Abstract: A calibration data acquisition unit (a) acquires Q optical spectra and S evaluation spectra, (b) extracts R subsets from a set of the Q optical spectra, (c) performs independent component analysis in which component amounts in each sample treated as independent components on each of R subsets so as to acquire R×N component calibration spectra, (d) obtains an inner product value between the R×N component calibration spectrum and an evaluation spectrum, (e) selects a component calibration spectrum for which a correlation degree between a component amount for the target component and the inner product value is the maximum as the target component calibration spectrum from among the R×N component calibration spectra, and (f) creates a calibration curve by using the target component calibration spectrum.

Abstract: A spectroscopic system includes a main body including a light source that radiates light to a light transmissive measurement target, an imaging device that captures an image based on transmitted light having passed through the measurement target, and a spectroscopy section that is provided in an optical path between the light source and the imaging device and selectively transmits light that belongs to a specific wavelength region, and an attachment that includes an optical path changer that changes the direction of the optical path of the light outputted from the light source and is so attached to the main body as to form a placement space which is located between the optical path changer and the main body and in which the measurement target is placed.

Abstract: An identification method for identifying a target to be measured includes accepting, from an input section, an information representing a condition for acquiring spectral information specific to a target to be measured, capturing, by a spectrometry camera, an image of the target, acquiring the spectral information specific to the target based on the captured image, and identifying the target based on (i) the spectral information and (ii) a database, stored in a memory, containing a plurality of pieces of spectral information corresponding to a plurality of objects. Acquiring the spectral information includes preferentially acquiring the spectral information specific to the target in a specific wavelength region where the target is identifiable.

Abstract: An information system according to the present disclosure includes an input section to which conditions for acquiring spectral information of a target that should be measured are input, a spectral measuring section configured to acquire the spectral information of the target, a storing section in which a database including a plurality of kinds of spectral information is stored, an analysis processing section configured to specify the target by comparing the spectral information of the target and the database stored in advance, and a display section configured to display information concerning the specified target.

Abstract: A calibration data acquisition unit uses an equation X=YW in which an optical spectrum matrix X is the same as a product between a component natural spectrum matrix Y and a component amount matrix X, performs independent component analysis in which column vectors of the component amount matrix W are treated as independent components so as to determine the component natural spectrum matrix Y, and employs row vectors of a general inverse matrix Y† of the component natural spectrum matrix Y as component calibration spectra corresponding to a plurality of components. A calibration curve is created by using a target component calibration spectrum corresponding to a target component.

Abstract: A calibration data acquisition unit uses an equation X=YW in which an optical spectrum matrix X is the same as a product between a component natural spectrum matrix Y and a component amount matrix X, performs independent component analysis in which column vectors of the component amount matrix W are treated as independent components so as to determine the component natural spectrum matrix Y, and employs row vectors of a general inverse matrix Y† of the component natural spectrum matrix Y as component calibration spectra corresponding to a plurality of components. A calibration curve is created by using a target component calibration spectrum corresponding to a target component.

Abstract: A calibration data acquisition unit (a) acquires Q optical spectra, S evaluation spectra, and a reference spectrum of a target component, (b) extracts R subsets from a set of the Q optical spectra, (c) performs independent component analysis in which component amounts for respective components in each sample are treated as independent components on each of the R subsets so as to acquire R×N component natural spectra and component calibration spectra, (d) obtains a similarity between each component natural spectrum and a reference spectrum, (e) selects a component calibration spectrum causing the similarity to be greatest as the target component calibration spectrum from among the R×N component calibration spectra, and (h) creates a calibration curve by using the target component calibration spectrum.

Abstract: A calibration data acquisition unit (a) acquires Q optical spectra, S evaluation spectra, and a reference spectrum of a target component, (b) extracts R subsets from a set of the optical spectra, (c) performs independent component analysis in which component amounts are treated as independent components on each subset so as to acquire component natural spectra and component calibration spectra, (d) obtains an inner product value between the component calibration spectrum and the evaluation spectrum, (e) obtains a correlation degree between a component amount for the target component and the inner product value with respect to each component calibration spectrum, (f) obtains a similarity between each component natural spectrum and a reference spectrum, (g) selects a component calibration spectrum causing a comprehensive evaluation value based on the correlation degree and the similarity to be greatest as the target component calibration spectrum, and (h) creates a calibration curve.

Abstract: A calibration data acquisition unit (a) acquires Q optical spectra and S evaluation spectra, (b) extracts R subsets from a set of the Q optical spectra, (c) performs independent component analysis in which component amounts in each sample treated as independent components on each of R subsets so as to acquire R×N component calibration spectra, (d) obtains an inner product value between the R×N component calibration spectrum and an evaluation spectrum, (e) selects a component calibration spectrum for which a correlation degree between a component amount for the target component and the inner product value is the maximum as the target component calibration spectrum from among the R×N component calibration spectra, and (f) creates a calibration curve by using the target component calibration spectrum.

Abstract: A calibration curve creating method includes: (a) acquiring observation data of a plurality of samples of a test object; (b) acquiring content of a target component of each sample; (c) estimating a plurality of independent components when the observation data of each sample is separated into the plurality of independent components, and acquiring a mixing coefficient corresponding to the target component for each sample; and (d) acquiring a regression equation of a calibration curve. (c) includes acquiring an independent component matrix by performing a first preprocessing including normalization of the observation data, a second preprocessing including whitening, and an independent component analysis process in this order. ? divergence is used as an independence index of the independent component analysis process, and a robust regression method is used in (d).

Abstract: A calibration curve generation device includes an estimation unit that estimates a plurality of independent components or main components constituting observation spectral data of a plurality of samples and a regression formula calculation unit that acquires a regression formula of a calibration curve based on glucose concentration of the plurality of samples and a mixing coefficient of the independent components or the main components in the observation spectral data for each of the samples. The estimation unit selects a component waveform signal having a peak in a wavelength selected in advance as the independent components or the main components.

Abstract: A calibration curve generation method includes acquiring an independent component matrix including independent components of each sample the independent component matrix by performing first pre-processing that includes normalizing observation data, second pre-processing that includes whitening, and independent component analysis processing in order. Further, the same noise is added to the observation data related to a plurality of samples in the first pre-processing.

Abstract: A target component calibration device includes a mixing coefficient calculating section that calculates mixing coefficients of target components regarding a test object based on observational data regarding the test object and calibration data, and a target component content calculating section that calculates the content of target components based on the mixing coefficients calculated by the mixing coefficient calculating section and a simple regression equation representing the relationship between the content and the mixing coefficients corresponding to the target components. The target component content calculating section adjusts at least one of two constants of the simple regression equation depending on a measurement condition when the observational data is obtained.

Abstract: 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 ? (where ? is an integer from 1 to N) of a data length N of the observation data, not a power function of ? with an exponent of an integer but a single-variable function which monotonously increases according to an increase in ? in a range of the value of ? from 1 to N is used.

Abstract: A calibration curve creating method includes: (a) acquiring observation data of a plurality of samples of a test object; (b) acquiring content of a target component of each sample; (c) estimating a plurality of independent components when the observation data of each sample is separated into the plurality of independent components, and acquiring a mixing coefficient corresponding to the target component for each sample; and (d) acquiring a regression equation of a calibration curve. (c) includes acquiring an independent component matrix by performing a first preprocessing including normalization of the observation data, a second preprocessing including whitening, and an independent component analysis process in this order. ? divergence is used as an independence index of the independent component analysis process, and a robust regression method is used in (d).

Abstract: A calibration curve creating method includes: (a) acquiring observation data of a plurality of samples of a living body, when near infrared light is emitted to the living body and an absorbance spectrum obtained from transmitted light or diffusely-reflected light thereof is set as the observation data; (b) acquiring content of a target component of each sample; (c) estimating a plurality of independent components when the observation data of each sample is separated into the plurality of independent components, and acquiring a mixing coefficient corresponding to the target component for each sample; and (d) acquiring a regression equation of a calibration curve. (c) includes acquiring an independent component matrix by performing processes of normalization, whitening, and independent component analysis of the observation data, and the normalization is performed after a process performed by project on null space.

Abstract: A calibration curve creation method is capable of performing accurate measurement from a piece of observation data. The calibration curve creation method includes (a) acquiring observation data regarding a plurality of samples of a subject, (b) acquiring the content of a target component in each sample, (c) estimating a plurality of independent components at the time of separation into a plurality of independent components of each sample and calculating a mixing coefficient corresponding to the target component for each sample, and (d) calculating the regression equation of the calibration curve. The process (c) includes a step of calculating an independent component matrix by executing first pre-processing including correcting the observation data, second pre-processing including whitening, and independent component analysis processing in this order. A process suitable for the observation data is selected from a plurality of processes, and is used as the first pre-processing and the second pre-processing.

Abstract: A method sequentially obtains observation data with respect to a plurality of samples for a test subject and obtains content of a target component contained in each of the plurality of samples. The method subsequently estimates a plurality of independent components, which are separated from the observation data with respect to each of the samples, and calculates a mixing coefficient corresponding to the target component with respect to each of the samples, based on the estimated plurality of independent components. The method then determines a regression equation of a calibration curve, based on the obtained contents of the target component contained in the plurality of samples and the mixing coefficients of the respective samples.