Shimadzu Corporation

Kabushiki Kaisha Toshiba

Hitachi, Ltd.

Japan Atomic Energy Agency

Akira Noda

Kazuo Hiramoto

Masahiro Tadokoro

Masatsugu Nishi

Masahide Gunji

Yasuhiro Mito

Junichi Hirota

Hiroaki Kozawa

Yoshikatsu Umemura

Takeshi Yoshiyuki

Yasushi Iseki

Hiroyuki Daido

Masahiro Ikegami

Mamiko Nishiuchi

Kensuke Otake

Makoto Inoue

A sample measurement device includes a measurement unit () configured to measure a sample, and a controller () configured to analyze a measurement result of the measurement unit. The controller () is configured to estimate and acquire a measurement result under another measurement condition using a model formula based on measurement results under a plurality of measurement conditions with different measurement parameter conditions, and estimate a distribution of a measurement quality indicator with respect to a measurement parameter based on the estimated measurement result.

Sample measurement device and measurement parameter setting assistance device

A peak tracking device includes a chromatogram acquirer that acquires chromatograms based on measurement data obtained by providing an analysis device with analysis condition data, and a peak associator that associates each peak included in each chromatogram with one another. The peak associator includes a peak spectrum extractor that extracts peak spectral data being a spectrum derived from a peak from measurement spectral data acquired from each measurement data, an orthogonal spectrum extractors that extracts, from the measurement spectral data, spectral data orthogonal to a dominant component among components of the peak spectral data, and a similarity determiner that associates each peak with one another based on similarity of the spectral data extracted from the orthogonal spectrum extractor.

Peak tracking device, peak tracking method and non-transitory computer readable medium storing peak tracking program

An analysis data processing method for processing analysis data collected with an analyzing device for each of a plurality of samples, by applying an analytical technique using statistical machine learning to multidimensional analysis data formed by output values obtained from a plurality of channels of a multichannel detector provided in the analyzing device, the method including: acquiring a non-linear regression or non-linear discrimination function expressing analysis data obtained for known samples; calculating a contribution value of each of the output values obtained from the plurality of channels forming the analysis data of the known samples, to the acquired non-linear regression or non-linear discrimination function, based on a differential value of the non-linear regression function or non-linear discrimination function; and identifying one or more of the plurality of channels of the detector, which are to be used for processing analysis data obtained for an unknown sample, based on the contribution value.

Analysis data processing method and analysis data processing device

Using training data, machine learning is performed to construct a learning model which is a non-linear function for discrimination or regression analysis (S). A degree of contribution of each input dimension is calculated from a partial differential value of that function. Input dimensions to be invalidated are determined using a threshold defined by a Gaussian distribution function based on the degrees of contribution (S-S). Machine learning is once more performed using the training data with the partially-invalidated input dimensions (S). A new value of the degree of contribution of each input dimension is determined from the obtained learning model, and the degree of contribution is updated using the old and new values (S-S). After the processes of Steps S-Sare iterated a specified number of times (S), useful dimensions are determined based on the finally obtained degrees of contribution, and the machine-learning model is constructed (S).

Analysis-data analyzing device and analysis-data analyzing method that calculates or updates a degree of usefulness of each dimension of an input in a machine-learning model

Wavelength spectrums of peaks detected on a chromatogram based on observation data to be processed are extracted to create a spectrum set {S'} in which the intensity values of the spectrums are normalized (S, S). One wavelength spectrum is selected from the set, and a vector of the wavelength spectrum at each point in time of measurement based on the observation data is projected so as to be perpendicular to the vector of the selected spectrum (Sto S). The vectors of the wavelength spectrums in the set {S&#x2032; } are also similarly projected (S). Consequently, the selected spectrum is erased from the set {S&#x2032;}. The processes from Sto Sare repeated until the set {S&#x2032; } does not include a spectrum, and the obtained signals are added (S). The signal resulting from the addition is a signal indicating the waveform shape of an unknown baseline. A baseline spectrum is obtained by fitting the signal to a chromatogram at each wavelength obtained from the observation data, and a baseline signal at each wavelength is calculated from the baseline spectrum and the baseline chromatogram. As a result, a baseline can be automatically estimated without setting of a parameter and the like by a user.

Chromatogram data processing method and device

For vector A which expresses an absorption spectrum of a target component, vector F orthogonal to vector A is designated as a filter for extracting an impurity superposed on the target component on a chromatogram. For vector I which expresses a measured spectrum obtained by a chromatographic analysis performed on a sample, the inner product of vectors I and F is defined as an index value u of the amount of impurity. If an impurity is present, a peak-like waveform appears on a graph which shows a temporal change in the index value u for the measured spectrum obtained at each point in time of the measurement. By detecting this waveform, the presence or absence of the impurity can be correctly determined. The direction of vector F may be determined so that, when vector B which expresses a spectrum of the impurity is decomposed into vector Ba parallel to vector A and vector Bo orthogonal to vector A, vector F becomes nearly parallel to vector Bo (i.e. the cosine similarity index is maximized).

Chromatogram data processing system

The EM algorithm for a Gaussian mixture model is applied to the separation of peaks that overlap one another on a chromatogram. However, the number of overlapping components is unknown. Thus, a suitable number of models is set, and the fitting of model parameters is performed while an actually measured signal is appropriately divided for each model by the EM algorithm. Then, when a solution converges, a determination is made as to whether a peak-like waveform is present in a residue signal that is not divided. When the peak-like waveform is present, a peak model is added. The EM algorithm is executed again. In the M step, optimization is performed using, not only a simple Gaussian function, but also a modified Gaussian function assuming a tailing. In the M step, the estimation of a spectrum assuming a chromatogram and the estimation of a chromatogram assuming a spectrum are repeatedly performed.

Chromatogram data processing method and chromatogram data processing apparatus

An upsamplerperforms upsampling based on actual measurement data forming a profile spectrum obtained with a time-of-flight mass spectrometer, to insert interpolation data between the temporally adjacent actual measurement data and make the waveform smoother. Subsequently, a peak waveform processordetermines the centroid position, peak area or other relevant values by performing centroid processing which employs trapezoidal approximation or similar technique. The smoothing of the waveform between adjacent measurement data improves the accuracy of the centroid processing, whereby a systematic error in the estimation of the centroid position or calculation of the peak area is reduced. Therefore, even when the number of data points forming one peak on a measured waveform is small, the centroid position and other kinds of peak information can be obtained with a high level of accuracy, and the performance of qualitative or quantitative determination is thereby improved.

Peak waveform processing device

For a signal waveform to be processed, the continuous wavelet transform is performed with various scale factors, and a wavelet coefficient at each point in time is calculated. On an image showing the strength of the wavelet coefficient with respect to the scale factor and time, ridge lines are detected, and based on these ridge lines, positive and negative peak candidates are extracted, after which an error in the position and width of the peak due to the influence of a neighboring peak is corrected. Subsequently, the degree of non-symmetry of the peak shape or other features are examined to remove false negative peaks due to negative peak artifacts. Subsequently, a true peak cluster, a false peak cluster resulting from the removal of high-frequency components of a high-frequency noise or other causes, and other kinds of peaks are identified, and the obtained result is used to remove false peaks.

Peak detection method

In a spectrum analysis apparatus, a controller controls selection of an adequate the correction device based on a plurality of corrected absorption spectra corrected by a plurality of correction devices acquired in advance for eliminating an effect of stray light.

Spectrum analysis apparatus and calibration method

A problem addressed by the present invention is to reduce the influence of stray light incident on each light-receiving element in the case of receiving each wavelength of light using a plurality of light-receiving elements. The multichannel spectrophotometer according to the present invention is a detector for simultaneously detecting the entirety of wavelength-dispersed light obtained by introducing light from a sample to a light-dispersing element () and dispersing this light into wavelengths by the light-dispersing element (), including: a multichannel-type detector () including a plurality of light-receiving elements arranged in a one-dimensional form in a wavelength-dispersing direction of the light-dispersing element; a light amount calculator () for calculating the amount of light from a detection signal of each of the plurality of light-receiving elements (PD); a spectrum creator () for creating, from the amounts of light calculated by the light amount calculator (), a spectrum showing a relationship between wavelength and the amount of light; and a computing section () for estimating, from the spectrum, the amount of stray light incident on each light-receiving element (PD) and correcting the spectrum by subtracting, from the amount of wavelength-dispersed light incident on each light-receiving element, the amount of stray light.

Multichannel spectrophotometer and data processing method for multichannel spectrophotometer

Provided is an analysis target region setting apparatus that can accurately set an analysis target region, based on an observation image of a sample obtained with an optical microscope and the like irrespective of texture on the sample surface when the analysis target region is set therein. The analysis target region setting apparatus according to the present invention divides the observation image into a plurality of sub-regions based on pixel information on each pixel constituting the observation image. Subsequently, consolidation information on each sub-region is calculated, and two adjacent sub-regions themselves are consolidated based on the consolidation information. According to this, it is possible to divide the observation image into sub-regions having similar pixel information with a disregard of noise attributed to the shape of a surface and the like. A user designates one sub-region from among the sub-regions finally obtained, as the analysis target region.

Analysis target region setting apparatus

Before executing a search for optimal correspondence relationship with a coarse-to-fine DP (dynamic programming) algorithm using time information of peaks appearing in a chromatogram as input data, a simplified linear correction is executed based on detection of start point and end point of the section in which the peaks are present. It is sufficient to correct only nonlinear time deviations in coarse-to-fine DP, and the space to be searched is thus narrowed down. In addition, in coarse stage DP, the number of pieces of data to be processed is reduced by selecting peaks based on peak intensity. Between coarse stage DP and fine stage DP, local inappropriate matching is eliminated by performing filtering processing in accordance with trend of time deviations over the entire chromatogram. The space to be searched in fine stage DP becomes narrow, which reduces the number of candidates to be searched and shortens the calculation time.

For an input signal with a ringing superposed thereon, a ringing-generating filter generates an analogous ringing waveform from only a peak portion of the signal which precedes the ringing. A subtractor subtracts the analogous ringing waveform from the input signal to eliminate the ringing. The coefficient of the filter is determined by applying a calculation method similar to a polynomial division based on the complete pivoting Gaussian elimination to polynomials using a reference data expressing a peak waveform and a ringing waveform, and by using a least squares method for minimizing the square of the covariance so as to allow the presence of noise in the data. Furthermore, by a repetitive process on a plurality of the same datasets, the calculation accuracy of the coefficient is improved even under the condition that the ringing frequency is high and the number of samples in one cycle is small.

Data processing method

The present invention aims at providing a method and apparatus for analyzing a mass spectrum on which multivalent ion peaks originating from a target compound appear, and calculating the mass of the target compound. First, each peak on the mass spectrum is analyzed to detect isotopic clusters, and the valence and the representative point (m/z value) of each isotopic cluster are obtained (Sthrough S). Since the range of the m/z value of the component which is added to or desorbed from the compound is limited, by using this factor, the isotopic clusters originating from the same compound are deduced. By combining the deduced isotopic clusters, the candidates for the m/z value of the added/desorbed component are deduced (S). Among the plurality of selected candidates, clearly abnormal candidates are eliminated by using a plurality of conditions such as the degree of distribution of the m/z values and the similarity of the relative intensities of the representative points of the isotopic clusters (Sthrough S). The candidate having the smallest distribution of m/z values or the candidate having the highest similarity of the relative intensities of the representative points is finally selected. After the m/z value of the added/desorbed component is determined, the mass of the compound is calculated (Sthrough S).

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Kyoto, Japan

Akira Noda