| Patents for GC/LC-MS data evaluation Patents are nice sources for a special view inside. I will list some interesting patents from mass spectrometry, data evaluation, device
development. As many services are outdated very fast, you can also search for yourself at the US Patent Office or Delphion. Mass spectrometry US5218529: Neural network system and
methods for analysis of organic materials and structures using spectral data; Meyer; Bernd J. , Athens, GA; Sellers; Jeffrey P. , Suwanee, GA; Thomsen; Jan U. , Fredricksberg, Denmark; University of Georgia Research Foundation, Inc. (Athens, GA) Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared
absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as
complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation. [PDF][PDF2] Time-of-Flight mass spectrometry
US5175430: Time-compressed chromatography in mass spectrometry; Enke; Christie G. , East Lansing, MI; Holland; John F. , Lansing, MI; McLane; Richard D. , Lansing, MI; Yefchak; George E. , Lansing, MI; Meridian Instruments, Inc., Okemos, MI A process and
apparatus employing the time compression of chromatography in mass spectrometry with array detection in which the time compressed information is deconvoluted by mathematical analysis for recovery of analytical information made inaccessible in the time compression and thereby resulting in a decrease in analysis time and improved component identification without loss of sensitivity.[PDF][PDF2] Deconvolution in Mass spectrometry EP0708475: Mass spectra deconvolution method; Gray, Zachary A.; Abel, Roger H. Hewlett-Packard Company; 1995
Mass spectral analyzer providing automated discovery, deconvolution and identification of mass spectrum.
Conventionally acquired mass data file (180) is re-sorted from chronological (181) to primarily ion-mass order (182) and secondarily to chronological order (183) within each ion-mass grouping. For each ion-mass measured, local peaks or maximums are identified through an integrator means. All local maximums are then sorted and partitioned (184) such that a set of deconvoluted spectra is obtained such that each element of the set constitutes an identifiable compound. Compounds are then matched
to reference spectra in library datafiles (185) by conventional probabilistic matching routines. [PDF] [LINK] US4807148: Deconvolving chromatographic peaks; Lacey; Richard F. , Palo Alto, CA; Hewlett-Packard Company, Palo Alto, CA; 1989
: Deconvolution of up to three overlapping chromatographic peaks is provided in which pure spectral components are extrapolated from a Euclidean-normalized
expression of chromatographic data in the space of the three principal factors. A coordinate transformation to planar coordinates after expansion in factor space and before extrapolation yields the simplicity of linear extrapolation in combination with the inherent accuracy of Euclidean, as opposed to standard, normalization. The estimation of the pure component spectra permits the constructions of a concentration matrix. Improved estimates and an error bound are provided by applying
assumptions of non-negativity and limited deviation from the means to the concentration matrix. [PDF] US4752888: Method of
determining major and minor peaks in a chromatogram using a data processor; Yoshihara; Touhachi , Katsuta, Japan;Hitachi, Ltd., Tokyo, Japan; 1985
A data processor for chromatography operates to separately recognize the peaks of a composite peak of a chromatogram. At first, in order to separate the peaks, the maximum point of the peak of the chromatogram is determined, and extrapolation lines are drawn from said maximum point to the individual points of the
chromatogram, and then the gradients of the lines changing from the minimum to the maximum are determined. By determining the changes of the gradients of the extrapolation lines and intersection points of the extrapolation lines and the chromatogram, the major and minor peak are separated so that the shoulder peak can be judged remarkably accurately. [PDF] [PDF2] US5453613: Mass spectra interpretation system including spectra extraction; Gray; Zachary A. , Palo Alto, CA; Abel; Roger H. , Cupertino, CA; 1994
A
mass spectral analyzer system providing automated discovery, deconvolution and identification of mass spectrum is taught. Conventionally acquired mass data files are re-sorted from chronological to primarily ion-mass order and secondarily to chronological order within each ion-mass grouping. For each ion-mass measured, local peaks or maximums are identified through an integrator means. All local maximums are then sorted and partitioned such that a set of deconvoluted spectra is obtained such
that each element of the set constitutes an identifiable compound. Compounds are then matched to reference spectra in library datafiles by conventional probabilistic matching routines. [PDF] US4837434:
Mass spectrometry system and method employing measurement/survey scan strategy; James; Craig A. , Palo Alto, CA; Hewlett-Packard Company, 1989 A gas chromatography plus mass spectrometry system implements a scan strategy in which each full range scan alternates between a normal measurement mode and a survey mode based on a block/gap map made
during the previous scan. Survey mode is used within regions that were determined in the previous scan to
lack signal above a predetermined threshold. Spectral data is generated during measurement mode operation.
Each scan serves both measurement and mapping functions in a way that avoids mass filter jumps, since each scan is monotonic over the entire scanning range.[PDF] [PDF2] US6147344: Method for identifying compounds in a chemical mixture; Annis; D. Allen , Cambridge, MA; Birnbaum; Mark , New York, NY; Birnbaum; Seth N. , Boston, MA; Tyler; Andrew N. , Reading, MA; Neogenesis, Inc, Cambridge, MA; 2000 A
technique for automatically analyzing mass spectrographic data from mixtures of chemical compounds is described consisting a series of screens designed to eliminate or reduce incorrect peak identifications due to
background noise, system resolution, system contamination, multiply charged ions and isotope substitutions. The technique performs a mass spectrum operation on a control sample, producing a first group of output values. Next, perform a mass spectrographic operation on a sample to
be analyzed, producing a second group of output values. Select a first m/z ratio for a material expected to be present in the mixture from a predetermined library of calculated mass spectrometer output spectrums and subtract the value of the
control sample at the expected output value from the value of the analyzed sample, and compare the difference to a predetermined value. If the value is greater than the predetermined value thus indicating that the signal is above the background noise
level, generating a record at that m/z value for an expected material. Performing the same mass spectrum operation several times to eliminate random noise and background contamination. Next, identify peak values that dont have the expected peak width or proper retention time for the separation method. Identify multiply charged ions by examining peak separation. Examine the m/z location of the expected
material and compare intensity at the expected m/z location with the intensity at the
next lower m/z recorded peak to identify peaks related to atomic isotope substitution. With such a technique, mass spectrograph data analysis may be greatly simplified by the identification of probable spurious signals, and analysis will become simpler and more accurate.[PDF] [PDF2] |
