An instrument for separating ions may include an ion source configured to generate ions from a sample, at least one ion separation instrument configured to separate the generated ions as a function of at least one molecular characteristic and an electrostatic linear ion trap (ELIT) positioned to receive ions exiting the at least one ion separation instrument. The ELIT has first and second ion mirrors separated by a charge detection cylinder, and is configured such that an ion trapped therein oscillates back and forth through the charge detection cylinder between the first and second ion mirrors with a duty cycle, corresponding to a ratio of time spent by the trapped ion traversing the charge detection cylinder and total time spent by the trapped ion traversing a combination of the first and second ion mirrors and the charge detection cylinder during one complete oscillation cycle, of approximately 50%.

A system includes a first type of sensor and an estimation system that is connected to first type of sensor. The estimation system is configured to (a) identify a best peak shape for estimation of known gas mixtures by analyzing characterization data across known gas mixtures, with added noise, using machine learning, (b) generate a plurality of actual peak shapes, in first type of sensor, for several different instances using standard gas mixtures to provide an actual peak shape among the plurality of peak shapes as calibrating input to calibrate first type of sensor and (c) calibrate first type of sensor by automatically adjusting parameters of first type of sensor for optimizing actual peak shape to match with desired peak shape.

A method of analysis using mass spectrometry and/or ion mobility spectrometry is disclosed. The method comprises: using a first device to generate smoke, aerosol or vapour from a target comprising or consisting of a microbial population; mass analysing and/or ion mobility analysing said smoke, aerosol or vapour, or ions derived therefrom, in order to obtain spectrometric data; and analysing said spectrometric data in order to analyse said microbial population.

One mode of the present invention provides a laser power adjustment method for ionization in a laser desorption/ionization mass spectrometer, the laser power adjustment method including: a measurement step (S1) in which intensity information on ions derived from a specific component in a specimen are acquired while changing laser power in n stages (n is 3 or more) for the identical specimen; and a processing step (S2, S6, and S7) in which a slope of a straight line connecting two adjacent plot points on a laser power axis is calculated in a two-axis graph in which a relationship between n ionic intensities obtained by the measurement step or a signal value, which is an SN ratio obtained from the ionic intensities, and laser power is plotted; an index value reflecting a ratio between a forward slope value, which is a slope of a straight line on a front side of the plot point, and a backward slope value, which is a slope of a straight line on a rear side, is obtained for each plot point; and appropriate laser power is selected using the index value.

Methods and systems for determining a measure of a rate of decay of an ion sample. Specifically, the present disclosure provides methods and apparatus for determining decay constants and cross-section measurements in parallel to mass measurement and decay time correction. The disclosure particularly relates to methods and apparatus for performing Fourier transform mass spectrometry (FTMS).

A trace of intensity versus time values is received for a series of samples produced by a mass spectrometer. Also, a series of ejections times corresponding to the series of samples produced by a sample introduction system is received. A series of expected peak times corresponding to the series of ejection times are calculated using a known delay time from ejection to mass analysis. At least one isolated peak of the trace is identified using the series of expected peak times. A peak profile is calculated by fitting a mixture of at least two different distribution functions to the at least one isolated peak. For at least one time of the series of expected peak times, an area of a peak at the one time is calculated by fitting the peak profile to the trace at the one time and calculating an area of the fitted peak profile.

Targeted quantification for mass spectrometry is described. In one aspect, a mass spectrometer can generate survey mass spectra and identify the compounds of a sample using the survey mass spectra. Compounds that elute within a same time range and do not form interfering product ions upon fragmentation can be identified, and grouped together for an MS2 scan. A series of MS2 scans can then be generated to acquire MS2 mass spectra.

A system and method to screen a plurality of molecules in datasets obtained from mass spectroscopy, including selecting and receiving at least one dataset of mass spectral data, and selecting customizable m/z mass tolerance peaks to assign initial compound assignments from at least one adduct ion hierarchy database for at least one compound having a parent molecule. Adduct ion hierarchy screening is applied to at least a portion of the dataset, wherein selected dataset features are tested to determine if they represent the most abundant expected adduct of the parent molecule class and if the expected adduct assignment hierarchy are present in the dataset.

A system, method and computer program product are provided for calculating one or more indicative properties, e.g., one or more of the cetane number, octane number, pour point, cloud point and aniline point of oil fractions, from the density and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) of a sample of an oil sample.

When performing an analysis of the difference between a specific sample group and a nonspecific sample group, a principle component analysis processing unit (33) performs principle component analysis on a collection of a plurality of mass spectrums created from data obtained for a single specific sample, and a characteristic spectrum acquisition unit (34) acquires a characteristic spectrum for each of a plurality of principle components using factor loadings. A spectrum similarity calculation unit (35) calculates the similarities between all mass spectrums and the characteristic spectrum for each sample, and obtains a representative value for the same. The similarity representative value for each sample is obtained for all the characteristic spectrums. A difference determination unit (36) checks whether there is a significant difference between the distribution of the similarity representative values of the specific sample group and the distribution of the similarity representative values of the nonspecific sample group and determines that the characteristic spectrum which is the source of the similarities having a significant difference is a difference spectrum. The difference spectrum reflects component information characterizing a sample group difference, so a component identification unit (37) searches for the difference spectrum in a library to identify a component. This makes it possible to perform different analysis without performing spectrum peak detection.