| 摘要 |
A calibration curve creation method includes a step of obtaining an independent component matrix including independent components of each sample, and this step includes a step of obtaining the independent component matrix by performing a first preprocess including normalization of the observation data, a second preprocess including whitening, and independent component analysis in this order. In the first preprocess, normalization is performed after a process based on project on null space (PNS) is performed. In the PNS, as a single-variable function representing a variation which depends on an ordinal number λ (where λ is an integer from 1 to N) of a data length N of the observation data, not a power function of λ with an exponent of an integer but a single-variable function which monotonously increases according to an increase in λ in a range of the value of λ from 1 to N is used. |
| 主权项 |
1. A calibration curve creation method of creating a calibration curve used to derive a content of a target component for a test object from observation data of the test object, the method comprising:
(a) causing a computer to acquire the observation data for a plurality of samples of the test object; (b) causing the computer to acquire a content of the target component for each sample; (c) causing the computer to estimate a plurality of independent components obtained when the observation data of each sample is separated into the plurality of independent components, and to obtain a mixing coefficient corresponding to the target component for each sample on the basis of the plurality of independent components; and (d) causing the computer to obtain a regression formula of the calibration curve on the basis of the content of the target component of the plurality of samples and the mixing coefficient for each sample, wherein (c) includes
(i) causing the computer to obtain an independent component matrix including the independent components of each sample;(ii) causing the computer to obtain an estimated mixing matrix indicating a set of vectors for defining a ratio of an independent component element for each independent component in each sample on the basis of the independent component matrix; and(iii) causing the computer to obtain a correlation of the content of the target component of the plurality of samples for each vector included in the estimated mixing matrix, and to select the vector which is determined as having the highest correlation as a mixing coefficient corresponding to the target component, wherein, in (i), the computer obtains the independent component matrix by performing a first preprocess including normalization of the observation data, a second preprocess including whitening, and independent component analysis in this order, wherein, in the first preprocess, the computer performs the normalization after a process based on project on null space (PNS) is performed, and wherein, in the PNS, the computer uses, as a single-variable function representing a variation which depends on an ordinal number λ (where λ is an integer from 1 to N) of a data length N of the observation data, not a power function of λ with an exponent of an integer, the single-variable function monotonously increasing according to an increase in λ in a range of the value of λ from 1 to N. |
