Analysis of chemically samples using gas chromatography (GC) separation with vacuum ultra-violet spectroscopy detection is described. One technique focuses on assigning a specific analysis methodology to each constituent in a sample. Constituents can elute from the GC by themselves or with other constituents, in which case a deconvolution is done using VUV spectroscopic data. In an exemplary embodiment, each constituent may be specifically included in an analysis method during a setup procedure, after which the same series of analyses are done on subsequent sample runs. The second approach essentially integrates an entire chromatogram by first reducing it into a series of analysis windows, or time slices, that are analyzed automatically. The analysis at each time slice determines the molecular constituents that are present as well as their contributions to the total response. Either approach can be used to quantify specific analytes or to do bulk classification.

The present invention relates to a method for determining the logarithmic reduction value LRV of a size-exclusion filter for a particle of a process solution, which particle is to be clarified, the size-exclusion filter being protected from a blocking adsorbing species present in the process solution by a process adsorber which is connected upstream in series.

Wavelength spectrums of peaks detected on a chromatogram based on observation data to be processed are extracted to create a spectrum set in which the intensity values of the spectrums are normalized. 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. The vectors of the wavelength spectrums in the set are also similarly projected. Consequently, the selected spectrum is erased from the set. The processes are repeated until the set does not include a spectrum, and the obtained signals are added. The signal resulting from the addition is a signal indicating the waveform shape of an unknown baseline.

A mass spectrometry data processing apparatus includes a computation unit and a seeking unit. The computation unit calculates the mass difference m/z between the peaks of two molecules selected from mass spectrum data or obtains the mass difference m/z. The seeking unit estimates a combination of atoms between the peaks of the two molecules in a range of the mass difference m/z. The seeking unit seeks the combination of atoms having a mass difference, which matches the mass difference m/z, between a set of atoms desorbed from a first molecule of the two molecules and a set of atoms added to the first molecule.

The present disclosure relates to methods and apparatus for identifying metabolic pathways and metabolites in complex biological samples. In particular, the present disclosure relates to a method and apparatus to increase the confidence of metabolite identification in metabolomics, such as in untargeted metabolomics data, using various statistical tools, such as over representation and enrichment analysis.

A method to filter out pump pulses from data collected with a chromatography system is disclosed. Baseline data is collected as a pump delivers solvent to an analytical instrument, which may be the IP signal of a capillary bridge viscometer. A Fourier transform is applied to the data to generate the power spectrum of the baseline signal. Fundamental and harmonic frequencies are determined and a comb filter is constructed therefrom and applied to sample collected from all of the affected instruments. The comb filter may be correlated to the pump and flow rate and stored in data analysis software or database. Other systems using other pumps may also generate associated comb filters, and the resulting filters and the flow rates at which they were generated may be stored in a database accessible to the data analysis software.

Embodiments of the present disclosure are directed to methods and systems for assessing integrity of chromatography columns, systems, and processes. The methods and systems can comprise one or more of extracting a block and signal combination for analysis, performing a transition analysis, performing one or more statistical process controls, and/or implementing in-process controls based on the statistical process controls.

Methods for the monitoring of quality and efficiency of chromatography columns operated in continuous mode without disruption of column operation by monitoring buffer change followed by smoothing and derivation of the recorded values.

Exemplary embodiments provide methods, mediums, and systems for analyzing spectrometry and/or chromatography data, and in particular to techniques to improve the reproducibility of results of spectrographic and/or chromatographic experiments. For example, some embodiments provide techniques for normalizing mass spectrometry (MS) and/or liquid chromatography (LC) data across different experimental devices, allowing data from different cohorts to be directly compared. To this end, exemplary embodiments provide a reliable, reproducible target library usable across different platforms, laboratories, and users. One embodiment leverages statistical techniques to select experimental parameters configured to reduce or minimize the chance of misidentifying a target molecule. Another embodiment leverages the law of large numbers to produce a composite product ion spectrum usable across different experiments. The composite product ion spectrum allows regression curves to be generated, where the regression curves can be used to normalize an experimental mass spectrum.

A method for generating an extracted ion chromatogram (XIC) from mass spectrometry data is disclosed. Mass spectrometry data are received comprised of a plurality of data points, each data point representing a measured ion intensity at a mass to charge ratio at a chromatographic retention time and these data are filtered to produce a filtered dataset. A minimal spanning tree is then generated connecting the data points of the filtered dataset and tree branches are pruned in accordance with a specified length threshold to yield one or more sub-trees. The sub-trees are then interpreted as a set of XICs and displayed on a display device.