An analyzer configured to acquire a chromatogram or spectrum by performing a predetermined analysis of a sample and perform a qualitative or quantitative analysis of components contained in the sample. The analyzer includes: a peak detection unit configured, based on information regarding a plurality of target components that need to be checked whether contained in the sample or that need to be quantified, to detect a peak or peaks in the chromatogram or spectrum acquired by the predetermined analysis of the sample corresponding to one of the target components, configured to acquire peak information regarding each of the peak or peaks, and configured to obtain confidence information for each of the peak or peaks, the confidence information being an indicative value of certainty of detecting a peak; and a display processing unit configured to display on a display unit a list of at least a part of the target components.

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.

Provided are a method and system for measuring a composition and property of a formation fluid. The method includes: acquiring a measuring model used for measuring a composition and a property of a formation fluid; using a signal measured by a sensor on a downhole hydrocarbon formation tester in real-time as input data and inputting the input data into the measuring model; processing the input data by the measuring model; and directly outputting a processing result as data on the composition and the property of the real-time formation fluid, or parsing the data on the composition and the property of the real-time formation fluid according to the processing result.

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 computer-implemented method for detecting at least one interference and/or at least one artefact in at least one chromatogram determined by at least one mass spectrometry device (110) is proposed. The chromatogram comprises a plurality of raw data points. The method comprises the following steps: a) retrieving the at least one chromatogram by at least one processing device (126); b) applying at least one peak fit modelling to the chromatogram by using the processing device (126); c) determining information about residuals of the raw data points by using the processing device (126); d) detecting the at least one interference and/or the at least one artefact by using the processing device (126) by comparing the determined information about the residuals with at least one pre-determined threshold, wherein, if the determined information about the residuals exceed the pre-determined threshold, the at least one interference and/or the at least one artefact is detected.

Training a neural network processor is described for providing diagnostic information of a controlled liquid chromatography pump unit. The training includes executing a sequence of operations wherein the neural network processor is trained with input signals obtained from a simulated version of the controlled liquid chromatography pump unit and associated sensors, while modifying the simulated version of the liquid chromatography pump unit to a pump fault simulation signal. Dependent on a value of the pump fault simulation signal, the simulated version of the liquid chromatography pump unit simulates operation of the liquid chromatography pump unit free from faults or the operation thereof with one or more pump faults. The trained neural network processor obtained therewith is thereafter integrated with a controlled liquid chromatography pump unit to provide for auto-diagnostic capabilities or used in a separate diagnostic unit for diagnosing one or more controlled liquid chromatography pump units not having auto-diagnostic capabilities.

Various machine learning techniques can detect errors (e.g., leaking valves, column plugging) and other conditions (e.g., system readiness conditions like equilibration and priming) in LC devices. Examples of suitable AI/ML models include Bayesian hierarchical models, gradient boosted trees, and recurrent neural networks. Embodiments have shown expert-level identification of conditions based on a limited amount of signals data from the instrument (about 2 minutes' worth of data).

The present invention relates to an early warning method before the occurrence of aflatoxin contamination. The steps are as follows: extracting toxins from the sample to obtain a sample extract, and subjecting the sample extract to detection and analysis by liquid chromatography-high resolution mass spectrometer, performing qualitative analysis based on the mass spectrometry information to obtain qualitative results, performing quantitative analysis based on a standard curve of the chromatographic peak area of each warning molecule/the peak area of the internal standard-warning molecule concentration to obtain quantitative results of these warning molecules, wherein a risk of aflatoxin contamination of the sample is assessed to obtain a classification prediction model, inputting the quantitative results of the warning molecules for a toxigenic strain of Aspergillus flavus, and outputting a risk assessment result based on the classification prediction model, thereby achieving the early warning before aflatoxin contamination occurs.

A certainty factor of peak picking can be calculated when the peak picking is performed using semantic segmentation technology. An analysis device divides a target waveform into a plurality of partial waveforms, determines a peak waveform that becomes a peak portion among the plurality of divided partial waveforms using a trained model, and calculates the certainty factor of a determination result of the peak waveform using data output from the trained model when the peak portion of the target waveform is determined using the trained model.

An acquirer that acquires, based on measurement data acquired over time using an analysis device, measurement waveform data, the measurement data representing a change in domain direction of the measurement data, and an estimator that acquires estimation waveform data that is what noise data is at least partially removed from the measurement waveform data, are included. The estimator acquires the estimation waveform data as data such that the noise data included in the measurement waveform data has a correlation in the domain direction. Thus, a peak shape can be correctly estimated based on measurement waveform data to which noise is added.