Techniques for real-time or substantially real-time peak detection are described. In one embodiment, for example, logic coupled to memory may be configured to receive data from at least one analytical instrument and perform processing or analysis on the received data. Moreover, the logic may be configured to determine, via one or more GPUs or CPUs (or both), one or more peaks based on the processing or the analysis of the received data and generate peak detection data based on the detected one or more peaks in real-time or substantially real-time. Other embodiments are described.

A method includes: performing a time-frequency analysis on measurement data to obtain waveform data representing a temporal change in the intensity of each of a plurality of frequency components; dividing the waveform data of each of a plurality of predetermined frequencies into a plurality of segments so that each section where positive values successively occur and each section where negative values successively occur in a time-axis direction are defined as one segment; calculating the area of each of the segments to obtain segment values; creating, for the waveform data of each of the predetermined frequency components, a selected segment group by excluding a segment whose segment value exceeds a predetermined reference value from the segments in the waveform data; and determining a noise level of each of the predetermined frequency components based on the average value of the segment values of the segments included in the selected segment group.

Described are techniques for processing data. Sample analysis is performed generating scans of data. Each scan comprises a set of data elements each associating an ion intensity count with a plurality of dimensions including a retention time dimension and a mass to charge ratio dimension. The scans are analyzed to identify one or more ion peaks. Analyzing includes filtering a first plurality of the scans producing a first plurality of filtered output scans. The filtering including first filtering producing a first filtering output, wherein the first filtering includes executing a plurality of threads in parallel which apply a first filter to the first plurality of scans to produce the first filtering output. Each of the plurality of threads computes at least one filtered output point for at least one corresponding input point included in the plurality of scans. Analyzing includes detecting one or more peaks using the filtered output scans.

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.

Methods and systems for measuring, in a gas stream, an analyte concentration level from a gas chromatography elution peak outputted by a gas chromatography system are provided. The method includes receiving an analyte signal representative of the gas chromatography elution peak in the time domain, converting the analyte signal from the time-domain to the frequency domain, in the frequency domain, preprocessing the analyte signal to distinguish frequencies of the analyte signal, integrating the analyte signal after preprocessing to obtain a redressed analyte signal in the time domain, the redressed analyte signal having a substantially Gaussian shape, and processing the redressed analyte signal to obtain the analyte concentration level. The system includes a detector operable for generating the analyte signal and one or more processors configured for preprocessing and integrating the analyte signal to obtain the redressed analyte signal and processing the redressed analyte signal to obtain the analyte concentration level.

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.

A method is provided for analyzing a sample and identifying species using chromatography and spectrometry. Possible candidate species to be used in a regression analysis are selected for consideration based on their retention indices in a chromatography column and peak locations in an infrared spectrum. By using such a selection process, the number of combinations of species to be used in the regression analysis can be significantly reduced. The species and respective concentrations in the sample are identified by using an iterative process with regression analysis and minimizing least squares errors between a sample spectrum and a computed spectrum associated with selected candidate species.

In this application is described a method for preparing nano-VLP composition, thereby permitting purification using chromatography and filtration. The nano-VLP composition has a more uniform size range of filovirus particles, roughly 230 nm diameter, allowing ease of manipulation of the composition, while retaining GP conformational integrity and the antigenic effectiveness of the vaccine. Additionally, the nano-VLP can be lyophilized without loss of nano-VLP structure, or GP immunogenicity. Lyophilized nano-VLP have greatly enhanced thermostability, allowing the creation of a filovirus nano-VLP vaccine without a cold chain requirement.

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.

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.