ANALYSIS DEVICE, ANALYSIS METHOD, PROGRAM FOR ANALYSIS DEVICE, LEARNING DEVICE FOR ANALYSIS, LEARNING METHOD FOR ANALYSIS, AND PROGRAM FOR LEARNING DEVICE FOR ANALYSIS

Abstract

An analysis device analyzes a measurement sample based on spectral data obtained from that measurement sample. This analysis device includes a correlation data storage portion that stores correlation data that shows a correlation between spectral data for a reference sample in which total analysis values for a predetermined plurality of components are already known, and a total analysis value of the reference sample, and a calculation main unit that applies the correlation data stored in the correlation data storage portion to the spectral data obtained from the measurement sample, and then calculates the total analysis values of the predetermined plurality of components contained in the measurement sample. The reference sample includes a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components contained in the first reference sample.

Description

Claims

1. An analysis device that analyzes a measurement sample based on spectral data obtained from that measurement sample, comprising: a correlation data storage portion that stores correlation data that shows a correlation between spectral data for a reference sample in which total analysis values for a predetermined plurality of components are already known, and a total analysis value of the reference sample; anda calculation main unit that applies the correlation data stored in the correlation data storage portion to the spectral data obtained from the measurement sample, and then calculates the total analysis values of the predetermined plurality of components contained in the measurement sample, wherein the reference sample contains a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components that are part of the first reference sample, and wherein the correlation data shows a machine learning model in which calculated as the training data are:first reference sample data that includes spectral data for the first reference sample and a total analysis value for the first reference sample; andsecond reference sample data that includes spectral data for the second reference sample and a total analysis value for the second reference sample.

2. The analysis device according to claim 1, wherein the second reference sample is either one or a plurality of the components that make up the predetermined plurality of components.

3. The analysis device according to claim 1, wherein the second reference sample is consisting of either one or a plurality of the components that do not contribute to the total analysis value, among the reference sample in which total analysis values for the predetermined plurality of components are already known.

4. The analysis device according to claim 1, wherein the second reference sample is consisting of either one or a plurality of the components for which a pseudo-correlation exists between itself and the total analysis value.

5. The analysis device according to claim 1, wherein the second reference sample is a fuel that generates exhaust gas.

6. The analysis device according to claim 1, wherein the measurement sample or the first reference sample is exhaust gas, and the predetermined plurality of components are hydrocarbons.

7. The analysis device according to claim 6, wherein the total analysis value of the predetermined plurality of components is the concentration of the total hydrocarbons contained in the exhaust gas.

8. The analysis device according to claim 1, wherein the analysis device is an FTIR-type device.

9. The analysis device according to claim 1, wherein the total analysis value of the first reference sample and the total analysis value of the second reference sample are obtained via measurements performed by an FID analyzer.

10. The analysis device according to claim 1, wherein a plurality of correlation data calculated for each one of various types of fuel is stored in the correlation data storage portion, and the calculation main unit switches the correlation data that is to be applied to the spectral data obtained from the measurement sample in accordance with the type of fuel used to generate the measurement sample.

11. A method of analyzing a measurement sample based on spectral data obtained from that measurement sample, in which: correlation data that shows a correlation between spectral data for a reference sample in which total analysis values for a predetermined plurality of components are already known, and a total analysis value of the reference sample is stored; andthe stored correlation data is applied to the spectral data obtained from the measurement sample, and the total analysis values of the predetermined plurality of components contained in the measurement sample are then calculated, wherein the reference sample includes a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components that are part of the first reference sample, and wherein the correlation data shows a machine learning model in which calculated as the training data are:first reference sample data that includes spectral data for the first reference sample and a total analysis value for the predetermined plurality of components contained in the first reference sample; andsecond reference sample data that includes spectral data for the second reference sample and a total analysis value for the predetermined plurality of components contained in the second reference sample.

12. A program for an analysis device that is installed in an analysis device that analyzes a measurement sample based on spectral data obtained from that measurement sample, and that causes the analysis device to perform: functions of a correlation data storage portion that stores correlation data that shows a correlation between spectral data for a reference sample in which total analysis values for a predetermined plurality of components are already known, and a total analysis value of the reference sample; andfunctions of a calculation main unit that applies the correlation data stored in the correlation data storage portion to the spectral data obtained from the measurement sample, and then calculates the total analysis values of the predetermined plurality of components contained in the measurement sample, wherein the reference sample contains a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components that are part of the first reference sample, and wherein the correlation data shows a machine learning model in which calculated as the training data are:first reference sample data that includes spectral data for the first reference sample and a total analysis value for the first reference sample; andsecond reference sample data that includes spectral data for the second reference sample and a total analysis value for the second reference sample.

13. A learning device for analysis comprising: a receiving portion that receives spectral data obtained from a reference sample in which total analysis values for a predetermined plurality of components are already known;a reference sample data storage portion that stores reference sample data that includes total analysis values for a plurality of the reference samples that are mutually different from each other; anda correlation calculating portion that, taking the reference sample data as training data, employs machine learning to calculate a common correlation between the spectral data of each reference sample and the total analysis values, wherein the reference sample includes a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components that are part of the first reference sample, and wherein the reference sample data includes:first reference sample data that contains spectral data for the first reference sample and a total analysis value for the predetermined plurality of components contained in the first reference sample; andsecond reference sample data that contains spectral data for the second reference sample and a total analysis value for the predetermined plurality of components contained in the second reference sample.

14. A learning method for analysis comprising: receiving spectral data obtained from a reference sample in which total analysis values for a predetermined plurality of components are already known;storing reference sample data that includes total analysis values for a plurality of the reference samples that are mutually different from each other; andemploying machine learning to calculate a common correlation between the spectral data of each reference sample and the total analysis values, taking the reference sample data as training data, wherein the reference sample includes a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components that are part of the first reference sample, and wherein the reference sample data includes:first reference sample data that contains spectral data for the first reference sample and a total analysis value for the predetermined plurality of components contained in the first reference sample; andsecond reference sample data that contains spectral data for the second reference sample and a total analysis value for the predetermined plurality of components contained in the second reference sample.

15. A program for a learning device for analysis that causes the learning device for analysis to perform: functions of a receiving portion that receives spectral data obtained from a reference sample in which total analysis values for a predetermined plurality of components are already known;functions of a reference sample data storage portion that stores reference sample data that includes total analysis values for a plurality of the reference samples that are mutually different from each other; andfunctions of a correlation calculating portion that, taking the reference sample data as training data, employs machine learning to calculate a common correlation between the spectral data of each reference sample and the total analysis values, wherein the reference sample includes a first reference sample that contains the predetermined plurality of components, and a second reference sample that is consisting of either one or a plurality of the components that are part of the first reference sample, and wherein the reference sample data includes:first reference sample data that contains spectral data for the first reference sample and a total analysis value for the predetermined plurality of components contained in the first reference sample; andsecond reference sample data that contains spectral data for the second reference sample and a total analysis value for the predetermined plurality of components contained in the second reference sample.