Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Electrical signal parameter measurement system
Reexamination Certificate
2000-08-10
2003-04-15
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Electrical signal parameter measurement system
C702S076000, C702S179000, C702S190000, C702S022000, C073S659000, C073S570000, C073S019030, C706S055000, C706S022000, C356S939000, C356S931000
Reexamination Certificate
active
06549861
ABSTRACT:
Throughout this application, various patents and publications are referred to. Disclosure of these publications and patents in their entirety are hereby incorporated by reference into this application to more fully describe the state of the art to which this invention pertains.
FIELD OF THE INVENTION
The present invention relates to the field of qualitative and quantitative spectroscopic analysis.
BACKGROUND OF THE INVENTION
Infrared spectroscopy is a technique which is based upon the vibrational changes of the atoms of a molecule. In accordance with infrared spectroscopy, an infrared spectrum is generated by transmitting infrared radiation through a sample of an organic compound and determining what portion of the incident radiation are absorbed by the sample. An infrared spectrum is a plot of absorbance (or transmittance) against wavenumber, wavelength, or frequency. Infrared radiation is radiation having a wavelength between about 750 nm and about 1000 &mgr;m. Near-infrared radiation is radiation having a wavelength between about 750 nm and about 2500 nm.
In order to identify the presence and/or concentration of an analyte in a sample, the near-infrared reflectance or transmittance of a sample is measured at several discrete wavelengths, converted to absorbance or its equivalent reflectance term and then multiplied by a series of regression or weighting coefficients calculated through multiple-linear-regression mathematics.
In the past, analysis was done only via transmission measurements from clear solutions, using solvents having little or no absorbency at the wavelength of the analyte. The absorbance (A) of an analyte in a non-absorbing solution at a specified wavelength, is represented by the equation abc, wherein a is the absorptivity constant, b is the pathlength of light through the samples and c is the concentration of the analyte. In this prior art system, the calibration sample consisted of a predetermined set of standards (i.e. samples of a known composition) which were run under the same conditions as the unknown samples, thereby allowing for the determination of the concentration of the unknowns.
In early infrared radiation analysis, deviations from Beer's law caused for example, by instrument noise or a nonlinear relationship between absorbency and concentration were common. Calibration curves, determined empirically, were required for quantitative work. The analytical errors associated with quantitative infrared analysis needed to be reduced to the level associated with ultraviolet and visible methods. Least-squares analysis allowed the chemist to determine a calibration equation. The spectroscopic data (Y) was the dependent variable and the standard concentrations were the independent variable (X).
Various methods have been developed to improve and expedite the interpretation of NIRA spectra. Examples of methods of processing NIRA spectral data to generate a comparison factor to be used in determining the similarity between the composition of a test sample and a standard material are found in U.S. Pat. No. 5,023,804 issued Aug. 23, 1988 to Hoult; U.S. Pat. No. 4,766,551 issued Aug. 23, 1988 to Begley; U.S. Pat. No. 5,900,634 issued May 4, 1999 to Soloman; U.S. Pat. No. 5,610,836 issued Mar. 11, 1997 to Alsmeyer et al.; U.S. Pat. No. 5,481,476 issued Jan. 2, 1996 to Windig; U.S. Pat. No. 5,822,219 issued Oct. 13, 1998 to Chen et al.
Instruments have improved enormously. The noise and drifts associated with earlier instruments have improved with the changeover of electronic circuitry from tubes to semiconductor circuits. Modem applications of spectroscopy, and particularly near-infrared spectroscopy, have gone away from the simple two-component mixtures to analysis of multi-component mixtures of an unknown nature (e.g., natural products). However, because of the of the large amount of sample variance in multi-component mixtures use of the standard set is no longer possible.
The net result of the evolution of NIRA is to interchange the roles of the spectroscopic and standard values for the calibration samples. Previously, the standard values (i.e., the composition of the known samples) were considered to be more accurate than the spectral data. However, now that the calibration samples are a multi-component mixtures of unknown nature, it is the spectroscopic values that are known with better precision and accuracy.
SUMMARY OF THE INVENTION
In accordance with the present invention, an automated method for modeling spectral data is provided. The samples are analyzed and spectral data is collected by the method of diffuse reflectance, clear transmission, or diffuse transmission. In addition, for each sample, one or more constituent values are measured. In this regard, a constituent value is a reference value for the target substance in the sample which is measured by a independent measurement technique. As an example, a constituent value used in conjunction with identifying a target substance in a pharmaceutical tablet sample might be the concentration of that substance in the tablet sample as measured by high pressure liquid chromatography (HPLC) analysis. In this manner, the spectral data for each sample has associated therewith at least one constituent value for that sample.
The set of spectral data (with its associated constituent values) is divided into a calibration sub-set and a validation sub-set. The calibration sub-set is selected to represent the variability likely to be encountered in the validation sub-set.
In accordance with a first embodiment of the present invention, a plurality of data transforms is then applied to the set of spectral data. Preferably, the transforms are applied singularly and two-at-a-time. The particular transforms used, and the particular combination pairs used, are selected based upon the particular method used to analyze the spectral data (e.g. diffuse reflectance, clear transmission, or diffuse transmission as discussed in the detailed description). Preferably, the entries are contained in an external data file, so that the user may change the list to conform to his own needs and judgement as to what constitutes sensible transform pairs. Preferably, the plurality of transforms applied to the spectral data includes at least a second derivative and a baseline correction. In accordance with a further embodiment of the, present invention, transforms include, but are not limited to the following: performing a normalization of the spectral data, performing a first derivative on the spectral data, performing a second derivative on the spectral data, performing a multiplicative scatter correction on the spectral data, in performing smoothing transforms on the spectral data. In this regard, it should be noted that both the normalization transform and the multiplicative scatter correction transform inherently also perform baseline corrections.
Preferably, the normalization transform is combined with each of the first derivative, second derivative, and smoothing transforms; the first derivative transform is combined with the normalization, and smoothing transforms; the second derivative transform is combined with the normalization and smoothing transforms; the multiplicative scatter correction transform is combined with absorption-to-reflection, first derivative, second derivative, Kubelka-Munk, and smoothing transforms; the Kubelka-Munk transform is combined with the normalization, first derivative, second derivative, multiplicative scatter correction, and smoothing transforms; the smoothing transform is combined with the absorption-to-reflection, normalization, first derivative, second derivative, multiplicative scatter correction, and Kubelka-Munk transforms; and the absorption-to-reflection transform is combined with the normalization, first derivative, second derivative, multiplicative scatter correction, and smoothing transforms. In this manner a set of transformed and untransformed calibration and validation data sets are created.
In a further preferred embodiment, the plurality of transforms applied to the spectral data m
Ciurczak Emil
Mark Howard
Ritchie Gary
Davidson Davidson & Kappel LLC
Desta Elias
Euro-Celtique S.A.
Hoff Marc S.
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