ON-SITE ANALYSIS SYSTEM WITH CENTRAL PROCESSOR AND METHOD OF ANALYZING

• US 20110054864A1
• Filed: 10/28/2010
• Published: 03/03/2011
• Est. Priority Date: 07/23/2001
• Status: Active Grant

• Current Assignee: Eurofins QTA, Inc. (Eurofins Scientific SE)
• Original Assignee: Cognis International GmbH (BASF SE), Institut Univ. de Ciencia i Tecnologia, S.A.
• Inventors: LUNDSTEDT, ALAN P., HALL, ALLEN L., TSENG, CHING-HUI
• Granted Patent: US 8,010,309 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G01N 2035/00881 network configurations
• CPC Class Codes: G01N 21/274 Calibration, base line adjustment, drift correction
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/12753 and storage
• CPC Class Codes: G01N 2201/12792 Compensating own radiation in apparatus
• CPC Class Codes: G01N 35/00712 Automatic status testing, e.g. at start-up or periodic
• CPC Class Codes: G01N 35/00871 Communications between instruments or with remote terminals

Abstract

A method of analysis, analysis system, program product, apparatus, and method of supplying analysis of value incorporating the use of at least one data acquisition device, a central processor, and a communication link that is connectable between the data acquisition device and the central processor. The central processor is loaded with multivariate calibration models developed for predicting values for various properties of interest, wherein the calibration models are capable of compensating for variations in an effectively comprehensive set of measurement conditions and secondary material characteristics. As so configured, the calibration models can compensate for instrument variance without instrument-specific calibration transfer. Measurement results generated by the central processor can be transmitted to an output device of a user interface.

Claims

1. (canceled)

2. A method for developing a calibration model for use in a central processor configured to generate a predicted value of a property of interest based on an input from one or more data acquisition instruments, comprising:

obtaining a set of samples having the property of interest;

generating a first set of data by measuring the property of interest in the samples;

generating a mathematical model of the property of interest using the first set of data;

generating predicted values for the property of interest using the mathematical model;

determining whether the predicted values for the property of interest correlate with the measured values of the property of interest to within a predetermined criterion;

identifying the type and range of a secondary variable that is potentially influential of the measurement of the property of interest;

determining whether the secondary variable is actually influential of the measurement of the property of interest;

adjusting the preliminary model to compensate for the secondary variable and/or identifying an appropriate method of treating responses of the one or more data acquisition instruments to compensate for the secondary variable;

determining whether predicted values of the property of interest generated by the adjusted mathematical model and/or using the treatment method correlate with the measured values within another predetermined criterion when the secondary variable is varied;

generating a second set of data by measuring the property of interest in the samples using the treatment method;

generating a prediction of the property of interest using the second set of data and the revised property model;

determining whether a probable outlier is present in the prediction of the property of interest;

generating a third set of data using the pretreatment method while varying the influential factor;

generating a prediction of the property of interest using the revised property model and the third set of data; and

determining whether a probable outlier is present in the prediction of the property of interest generated using the revised property model.

3. The method of claim 2, further comprising determining whether the probable outlier is a good outlier in accordance with another predetermined criterion, and if so, adding the probable outlier to the second set of data, determining a method of treatment to compensate for the probable outlier, and revising the revised model to compensate for the probable outlier.

4. The method of claim 2, wherein the property of interest in the samples spans the range over which the property of interest is expected to vary during actual measurements in the future.

5. The method of claim 2, further comprising determining whether the property of interest is measurable in the presence of variations in the secondary variable to within another predetermined criterion.

6. The method of claim 2, wherein the predetermined criterion is a limit of desired precision.

7. The method of claim 2, wherein determining whether the secondary variable is actually influential of the measurement of the property of interest comprises varying the secondary variable and obtaining measurements of the property of interest.

8. The method of claim 7, wherein determining whether the secondary variable is actually influential of the measurement of the property of interest comprises determining whether the variation of the secondary variable produces a change in the measured value of the property of interest that is statistically significant to a limit of desired precision in the measured value.

9. The method of claim 2, wherein identifying an appropriate method of treating responses of the one or more data acquisition instruments to compensate for the secondary variable comprises determining mathematical operations that are used on responses of the data acquisition instrument to compensate for factors that influence the response of the instrument when measuring the property of interest.

10. The method of claim 2, further comprising installing the revised model in the central processor.

11. The method of claim 11, further comprising obtaining a measurement of the property of interest on site and determining whether any outliers exist in the measurement and adjusting the revised model and/or the method of treatment to compensate for the outliers.

12. The method of claim 12, further comprising notifying the user of the presence of the outliers.