# US 2025/0095976 A1 · Systems and methods of ion population regulation in mass spectrometry

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2025/0095976 A1
- Document ID: US20250095976A1
- Status: Published application
- Kind: A1
- Application number: 18/961,044
- Filed: 2024-11-26
- Published: 2025-03-20
- Earliest claimed: 2022-05-16
- Assignees: Thermo Finnigan LLC
- Applicants: Thermo Finnigan LLC
- Inventors: Philip M. REMES; Jesse D. CANTERBURY
- Web page: https://patentgrep.com/patent/US20250095976A1

## CPC

- G01N 30/8651: {Recording, data aquisition, archiving and storage}
- H01J 49/0036: {Step by step routines describing the handling of the data generated during a measurement}
- G01N 30/7233: {interfaced to liquid or supercritical fluid chromatograph (interfaces in general for introducing or extracting samples to be analysed with specially adapted mass spectrometer, see H01J49/04)}
- G01N 30/8675: {Evaluation, i.e. decoding of the signal into analytical information (for analysis of specific compounds see also G01N30/88 and subgroups of G01N33/00; chemical libraries per se C40B)}
- G01N 30/8693: {Models, e.g. prediction of retention times, method development and validation}
- G06N 3/08: Learning methods
- H01J 49/0031: {Step by step routines describing the use of the apparatus (H01J49/0081 takes precedence)}
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- H01J: ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS (spark-gaps H01T; electric arc lamps H05B31/00; particle accelerators H05H)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A computer-implemented method of training a machine learning model comprises: accessing an elution profile comprising a plurality of detection points representing intensity of ions derived from components eluting from a chromatography column and detected by a mass analyzer as a function of time; generating, based on the elution profile, training data comprising a plurality of training examples, a training example of the plurality of training examples comprising a set of detection points and a target next detection point, the target next detection point comprising a detection point of the plurality of detection points following the set of detection points; and training, using the training data, the machine learning model to determine a predicted next detection point, the predicted next detection point following the set of detection points.

## Claims

### Claim 1

A computer-implemented method of training a machine learning model, the method comprising:
accessing an elution profile comprising a plurality of detection points representing intensity of ions derived from components eluting from a chromatography column and detected by a mass analyzer as a function of time;
generating, based on the elution profile, training data comprising a plurality of training examples, a training example of the plurality of training examples comprising a set of detection points and a target next detection point, the target next detection point comprising a detection point of the plurality of detection points following the set of detection points; and
training, using the training data, the machine learning model to determine a predicted next detection point, the predicted next detection point following the set of detection points.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1, wherein training the machine learning model comprises:
determining, using the machine learning model and based on the set of detection points in the training example, the predicted next detection point of the elution profile;
determining an evaluation value based on the predicted next detection point and the target next detection point in the training example; and
adjusting one or more model parameters of the machine learning model based on the evaluation value.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1, further comprising:
accessing a series of mass spectra of the detected ions; and
obtaining the elution profile based on the series of mass spectra.

### Claim 4 (depends on 3)

The computer-implemented method of claim 3, wherein generating the training data comprises interpolating the plurality of detection points to a uniform time scale.

### Claim 5 (depends on 3)

The computer-implemented method of claim 3, wherein generating the training data comprises normalizing the plurality of detection points relative to a reference intensity value.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1, wherein the machine learning model comprises a Recurrent Neural Network (RNN).

### Claim 7 (depends on 1)

The computer-implemented method of claim 1, wherein the machine learning model comprises a Long Short-Term Memory (LSTM) neural network or a Gated Recurrent Unit (GRU) neural network.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1, wherein the training data comprises a plurality of subsets of training data, each subset of training data based on a distinct set of experiment conditions.

### Claim 9 (depends on 8)

The computer-implemented method of claim 8, wherein training the machine learning model using the training data comprises training the machine learning model on each subset of training data included in the plurality of subsets of training data serially in a plurality of training stages.

### Claim 10 (depends on 8)

The computer-implemented method of claim 8, wherein training the machine learning model using the training data comprises:
mixing the plurality of subsets of training data; and
training the machine learning model on the mixed subsets of training data in a same training stage.

### Claim 11 (depends on 8)

The computer-implemented method of claim 8, wherein each set of experiment conditions specifies at least one of a flow rate of the chromatography column, a gradient of the chromatography column, a list of target analytes included in a sample comprising the components eluting from the chromatography column, a type of chromatography performed, or a type of stationary phase and/or mobile phase of the chromatography column.

### Claim 12

A non-transitory computer-readable medium storing instructions that, when executed, cause a processor of a computing device to:
obtain, based on a series of mass spectra of detected ions derived from components eluting from a chromatography column, an elution profile comprising a plurality of detection points representing intensity of the detected ions as a function of time; and
determine, based on a set of detection points included in the plurality of detection points, a predicted next detection point of the elution profile to be obtained based on a next mass spectrum to be acquired.

### Claim 13 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the instructions, when executed, cause the processor to determine the predicted next detection point by applying the set of detection points to a trained machine learning model configured to determine the predicted next detection point based on the set of detection points.

### Claim 14 (depends on 13)

The non-transitory computer-readable medium of claim 13, wherein the trained machine learning model comprises a Recurrent Neural Network (RNN).

### Claim 15 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the instructions, when executed, further cause the processor to set, based on the predicted next detection point and for an acquisition of the next mass spectrum, an accumulation time for accumulating the ions produced from the components eluting from the chromatography column.

### Claim 16 (depends on 15)

The non-transitory computer-readable medium of claim 15, wherein the instructions, when executed, cause the processor to set the accumulation time by:
estimating, based on the predicted next detection point, an ion flux of the ions produced from the components eluting from the chromatography column during the acquisition of the next mass spectrum; and
determining the accumulation time as a target population of ions divided by the estimated ion flux during the acquisition of the next mass spectrum.

### Claim 17 (depends on 12)

The non-transitory computer-readable medium of claim 12, wherein the elution profile comprises an extracted ion chromatogram for a selected m/z.

### Claim 18 (depends on 17)

The non-transitory computer-readable medium of claim 17, wherein the instructions, when executed, further cause the processor to:
detect, based on the series of mass spectra, precursor ions having the selected m/z;
perform, based on the detected the ions having the selected m/z, the determining the predicted next detection point; and
performing, based on the predicted next detection point, a data-dependent acquisition of product ions produced from the precursor ions.