Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Chemical analysis
Patent
1997-10-28
1999-10-19
Barlow, John
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Chemical analysis
702 28, G01N 2135
Patent
active
059704241
DESCRIPTION:
BRIEF SUMMARY
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention is a method and apparatus for qualifying an object.
2. Discussion of the Background
By plotting alterations of a physical characteristic measurable on a material as a function of another physical characteristic a spectrum is obtained. A spectrum is obtained for example, when an optical characteristic (transmission or reflection) of a material is plotted as a function of the wavelength of the illuminating radiation. Spectra are useful information sources relating to qualities, e.g. composition, of a material examined. However, the relationship between data representing qualities of the examined material and a certain spectrum of the sample cannot be described today in most cases in the form of mathematical equations. Therefore, determining relationships in an empirical way is relied upon. For this, a large number of samples with known qualities are required and spectra of all samples must be measured. In present day measurement technology it is not rare that a single spectrum is represented by a thousand spectrum values. The processing of the spectrum means that operations are to be carried out with these one thousand data.
Obtaining and interpreting useful information require in most cases the application of complicated methods of mathematical statistics. Even the majority of such spectrum recording and evaluating instruments and methods become suitable for determining qualities of samples of unknown composition from their spectra after a "learning" process of a large number of measurements (calibration) carried out on a series of samples of known qualities. Of methods of mathematical statistics applied for processing spectra, a relatively simple and widely applied method is the MLR (Multiple Linear Regression) method. Good results can be achieved by the MLR method, however, it has the disadvantage that only spectrum values associated with some discrete values of independent variable are used in determining the quality or qualities sought, although other spectrum values also carry valuable information. Further, when the program is run on a PC configuration, the calculation period may be rather long.
Shortening of the calculation period and the learning process may be achieved by transforming spectra into Fourier domain and taking into consideration the first 20 to 50 members of the Fourier series, only. This method represents a substantial data reduction, generally by one order of magnitude, in a way that in determining the reduced data all original spectrum data play a role. A spectral pattern classification system using discrete Fourier transformation is described in U.S. Pat. No. 4,783,754. The signal to be classified is sampled and the samples are multiplied by weighting constants inherent in the system prior to performing discrete Fourier transformation. The preprocessing ensures that data blocks from similar sources will have spectra that are close to one another in the Fourier domain.
A method different from those above, implementing a large-scale data reduction based on geometry, has also been suggested by K. J. Kaffka and L. S. Gyarmati in an article entitled Qualitative (Comparative) Analysis by Near Infrared Spectroscopy, Proceedings of the Third International Conference on Near Infrared Spectroscopy, Jun. 25-29, 1990, Brussels, Belgium, pp. 135-144. According to the method, spectrum values measured are not plotted in a usual rectangular coordinate system, but in a polar coordinate system, hypothetical masses of equal amount are assigned to the spectrum points so obtained, and their center of mass, that is the center of gravity is determined. The polar coordinate system was called "quality plane", the center of gravity was named "quality point" and the vector drawn from the origin of the coordinate system to the quality point was called "quality vector". According to this method, the quality, e.g. composition, of materials that can be characterised by their spectra can be described by the quality point or quality vector, respec
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Proceeding Third International Conference on Near Infrared Spectroscopy, Brussels, BE, Jun. 25-29 1990, pp. 135-144, XP000577940 cited in the application K.J. Kaffka et al: "Qualitative (comparative) analysis by near infrared spectroscopy".
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Proceedings of the International Diffuse Spectroscopy Conferences, Aug. 1992, Qualitative Analysis Applying NIR Spectroscopy, Karoly J. Kaffka, Central Food Research Institute, Budapest, Hungary.
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Domjan Gyula
Godolle Laszlo
Gyarmati Laszlo
Jako Janos
Kaffka Karoly
Barlow John
Miller Craig Steven
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