Abstract: A glass plate includes, in terms of molar percentage based on oxides: 70%?SiO2?85%; 0.0%?Al2O3?10%; 0.0%?B2O3?15%; 1.5%?MgO?20%; 0.0%?CaO?20%; 0.0% 0 SrO?5.0%; 0.0%?BaO?1.0%; 0.0% 0 ZnO?5.0%; 1.0%?Li2O?11%; 0.0%?Na2O?10%; 0.0% K2O?10%; 3.0%?R2O 11%; 0.01% Fe2O3?1.00%; and 2.0%?RO?20%, in which a temperature T2 at which a glass viscosity is 102 dPa·s is 1,650° C. or lower, a temperature T12 at which a glass viscosity is 1012 dPa·s is 730° C. or lower, a relative dielectric constant (Fr) at a frequency of 10 GHz is 6.5 or less, and a loss tangent (tan ?) at a frequency of 10 GHz is 0.0090 or less.

Abstract: To improve the reliability of mutual diagnosis in a cancer determination by machine learning, m/z values of ions originating from tumor markers or similar substances used in other related tests are stored in a particular m/z-value database. A spectrum information filtering section deletes signal intensities at the m/z values stored in the particular m/z-value database from a large number of mass spectra classified by the presence or absence of cancer. Using the data which remain after the deletion as training data, a training processor obtains training-result information and stores it in a training result database. A judgment processor similarly deletes signal intensities at the predetermined m/z values from mass spectrum data obtained for a target sample to be judged. Then, based on the training-result information stored in the training-result database, the judgment processor determines whether the target sample should be classified into a cancerous group or non-cancerous group.

Abstract: This analytical data analysis method uses machine learning of analysis result data (31) measured by an analyzer (1), and includes generating simulated data (32) in which a data variation has been added to the analysis result data (31) within a range that does not affect identification, performing the machine learning using the generated simulated data (32), and performing discrimination using a discrimination criterion (23b) obtained through the machine learning.

Abstract: Provided is a method for sorting a number of samples into an appropriate number of clusters according to their characteristics. Highly-correlated peaks are extracted from mass spectrum data obtained for the samples (S2). Using the extracted peaks, highly-correlated sample pairs are extracted (S3). While removing samples having low degrees of correlation, highly-correlated sample pairs are integrated to form core clusters (S4). Using singular peaks characterizing each core cluster, two or more core clusters are integrated to form clusters (S5-S7). These clusters include mixed clusters in which two or more clusters are mixed. Member determination formulae are created based on the singular peaks of each cluster (S8-S12). All samples, including those which have been excluded from the cluster determination process, are classified into clusters based on the member determination formulae (S14). The member determination formulae can also be used to assign a new sample to one of the cluster.

Abstract: When conducting imaging mass analysis for a region to be measured on a sample, an individual reference value calculating part obtains a maximum value in Pi/Ii of respective measuring points, and stores the value together with measured data as an individual reference value. When performing comparison analysis for a plurality of the data obtained from different samples, a common reference value determining part reads out corresponding a plurality of the individual reference values and determines a minimum value as a common reference value Fmin. A normalization calculation processing part normalizes the respective intensity values by multiplying the intensity values read out from an external memory device by a normalization coefficient long_Max×(Fmin/Pi) obtained from the common reference value Fmin, TIC values Pi at the respective measuring points, and a maximum allowable value long_Max of a variable storing the intensity values at the time of operation.

Abstract: This analytical data analysis method uses machine learning of analysis result data (31) measured by an analyzer (1), and includes generating simulated data (32) in which a data variation has been added to the analysis result data (31) within a range that does not affect identification, performing the machine learning using the generated simulated data (32), and performing discrimination using a discrimination criterion (23b) obtained through the machine learning.

Abstract: To improve the reliability of mutual diagnosis in a cancer determination by machine learning, m/z values of ions originating from tumor markers or similar substances used in other related tests are stored in a particular m/z-value database. A spectrum information filtering section deletes signal intensities at the m/z values stored in the particular m/z-value database from a large number of mass spectra classified by the presence or absence of cancer. Using the data which remain after the deletion as training data, a training processor obtains training-result information and stores it in a training result database. A judgment processor similarly deletes signal intensities at the predetermined m/z values from mass spectrum data obtained for a target sample to be judged. Then, based on the training-result information stored in the training-result database, the judgment processor determines whether the target sample should be classified into a cancerous group or non-cancerous group.

Abstract: If spatial measurement point intervals in imaging mass analysis data of two samples to be compared are different and the degrees of spatial distribution spreading of substances are compared, one of the data is defined as a reference, the measurement point intervals in the other of the data are redefined so as to be equalized to the reference, and a mass spectrum at each virtual measurement point set as a result of the redefinition is obtained through interpolation or extrapolation based on a mass spectrum at an actual measurement points. If the arrays of the m/z values of mass spectra are different for each sample, the m/z value positions of the mass spectrum in one of the data are defined as a reference, and the intensity values corresponding to the reference m/z values are obtained through interpolation or extrapolation for the mass spectrum of the other of the data.

Abstract: Provided is a technique for using an optical microscope image of an area on a sample to collect area-specific information characterizing each kind of biological tissue from imaging mass analysis data. On an optical image of a two-dimensional target area on a sample, a difference is examined in the kind of tissue or other features and areas are specified, each regarded as the same kind of tissue. When data processing is initiated, peak information is extracted, for each specified area, from mass spectrum data of all the measurement points. A peak method is applied to each area to extract peak information. Then, when a command to compare a set of areas is given, the peak information of those areas is collected. By comparing the peak information of different areas by a machine learning algorithm or similar judging technique, area-specific peak information is obtained, and this information is stored in memory.

Abstract: Provided is a method for sorting a number of samples into an appropriate number of clusters according to their characteristics. Highly-correlated peaks are extracted from mass spectrum data obtained for the samples (S2). Using the extracted peaks, highly-correlated sample pairs are extracted (S3). While removing samples having low degrees of correlation, highly-correlated sample pairs are integrated to form core clusters (S4). Using singular peaks characterizing each core cluster, two or more core clusters are integrated to form clusters (S5-S7). These clusters include mixed clusters in which two or more clusters are mixed. Member determination formulae are created based on the singular peaks of each cluster (S8-S12). All samples, including those which have been excluded from the cluster determination process, are classified into clusters based on the member determination formulae (S14). The member determination formulae can also be used to assign a new sample to one of the cluster.

Abstract: When conducting imaging mass analysis for a region to be measured on a sample, an individual reference value calculating part obtains a maximum value in Pi/Ii of respective measuring points, and stores the value together with measured data as an individual reference value. When performing comparison analysis for a plurality of the data obtained from different samples, a common reference value determining part reads out corresponding a plurality of the individual reference values and determines a minimum value as a common reference value Fmin. A normalization calculation processing part normalizes the respective intensity values by multiplying the intensity values read out from an external memory device by a normalization coefficient long_Max×(Fmin/Pi) obtained from the common reference value Fmin, TIC values Pi at the respective measuring points, and a maximum allowable value long_Max of a variable storing the intensity values at the time of operation.

Abstract: Even when only mass spectra wherein the reproducibility of peak intensities is low are obtained in a mass spectrometry apparatus using, for example, a MALDI ion source, the correction of shifts in retention time using TICs for a plurality of specimens is performed with good precision. For each mass spectrum, variable scaling is executed which combines such first scaling as to equalize the extent of variations in signal intensity values in one mass spectrum, among different mass spectra, and second scaling for performing weighting according to relative variations in signal intensity values for each mass spectrum (S3). The signal intensity values after the scaling are added to obtain a total signal intensity value for one measurement time point (S4). From a plurality of total signal intensity values thus obtained, a TIC is created (S6). Using these TICs, RT alignment is executed (S8). Thus, the similarity in TIC waveforms increases, and RT alignment can be suitably performed.

Abstract: MS1 and MS2 measurements of fractionated samples are performed. Based on the identification results and the S/N ratios of the MS1 peaks, an identification probability estimation model showing a relationship between the cumulative number of MS1 peaks and the number of MS1 peaks successfully identified through the MS2 measurements and identifications performed in ascending order of S/N ratio is created. S/N ratios of the MS1 peaks obtained by MS1 measurements are determined, and probabilities of substances in a target sample are estimated from S/N ratios using the aforementioned model. Optimization of precursor-ion selection and data-accumulation number is defined as the problem of maximizing the sum of identification probabilities of MS1 peaks selected for MS2 measurement, and formulated as an objective function using 0-1 variables. This function is solved as a 0-1 integer programming problem under preset conditions.

Abstract: The present invention provides a method and apparatus for efficiently handling a large amount of data collected by an imaging mass analysis to present significant information for the analysis of the tissue structure of a biological sample or other objects in an intuitively understandable form for analysis operators. For each pixel 8b on a sample 8, a mass-to-charge ratio m/z(i) corresponding to the maximum intensity MI(i) in a mass spectrum is listed, and the largest value MII of the maximum intensities of all the pixels are extracted. A color scale corresponding to the intensity values within a range of 0 to MII is defined. For each pixel, the maximum intensity MI is compared with the color scale to assign a color to that pixel. A mapping image with the pixels shown in the respective colors is created and displayed.

Abstract: If spatial measurement point intervals in imaging mass analysis data of two samples to be compared are different and the degrees of spatial distribution spreading of substances are compared, one of the data is defined as a reference, the measurement point intervals in the other of the data are redefined so as to be equalized to the reference, and a mass spectrum at each virtual measurement point set as a result of the redefinition is obtained through interpolation or extrapolation based on a mass spectrum at an actual measurement points. If the arrays of the m/z values of mass spectra are different for each sample, the m/z value positions of the mass spectrum in one of the data are defined as a reference, and the intensity values corresponding to the reference m/z values are obtained through interpolation or extrapolation for the mass spectrum of the other of the data.

Abstract: Even when only mass spectra wherein the reproducibility of peak intensities is low are obtained in a mass spectrometry apparatus using, for example, a MALDI ion source, the correction of shifts in retention time using TICs for a plurality of specimens is performed with good precision. For each mass spectrum, variable scaling is executed which combines such first scaling as to equalize the extent of variations in signal intensity values in one mass spectrum, among different mass spectra, and second scaling for performing weighting according to relative variations in signal intensity values for each mass spectrum (S3). The signal intensity values after the scaling are added to obtain a total signal intensity value for one measurement time point (S4). From a plurality of total signal intensity values thus obtained, a TIC is created (S6). Using these TICs, RT alignment is executed (S8). Thus, the similarity in TIC waveforms increases, and RT alignment can be suitably performed.

Abstract: In an imaging mass analysis, image information of a sample allows users to grasp specific information about the sample, such as distribution of a portion with a particular function or effect. The mass spectrum intensity data are normalized for each pixel so that the sum of the intensities over the entire mass-to-charge ratio (m/z) is one. Entropy is calculated by totaling the product of the intensity normalizing at each m/z and the logarithm of that intensity over the entire m/z range. After the entropy is calculated for each pixel, the pixels are colored according to their entropy values to display a two-dimensional color image of entropy distribution. The entropy of a cancerous portion is relatively small because of a high content of a specific kind of substance and the simplified composition of the substances. Thus, the cancerous part and the normal part of the entropy image can be distinguished.

Abstract: (EN) MS analysis, MS2 analysis, . . . , MSP analysis for peptide mixture are sequentially executed to obtain respective mass spectra (S1). At this time, an analysis in which precursor ion is changed or a different cleavage condition is set for the same precursor ion is performed plural times to put together peaks appearing in mass spectra that are obtained respectively. After the number of peaks is increased, a useful peak is extracted using commonality and complementarity of the peaks of MSm spectrum and MSm+1 spectrum and classification is performed for each type of peaks extracted to obtain an appearance frequency for each classification (S3, S4). An evaluation score on whether the extracted peak is a product ion and on a terminal is calculated based on reliability and appearance frequency that are obtained in advance (S8). The evaluation score is used in estimating sequence with the use of the extracted peak to decide and output, for example, the priority of sequence candidates (S8, S9).

Abstract: A method creates an accurate mass spectrum with a high resolving power based on a plurality of TOF spectra, while reducing the computation to assure real-time processing. TOF spectra are measured when ions are ejected from the loop orbit. Then a coincidence detection method determines what mass-to-charge ratio a peak appearing on the TOF spectra originates from. The time range in which a corresponding peak appears on other TOF spectra is set, and the existence of the peak in that range is determined. When the corresponding peak is found on other TOF spectra, the m/z is deduced from the peak on the TOF spectrum with the highest resolving power and a mass spectrum is created. From the peak density around the peak of interest, the reliability of the deduction is computed. For a low reliability peak, the ion ejection time is optimized and the TOF spectrum is measured again.

Abstract: The present invention aims at providing a method and apparatus for presenting, based on an enormous amount of data collected by an imaging mass analysis, information which is significant for understanding the tissue structure and other information of a biological sample and which is intuitively easy to understand to analysis operator. For each pixel 8b on a sample 8, the mass-to-charge ratio m/z (i) corresponding to the maximum intensity MI(i) in the mass spectrum is extracted, and all the pixels are grouped into clusters in accordance with their m/z (i). One cluster corresponds to one substance. Then, the largest maximum intensity MI(i) among the maximum intensities of the pixels included in a cluster is extracted as the representative maximum intensity MI(cj) for each cluster, and these representative maximum intensities MI(cj) are displayed with cluster number cj.