The present disclosure provides component analysis methods including a measurement process and an analysis process.

When a user inputs samples per group, a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. A multivariate analysis is applied to the generated peak matrix. The sample tree, peak matrix, score plot, and loading plot are displayed. When the user clicks a plotted point on the loading plot, a row indicating a corresponding peak on the peak matrix is discriminated. When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded. The score plot and other data are updated. When separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation.

A peak extraction method for extracting a true peak from a measured waveform, including acquiring a second derivative waveform; extracting a provisional peak on the basis of a maximum value and/or a minimum value of the second derivative waveform; determining the peak width of the provisional peak on the basis of a model peak function; computing, on the basis of the model peak function, a theoretical value for the height of the provisional peak using two points corresponding to the two ends of the peak width; computing, based on the second derivative waveform, an index value for a variation in the noise on the measured waveform; and computing an S/N ratio, which is a ratio of the peak height theoretical value and the index value, and extracting the provisional peak that is equal to or greater than a preset value as the true peak.

A chromatogram data processing system having an impurity detector including a differential chromatogram creator, a purity curve creator and a determiner. The differential chromatogram creator calculates a differential coefficient of an absorbance spectrum with respect to wavelength at a local maximum or local minimum absorption wavelength of the target component, and creates a differential chromatogram which shows a temporal change of the differential coefficient. The purity curve creator creates a purity curve which shows a temporal change of the difference between the degree of similarity of a spectrum on the target peak to a reference spectrum and a threshold of the degree of similarity which is determined taking into account the influence of noise components. The differential chromatogram and the purity curve are displayed on a display unit in a superposed form on a wavelength chromatogram created by a wavelength chromatogram creator.

An analytical method and an analytical system capable of more accurate analysis, in which a sample is analyzed by a capillary electrophoresis technique in which a voltage is applied to a sample solution introduced to a micro flow path, a separation analysis is performed for a component contained in the sample solution, and an optically measured value corresponding to an elapsed time after starting a measurement is measured. The analytical method comprises: a process of determining an interface arrival time point, based on the optically measured value when an interface between the sample solution and a migration liquid reaches a predetermined measurement position in the micro flow path; and a process of identifying the component contained in the sample solution using the optically measured value at the elapsed time after the interface arrival time point.

The present disclosure provides component analysis methods including a measurement process and an analysis process.

It is intended to provide a rapid, convenient, and highly accurate method for determining the prognosis of cancer. The present invention provides a method for determining a tissue having renal cell carcinoma, comprising: (1) subjecting sample DNA to ion exchange chromatography, wherein the sample DNA is obtained by treating target genomic DNA prepared from a renal tissue of a subject with bisulfite, followed by PCR amplification; (2) calculating a derivative value of a detection signal of the chromatography; and (3) determining the renal tissue as being a tissue having renal cell carcinoma having poor prognosis when the derivative value calculated in the step (2) has two or more maximums.

A two-dimensional chromatogram creator creates a two-dimensional chromatogram based on data obtained for each of two samples. A chromatogram difference calculator creates a two-dimensional differential chromatogram showing the intensity difference between the two two-dimensional chromatograms. A blob detector detects blobs on each chromatogram. A matching-blob extractor compares the blobs located on the differential chromatogram with those located on each of the two other chromatograms, to extract blobs which can be considered to be located at the same temporal position. If there are two blobs extracted at the same temporal position on the two chromatograms, a normalized value of the intensity difference between the two blobs is calculated, and the line type of the boundary line for indicating the extracted blob on the display is chosen according to that value. Important blobs having significant intensity differences on the two chromatograms being compared can be automatically selected and presented to analysis operators.

In a method for estimating a noise level representing the magnitude of a noise component from measurement data, first waveform data composed of high frequency noise components extracted from assumed noise data are divided into segments so that each section where positive values successively occur or each section where negative values successively occur in the first waveform data is defined as one segment. A segment-width threshold is determined based on the distribution of the widths of the segments. Second waveform data composed of high frequency noise components extracted from measurement data are divided into segments in the same manner. Each segment having a width larger than the threshold is excluded from the segments in the second waveform data, to create a first segment group. The noise level is determined based on the heights or areas of the plurality of segments included in the first segment group.

When a user inputs samples per group (S2), a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. Then, a multivariate analysis is applied to the generated peak matrix (S3-S4). The sample tree, peak matrix, score plot, and loading plot are displayed on an analysis main screen. When the user clicks a desired plotted point on the loading plot, a row indicative of a corresponding peak on the peak matrix is discriminated (S5-S7). When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded, and the score plot and other data are updated (S9-S10). When the separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation. Thus, the marker search can be accurately performed in a simplified manner.