A calibration curve creation method of creating a calibration curve, which is used to derive a content of a target component in a subject, from observation data of the subject, comprising: (a) acquiring the observation data for a plurality of samples of the subject; (b) acquiring the content of the target component in each sample; (c) executing pre-processing for the observation data of each sample, a pre-processing method is selected from a plurality of options; (d) estimating a plurality of independent components when separating the pre-processed observation data of each sample into a plurality of independent components and calculating a mixing coefficient corresponding to the target component for each sample based on the plurality of independent components; (e) calculating a regression equation of the calibration curve based on the content of the target component of each of the plurality of samples and the mixing coefficient of each sample; and (f) evaluating accuracy of the calibration curve; wherein, in the process (c), the pre-processing includes first pre-processing including processing for correcting the observation data and second pre-processing including whitening, and a plurality of processing methods are prepared as processing methods of each of the first pre-processing and the second pre-processing and the pre-processing method is set by combining one or more of the processing methods of each of the first pre-processing and the second pre-processing, the process (d) includes: (i) calculating an independent component matrix including the independent component of each sample; (ii) calculating an estimated mixing matrix, which indicates a set of vectors defining a ratio of an independent component element of each independent component in each sample, from the independent component matrix; and (iii) calculating a correlation between each of the vectors included in the estimated mixing matrix and the content of the target component of each of the plurality of samples and selecting the vector, which is determined to have the highest correlation, as a mixing coefficient corresponding to the target component, in the process (i), the first pre-processing, the second pre-processing, and independent component analysis processing are excuted in this order using the pre-processing method selected in the process (c), and processes (c) to (f) are repeated to select a plurality of combinations of pre-processing, evaluate the accuracy for each of said combinations, and select an optimal combination among said combinations. (a) acquiring the observation data for a plurality of samples of the subject; (b) acquiring the content of the target component in each sample; (c) executing pre-processing for the observation data of each sample, a pre-processing method is selected from a plurality of options; (d) estimating a plurality of independent components when separating the pre-processed observation data of each sample into a plurality of independent components and calculating a mixing coefficient corresponding to the target component for each sample based on the plurality of independent components; (e) calculating a regression equation of the calibration curve based on the content of the target component of each of the plurality of samples and the mixing coefficient of each sample; and (f) evaluating accuracy of the calibration curve; (i) calculating an independent component matrix including the independent component of each sample; (ii) calculating an estimated mixing matrix, which indicates a set of vectors defining a ratio of an independent component element of each independent component in each sample, from the independent component matrix; and (iii) calculating a correlation between each of the vectors included in the estimated mixing matrix and the content of the target component of each of the plurality of samples and selecting the vector, which is determined to have the highest correlation, as a mixing coefficient corresponding to the target component,