Abstract:
A model constructed by a training process using the technique of deep learning using the training data including images created from a large number of chromatograms and correct peak information is previously stored in a trained model storage section. When chromatogram data for a target sample acquired with an LC measurement unit are inputted, an image creator converts the chromatogram into an image and creates an input image in which one of the two areas divided by the chromatogram curve as the boundary in the image is filled. A peak position estimator inputs the pixel values of the input image into a trained model using a neural network, and obtains the position information of the starting point and/or ending point of the peak and a peak detection confidence as the output. A peak determiner determines the starting point and/or ending point of each peak based on the peak detection confidence.
Abstract:
A data processing device (1) is provided with: a data base storage region (32c) for storing MSn mass spectra of a great number of known compounds in advance; an acquisition unit for acquiring the MSn mass spectrum of an unknown compound (31a); and a score calculation unit (31d) for finding respective scores indicating similarities between the MSn mass spectrum of the unknown compound and MSn mass spectra of the great number of known compounds. The data processing device (1) is characterized by being further provided with: an intensity score calculation unit (31b) for classifying the peaks in the MSn mass spectrum of the unknown compound and the peaks in the MSn mass spectra of the known compounds into intensity scores of several ranks depending on the dimensions of the ion intensities, respectively; and a position score calculation unit (31c) for finding the respective position scores indicating error in the mass-to-charge ratio for each peak, wherein the score calculation unit (31d) adds up the intensity score of each peak in the MSn mass spectrum of a known compound, the intensity score of the corresponding peak in the MSn mass spectrum of the unknown compound and the position score, and calculates as a score the sum total of the values that have been added up for all peaks.
Abstract:
In a method for creating isotope distribution data, samples for analysis having different concentrations of metabolites are prepared as samples containing metabolites of cells cultured in a medium containing a substrate labeled with a stable isotope, mass spectrometry is performed on each under the same analysis condition, mass spectrum data is analyzed for each to identify the type of the metabolites, and there are determined the number of metabolites included in a metabolite group made of unlabeled metabolites and/or isotopic isomers, and the signal intensities of mass peaks corresponding to all isotope isomers included in the metabolite group. The number of metabolites corresponding to all types of metabolites and the signal intensity are compared among the samples to select a sample for analysis for obtaining the isotope distribution, and data on the isotope distribution of the metabolite is integrated to create the isotope distribution data of the metabolite.
Abstract:
A mass spectrometry data display device in which the mass axis (m/z axis) is made into a ring shape and the intensity axis is the radial direction thereof, and peak information (in the drawing, the compound name and structural formula candidates) are arranged in a ring shape in correspondence with the peaks along the outer circumference of the mass spectrum and displayed together therewith on a screen.
Abstract:
Provided is a literature information service method using a single computer or a plurality of computers connected to each other via a network. The literature information service method includes: transmitting a first character string to a plurality of first servers connected respectively to a plurality of databases each including information of enzymes, and receiving a plurality of pieces of data obtained by searching the plurality of databases with the first character string; extracting a plurality of second character strings indicating information of enzymes from the plurality of pieces of data; generating a search expression using at least one of the plurality of extracted second character strings; and searching a literature database using the search expression to acquire search result data.
Abstract:
An analyzer configured to acquire a chromatogram or spectrum by performing a predetermined analysis of a sample and perform a qualitative or quantitative analysis of components contained in the sample. The analyzer includes: a peak detection unit configured, based on information regarding a plurality of target components that need to be checked whether contained in the sample or that need to be quantified, to detect a peak or peaks in the chromatogram or spectrum acquired by the predetermined analysis of the sample corresponding to one of the target components, configured to acquire peak information regarding each of the peak or peaks, and configured to obtain confidence information for each of the peak or peaks, the confidence information being an indicative value of certainty of detecting a peak; and a display processing unit configured to display on a display unit a list of at least a part of the target components.
Abstract:
When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.