A method for anomaly detection and diagnosis in a chromatography system including a sample handling unit, a chromatographic separation unit, and a detection unit is disclosed. The method can include performing, with the chromatography system, a chromatography analysis of a sample to obtain a chromatogram of the sample, the sample including a known quantity of a reference standard. The method can also include determining peak information corresponding to the reference standard in the chromatogram and determining whether the peak information conforms with an expected response of the chromatography system associated with the reference standard. If the peak information does not conform with the expected response, the method can include determining that there is an anomaly in the operation of the chromatography system and diagnosing a cause of the anomaly as relating to the operation of at least one the sample handling unit, the chromatographic separation unit, and the detection unit.

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.

In a quantitative determination device 10 for brominated flame-retardant compounds, a storage section 41 holds a relative response factor 411 representing a relationship of a measured intensity of a compared compound to that of a reference compound selected from target compounds. A standard-sample measurer 43 acquires the intensity of the reference compound by measuring a standard sample, using an analyzer 10, 20. A target-sample measurer 45 acquires the intensities of the reference and compared compounds by measuring a target sample, using the analyzer. A reference-compound quantity determiner 46 determines a quantitative value of the reference compound in the target sample. A compared-compound quantity determiner 47 determines a quantitative value of the compared compound based on the quantity of the reference compound in the standard sample, intensity of the reference compound acquired by the standard-sample measurer, intensity of the compared compound acquired by the target-sample measurer, and relative response factor of the compared compound.

A method for monitoring an analyzer including a liquid chromatography device (LC) having at least two liquid chromatography (LC) streams, the method including continuously monitoring one or more parameters in measurement data of samples in each of the at least two LC streams, the one or more parameters being independent of an analyte concentration of the respective sample, determining if the one or more monitored parameters show an expected behavior and triggering a response upon detection that the one or more monitored parameters deviate from the expected behavior.

When chromatogram data for a target sample have been acquired, a peak position estimator determines an estimated result of the position of the starting and/or ending point of a peak as well as the confidence value representing the reliability of the estimation, using a trained model stored in the trained model storage section. Normally, a plurality of estimated results of the starting point and/or ending point of the peak are acquired for one peak. A peak information correction processor identifies a candidate having the highest confidence as a prime candidate, and superposes a plurality of candidates including the prime candidate, with their respective confidence values, on a displayed chromatogram. An operator referring to the confidence values selects a peak which needs close checking or correction, and corrects the starting point and/or ending point of the selected peak, for example, by selecting and indicating a candidate other than the prime candidate.

Disclosed is a method 100 for classifying a fluid sample. The method comprises the steps of: a. at least partially separating 110 one or more of the chemical constituents of the fluid sample; b. measuring and recording 120 the amount of separated chemical constituents of the sample during, or after, the chemical separation; c. measuring and recording 130 the spatial or time separation profile of sample constituents, during or after separation, and providing a data set of the same; d. comparing 140 the amount of said separated constituents to one or more reference samples; e. comparing 150 the spatial or time separation profile to the corresponding profile of the or each reference sample; f. assigning 160 a similarity score to the sample based on the similarity of the amount or the profile comparisons of the separated constituents, as performed under steps d 140 and e 150 above, or both, with the equivalent amount and/or profile of the or each reference sample respectively; g. providing 170 a classification of the sample based on the similarity score.

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 peak tracking device includes a chromatogram acquirer that acquires chromatograms based on measurement data pieces obtained from measurements by an analyzer in accordance with analysis condition data pieces, a score calculator that calculates score data based on a belonging probability, the probability being calculated for each peak appeared in each chromatogram to belong to one of substances included in a sample, and a score displayer that displays on a display the score data calculated by the score calculator.

A scan of a separating sample is received by a mass spectrometer at each interval of a plurality of intervals. The spectrometer performs at each interval one or more mass spectrometry scans. The scans have one or more sequential mass window widths in order to span an entire mass range at each interval and produce a collection of spectra for the entire mass range for the plurality of intervals. One or more peaks at one or more different intervals in the collection of spectra are identified for a fragment ion. A mass spectrum of the entire mass range is retrieved for each interval of each peak. Values for one or more ion characteristics of a mass-to-charge ratio peak in the mass spectrum corresponding to each peak are compared to one or more known values for the fragment ion. Each peak is scored based on the comparison.

A method for operating a data processing system to find peaks in a mass spectrum that includes an ordered set of measurements of the abundances of species as a function of the mass/charge ratio of the species is disclosed. The method includes selecting a candidate blob that has a plurality of blob peaks from the mass spectrum. The data processing system selects a candidate blob peak for characterization. The candidate blob peak is approximated by a first species peak using a species peak model having a plurality of parameters by fitting the species peak model to a portion of the blob that has values that are substantially free of contributions from other species peaks that overlap with the first species peak and that are not represented by the species peak model. The first species peak is then subtracted from the candidate blob.