The present invention relates to a method for reducing the complexity of bio-crudes. The method includes (a) obtaining experimental data of quantitative and qualitative analyses for the bio-crudes, (b) grouping compounds contained in the bio-crudes according to a predetermined basis based on the experimental data, (c) selecting representative compounds from among the compounds belonging to the same group, and (d) reconstituting the bio-crudes as a mixture of the representative compounds.

A computer implemented method for identifying at least one peak in a mass spectrometry response curve is provided comprising: a) providing at least one mass spectrometry response curve by using at least one mass spectrometry device; b) evaluating the mass spectrometry response curve by using at least one trained model thereby identifying a start point and an end point of at least one peak of the mass spectrometry response curve, wherein the model was trained using a deep learning regression architecture.

Disclosed is a method for determining an operating flow rate for a chromatographic column (4) in an HPLC system (1). The method comprises: measuring/calculating a pressure of the system (1) without the chromatographic column (4) for one or more flow rates; fitting a function to the flow rate(s) and corresponding pressure(s), calculating from the function and a predetermined recommended flow rate for the chromatographic column (4) a system pressure drop at the predetermined recommended flow rate. An operating flow rate is determined by summing the system pressure drop and a maximal column pressure limit, and determining a contribution of the system pressure drop to the summed pressure. If this contribution exceeds 1% an operating flow rate for the column is determined to a flow rate that corresponds to a pressure at a pressure monitor arranged before the column that is lower than the predetermined maximum column pressure limit.

The present invention discloses a method for realizing multi-column continuous chromatography design and analysis based on a chromatography model, and a method for realizing multi-column continuous chromatography design and analysis based on an artificial neural network. The method based on the chromatography model includes the following steps: step 101, experimental breakthrough curve fitting: performing fitting using a chromatography model to obtain model parameters; step 102: breakthrough curve prediction: substituting the model parameters into the chromatography model to obtain a breakthrough curve under different operation conditions; step 103, process analysis of continuous chromatography: substituting the predicted breakthrough curve and the continuous chromatography operation parameters into a continuous chromatography model to obtain performance indexes such as process productivity and resin capacity utilization; and step 104, operation space optimization of continuous chromatography: obtaining the operation space of the continuous chromatography design parameters based on a specific separation target. The method based on the artificial neutral network completes the respective steps above by replacing the chromatography model with artificial neural network.

An analysis method for analyzing a sample includes a first step of acquiring measurement data including a first signal based on the sample and a second signal based on noise added to the first signal as a result of analysis of the sample, a second step of assuming a shape representing the first signal and a shape representing the second signal and modeling the measurement data using Bayesian inference, and a third step of estimating a probability distribution of characteristics of the sample based on the modeled measurement data.

The present invention relates to a method for determining the logarithmic reduction value LRV of a size-exclusion filter for a particle of a process solution, which particle is to be clarified, the size-exclusion filter being protected from a blocking adsorbing species present in the process solution by a process adsorber which is connected upstream in series.

A program for assisting in determining a combination of parameter values of analysis conditions suitable for an intended analysis, makes a computer operate as: a graph-displaying section which displays, on a screen, a graph created based on data collected with a chromatograph device, to show a relationship between the parameter values and an analysis result obtained with the chromatograph device or a result of analytical processing based on the analysis result; an input-receiving section which receives a user's input of a constraint condition which specifies the upper/lower limit or range of a numerical value representing the analysis result or analytical-processing result; a search-conducting section which conducts a search for a combination of the parameter values which yields the analysis result or analytical-processing result in which the numerical value satisfies a predetermined search condition under the constraint condition; and a search-result-displaying section which displays a search result on the graph.

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.

Examples are directed toward collecting, by a LC-MS device, a full scan of ion chromatograms of a sample. The LC-MS device determines observed ions contained in the full scan, based on mass-to-charge ratios (m/z), and determines, for a formation curve of an observed ion, a formation point at which fifty percent of the observed ion has formed. The LC-MS device determines a fragmentation curve of a precursor ion, based on a fragmentation point of the fragmentation curve equivalent to the formation point at which fifty percent of the precursor ion has fragmented, and identifies the precursor ion by referencing the LC-MS library to confirm that the observed ion is a product of the fragmentation of the precursor ion. The LC-MS device indicates a goodness of fit between the fragmentation curve, as observed, and a model fragmentation curve, as stored in the LC-MS library.

Systems and methods are disclosed for identifying actual XIC peaks of compounds of interest from samples so that more accurate expected retention times and more accurate expected retention time windows can be calculated. In one system, an actual XIC peak is identified using standard samples. The ratio of the quantity of the compound of interest in any two different samples is known, so this ratios is compared to the intensities of the XIC peak calculated in the two samples to identify an actual XIC peak. In another system, an actual XIC peak is identified using information about other compounds of interest in a plurality of samples. It is known that the XIC peaks of compounds of interest in the same samples have a similar distribution of retention times across those samples, so the distributions of retention times of XIC peaks are compared to identify actual XIC peaks.