Method and system for analyzing and detecting components of amino acid polypeptide protein powder

Abstract

The invention discloses an amino acid polypeptide protein powder component analysis and detection method and system, and belongs to the field of component analysis. The method comprises the following steps: carrying out HPLC (High Performance Liquid Chromatography) separation on an amino acid polypeptide protein powder sample to obtain a separated sample solution, a sample chromatogram and separation time; determining initial sample components and component concentrations of the initial sample components; calculating the initial sample complexity of the amino acid polypeptide protein powder sample; performing mass spectrometry on the sample solution to obtain a mass spectrum of the sample solution, and determining detection sensitivity and background noise; and determining the analysis efficiency of mass spectrometry, and if the analysis efficiency exceeds a threshold value, determining the final sample components of the amino acid polypeptide protein powder sample according to the mass spectrum and a preset second standard curve. According to the scheme, high accuracy of component analysis is ensured, and errors caused by a single detection means are avoided. The automation degree is high, the objectivity and consistency of analysis results are ensured, and the analysis efficiency and reliable quantitative and qualitative results are improved.

Description

Technical Field

[0001] The present application relates to the technical field of component analysis, and in particular to a method and system for analyzing and detecting the components of amino acid polypeptide protein powder. Background Art

[0002] In the food, health care products, medicine and other industries, amino acid peptide protein powder is an important raw material or finished product, and its quality control is directly related to the efficacy and safety of the product. Through component analysis and testing, the content of various amino acids and peptides in the product can be accurately determined to ensure that the product meets the requirements of relevant standards and achieve quality control and standardized production.

[0003] Nowadays, amino acid peptide protein powder samples are manually weighed and pre-treated to remove impurities and prepare a suitable analytical environment. Subsequently, chemical or biochemical methods such as hydrolysis and derivatization are used to decompose the protein powder into analyzable amino acids or peptide fragments. Next, the components in the sample are separated and detected through analytical techniques such as chromatography, electrophoresis or spectroscopy. Finally, based on the test results, data analysis is performed in combination with professional knowledge to determine the content and proportion of each component in the sample.

[0004] However, the manual analysis process involves multiple steps, including sample preparation, pretreatment, separation, detection and data analysis, which usually take a long time to complete, resulting in a long overall analysis cycle and low efficiency. It is also easily affected by various factors, such as the operator's skill level, slight changes in experimental conditions, and the complexity of the sample itself, which can increase the error of the analysis results. Summary of the invention

[0005] The embodiments of the present application provide an amino acid polypeptide protein powder component analysis and detection method and system, which solves the problem that the process of manual analysis of the amino acid polypeptide protein powder component in the prior art takes a long time to complete, resulting in a long overall analysis cycle and low efficiency; and is easily affected by various factors, such as the operator's skill level, slight changes in experimental conditions, the complexity of the sample itself, etc., which will lead to increased errors in the analysis results.

[0006] In a first aspect, the present application provides a method for analyzing and detecting the components of amino acid polypeptide protein powder, the method comprising: Obtaining an amino acid polypeptide protein powder sample, performing HPLC separation on the amino acid polypeptide protein powder sample according to a preset HPLC separation parameter initial value, and obtaining a separated sample solution, a sample chromatogram, and a separation time; Determine the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve; Determining the number of initial sample types according to the initial sample components, and calculating the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the component concentrations of each initial sample component, the number of initial sample types, and a preset sample complexity calculation formula; Performing mass spectrometry analysis on the sample solution to obtain a mass spectrum of the sample solution, and determining the detection sensitivity and background noise; The analysis efficiency of the mass spectrometry analysis is obtained according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula; If the analysis efficiency exceeds a preset analysis efficiency threshold, the final sample composition of the amino acid polypeptide protein powder sample is determined according to the mass spectrum and a preset second standard curve.

[0007] Further, after obtaining the analysis efficiency of the mass spectrometry analysis, the method further comprises: If the analysis efficiency does not exceed a preset analysis efficiency threshold, the preset HPLC separation parameter initial value, initial sample complexity, detection sensitivity and background noise are input into a preset HPLC separation parameter adjustment model to determine an HPLC separation parameter adjustment value; Re-obtain the amino acid polypeptide protein powder sample, re-perform HPLC separation and mass spectrometry analysis on the amino acid polypeptide protein powder sample according to the HPLC separation parameter adjustment value and the preset HPLC separation parameter initial value, and calculate the analysis efficiency of the mass spectrometry analysis; if the analysis efficiency still does not exceed the preset analysis efficiency threshold, re-determine the HPLC separation parameter adjustment value until the analysis efficiency exceeds the preset analysis efficiency threshold.

[0008] Furthermore, the preset sample complexity calculation formula is: ; Among them, S is the complexity of the initial sample; is the number of initial sample types; is the component concentration of the i-th initial sample component; is a similarity matrix, which represents the similarity value between the i-th component and the j-th component.

[0009] Furthermore, the process of obtaining the similarity matrix includes: Determine the feature set of each initial sample component, and calculate the similarity matrix based on the feature set and a preset similarity matrix calculation formula; wherein the preset similarity matrix calculation formula is: ; in, is the feature set of the i-th initial sample component; is the feature set of the jth initial sample component.

[0010] Furthermore, the preset analysis efficiency calculation formula is: ; E is the analysis efficiency; S is the initial sample complexity; T is the separation time; D is the detection sensitivity; C is the background noise.

[0011] Furthermore, the detection sensitivity and background noise are determined, including: Determine a baseline region according to the mass spectrum, obtain a signal intensity value of each signal intensity in the baseline region, and determine the number of signal intensity values; Calculating background noise according to the signal strength value, the number of signal strength values ​​and a preset background noise calculation formula; The detection sensitivity is determined according to the background noise, a preset second standard curve and a preset standard deviation.

[0012] Furthermore, the preset background noise calculation formula is: ; Among them, SD is the background noise; is the number of signal strength values; is the signal strength value of each signal strength; is the average signal strength.

[0013] Furthermore, the training process of the preset HPLC separation parameter adjustment model includes: Obtaining historical adjustment records, determining historical sample complexity, historical HPLC separation parameter initial values, historical detection sensitivity, and historical background noise when performing HPLC separation on historical amino acid polypeptide protein powder samples according to the historical adjustment records, and creating a first data set according to the historical sample complexity, historical separation parameter initial values, historical detection sensitivity, and historical background noise; Determining a historical HPLC separation parameter adjustment value according to the historical adjustment record, and marking the HPLC separation parameter adjustment value label of the first data set according to the historical HPLC separation parameter adjustment value; A HPLC separation parameter adjustment model is constructed, and the HPLC separation parameter adjustment model is trained according to the first data set and the HPLC separation parameter adjustment value label until the HPLC separation parameter adjustment model reaches a preset training standard.

[0014] Further, after determining the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and the preset second standard, the method further comprises: The sample component information is determined according to the final sample components and the amino acid polypeptide protein powder sample, and the sample component information is transmitted to a control center.

[0015] According to the second aspect of the present application, there is provided an amino acid polypeptide protein powder component analysis and detection system, the system comprising: A sample acquisition module is used to acquire an amino acid polypeptide protein powder sample, perform HPLC separation on the amino acid polypeptide protein powder sample according to a preset HPLC separation parameter initial value, and obtain a separated sample solution, a sample chromatogram, and a separation time; An initial sample analysis module, used to determine the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve; An initial sample complexity calculation module, used to determine the number of initial sample types according to the initial sample components, and calculate the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the number of initial sample types and a preset sample complexity calculation formula; A mass spectrometry analysis module, used to perform mass spectrometry analysis on the sample solution, obtain a mass spectrum of the sample solution, and determine the detection sensitivity and background noise; An analysis efficiency calculation module, used to obtain the analysis efficiency of mass spectrometry analysis according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula; The final sample composition determination module cancels the determination of the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and the preset second standard if the analysis efficiency exceeds a preset analysis efficiency threshold.

[0016] In an embodiment of the present application, an amino acid polypeptide protein powder sample is obtained, and HPLC separation is performed on the amino acid polypeptide protein powder sample according to preset initial values ​​of HPLC separation parameters to obtain a separated sample solution, a sample chromatogram, and a separation time; the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component are determined according to the sample chromatogram and a preset first standard curve; the number of initial sample types is determined according to the initial sample components, and the initial sample complexity of the amino acid polypeptide protein powder sample is calculated according to the initial sample components, the component concentrations of each initial sample component, the number of initial sample types, and a preset sample complexity calculation formula; mass spectrometry analysis is performed on the sample solution to obtain a mass spectrum of the sample solution, and the detection sensitivity and background noise are determined; the analysis efficiency of the mass spectrometry analysis is obtained according to the initial sample complexity, separation time, detection sensitivity, background noise, and a preset analysis efficiency calculation formula; if the analysis efficiency exceeds a preset analysis efficiency threshold, the final sample components of the amino acid polypeptide protein powder sample are determined according to the mass spectrum and a preset second standard curve. Through the above-mentioned amino acid polypeptide protein powder component analysis and detection method, multi-step separation and analysis ensures high accuracy of component analysis of amino acid polypeptide protein powder, avoiding the errors that may be caused by a single detection method. The high degree of automation reduces human subjective judgment, ensures the objectivity and consistency of the analysis results, improves the analysis efficiency, and provides reliable quantitative and qualitative results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the amino acid polypeptide protein powder component analysis and detection method provided in Example 1 of the present application; Figure 2 It is a schematic diagram of the process of the amino acid polypeptide protein powder component analysis and detection method provided in Example 2 of the present application; Figure 3 It is a schematic diagram of the process of the amino acid polypeptide protein powder component analysis and detection method provided in Example 3 of the present application; Figure 4 It is a structural schematic diagram of the amino acid polypeptide protein powder component analysis and detection system provided in Example 4 of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only part of the present application is shown in the accompanying drawings, but not all of the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0020] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0021] The amino acid polypeptide protein powder component analysis and detection method provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0022] Embodiment 1

[0023] Figure 1 Schematic diagram of the process of analyzing and detecting the amino acid polypeptide protein powder components provided in Example 1 of the present application. Figure 1 As shown, the specific steps include: S101, obtaining an amino acid polypeptide protein powder sample, performing HPLC separation on the amino acid polypeptide protein powder sample according to preset HPLC separation parameter initial values, and obtaining a separated sample solution, a sample chromatogram, and a separation time.

[0024] First, the application scenario of this solution can be to analyze the amino acid polypeptide protein powder sample and determine the composition of the amino acid polypeptide protein powder sample.

[0025] Based on the above usage scenarios, it can be understood that the executor of the present application can be an amino acid polypeptide protein powder component analysis and detection system, and no excessive limitations are made here.

[0026] Amino acid polypeptide protein powder can refer to a sample composed of amino acids, polypeptides, or small molecule proteins. It can be an extract from food, medicine or biological preparations, and is used to detect its components.

[0027] The preset initial values ​​of HPLC separation parameters can be the separation parameters of the HPLC system set according to the properties of the sample and the target component. Specifically, it can include the type of chromatographic column: for example, reverse phase column (C18), ion exchange column, etc., selected according to the polarity, molecular weight and other properties of the sample. Flow rate initial value: refers to the mobile phase flow rate set at the beginning of the analysis, such as 0.5mL / min. This value is the default flow rate of the HPLC system without adjustment. Temperature initial value: refers to the initial temperature of the chromatographic column, such as 25°C. This value is the default temperature without adjustment, which affects the selectivity and efficiency of the separation. Mobile phase composition initial value: includes the initial ratio of aqueous phase and organic phase, such as 70% aqueous phase and 30% organic phase (such as acetonitrile or methanol). This is the mobile phase composition without adjustment. pH value initial value: refers to the initial pH value of the mobile phase, such as 3.0 or 7.0. This value is the default pH without adjustment, which is used to affect the ionization state of the analyte.

[0028] The sample solution can be a separated solution obtained when the sample is separated through a chromatographic column along with a mobile phase during HPLC separation.

[0029] The sample chromatogram can be the retention time and corresponding signal intensity (peak area / height) of each component detected by an HPLC detector (such as UV, DAD or mass spectrometer). Each chromatographic peak corresponds to a component in the sample, and the position (retention time) and size (signal intensity) of the peak indicate the content and nature of the component.

[0030] The separation time can be the time it takes for each component in the sample to pass through the chromatographic column. The total separation time is usually from several minutes to tens of minutes.

[0031] You can weigh an appropriate amount of amino acid, peptide and protein powder (usually a few milligrams according to requirements) and dissolve it with an appropriate solvent (such as water or buffer). Then select the chromatographic column according to the properties of the sample. Usually, a C18 reverse phase column is selected for peptide separation. Set the mobile phase (for example: water + 0.1% trifluoroacetic acid as phase A, acetonitrile + 0.1% trifluoroacetic acid as phase B). Set the initial flow rate (for example, 1mL / min), the initial gradient program (such as 5% B to 95% B, for 30 minutes). Set the column temperature (for example, 40°C) and detector parameters (such as UV detection wavelength of 210 nm). Then inject the prepared sample solution into the HPLC system. The amino acids, peptides, and proteins in the sample will be separated with the mobile phase through the chromatographic column based on their interaction with the chromatographic column and molecular characteristics. During the separation process, the HPLC detector will generate a chromatogram showing the retention time and peak value of each component. Record the chromatogram of the sample, including the retention time, peak area and peak height of each component, and calculate the separation time of each component.

[0032] S102, determining the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve.

[0033] The preset first standard curve can be a curve of the relationship between peak area / height and sample concentration drawn by performing HPLC analysis on a series of standard samples with known concentrations, and the curve is used to determine the concentration of each component in the unknown sample.

[0034] Initial sample components refer to the specific substances represented by the individual peaks separated from the HPLC chromatogram. These components may be amino acids, peptides, or proteins, depending on the sample being analyzed.

[0035] Component concentration can refer to the content of a specific chemical component in a sample, usually expressed as mass-to-volume ratio (such as mg / mL) or molar concentration (such as μmol / L).

[0036] A standard solution of known composition and known concentration (such as a standard sample of amino acid, peptide or protein of known concentration) can be prepared. Standards of different concentrations are injected into the HPLC system, the chromatogram of each standard is recorded, and the peak area or peak height of each standard is measured. The first standard curve is drawn using the horizontal axis as the concentration and the vertical axis as the peak area or peak height. Usually, there is a linear relationship between the peak area or peak height and the sample concentration. Then, based on the retention time of the chromatographic peak in the sample chromatogram (the separation time of each component in the chromatographic column), the initial sample components corresponding to each peak are determined by comparing the retention time of the known standard sample. Calculate the area or height of each chromatographic peak in the sample chromatogram. Specifically, these values ​​can be automatically calculated by the HPLC system software. Substitute the measured peak area or height into the preset first standard curve to determine the component concentration of each component.

[0037] S103, determining the number of initial sample types according to the initial sample components, and calculating the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the component concentrations of the initial sample components, the number of initial sample types and a preset sample complexity calculation formula.

[0038] The number of initial sample types can be the number of each chemical component in the sample determined by HPLC and mass spectrometry. In the amino acid polypeptide protein powder sample, the initial sample components can include multiple amino acids, polypeptides and proteins, and the number of initial sample types represents the total number of these components.

[0039] The preset sample complexity calculation formula can be used to quantify the complexity of different components in the sample. This formula combines the concentration of each component in the sample and the similarity between the components, comprehensively reflecting the diversity and structural complexity of the sample.

[0040] The complexity of the initial sample can be a quantitative indicator used to measure the number of different components in the sample, the similarity and correlation between the components. The higher the complexity, the more types of components in the sample, and the higher the correlation or structural complexity between the components.

[0041] The number of components in the sample can be preliminarily determined through the sample chromatogram, and each chromatographic peak represents a potential chemical component. Then, the required parameters of the preset sample complexity calculation formula are calculated based on the initial sample components, the component concentrations of each initial sample component, and the number of initial sample types to determine the initial sample complexity.

[0042] On the basis of the above technical solution, an optional, preset sample complexity calculation formula is: ; Among them, S is the complexity of the initial sample; is the number of initial sample types; is the component concentration of the i-th initial sample component; is a similarity matrix, which represents the similarity value between the i-th component and the j-th component.

[0043] In this scheme, the component corresponding to each peak and its concentration can be determined by comparing the peak position (retention time) in the sample chromatogram with the standard curve. , and then select appropriate molecular descriptors based on the properties of the components, such as molecular weight, polarity, hydrophobicity, etc. Use the Tanimoto coefficient or other similarity calculation methods to calculate the similarity matrix between each pair of components ,Will , as well as Substitute into the formula to calculate the sample complexity.

[0044] Based on the above technical solution, optionally, the process of obtaining the similarity matrix includes: Determine the feature set of each initial sample component, and calculate the similarity matrix based on the feature set and a preset similarity matrix calculation formula; wherein the preset similarity matrix calculation formula is: ; in, is the feature set of the i-th initial sample component; is the feature set of the jth initial sample component.

[0045] In this scenario, a feature set can be a variety of properties or characteristics used to describe the components of a sample. These features are usually quantifiable descriptors that can be used to compare the similarities between different components. For amino acids, peptides, and proteins, feature sets can include the following attributes: Molecular Weight: The molecular weight of the component, usually in Daltons (Da). Polarity: The polarity of the component in different solvents. Hydrophobicity: A property that describes the ability of a component to dissolve in water, usually expressed by a hydrophobicity index or hydrophobicity score. Amino Acid Sequence: For peptides and proteins, the specific order of amino acids. Conformational Features: For example, the presence of secondary structure (α-helix, β-sheet, etc.). Charge State: The charge state of a component at a specific pH value.

[0046] According to the research objectives and the properties of the components, appropriate molecular descriptors can be selected as feature sets. Molecular features can be obtained using computational chemistry software or databases (such as PubChem, ChEMBL, etc.). These features are collected for each component (such as amino acids, peptides) and organized into feature vectors or feature matrices. For example, for the component , the feature set can be expressed as: ; in represents molecular weight, and so on for other features. Then the similarity matrix is ​​calculated using the formula: is a feature set and The intersection of , that is, the number of features shared by the two. | | is the feature set and The union of is the number of all unique features of the two. For example, there are two sample components A and B, whose feature sets are as follows: The feature set of component A ,

[0047] The feature set of component B , intersection |A∩B|: common features: molecular weight, polarity (assuming they have the same feature types) |A∩B|=2 (assuming only molecular weight and polarity are the same) Union |A∪B|: All characteristics: molecular weight, polarity, hydrophobicity, charge state |A∪B|=4, according to the similarity matrix formula: ; S104, performing mass spectrometry analysis on the sample solution to obtain a mass spectrum of the sample solution, and determining detection sensitivity and background noise.

[0048] A mass spectrum can be a graphical result generated by a mass spectrometer after analyzing a sample, which is used to display the mass-to-charge ratio (m / z) of each chemical component in the sample and the corresponding signal intensity (usually peak height or peak area). The horizontal axis of the mass spectrum is the mass-to-charge ratio (m / z), and the vertical axis is the corresponding ion signal intensity. Among them, the mass-to-charge ratio represents the ratio of the mass of the ion detected by the mass spectrometer to its charge. The signal intensity represents the relative abundance or concentration of each ion. The stronger the signal, the higher the content of the ion in the sample. In the mass spectrum, each peak corresponds to an ion or chemical component, its position is determined by the mass-to-charge ratio, and the peak height or peak area reflects the abundance of the component.

[0049] Detection sensitivity can be the ability of a mass spectrometer to detect the lowest concentration of a sample component. Generally, the higher the sensitivity, the lower the concentration of the sample component that the instrument can detect. Sensitivity is often determined by the minimum detectable concentration in the standard curve, which means the lowest concentration at which the detected signal intensity is still significantly higher than the background noise.

[0050] Background noise can be a low-intensity signal or random noise generated by the mass spectrometer when there is no sample component or only a very low concentration of sample. Background noise reflects the baseline level of the instrument and is an invalid signal in the mass spectrum. Its intensity is usually calculated by the signal change in the baseline area and is expressed as the standard deviation of the signal. Background noise has an important impact on detection sensitivity. The higher the noise, the lower the sensitivity.

[0051] The sample solution can be injected into the mass spectrometer through an injection device (such as liquid chromatography-mass spectrometer, HPLC-MS). The sample generates ions through ionization (such as electrospray ionization ESI or matrix-assisted laser desorption ionization MALDI and other technologies), and the ions enter the mass spectrometer and are separated according to their mass-to-charge ratio. The instrument detects ion signals of different mass-to-charge ratios and generates corresponding mass spectra. The mass spectrum shows the ion signal intensity corresponding to each mass-to-charge ratio. Then, a series of standard samples with known concentrations are analyzed by mass spectrometry, and a curve of concentration and signal intensity (peak area or peak height) is plotted. This curve can be used to determine the concentration of components in unknown samples. Find the minimum concentration point in the standard curve where the signal intensity is significantly higher than the background noise, and determine this concentration as the minimum detectable concentration, which is the detection sensitivity. This concentration is the lowest concentration of the component that the mass spectrometer can reliably detect. Select a baseline area in the mass spectrum without a signal peak. This area does not contain the signal of the sample components, but mainly the noise generated by the instrument. The background noise is calculated by the standard deviation of the signal intensity in the baseline area. The smaller the noise, the more stable the baseline of the instrument and the higher the detection sensitivity. In order to evaluate whether a signal is significant, the signal-to-noise ratio can be calculated, which is the ratio of the signal intensity to the background noise. It is generally believed that when the signal-to-noise ratio is greater than 3, the signal can be considered to be higher than the background noise, thereby determining the sensitivity.

[0052] S105, obtaining the analysis efficiency of the mass spectrometry analysis according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula.

[0053] The preset analysis efficiency calculation formula can be a mathematical expression used to quantify the influence of different factors on the analysis efficiency in mass spectrometry analysis. This formula usually combines factors such as sample complexity, separation time, detection sensitivity, background noise, etc. to evaluate the efficiency of the entire mass spectrometry analysis process.

[0054] Analytical efficiency can be a quantitative indicator to measure the performance and effect of mass spectrometry analysis. It can be used to evaluate the overall performance of mass spectrometry analysis methods.

[0055] The initial sample complexity, separation time, detection sensitivity, and background noise can be substituted into a preset analysis efficiency calculation formula to calculate the analysis efficiency of mass spectrometry analysis.

[0056] On the basis of the above technical solution, optionally, after obtaining the analysis efficiency of the mass spectrometry analysis, the method further comprises: If the analysis efficiency does not exceed a preset analysis efficiency threshold, the preset HPLC separation parameter initial value, initial sample complexity, detection sensitivity and background noise are input into a preset HPLC separation parameter adjustment model to determine an HPLC separation parameter adjustment value; Re-obtain the amino acid polypeptide protein powder sample, re-perform HPLC separation and mass spectrometry analysis on the amino acid polypeptide protein powder sample according to the HPLC separation parameter adjustment value and the preset HPLC separation parameter initial value, and calculate the analysis efficiency of the mass spectrometry analysis; if the analysis efficiency still does not exceed the preset analysis efficiency threshold, re-determine the HPLC separation parameter adjustment value until the analysis efficiency exceeds the preset analysis efficiency threshold.

[0057] In this scheme, the preset HPLC separation parameter adjustment model can be a model based on historical data, experimental results or algorithms (such as machine learning or optimization algorithms) to dynamically adjust the operating conditions of HPLC separation to achieve better separation effect and mass spectrometry analysis efficiency. The model can give optimized HPLC separation conditions based on multiple input parameters, such as sample complexity, detection sensitivity, background noise, etc.

[0058] HPLC separation parameter adjustment values ​​may refer to parameter change values ​​calculated or set according to specific analysis objectives and sample characteristics in order to optimize the separation effect during the high performance liquid chromatography (HPLC) analysis process. These adjustment values ​​are used to guide the HPLC system to make appropriate modifications to the initial separation conditions, thereby improving the sensitivity, separation and efficiency of the analysis. Specifically, they may include flow rate adjustment values: used to change the flow rate of the mobile phase, affecting the residence time of the sample in the chromatographic column. Temperature adjustment values: affect the temperature of the chromatographic column, thereby affecting the selectivity and efficiency of the separation. Mobile phase composition adjustment values: including changes in the ratio of aqueous phase and organic phase, used to improve the separation effect of sample components. pH adjustment values: changing the pH of the mobile phase affects the ionization state of the analyte, thereby changing its separation behavior.

[0059] The preset HPLC separation parameter initial value, initial sample complexity, detection sensitivity and background noise can be input into the preset HPLC separation parameter adjustment model, and the model outputs the HPLC separation parameter adjustment value, for example, the flow rate adjustment value can be an increased or decreased value. The mobile phase ratio adjustment value can represent the percentage of the aqueous phase or organic phase that needs to change. These adjustment values ​​need to be added and subtracted from the preset HPLC separation parameter initial value initially set to determine the new operating value. For example, flow rate operating value = preset flow rate initial value + flow rate adjustment value. Mobile phase ratio operating value = preset mobile phase ratio initial value + mobile phase ratio adjustment value. The new operating value will be used as the input parameter of the HPLC instrument. For example, if the flow rate initial value is 0.5mL / min and the adjustment value is +0.1mL / min, the new flow rate operating value will be 0.6mL / min. If the mobile phase initial ratio is 80% water / 20% organic solvent and the adjustment value is -5% water / +5% organic solvent, the new operating value will be 75% water / 25% organic solvent. In this way, the HPLC separation operation will be re-executed according to the new parameter settings, with the goal of improving the sample separation effect and ultimately improving the efficiency of mass spectrometry analysis. After the separation operation is completed, the mass spectrometry analysis will produce new results. Based on these new results, the analysis efficiency of the mass spectrometry analysis is recalculated and it is determined whether it exceeds the preset analysis efficiency threshold. If the analysis efficiency still does not meet the requirements, repeat the above steps to further optimize the separation parameters until the expected analysis efficiency is met.

[0060] In this scheme, by continuously adjusting the HPLC separation parameters, the separation process can be optimized, the components in the sample can be better separated, and the accuracy and sensitivity of the analysis can be improved. By continuously testing and adjusting the parameters, the reliability and repeatability of the analysis results can be ensured.

[0061] On the basis of the above technical solution, an optional, preset analysis efficiency calculation formula is: ; E is the analysis efficiency; S is the initial sample complexity; T is the separation time; D is the detection sensitivity; C is the background noise.

[0062] The obtained initial sample complexity, separation time, detection sensitivity and background noise can be substituted into the formula to obtain the analysis efficiency.

[0063] S106, if the analysis efficiency exceeds a preset analysis efficiency threshold, determining the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and a preset second standard curve.

[0064] The preset analysis efficiency threshold can be a critical value set during the mass spectrometry analysis process to measure the effectiveness of the mass spectrometry analysis. If the analysis efficiency (calculated by comprehensive factors such as detection sensitivity, separation time, background noise, etc.) is higher than this threshold, it means that the analysis is accurate and efficient enough, and the component analysis can be performed directly based on the mass spectrum, eliminating further separation or processing steps.

[0065] The preset second standard curve can be a standard reference curve for quantitative analysis of mass spectrometry data, similar to the first standard curve in HPLC, but specifically used for interpretation of mass spectra. Its function is to determine the concentration of components in unknown samples based on the relationship between the peaks in the mass spectrometer and the known sample concentrations. This curve is usually obtained by analyzing a series of standard samples of known concentrations and plotting the relationship between their signal intensity (such as peak area or peak height) and concentration in mass spectrometry analysis.

[0066] The final sample composition may refer to the specific components in the amino acid polypeptide protein powder sample determined according to the mass spectrum and the second standard curve after mass spectrometry analysis. Specifically, it may include amino acids, peptide chains and proteins in the sample: the molecular weight and structural information of these components separated by mass spectrometry analysis. The concentration of each component: the concentration corresponding to the intensity of different peaks in the mass spectrum. The distribution of components: the proportional distribution of different types of amino acids, polypeptides and proteins in the sample.

[0067] The preset analysis efficiency threshold can be read from the database. If the analysis efficiency exceeds the preset analysis efficiency threshold, it means that the analysis process is reliable enough. Then use the information in the mass spectrum to extract the mass-to-charge ratio of the sample components, and identify the different components in the sample by analyzing the peaks in the mass spectrum. Compare the peaks in the mass spectrum with the second standard curve. Through the comparison results, determine the specific components corresponding to each mass spectrum peak, and determine the concentrations of these components through the relationship in the curve. After completing the comparison of mass spectrum peaks and concentration calculation, combine the information of all mass spectrum peaks to determine the final component list of the amino acid polypeptide protein powder sample. This includes the types of all amino acids, polypeptides and proteins in the sample and their corresponding concentrations.

[0068] In an embodiment of the present application, an amino acid polypeptide protein powder sample is obtained, and HPLC separation is performed on the amino acid polypeptide protein powder sample according to preset initial values ​​of HPLC separation parameters to obtain a separated sample solution, a sample chromatogram, and a separation time; the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component are determined according to the sample chromatogram and a preset first standard curve; the number of initial sample types is determined according to the initial sample components, and the initial sample complexity of the amino acid polypeptide protein powder sample is calculated according to the initial sample components, the component concentrations of each initial sample component, the number of initial sample types, and a preset sample complexity calculation formula; mass spectrometry analysis is performed on the sample solution to obtain a mass spectrum of the sample solution, and the detection sensitivity and background noise are determined; the analysis efficiency of the mass spectrometry analysis is obtained according to the initial sample complexity, separation time, detection sensitivity, background noise, and a preset analysis efficiency calculation formula; if the analysis efficiency exceeds a preset analysis efficiency threshold, the final sample components of the amino acid polypeptide protein powder sample are determined according to the mass spectrum and a preset second standard curve. Through the above-mentioned amino acid polypeptide protein powder component analysis and detection method, multi-step separation and analysis ensures high accuracy of component analysis of amino acid polypeptide protein powder, avoiding the errors that may be caused by a single detection method. The high degree of automation reduces human subjective judgment, ensures the objectivity and consistency of the analysis results, improves the analysis efficiency, and provides reliable quantitative and qualitative results.

[0069] On the basis of the above technical solution, optionally, after determining the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and the preset second standard curve, the method further comprises: The sample component information is determined according to the final sample components and the amino acid polypeptide protein powder sample, and the sample component information is transmitted to a control center.

[0070] In this solution, the sample component information can be the chemical or physical property information obtained after analyzing the amino acid, polypeptide and protein powder samples. Specifically, it can include chemical composition: molecular weight, molecular structure, content, purity, etc. of amino acids, polypeptides, proteins and other components. Concentration information: quantitative analysis results of each component (such as the concentration and mass fraction of each component). Peak information: component separation information corresponding to the chromatographic peak obtained from HPLC or mass spectrometry, such as peak area, peak height, retention time, etc. Other attributes: such as molecular weight distribution, polymerization state, molecular structure characteristics, etc.

[0071] The control center can be a central system that receives and processes sample composition information. It can be a software platform, a cloud server, or a local laboratory information management system.

[0072] Amino acid, polypeptide and protein powder samples can be separated and quantitatively analyzed by analytical instruments such as HPLC or mass spectrometry to obtain their chemical composition information. The analysis results are organized into sample composition information, such as the name, concentration, chromatographic peak information, etc. of each amino acid, polypeptide or protein. The organized sample composition information is transmitted to the control center through data transmission protocols (such as HTTP, FTP, database connection, etc.), which can be achieved through network connection, local area network, cloud transmission and other methods.

[0073] In this solution, the experimental data is directly transmitted from the instrument to the control center, which saves the time of manual data sorting and transmission and speeds up the experimental process. All sample composition information is centrally stored and managed in the control center, which facilitates the long-term preservation and rapid retrieval of experimental data.

[0074] Embodiment 2

[0075] Figure 2 : is a schematic diagram of the process of the amino acid polypeptide protein powder component analysis and detection method provided in Example 2 of the present application, such as Figure 2 As shown, the specific method includes the following steps: S201, obtaining an amino acid polypeptide protein powder sample, performing HPLC separation on the amino acid polypeptide protein powder sample according to preset HPLC separation parameter initial values, and obtaining a separated sample solution, a sample chromatogram, and a separation time.

[0076] S202, determining the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve.

[0077] S203, determining the number of initial sample types according to the initial sample components, and calculating the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the component concentrations of the initial sample components, the number of initial sample types and a preset sample complexity calculation formula.

[0078] S204, performing mass spectrometry analysis on the sample solution to obtain a mass spectrum of the sample solution, determining a baseline region according to the mass spectrum, obtaining signal intensity values ​​of each signal intensity in the baseline region, and determining the number of signal intensity values.

[0079] The baseline region can be the area in the mass spectrum where no significant peaks appear, that is, the area of ​​background noise. It represents the area where there is no obvious signal and usually reflects the instrument noise level. The baseline region is often used to calculate the background noise and evaluate the significance of the signal.

[0080] Signal intensity is the signal response value corresponding to a certain time point or mass-to-charge ratio in the mass spectrum, usually representing the relative intensity of a certain ion peak. This can be understood as a relative indication of the number or concentration of ions detected by the mass spectrometer.

[0081] The signal strength value can be the actual response value corresponding to each detection point or m / z. In the baseline area, these values ​​correspond to the size of the background noise. Each data point will have a signal strength value, which can be a physical quantity such as voltage, current, or count rate.

[0082] The number of signal intensity values ​​may refer to the total number of signal intensity values ​​of all sampling points in the selected baseline region. The mass spectrum is composed of a large number of sampling points, so the number of signal intensity values ​​is the number of signal points detected in the baseline region.

[0083] In a mass spectrum, the baseline is usually an area where no large peaks appear, and can be any relatively flat section. The baseline area can be automatically identified by data analysis software, and the area without significant signal can be detected based on smoothness or noise level. For example, there is no significant peak in the range of 10-20 seconds in the mass spectrum, which is determined to be the baseline area. For example, in the baseline area, the signal intensity may fluctuate between 100 and 120. Then count the number of all sampling points in the baseline area, and each point has a corresponding signal intensity value. For example, if there are 50 sampling points in the baseline area, then the number of signal intensity values ​​is 50.

[0084] S205: Calculate background noise according to the signal strength value, the number of signal strength values ​​and a preset background noise calculation formula.

[0085] Background noise can refer to the noise level when there is no actual signal peak in mass spectrometry analysis. It usually reflects the background noise of the instrument and is used to judge the significance of the signal. When we determine the background noise, we can use it to compare the mass spectrometry signal to identify the real signal peak.

[0086] The preset background noise calculation formula can be used to measure the volatility or irregularity of the signal in the baseline area to help determine the difference between the real signal and noise in mass spectrometry analysis. The larger the background noise, the lower the significance of the signal peak relative to the noise; the smaller the background noise, the easier it is to identify the signal peak.

[0087] The required parameters of a preset background noise calculation formula can be calculated according to the signal strength value, and the signal strength value is substituted into the formula to calculate the background noise.

[0088] Based on the above technical solution, optionally, the preset background noise calculation formula is: ; Among them, SD is the background noise; is the number of signal strength values; is the signal strength value of each signal strength; is the average signal strength.

[0089] In this solution, you can select the baseline area in the mass spectrum and read the signal intensity value of each point , , ..., For example, in the baseline area, there are 5 signal intensity values: 100, 105, 98, 102, 101. The formula to calculate the average of these signal intensity values ​​is: ; For each signal strength value ,calculate , and calculate the sum of all square differences , and finally substitute it into the preset background noise calculation formula to obtain the background noise.

[0090] S206, determining the detection sensitivity according to the background noise, a preset second standard curve, and a preset standard deviation.

[0091] The preset standard deviation can be a parameter used to quantify data volatility or uncertainty, which reflects the allowable range of signal intensity fluctuation based on background noise. This value is usually derived from the analysis results of multiple standard samples (known composition and concentration), through which a standard curve is established and the signal deviation of each sample is calculated.

[0092] The standard deviation of the signal intensity of each sample can be calculated by analyzing the measurement results of the standard sample. The standard deviation represents the range of signal fluctuations that the system can accept at a given concentration. The detection sensitivity is the minimum sample concentration found from the second standard curve, whose signal intensity significantly exceeds the background noise. It is usually equal to a multiple of the standard deviation. For example, sensitivity can be defined as the standard deviation of the signal exceeding the background noise by 3 times. The reason for this is that a signal of 3 times the standard deviation can usually guarantee sufficient confidence that the signal comes from the sample rather than the noise. The background noise is then compared with the lowest signal intensity in the standard curve to determine the detection limit of the standard curve. If the lowest signal intensity exceeds the background noise plus the standard deviation, the concentration corresponding to the signal is the detection sensitivity. For example, by calculating the standard deviation of the signal in the baseline area, the background noise is determined to be 10 units. Assume that on the standard curve, the minimum detectable signal of the sample is 30 units, and the preset standard deviation is 5 units. If the signal intensity exceeds the standard deviation of 3 times the background noise (i.e., 10+3×5=25 units), the signal can be considered as a valid signal detected.

[0093] S207, obtaining the analysis efficiency of the mass spectrometry analysis according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula.

[0094] S208, if the analysis efficiency exceeds a preset analysis efficiency threshold, determining the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and a preset second standard curve.

[0095] In this embodiment, by calculating the background noise and determining the detection sensitivity according to the standard curve and standard deviation, the detection limit of the mass spectrometer for low-concentration samples can be accurately defined, which can help the instrument maintain high detection efficiency and sensitivity under low-concentration conditions and ensure the detection of trace samples. The standard curve provides a benchmark for quantitative analysis, and combined with the background noise and standard deviation, it ensures that the numerical value of the sample concentration can still be accurately obtained under low-concentration samples. This can make mass spectrometry quantitative analysis more accurate, especially in complex samples.

[0096] Embodiment 3

[0097] Figure 3 : is a schematic diagram of the process of the amino acid polypeptide protein powder component analysis and detection method provided in Example 3 of the present application, such as Figure 3 As shown, the specific method includes the following steps: The training process of the preset HPLC separation parameter adjustment model includes: S301, obtaining historical adjustment records, determining historical sample complexity, historical HPLC separation parameter initial values, historical detection sensitivity, and historical background noise of historical amino acid polypeptide protein powder samples during HPLC separation based on the historical adjustment records, and creating a first data set based on the historical sample complexity, historical separation parameter initial values, historical detection sensitivity, and historical background noise.

[0098] Historical adjustment records can be records of all adjustments made to experimental parameters, equipment settings, sample processing, etc. during previous experiments or analyses. These records can include changes to HPLC separation parameters, detection sensitivity, sample complexity adjustments, etc.

[0099] Historical amino acid peptide protein powder samples can be samples used in previous experiments. The relevant information of these samples has been recorded and associated with their corresponding experimental parameters and test results.

[0100] Historical sample complexity can be the sample complexity value calculated in previous experiments, which is usually determined by the number, type and relationship of multiple sample components (such as similarity matrix). Sample complexity can reflect the diversity of sample components and the difficulty of separation.

[0101] The historical HPLC separation parameter initial values ​​may refer to the initial setting parameters of the HPLC separation method used for the amino acid polypeptide protein powder sample in the previous experiment.

[0102] Historical detection sensitivity can be the minimum detectable concentration or signal response level of a mass spectrometer or other analytical method determined in previous experiments. This value is usually compared to the background noise.

[0103] Historical background noise can be a noise value obtained by calculating the signal fluctuation in the baseline region of the mass spectrum or chromatogram in previous experiments. This value represents the noise caused by the environment or equipment detected by the instrument in the absence of sample signal.

[0104] The first data set may be a collection of the above historical data such as sample complexity, initial values ​​of HPLC separation parameters, detection sensitivity, background noise, etc. It can be used as input for modeling, prediction or further analysis.

[0105] All adjustment records from previous experiments can be extracted from the experimental system or experimental records, including but not limited to HPLC separation parameters, detection sensitivity, sample complexity, etc. The complexity of each sample is determined based on the sample complexity value calculated in the historical experiment (such as based on composition, type, similarity matrix, etc.). Based on the historical adjustment records, the initial HPLC parameters used for sample separation are determined, including mobile phase, column temperature, flow rate, etc. The detection sensitivity of each experiment is then obtained or recalculated from the historical experimental data (determined by the signal-to-noise ratio and the standard curve). The background noise of each experiment is then obtained from the previous mass spectrum or chromatogram, usually by calculating the signal fluctuation in the baseline area. All of the above historical data are sorted and summarized to form a first data set containing multiple experimental samples and corresponding parameters. This data set can serve as the basis for subsequent analysis, modeling, or experimental optimization.

[0106] S302, determining a historical HPLC separation parameter adjustment value according to the historical adjustment record, and marking an HPLC separation parameter adjustment value label of the first data set according to the historical HPLC separation parameter adjustment value.

[0107] The historical HPLC separation parameter adjustment values ​​can be the final setting values ​​of the HPLC parameters (such as mobile phase, gradient, column temperature, flow rate, pressure, etc.) after adjustment in the previous experiment. The separation parameters of each experiment may be adjusted due to different sample characteristics, and the records of these adjustments are the historical HPLC separation parameter adjustment values.

[0108] The HPLC separation parameter adjustment value label can be a label that associates each experimental sample in the first data set with a corresponding HPLC separation parameter adjustment value. Through labeling, samples can be classified to indicate the separation parameter adjustments made under different experimental conditions.

[0109] The HPLC separation parameter adjustment values ​​used in each experiment can be extracted from the historical adjustment records, and the parameter adjustment information is summarized and matched with the corresponding experimental records. Then, each experimental sample in the first data set and its corresponding HPLC separation results and experimental conditions are determined. This data set contains the sample information, separation effect and corresponding test results in the historical experiments. Each experimental sample in the first data set and its corresponding HPLC separation results and experimental conditions are determined. This data set contains the sample information, separation effect and corresponding test results in the historical experiments. Each experimental sample is associated with its corresponding historical HPLC separation parameter adjustment value. The experiment and separation parameter adjustment value can be matched by the experiment number, date or other identifier. For each experimental sample, its corresponding HPLC separation parameter adjustment value is annotated as a new attribute. Based on the HPLC parameter adjustment value in each experiment, a separation parameter adjustment label is generated for each sample. The labels may include: Qualitative labels: such as "high flow rate adjustment", "low gradient", "high temperature separation", etc., to help classify the characteristics of the experimental parameters. Quantitative labels: directly use the adjusted specific values ​​as labels, such as "flow rate = 1.0 mL / min", "gradient time = 20 min", etc. Finally, the generated labels are applied to each sample in the first data set.

[0110] S303, constructing an HPLC separation parameter adjustment model, and training the HPLC separation parameter adjustment model according to the first data set and the HPLC separation parameter adjustment value label until the HPLC separation parameter adjustment model reaches a preset training standard.

[0111] The preset training criteria can refer to the criteria and goals used to evaluate the quality of model training. These criteria can be set according to the experimental requirements. Specifically, they can include accuracy: the degree of agreement between the model's predicted values ​​and the true values. It is usually measured by the accuracy of the model's predictions, which indicates the ratio of the model's correct predictions on the test data. Mean square error (MSE): measures the square average of the errors between the model's predicted values ​​and the actual values. For regression models, mean square error (MSE) can be used as an evaluation criterion. The smaller the error, the better the model performs. Coefficient of determination (R²): indicates the model's ability to explain data fluctuations. The closer the coefficient of determination (R²) is to 1, the better the model's fit. Convergence: During the model training process, the value of the loss function gradually decreases and tends to stabilize. When the set convergence threshold is reached, the model training stops. Cross-validation scoring: The model is tested and evaluated for multiple rounds through cross-validation to ensure the generalization ability of the model on different data sets. Running time: the time consumed by model training. In the preset criteria, an upper limit for the training time can be set to ensure that the model converges within a reasonable time.

[0112] It is possible to ensure that there are no missing values ​​or outliers in the first data set, and preprocess the data, such as normalization and standardization, to ensure the stability of model training. Select the features that have the greatest impact on HPLC separation from the data set. For example, HPLC parameters such as mobile phase composition, flow rate, gradient, column temperature, and detection wavelength. Ensure that each sample is correctly labeled with the HPLC separation parameter adjustment value label. These labels will be used as the target output of the model. Then select the appropriate model algorithm according to the experimental requirements. Specifically, it can include regression models: if you want to predict continuous HPLC separation parameter adjustment values, you can use regression models such as linear regression and ridge regression. Classification model: If the HPLC separation parameter adjustment value label is discrete (such as high / medium / low flow rate), you can use classification models such as decision trees, random forests, and support vector machines. Neural network model: If the data set is large and complex, you can consider using a neural network model. Divide the first data set into a training set and a test set, usually 70% training set and 30% test set. Cross-validation can be used to ensure the generalization ability of the model. Input the training set into the model, and train the model to minimize the error function (such as mean square error) based on the HPLC separation parameter adjustment value label. Then use methods such as grid search or random search to optimize the model's hyperparameters to obtain the best results. During the training process, continue to observe the changes in the loss function value to ensure that the model gradually converges in iterations. Use the test set to evaluate the model. Common evaluation indicators include accuracy, mean square error, R², etc. Then use cross-validation technology to divide the data set into multiple subsets, train and test the model in turn, and ensure the model's predictive ability for different data subsets. Finally, continue to adjust the model parameters and train repeatedly until the model reaches the preset training standard.

[0113] In this embodiment, the HPLC separation parameter adjustment values ​​are automatically determined by a machine learning model, which reduces the need for manual adjustments during the experiment and significantly improves efficiency.

[0114] Embodiment 4

[0115] Figure 4 : is a schematic diagram of the structure of an amino acid polypeptide protein powder component analysis and detection system provided in Example 4 of the present application, such as Figure 4 As shown, the system is used to implement an amino acid polypeptide protein powder component analysis and detection system method provided in embodiments 1, 2, and 3, and the system specifically includes the following: The sample acquisition module 401 is used to acquire an amino acid polypeptide protein powder sample, and perform HPLC separation on the amino acid polypeptide protein powder sample according to a preset HPLC separation parameter initial value to obtain a separated sample solution, a sample chromatogram, and a separation time; An initial sample analysis module 402 is used to determine the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve; An initial sample complexity calculation module 403 is used to determine the number of initial sample types according to the initial sample components, and calculate the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the number of initial sample types and a preset sample complexity calculation formula; A mass spectrometry analysis module 404 is used to perform mass spectrometry analysis on the sample solution to obtain a mass spectrum of the sample solution and determine the detection sensitivity and background noise; An analysis efficiency calculation module 405 is used to obtain the analysis efficiency of mass spectrometry analysis according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula; The final sample composition determination module 406 cancels the determination of the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and the preset second standard if the analysis efficiency exceeds a preset analysis efficiency threshold.

[0116] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A method for analyzing and detecting the components of amino acid polypeptide protein powder, characterized in that: The method comprises: Obtaining an amino acid polypeptide protein powder sample, performing HPLC separation on the amino acid polypeptide protein powder sample according to a preset HPLC separation parameter initial value, and obtaining a separated sample solution, a sample chromatogram, and a separation time; Determine the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve; Determining the number of initial sample types according to the initial sample components, and calculating the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the component concentrations of each initial sample component, the number of initial sample types, and a preset sample complexity calculation formula; Performing mass spectrometry analysis on the sample solution to obtain a mass spectrum of the sample solution, and determining the detection sensitivity and background noise; The analysis efficiency of the mass spectrometry analysis is obtained according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula; If the analysis efficiency exceeds a preset analysis efficiency threshold, the final sample composition of the amino acid polypeptide protein powder sample is determined according to the mass spectrum and a preset second standard curve.

2. The amino acid polypeptide protein powder component analysis and detection method according to claim 1, characterized in that: After obtaining the analysis efficiency of the mass spectrometry analysis, the method further comprises: If the analysis efficiency does not exceed a preset analysis efficiency threshold, the preset HPLC separation parameter initial value, initial sample complexity, detection sensitivity and background noise are input into a preset HPLC separation parameter adjustment model to determine an HPLC separation parameter adjustment value; Re-obtain the amino acid polypeptide protein powder sample, re-perform HPLC separation and mass spectrometry analysis on the amino acid polypeptide protein powder sample according to the HPLC separation parameter adjustment value and the preset HPLC separation parameter initial value, and calculate the analysis efficiency of the mass spectrometry analysis; if the analysis efficiency still does not exceed the preset analysis efficiency threshold, re-determine the HPLC separation parameter adjustment value until the analysis efficiency exceeds the preset analysis efficiency threshold.

3. The amino acid polypeptide protein powder component analysis and detection method according to claim 1, characterized in that: The preset sample complexity calculation formula is: ； Among them, S is the complexity of the initial sample; is the number of initial sample types; is the component concentration of the i-th initial sample component; is a similarity matrix, which represents the similarity value between the i-th component and the j-th component.

4. The amino acid polypeptide protein powder component analysis and detection method according to claim 3, characterized in that: The process of obtaining the similarity matrix includes: Determine the feature set of each initial sample component, and calculate the similarity matrix based on the feature set and a preset similarity matrix calculation formula; wherein the preset similarity matrix calculation formula is: ； in, is the feature set of the i-th initial sample component; is the feature set of the jth initial sample component.

5. The amino acid polypeptide protein powder component analysis and detection method according to claim 1, characterized in that: The preset analysis efficiency calculation formula is: ； E is the analysis efficiency; S is the initial sample complexity; T is the separation time; D is the detection sensitivity; C is the background noise.

6. The amino acid polypeptide protein powder component analysis and detection method according to claim 5, characterized in that: Determine detection sensitivity and background noise, including: Determine a baseline region according to the mass spectrum, obtain a signal intensity value of each signal intensity in the baseline region, and determine the number of signal intensity values; Calculating background noise according to the signal strength value, the number of signal strength values ​​and a preset background noise calculation formula; The detection sensitivity is determined based on the background noise, a preset second standard curve, and a preset standard deviation.

7. The amino acid polypeptide protein powder component analysis and detection method according to claim 6, characterized in that: The preset background noise calculation formula is: ； Among them, SD is the background noise; is the number of signal strength values; is the signal strength value of each signal strength; is the average signal strength.

8. The amino acid polypeptide protein powder component analysis and detection method according to claim 2, characterized in that: The training process of the preset HPLC separation parameter adjustment model includes: Obtaining historical adjustment records, determining historical sample complexity, historical HPLC separation parameter initial values, historical detection sensitivity, and historical background noise when performing HPLC separation on historical amino acid polypeptide protein powder samples according to the historical adjustment records, and creating a first data set according to the historical sample complexity, historical separation parameter initial values, historical detection sensitivity, and historical background noise; Determining a historical HPLC separation parameter adjustment value according to the historical adjustment record, and marking the HPLC separation parameter adjustment value label of the first data set according to the historical HPLC separation parameter adjustment value; A HPLC separation parameter adjustment model is constructed, and the HPLC separation parameter adjustment model is trained according to the first data set and the HPLC separation parameter adjustment value label until the HPLC separation parameter adjustment model reaches a preset training standard.

9. The amino acid polypeptide protein powder component analysis and detection method according to claim 1, characterized in that: After determining the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and the preset second standard curve, the method further comprises: The sample component information is determined according to the final sample components and the amino acid polypeptide protein powder sample, and the sample component information is transmitted to a control center.

10. An amino acid polypeptide protein powder component analysis and detection system, characterized in that: The system comprises: A sample acquisition module is used to acquire an amino acid polypeptide protein powder sample, perform HPLC separation on the amino acid polypeptide protein powder sample according to a preset HPLC separation parameter initial value, and obtain a separated sample solution, a sample chromatogram, and a separation time; An initial sample analysis module, used to determine the initial sample components corresponding to each chromatographic peak in the sample chromatogram and the component concentration of each initial sample component according to the sample chromatogram and a preset first standard curve; An initial sample complexity calculation module, used to determine the number of initial sample types according to the initial sample components, and calculate the initial sample complexity of the amino acid polypeptide protein powder sample according to the initial sample components, the number of initial sample types and a preset sample complexity calculation formula; A mass spectrometry analysis module, used to perform mass spectrometry analysis on the sample solution, obtain a mass spectrum of the sample solution, and determine the detection sensitivity and background noise; An analysis efficiency calculation module, used to obtain the analysis efficiency of mass spectrometry analysis according to the initial sample complexity, separation time, detection sensitivity, background noise and a preset analysis efficiency calculation formula; The final sample composition determination module cancels the determination of the final sample composition of the amino acid polypeptide protein powder sample according to the mass spectrum and the preset second standard if the analysis efficiency exceeds a preset analysis efficiency threshold.

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