The present disclosure describes a computer implemented method, a system, and a computer program product of controlling the purification of a macromolecule solution via real-time multi-angle light scattering.

A cloud server platform end is used to construct a mass spectrum species classification model, extract a mass spectrum data feature, and construct a training model of the convolutional neural network; a user platform end is used to upload the mass spectrum, experiment condition and device data, directly screen and identify the type of the mass spectrum based on the mass spectrum species classification model or the mass spectrum information base, automatically compare and identify the species and name of the pesticides based on the neural network model trained by the cloud server platform end, and feedback the comparison result to the user. The disclosure solves the restriction on the purchase of standards for user, the use of the system is not limited by the location, and the pesticide residues could be detected automatically, quickly and accurately.

An exemplary chromatographic alignment system accesses a target file including data representative of a plurality of chromatographic features detected from a first sample and a reference file including data representative of a plurality of chromatographic features detected from a second sample. The system identifies, based on the target and reference files, a distinct retention time offset value for each chromatographic feature included in a first subset of the plurality of chromatographic features detected from the first sample. The system determines, based on the identified distinct retention time offset values for the chromatographic features included in the first subset and on a machine learning model, a distinct predicted retention time offset value for each chromatographic feature included in a second subset of the plurality of chromatographic features detected from the first sample. The system assigns the distinct predicted retention time offset value for each chromatographic feature included in the second subset.

A method of converting longer path cell signal data to shorter path cell signal data comprising: obtaining a longer path absorbance signal tracing and a shorter path absorbance signal tracing for at least one analyte band under the same conditions; obtaining an approximate superimposable match between the longer path absorbance signal tracing and the shorter path absorbance signal tracing using an amplitude scaling factor and one or more parameters derived from a dispersion model that accounts for dispersion differences between a short cell and a long cell; and applying the dispersion model in reverse using the derived parameters to future longer path absorbance signal traces from the longer path cell signal data to generate the shorter path cell signal data.

A cloud server platform end is used to construct a mass spectrum species classification model, extract a mass spectrum data feature, and construct a training model of the convolutional neural network; a user platform end is used to upload the mass spectrum, experiment condition and device data, directly screen and identify the type of the mass spectrum based on the mass spectrum species classification model or the mass spectrum information base, automatically compare and identify the species and name of the pesticides based on the neural network model trained by the cloud server platform end, and feedback the comparison result to the user. The disclosure solves the restriction on the purchase of standards for user, the use of the system is not limited by the location, and the pesticide residues could be detected automatically, quickly and accurately.

An object is to accurately detect peaks of various compositions, even in a case of unseparated peaks in which peaks of a plurality of compositions are superimposed. A computer acquires waveform data D1 having a peak P1 in a composition A measured by a data analysis device (S10). Next, the computer acquires waveform data D2 having a peak P2 in a composition B measured by the data analysis device (S20). Next, waveform data D12 including unseparated peaks by superimposing the waveform data D1 including the acquired peak P1 and the waveform data D2 including the acquired peak P2 (S30) is generated. Next, the generated waveform data D12 of the unseparated peaks is input as learning data, and the waveform data D1 and D2 corresponding to the waveform data D12 are input as training data in Step S40. Next, machine learning is performed using the waveform data D12, D1, and D2, and a learned model for estimating an accurate separation method of unseparated peaks is constructed based on the trained result (S50).

A chromatographic data system processing apparatus performs data processing based on plot data measured by a chromatograph. The chromatographic data system processing apparatus includes a virtual curve calculation portion which obtains a virtual curve based on the measured plot data, a tentative feature point acquisition portion which obtains a tentative feature point based on the obtained virtual curve, and an actual plot data feature point extraction portion which extracts an actual plot data feature point corresponding to the tentative feature point from the measured plot data.

A data analysis method for a component separation/tandem mass spectrometer system, including first and second MS steps which data therefrom includes respective sample components (MS1-SC, MS2-SC), includes analyzing per sample a data set for MS2-SC to determine mass-to-charge ratio and retention index (m/z-RI) for each MS2-SC. m/z-RI for each MS2-SC is compared to a known compound library and matching MS2-SC removed from the data set, the remaining MS2-SC being candidate MS2-SC. Clusters are formed across the candidate MS2-SC, each having m/z-RI within respective ranges per cluster. For each sample within each cluster, MS1-SC within the cluster ranges are retrieved. For each cluster, at most one consensus MS1-SC represents each sample, with corresponding consensus MS2-SC and m/z-RI, and designated as a molecular ion or derivative thereof. Clusters are grouped by consensus RI and candidate clusters from each group are selected and correlated by consensus parameters with an unknown compound.

Exemplary embodiments provide methods, mediums, and systems for analyzing spectrometry and/or chromatography data, and in particular to techniques to improve the reproducibility of results of spectrographic and/or chromatographic experiments. For example, some embodiments provide techniques for normalizing mass spectrometry (MS) and/or liquid chromatography (LC) data across different experimental devices, allowing data from different cohorts to be directly compared. To this end, exemplary embodiments provide a reliable, reproducible target library usable across different platforms, laboratories, and users. One embodiment leverages statistical techniques to select experimental parameters configured to reduce or minimize the chance of misidentifying a target molecule. Another embodiment leverages the law of large numbers to produce a composite product ion spectrum usable across different experiments. The composite product ion spectrum allows regression curves to be generated, where the regression curves can be used to normalize an experimental mass spectrum.

Methods for transferring a separation procedure from a first chromatographic system to a second one are disclosed that involve substantially matching a pressure profile. In some such methods, a length, an area, and a particle size of a first column in the first system and a flow rate in the first separation procedure are identifiable. Some such methods also involve selecting a combination of a length, an area, and a particle size of a second column in the second system and a flow rate for the second separation procedure. These methods may involve calculating a target length, a target area, or a target particle size for the second column in the second system or a target flow rate for the second separation procedure.