Method and system for the identification of compounds in complex biological or environmental samples by receiving (102) a mass spectrum (1) from a mass spectrometry coupled with a separation technique; for each data point (2) of the mass spectrum (1), annotating (106) in an annotation database (12) combinations of formulas and adducts the theoretical mass-to-charge ratio of which (m/z).sup.T corresponds to the mass-to-charge ratio (m/z) measured of the data point (2); for each formula and adduct annotated, detecting (108) regions of interest in a retention time range (RT.sub.0-RT.sub.1) according to characterisation criteria; generating (110) an inclusion list (14) with the retention time ranges (RT.sub.0-RT.sub.1) and the theoretical mass-to-charge ratios (m/z).sup.T of the formulas and adducts associated with the regions of interest; and sending (112) the inclusion list to a mass spectrometer for the identification of compounds in the sample by tandem mass spectrometry.

Methods for classification of hydrocarbon mixtures that include performing two-dimensional gas chromatography on a hydrocarbon mixture to obtain a chromatogram using a two-dimensional gas chromatograph equipped with a flame ionization detector, a reversed phase column configuration with a primary mid-polar or polar column and a secondary non-polar column, and a standard mixture. Classification is performed in which groups of hydrocarbons are identified and labeled based on peaks associated with the standard mixture, after which a quantification process is performed.

A waveform analytical device 4 which analyzes a target waveform which is a chromatogram or an optical spectrum includes a waveform division unit 54 configured to divide the target waveform into a plurality of partial waveforms, a determination unit 55 configured to determine whether each of the plurality of partial waveforms of the target waveform is a peak portion using a learned model created by machine learning using a plurality of sets of a plurality of partial waveforms created by dividing a reference waveform having a peak portion whose position is known, and a classification unit 56 configured to classify the target waveform into a peak region where the peak portion continues and a non-peak region other than the peak region based on a determination result from the determination unit.

A method for the identification of unknown compounds based on a novel Retention Index System having a TAGs homologous series, wherein such identification is performed by means of liquid chromatography (LC), or liquid chromatography coupled with mass spectrometry (LC-MS) is disclosed.

A method for analyzing spectra comprises identifying a set of sample peaks in a sample spectrum, where the sample peaks are associated with fragments of a sample, each having a sample fragment mass. A reference spectrum is selected with one or more reference peaks corresponding to fragments of a reference, each having a reference fragment mass. A mass difference can be determined between selected sample and reference peaks, and a group exchange can be selected based on the mass difference; e.g., where the group exchange represents a change in the sample or reference fragment masses associated with the selected peaks. The selected peaks can be shifted by the mass difference, and a fit value can be determined with respect to the reference spectrum. The fit value characterizes similarity between the respective sets of sample and reference peaks, responsive to the group exchange and corresponding peak shift.

Exemplary embodiments provide methods, mediums, and systems for visualization and advanced data science on information collected in an analytical data system. Embodiments identify correlations and patterns in chromatography metadata around areas of potential user error. Correlations between these data sources may point to compliance risk areas. Metadata from the analytical system may be combined with other data sources and/or analytical data to correlate an analytical outcome with compliance artifacts. Supervised and/or unsupervised machine learning techniques may be used to combine these data source and learn correlations between them and compliance risks. The results of these analyses may be displayed on a dashboard, allowing a user to visualize compliance risks across an entire enterprise or supply chain. Automatic notifications of compliance risks may be generated and presented on a user interface. A system may also use pattern recognition to provide insights around potential compliance risks that have not yet occurred.

Disclosed are a tea type differentiation method and system, belonging to the technical field of detection. The method comprises: building a differentiation function by using ionic strengths of 20 compounds as evaluation indexes to discriminate tea types. According to the disclosure, the tea types are discriminated by using relative abundance of 20 compounds in tea, problems in sensory differentiation can be solved, the tea is classified more objectively and scientifically, and the reliability and accuracy of differentiation results are improved. By using three algorithms, the feasibility and accuracy of using 20 discovered compounds for tea type differentiation in a combined manner are validated.

A glycopeptide analyzer that performs a structural analysis on glycoforms of a glycoprotein, including: a spectrum creator creating an MS/MS spectrum for each elution time based on data acquired by an LC/MS analysis of a sample containing glycopeptides originating from a target glycoprotein; a peptide mass calculator selecting a glycopeptide-related spectrum from a plurality of MS/MS spectra and calculating the mass of a peptide from the selected spectrum; a similarity determiner determining a similarity between the glycopeptide-related spectrum and each of the other MS/MS spectra; an elution-time range estimator estimating an elution-time range based on a distribution of the frequency of occurrence of an MS/MS spectrum for which a high level of similarity has been determined on a time axis; and a glycan composition estimator selecting an ion peak corresponding to a mass equal to or greater than a peptide mass and estimating a glycan composition based on the peak.

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.

The operation efficiency and accuracy of the simultaneous analysis of phospholipids, including fatty acid compositions are increased. After a first-time LC/MS/MS analysis for determining the phospholipid classes of the phospholipid contained in a sample is performed (S2-S3), a second-time LC/MS/MS analysis for determining fatty acid compositions is performed only for the detected phospholipids (S4-S8). By associating a method list in which an MRM transition for phospholipid class determination is recorded for each compound of phospholipid classes with a method list in which an MRM transition for fatty acid composition determination is recorded for each phospholipid compound, it is possible to promptly select MRM transitions for fatty acid composition determination that correspond to compounds of the detected phospholipid classes, and to easily create an analysis method for the second-time analysis.