The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 25, 2019

Filed:

Apr. 02, 2013
Applicant:

Shimadzu Corporation, Kyoto-shi, Kyoto, JP;

Inventor:

Kenichi Mishima, Kyoto, JP;

Assignee:

SHIMADZU CORPORATION, Kyoto-shi, Kyoto, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01N 30/86 (2006.01); G06F 17/10 (2006.01); G01N 21/27 (2006.01);
U.S. Cl.
CPC ...
G01N 30/86 (2013.01); G01N 21/27 (2013.01); G01N 30/8686 (2013.01); G01N 30/8696 (2013.01); G06F 17/10 (2013.01); G01N 2201/1293 (2013.01);
Abstract

Provided are an automatic analysis method, an automatic analysis apparatus, and a program for the automatic analysis apparatus capable of discriminating a number of components included in a sample more accurately and easily. Components are discriminated based on respective pieces of resolution spectral data obtained by multivariate curve resolution (MCR) using a provisional number of components k, and the number of components included in the sample is determined based on a discriminated result. At this time, the multivariate curve resolution is repeated (steps Sto S) until a border value between the provisional number of components k in a case where the components are discriminated as being different in all the pieces of the resolution spectral data obtained by the multivariate curve resolution and the provisional number of components k in a case where the components are discriminated as being matched in at least two pieces of the resolution spectral data in all the pieces of the resolution spectral data obtained by the multivariate curve resolution is obtained. As a result, the number of components included in the sample can be discriminated more accurately and easily based on the obtained border value (step S).


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