The present disclosure provides methods for determining adsorption isotherms for complex mixtures. In at least one embodiment, a method for obtaining adsorption isotherms for liquid mixtures includes providing a column comprising an adsorbent. The method includes delivering a composition to the column, the composition comprising a multi-component feed and a solvent. The method includes collecting a sample from the column and introducing the sample to a two dimensional gas chromatograph to determine a time-series concentration of one or more components of the sample. The method includes integrating the time-series concentration of at least one of the one or more components to determine an isotherm of the at least one component. The method includes obtaining quantitative information of the at least one component, based on the isotherm of the at least one component.

A waveform information inference device according to one mode of the present invention includes: a waveform extraction unit (31) configured to extract a partial waveform to be modeled from a signal waveform acquired based on actual measurement using a predetermined analysis device; and an adversarial learning unit (32) configured to acquire a model function corresponding to the partial waveform, or the model function and shape distribution information in the function by performing adversarial learning using two mutually adversarial models which are a generation model and a discriminative model using the partial waveform obtained by the waveform extraction unit as an input. The present invention can acquire a precision peak model function and its shape parameter distribution information.

An analysis assistance device includes an estimator that estimates a distribution of measurement quality indicator data using a regression analysis by using a plurality of analysis condition data pieces provided to an analysis device and a plurality of measurement data pieces obtained by the analysis device based on the plurality of analysis condition data pieces, an analysis assistance information displayer that displays the distribution on a display, and a saver that saves an analysis condition data piece of a point of interest designated in the distribution in a storage device in response to a saving operation.

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.

Disclosed is a method for predicting an elution order of compounds in a mixture. The method includes (a) building a quantitative structure-retention relationship (QSRR) model and (b) predicting a chromatographic elution order of the compounds in the mixture on the basis of the QSRR model using mathematical programming. The mathematical programming is a non-linear programming technique in which a predicted elution order of the compounds is used as a constraint or a multi-objective optimization (MOO) in which a retention time prediction error and an elution order prediction error are used as objective functions. With the use of the method of the present disclosure, it is possible to optimize separation of complex mixtures in reversed-phase chromatography by enabling identification of accurate positions of individual compounds that provides higher certainty in identifying a given compound, e.g., during an “omics” analysis (proteomics, metabolomics, etc.).

In a method for facilitating selection of chromatography parameters for manufacturing a therapeutic protein, one or more process parameter values associated with a hypothetical chromatography process, and one or more molecular descriptors descriptive of the therapeutic protein, are received. The method also includes predicting a performance indicator for the hypothetical chromatography process at least by analyzing the one or more process parameters and the one or more molecular descriptors using a machine learning model. The machine learning model is a regression tree model, an extreme gradient boost model, or an elastic net model. The method also includes causing the predicted performance indicator, and/or an indication of whether the predicted performance indicator satisfies one or more acceptability criteria, to be presented to a user via a user interface.

A method for determining optimal values of a mass spectral operating parameter for mass spectral analysis of each of a plurality of compounds comprises: acquiring a plurality of mass spectral measurements of each of at least one characteristic ion species of each respective compound during its introduction into a mass spectrometer while a quantity of each introduced compound varies with time wherein, for each characteristic ion species, the operational parameter is caused to vary between successive mass spectral measurements of the said species; calculating, for each characteristic ion species, a corrected intensity of at least a portion of the plurality of mass spectral measurements of said each species, based on a best-fit synthetic model curve that relates to the time variation of the respective corresponding compound; and determining the optimal values of the operating parameter from analyses of variation of the corrected intensities with respect to the operational parameter variation.

A system for analyzing a gas mixture, including at least one chromatography column, a mechanism injecting the mixture into the column, and a mechanism detecting compound(s) forming the gas mixture, the detection mechanism including at least one detector of nanosensor type of an outlet of the column and a detector of nanosensor type in the column, capable of detecting passage of the compounds. It is then possible to determine the velocity of each of the compounds within the system.

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 chromatographic data system processing apparatus includes a liquid feeder, a sample injector, a column that separates samples, a detector, a controller that processes a detected result of the detector, and a data processor that examines and sets operations of the liquid feeder, the column and the detector, and a measurement condition. The data processor generates a three-dimensional graph having three axes related to a pressure, a time, and a number of theoretical plates based on data or variables indicating a relationship between the number of theoretical plates and a flow rate, and data or variables indicating a relationship between the pressure and the flow rate. The chromatographic data system processing apparatus can easily obtain a separation condition for obtaining performance from a three-dimensional graph including a pressure drop, a hold-up time and a number of theoretical plates.