公开(公告)号：US20140303903A1

公开(公告)日：2014-10-09

申请号：US14177302

申请日：2014-02-11

Applicant: SHIMADZU CORPORATION

Inventor： Yuichiro FUJITA ,  Shigeki KAJIHARA

IPC: H01J49/00

CPC classification number: H01J49/0036 ,  G01N30/72 ,  G01N30/86 ,  G01N30/8675

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 translation: 即使在使用例如MALDI离子源的质谱装置中仅获得其峰值强度的再现性低的质谱也能够以高精度进行使用多个试样的TIC的保留时间偏移的校正。 对于每个质谱，执行可变缩放，其组合了这样的第一缩放比例，以在不同质谱中均衡一个质谱中的信号强度值的变化程度，以及根据信号强度值的相对变化执行加权的第二缩放 每个质谱（S3）。 添加缩放后的信号强度值，以获得一个测量时间点的总信号强度值（S4）。 从如此获得的多个总信号强度值中，创建TIC（S6）。 使用这些TIC，执行RT对齐（S8）。 因此，TIC波形的相似度增加，可以适当地进行RT对准。

公开(公告)号：US20190267222A1

公开(公告)日：2019-08-29

申请号：US16321130

申请日：2016-07-29

Applicant: SHIMADZU CORPORATION

Inventor： Hideaki IZUMI ,  Shigeki KAJIHARA

IPC: H01J49/00 ,  G01N33/574 ,  G06N20/00 ,  G01N27/62

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.

公开(公告)号：US20150066387A1

公开(公告)日：2015-03-05

申请号：US14471907

申请日：2014-08-28

Applicant: SHIMADZU CORPORATION

Inventor： Yoshihiro YAMADA ,  Shigeki KAJIHARA

IPC: H01J49/00

CPC classification number: H01J49/0027 ,  H01J49/004

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. Optimal precursor ions and data-accumulation numbers are determined from variables of the solution.

Abstract translation: 进行分离样品的MS1和MS2测量。 基于MS1峰值的识别结果和S / N比，识别概率估计模型显示了通过MS2测量成功识别的MS1峰值的累积数与MS1峰值之间的关系，并按照升序 创建S / N比。 确定通过MS1测量获得的MS1峰的S / N比，并且使用上述模型从S / N比估计目标样品中物质的概率。 前导离子选择和数据累积数的优化定义为最大化MS2测量选择的MS1峰的识别概率之和，并使用0-1变量表示为目标函数的问题。 该功能在预设条件下被解决为0-1整数编程问题。 最佳前体离子和数据累积数由溶液的变量确定。

公开(公告)号：US20170352525A1

公开(公告)日：2017-12-07

申请号：US15538685

申请日：2014-12-22

Applicant: Shimadzu Corporation

Inventor： Masahiro IKEGAMI ,  Shigeki KAJIHARA

IPC: H01J49/00

CPC classification number: H01J49/0036 ,  G01N27/62 ,  G06K9/0014 ,  G06K9/00503 ,  G06K9/38 ,  G06K9/40 ,  H01J49/0004

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.

公开(公告)号：US20200065699A1

公开(公告)日：2020-02-27

申请号：US16462611

申请日：2017-01-19

Applicant: SHIMADZU CORPORATION

Inventor： Yusuke TAMAI ,  Shigeki KAJIHARA ,  Shin FUJITA ,  Ryota AISU

IPC: G06N20/00

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.

公开(公告)号：US20140316717A1

公开(公告)日：2014-10-23

申请号：US14257025

申请日：2014-04-21

Applicant: SHIMADZU CORPORATION

Inventor： Masahiro IKEGAMI ,  Shigeki KAJIHARA

IPC: H01J49/00

CPC classification number: H01J49/0036 ,  H01J49/0004

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 translation: 如果要比较的两个样本的成像质量分析数据中的空间测量点间隔不同，并且比较物质的空间分布扩展程度，则将数据中的一个定义为参考，另一个数据中的测量点间隔 被重新定义为与参考相等，并且通过基于实际测量点处的质谱的内插或外推获得作为重新定义的结果设置的每个虚拟测量点处的质谱。 如果质谱的m / z值的阵列对于每个样品不同，则将其中一个数据中的质谱的m / z值位置定义为参考，并且对应于参考m / z的强度值 通过对另一个数据的质谱的插值或外推获得值。

公开(公告)号：US20130204537A1

公开(公告)日：2013-08-08

申请号：US13757439

申请日：2013-02-01

Applicant: SHIMADZU CORPORATION

Inventor： Shigeki KAJIHARA ,  Takayuki YOSHIMORI ,  Akiyasu YOSHIZAWA ,  Naoki KANEKO

IPC: G06F19/18

CPC classification number: G16B20/00 ,  G16B45/00

Abstract: The amino acid sequence is deduced by using de novo sequencing, to prevent the correct amino acid sequence from not being ranked high as candidates. Amino acid sequence candidates are computed by finding the longest path by a branch and using a bound method based on the spectrum data on the target peptide and the known amino acid sequence. A tree-structured directed graph is used where amino acid sequences are set as nodes and the peak intensities corresponding to the amino acids are set as branches. In a sequence put at a node in the highest layer, an amino acid is placed at a terminal, and as the layer goes deeper, amino acids are sequentially placed from both terminals toward the center of the sequence. The final score is estimated based on the remaining amino acids, and if the score is small, the search is halted.

Abstract translation: 通过使用从头测序推导出氨基酸序列，以防止正确的氨基酸序列不被列为候选者。 氨基酸序列候选是通过用分支找到最长路径并使用基于目标肽和已知氨基酸序列的光谱数据的结合方法计算的。 使用树形结构的有向图，其中将氨基酸序列设置为节点，并将对应于氨基酸的峰强度设置为分支。 在放置在最高层的节点上的序列中，氨基酸被置于末端，并且随着层变深，氨基酸从两个末端向序列的中心顺序放置。 根据其余的氨基酸估计最终得分，如果分数较小，则搜索停止。