calibration is used for measuring property values (e. g. water content, mixing consituent parts, hydroxy number, etc . ) .
PHU111/11.09.00 After measuring the samples the calibration is set up. Tt is ef-fected essentially according to the same course as with the qualitative calibration.
According to the type of calibration, qualitative or quantita-tive, according to the flaw diagram in Figure 8, the quality of the calibratio~l, the so-called Q-value, is laved down.
At the end of the calibration computations subsequently a table of the various Q-values is set up from which one may then select the optimal calibration.
Claims (10)
1. A calibration method for evaluating spectra of a spectrometer for examining solid, liquid or gaseous substances characterised by the following method steps:
- defining and determining the number of components of a vector-equivalent data set - producing the data sets on account of experience values, measuring methods, computation methods and/or specific substance properties - acquiring nominal spectra - calibration computation - selecting primary and/or secondary factors - determining and subsequent sorting of the calibration quality, in particular Q-value - selecting the calibration on account of the best Q-values wherein the calibration is effected automatically.
- defining and determining the number of components of a vector-equivalent data set - producing the data sets on account of experience values, measuring methods, computation methods and/or specific substance properties - acquiring nominal spectra - calibration computation - selecting primary and/or secondary factors - determining and subsequent sorting of the calibration quality, in particular Q-value - selecting the calibration on account of the best Q-values wherein the calibration is effected automatically.
2. A method according to claim 1, characterised in that as components of a data set the measuring device, in particular the sensor type, specifically optical and/or mechanical, the wavelength region, the adaptation of the required data as well as the number of measuring run-throughs are layed down.
3. A method according to claim 1 or 2, characterised in that as components the specific data of the substance to be examined is determined in a processed manner.
4. A method according to at least one of the claims 1, 2 or 3, characterised in that as a component the computation method is layed down.
5. A method according to claim 4, characterised in that as computation methods for the calibration the methods: MLR, PCR, PLS and/or CLU known per se in chemometry are provided.
6. A method according to at least one of the preceding claims, characterised in that as a component the behaviour of the calibration is provided.
7. A method according to at least one of the preceding claims, characterised in that the spectra of the substance to be examined serve as components.
8. A method according to at least one of the preceding claims, characterised in that the components of the data sets for optimising the calibration are completely and/or partly permutated.
9. A method according to at least one of the preceding claims, characterised in that the optimal number of primary factors for describing the spectra is determined up to the optical noise limit of the applied measuring method.
10. A method according to at least one of the preceding claims, characterised in that after the determining of the number of primary factors the selection factors described as secondary factors are determined which permit an unequivocal separation of the various Q-values of the calibrations.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE19810917.2 | 1998-03-13 | ||
| DE19810917A DE19810917A1 (en) | 1998-03-13 | 1998-03-13 | Calibration method used in evaluation of measured spectra |
| PCT/CH1998/000418 WO1999047909A1 (en) | 1998-03-13 | 1998-09-30 | Automatic calibration method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2323878A1 true CA2323878A1 (en) | 1999-09-23 |
Family
ID=7860766
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA002323878A Abandoned CA2323878A1 (en) | 1998-03-13 | 1998-09-30 | Automatic calibration method |
Country Status (7)
| Country | Link |
|---|---|
| US (1) | US6480795B1 (en) |
| EP (1) | EP1062496A1 (en) |
| JP (1) | JP2002506991A (en) |
| AU (1) | AU9150898A (en) |
| CA (1) | CA2323878A1 (en) |
| DE (1) | DE19810917A1 (en) |
| WO (1) | WO1999047909A1 (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SE9900030D0 (en) * | 1999-01-05 | 1999-01-05 | Astra Ab | Reaction monitoring |
| SE516343C2 (en) * | 2000-02-22 | 2001-12-17 | Johan Trygg | Method and apparatus for calibrating input data |
| US6774368B2 (en) * | 2001-03-08 | 2004-08-10 | Baylor University | Dispersive near-infrared spectrometer with automatic wavelength calibration |
| US6643597B1 (en) * | 2001-08-24 | 2003-11-04 | Agilent Technologies, Inc. | Calibrating a test system using unknown standards |
| US20050236563A1 (en) * | 2002-03-08 | 2005-10-27 | Busch Kenneth W | Dispersive near-infrared spectrometer with automatic wavelength calibration |
| WO2006088350A1 (en) * | 2005-02-21 | 2006-08-24 | Chemometry Consultancy | Method and system for selection of calibration model dimensionality, and use of such a calibration model |
| RU2308684C1 (en) * | 2006-06-20 | 2007-10-20 | Общество с ограниченной ответственностью "ВИНТЕЛ" | Method of producing multi-dimension calibrating models |
| US7672813B2 (en) * | 2007-12-03 | 2010-03-02 | Smiths Detection Inc. | Mixed statistical and numerical model for sensor array detection and classification |
Family Cites Families (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2066947B (en) * | 1980-01-09 | 1984-06-20 | Measurex Corp | Gas measuring apparatus with adjustable path length and method for operation and standardization therefor |
| US4627014A (en) * | 1984-04-09 | 1986-12-02 | Eastman Kodak Company | Method and apparatus for determination of an analyte and method of calibrating such apparatus |
| US4800279A (en) * | 1985-09-13 | 1989-01-24 | Indiana University Foundation | Methods and devices for near-infrared evaluation of physical properties of samples |
| CA2037324C (en) * | 1990-03-01 | 2000-01-11 | X-Rite, Incorporated | Apparatus and method for calibration in a spectrophotometer |
| US5243546A (en) * | 1991-01-10 | 1993-09-07 | Ashland Oil, Inc. | Spectroscopic instrument calibration |
| DE4128912C2 (en) * | 1991-08-30 | 1995-06-22 | Deutsche Forsch Luft Raumfahrt | Method and device for the calibration of spectroradiometers |
| US5335291A (en) * | 1991-09-20 | 1994-08-02 | Massachusetts Institute Of Technology | Method and apparatus for pattern mapping system with self-reliability check |
| CH683713A5 (en) * | 1992-02-18 | 1994-04-29 | Buehler Ag | Method and apparatus for detecting parameters of substances. |
| CH685807A5 (en) * | 1992-10-23 | 1995-10-13 | Buehler Ag | Automatic handling, sorting and sepn. of waste material |
| JPH08500898A (en) * | 1992-04-16 | 1996-01-30 | ザ ダウ ケミカル カンパニー | An improved method for interpreting complex data to detect abnormal equipment or processing behavior |
| DE4218971C2 (en) * | 1992-06-10 | 1994-09-22 | Grecon Greten Gmbh & Co Kg | Process for calibrating an image processing system |
| DE4331317A1 (en) * | 1993-09-15 | 1995-03-16 | Philips Patentverwaltung | Examination procedure for the evaluation of location-dependent spectra |
| CA2228844C (en) * | 1995-08-07 | 2006-03-14 | Boehringer Mannheim Corporation | Biological fluid analysis using distance outlier detection |
| DE19545178B4 (en) * | 1995-12-04 | 2008-04-10 | Berthold Gmbh & Co. Kg | spectral detector |
| DE19548378A1 (en) * | 1995-12-27 | 1997-07-03 | Bran & Luebbe | Process and device combination for establishing the comparability of spectrometer measurements |
| US5672869A (en) * | 1996-04-03 | 1997-09-30 | Eastman Kodak Company | Noise and background reduction method for component detection in chromatography/spectrometry |
| CA2201410A1 (en) * | 1996-04-16 | 1997-10-16 | Bogdan Kurtyka | System for matching absorbance spectra employing a library stabilization algorithm |
| US5822219A (en) * | 1996-05-13 | 1998-10-13 | Foss Nirsystems, Inc. | System for identifying materials by NIR spectrometry |
| US6015667A (en) * | 1996-06-03 | 2000-01-18 | The Perkin-Emer Corporation | Multicomponent analysis method including the determination of a statistical confidence interval |
-
1998
- 1998-03-13 DE DE19810917A patent/DE19810917A1/en not_active Ceased
- 1998-09-30 JP JP2000537053A patent/JP2002506991A/en active Pending
- 1998-09-30 CA CA002323878A patent/CA2323878A1/en not_active Abandoned
- 1998-09-30 US US09/623,469 patent/US6480795B1/en not_active Expired - Lifetime
- 1998-09-30 AU AU91508/98A patent/AU9150898A/en not_active Abandoned
- 1998-09-30 EP EP98943624A patent/EP1062496A1/en not_active Withdrawn
- 1998-09-30 WO PCT/CH1998/000418 patent/WO1999047909A1/en active Application Filing
Also Published As
| Publication number | Publication date |
|---|---|
| EP1062496A1 (en) | 2000-12-27 |
| US6480795B1 (en) | 2002-11-12 |
| WO1999047909A1 (en) | 1999-09-23 |
| JP2002506991A (en) | 2002-03-05 |
| AU9150898A (en) | 1999-10-11 |
| DE19810917A1 (en) | 1999-09-16 |
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