Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Chemical analysis
Reexamination Certificate
2000-03-03
2002-01-22
Wachsman, Hal (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Chemical analysis
C702S022000, C702S030000, C702S032000, C703S012000
Reexamination Certificate
active
06341257
ABSTRACT:
BACKGROUND OF THE INVENTION
This invention relates to methods for performing multivariate spectral analyses. More particularly, this invention relates to such methods which combine various features of both classical least squares analysis and the more modern inverse analysis methods such as partial least squares and principle components regression. The method may further include improved prediction ability enabled by adding spectral shapes (or selected spectral intensity data) for certain chemicals or other factors that affect the spectral response that may not have been present when spectral calibration data were obtained.
Classical least squares (CLS) quantitative multivariate calibration methods are based on an explicit or hard physical model (e.g., Beer's law), (see D. M. Haaland, “Multivariate Calibration Methods Applied to Quantitative FT-IR Analyses,” Chapter 8 in
Practical Fourier Transform Infrared Spectroscopy
, J. R. Ferraro and K. Krishnan, Editors, Academic Press, New York, pp. 396-468, (1989)). During calibration, the CLS method has the advantage that least-squares estimates of the pure-component spectra are obtained from mixture samples. Therefore, significant qualitative spectral information can be obtained from the CLS method about the pure-component spectra as they exist in the calibration mixtures. In addition, the method is readily understood, simple to apply, and when the model is valid, CLS requires fewer calibration samples than the popular inverse-based partial least squares (PLS) and principal component regression (PCR) factor analysis multivariate methods, (see D. M. Haaland and E. V. Thomas, “Partial Least-Squares Methods for Spectral Analyses 1: Relation to Other Multivariate Calibration Methods and the Extraction of Qualitative Information,” Analytical Chemistry 60 1193-1202 (1988)). However, CLS is more restrictive than inverse methods such as PLS and PCR, since CLS methods require that information be known about all spectral sources of variation in the samples (i.e., component concentrations and/or spectral shapes must be known, estimated, or derived). Inverse multivariate methods such as PLS and PCR can empirically model interferences and can approximate nonlinear behavior though their inverse soft-modeling approach.
BRIEF SUMMARY OF THE INVENTION
A method for estimating the quantity of at least one known constituent or property in a sample comprising first forming a classical least squares calibration model to estimate the responses of individual pure components of at least one of the constituents or parameters affecting the optical response of the sample and employing a cross validation of the samples in the calibration data set, then measuring the response of the mixture to the stimulus at a plurality of wavelengths to form a prediction data set, then estimating the quantity of one of the known constituents or parameters affecting the calibration data set by a classical least squares analysis of the prediction data set wherein such analysis produces residual errors, and then passing the residual errors to a partial least squares, principal components regression, or other inverse algorithm to provide an improved estimate of the quantity of the one known constituent or parameter affecting the sample. The estimation can be repeated for more of the known constituents in the calibration data set by repeating the last two steps for the other constituents or parameters. Overfitting of the prediction data set by the factor analysis algorithm can be minimized by using only factors derived from each step of the cross validation that are most effective in identifying the constituent or parameter. Also, the accuracy and precision of the classical least square estimation or prediction ability can be improved by adding spectral shapes to either or both of the calibration step or the prediction step that describe the effects on the sample response from constituents that are present in the sample or parameters that affect the optical response of the sample but whose concentrations or values are not in the calibration data base.
REFERENCES:
patent: 5435309 (1995-07-01), Thomas et al.
patent: 5606164 (1997-02-01), Price et al.
patent: 5610836 (1997-03-01), Alsmeyer et al.
patent: 5724268 (1998-03-01), Sodickson et al.
patent: 6031232 (2000-02-01), Cohenford et al.
Wentzell et al., “Maximum Likelihood Multivariate Calibration,” Anal. Chem 69, 22 99, pp. 2299-2311, Jul. 1979.*
Wentzell et al., “Maximum Likelihood Principal Component Analysis,” Journal of Chemometrics, 339, pp. 339-366, 1997 (No month).*
D. M. Haaland, “Multivariate Calibration Methods Applied to Quantitative FT-IR Analyses,” Chapter 8 inPractical Fourier Transform Infrared Spectroscopy, J. R. Ferraro and K. Krishnan, Editors, Academic Press, New York, pp. 396-468, (1989), (No month).
D. M. Haaland and E. V. Thomas, “Partial Least-Squares Methods for Spectral Analyses 1: Relation to Other Multivariate Calibration Methods and the Extraction of Qualitative Information,” Analytical Chemistry 60, 1193-1202 (1988), Jun. 1988.
D. M. Haaland, R. G. Easterling, and D. A. Vopicka, “Multivariate Least-Squares Methods Applied to the Quantitative Spectral Analysis of Multicomponent Samples,” Applied Spectroscopy 39, 73-84 (1985) (No month).
D. M. Haaland and R. G. Easterling, “Improved Sensitivity of Infrared Spectroscopy by the Application of Least Squares Methods,” Applied Spectroscopy 34, 539-548 (1980). (No month).
D. M. Haaland and R. G. Easterling, “Application of New Least Squares Methods for the Quantitative Infrared Analysis of Multicomponent Samples,” Applied Spectroscopy 36, 665-673 (1982). (No month).
A. Lorber, Error Propagation and Figures of Merit for Quantification by Solving Matrix Equations, Anal. Chem. 58 May 1986, pp. 1167-1172.
D. M. Haaland and D. K. Melgaard, “New Prediction-Augmented Classical Least Squares (PACLS) Methods: Application to Unmodeled Interferents,” submitted to Appl. Spectrosc. (Feb. 2000), pp. 1-39.
“Multivariate Calibration Based on the Linear Mixture Model” pp. 166-214. (No date).
D. M. Haaland, “Methods to Include Beer's Law Nonlinearities in Quantitative Spectral Analysis,” in ASTM Special Technical Publication,Computerized Quantitative Infrared Analysis, G. L. McClure, Editor, STP 934, 78-94 (1987) (No month).
H. Martens and T. Naes, “Multivariate Calibration by Data Compression,” in Near-infrared Technology in Agricultural and Food Industries, Ed. P, C. Williams and K. Norris, Am. Assoc. Cereal Chem. St. Paul Minnesota, 57-87 (1987). (No month).
“Orthogonal Decomposition in Certain Elementary Orthogonal Transformations,” pp. 9-17 (No date).
W. Windig, “Spectral data files for self-mideling curve resolution with examples using the SIMPLISMA approach,” Chemom. and Intell. Lab. Syst. 36, 3-16, 1997 (No month).
R. Tauler, A. Smilde, and B. Kowalski, “Selectivity, Local Rank, Three-Way Data Analysis and Ambiguity in Multivariate Curve Resolution,” J. Chemom. 9, 31-56, 1995) (No month).
J. W. Boardman, F. A. Kruxe & R. O. Green, “Mapping Target Signatures Via Partial Unmixing of a Viris Data,” pp. 23-26.(No date).
D. M. Haaland, L. Han, and T. M. Niemczyk, “Enhancing IR Detection Limits for Trace Polar Organics in Aqueous Solutions with Surface-Modified Sol-gel-coated ATR Sensors,” Applied Spectroscopy 53, 390-395 (1999). (No month).
D. M. Haaland and E. V. Thomas, “Partial Least-Squares Methods for Spectral Analyses. 2. Application to Simulated and Glass Spectral Data,” Jun. 1988, pp. 1-7.
D. M. Haaland, “Quantitative Infrared Analysis of Borophophosilicae Films Using Multivariate Statistical Methods,” 1988,(No month), pp. 1-10.
E. V. Thomas and D. M. Haaland, “Comparison of Multivariate Calibration Methods for Quantitative Spectral Analysis,” May 1990, pp. 1091-1099.
Bieg Kevin W.
Cone Gregory A.
Libman George H.
Sandia Corporation
Wachsman Hal
LandOfFree
Hybrid least squares multivariate spectral analysis methods does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Hybrid least squares multivariate spectral analysis methods, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Hybrid least squares multivariate spectral analysis methods will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2828970