Dextran quality spectrum detection analysis system and method

Abstract

The present application relates to the technical field of dextran detection, and particularly relates to a dextran quality spectrum detection and analysis system and method. The present application comprises a spectrum data set construction module, which acquires dextran sample spectrum data containing a plurality of continuously-acquired position point spectrum intensity value sequences; a characteristic peak identification module, which identifies primary and secondary characteristic peak position points; a characteristic peak position calibration module, which calibrates the primary characteristic peak position according to a preset dextran standard spectrum profile line, and generates a primary characteristic peak calibration position point; a deviation value calculation module, which calculates the offset of the primary characteristic peak calibration position point and the actual position point multiple times, and generates a position deviation value; a quality parameter evaluation module, which generates a dextran quality grade evaluation parameter in combination with the position deviation value and the secondary characteristic peak position point; a detection precision correction module, which corrects the preset spectrum acquisition position point coordinates according to the quality grade evaluation parameter; and a dynamic feedback module, which reacquires spectrum data after correction and calculates a new quality grade evaluation parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dextran detection, in particular to a dextran quality spectrum detection analysis system and method. BACKGROUND

[0002] In the production, processing and application process of dextran, accurate control of its quality is crucial, and spectrum detection technology has become a common technical means in the field of dextran quality analysis due to its rapid and non-destructive characteristics. However, the current dextran quality detection scheme based on spectrum technology still has many deficiencies, which is difficult to meet the high requirements of detection accuracy and stability in actual application.

[0003] The existing spectrum detection system stops at the preliminary positioning of a single main characteristic peak after obtaining the spectrum data of the dextran sample, and lacks effective identification and utilization of secondary characteristic peaks. Although the secondary characteristic peak has lower intensity than the main characteristic peak, it also contains key quality information such as dextran molecular structure and purity. Ignoring the secondary characteristic peak will result in incomplete quality analysis dimension, and further affect the accuracy of the final quality evaluation result. At the same time, in the positioning process of the main characteristic peak, the existing system does not establish a precise benchmarking mechanism with the preset dextran standard spectrum profile, and cannot standardize the calibration of the main characteristic peak position. Therefore, the main characteristic peak position data of different detection batches and different samples lack a unified reference benchmark, the data comparability is poor, and it is difficult to form a stable quality evaluation standard.

[0004] The existing system does not consider the influence of environmental factors and instrument errors in the detection process on the position of the main characteristic peak, and does not carry out multiple measurements and analysis of the deviation of the position of the main characteristic peak. This leads to detection results susceptible to external interference, insufficient stability of the main characteristic peak position data, and further reduces the reliability of quality evaluation. In terms of detection accuracy correction, the existing scheme mainly uses fixed correction parameters, lacks a mechanism for dynamic adjustment according to the actual quality grade evaluation results of dextran, and cannot realize individualized precision correction for dextran samples with different quality conditions. Therefore, the detection system has poor adaptability when facing diversified dextran samples. The existing system lacks a dynamic feedback link, and after completing spectrum acquisition and quality evaluation, it cannot reacquire spectrum data based on the evaluation results and carry out secondary analysis, making it difficult to realize closed-loop optimization of the detection process and continuously improve detection accuracy and quality evaluation accuracy. These problems restrict the further application and development of spectrum detection technology in the field of dextran quality analysis. SUMMARY

[0005] The main purpose of the present application is to provide a dextran quality spectrum detection analysis system and method, which aims to solve the technical problems in the prior art.

[0006] The present application provides a dextran quality spectrum detection analysis system, which comprises:

[0007] a spectrum data set construction module for obtaining spectrum data of the glucan sample, the spectrum data comprising a sequence of spectrum intensity values of a plurality of continuous acquisition position points;

[0008] a characteristic peak identification module for identifying characteristic peak position points in the spectrum data, the characteristic peak position points comprising primary characteristic peak position points and secondary characteristic peak position points;

[0009] a characteristic peak position calibration module for calibrating the primary characteristic peak position points according to a preset glucan standard spectrum profile to generate primary characteristic peak calibration position points;

[0010] a deviation value calculation module for measuring and analyzing the offset between the primary characteristic peak calibration position points and actual primary characteristic peak position points in the spectrum data to generate primary characteristic peak position deviation values;

[0011] a quality parameter evaluation module for generating a glucan quality grade evaluation parameter in combination with the primary characteristic peak position deviation values and the secondary characteristic peak position points;

[0012] a detection precision correction module for correcting the position coordinates of preset spectrum acquisition position points according to the glucan quality grade evaluation parameter;

[0013] a dynamic feedback module for reacquiring spectrum data after correction and calculating a new glucan quality grade evaluation parameter.

[0014] Preferably, the spectrum data set construction module obtains the specific manner of the continuous acquisition position points as follows:

[0015] The spectrum acquisition starting point is taken as the first position point, the ending point is taken as the last position point, the starting point, the ending point and the intermediate acquisition position points are numbered in the acquisition order, the adjacent position points form spectrum acquisition sections, each spectrum acquisition section is associated with the number of its two end position points, and the spectrum acquisition section associated with the intermediate acquisition position points is taken as the input of the characteristic peak identification module.

[0016] Preferably, the characteristic peak position calibration module generates the specific manner of the primary characteristic peak calibration position points as follows:

[0017] The wave peak vertex of the preset glucan standard spectrum profile is taken as a reference point, a perpendicular line to the spectrum intensity axis is drawn through the reference point, the perpendicular line is taken as a primary characteristic peak calibration position line, and the reference point is taken as the end point of the primary characteristic peak calibration position line.

[0018] Preferably, the deviation value calculation module generates the specific manner of the primary characteristic peak position deviation values as follows:

[0019] Randomly select one intermediate collection position point as a target position point, obtain the spectrum collection section associated with the target position point as a target collection section, and measure the offset angle of the main characteristic peak calibration position line and the actual main characteristic peak position line in the target collection section multiple times;

[0020] Screen the values in the offset angle that meet the preset fluctuation threshold value, and when the amount of screened values exceeds the preset amount threshold value, take the mean value thereof as the main characteristic peak position deviation value of the target position point, otherwise take the mean value of the extreme values of the offset angle as the main characteristic peak position deviation value;

[0021] Iterate through all intermediate collection position points to generate corresponding main characteristic peak position deviation values.

[0022] As a preferred embodiment, the specific way in which the quality parameter evaluation module generates the glucan quality grade evaluation parameter is as follows:

[0023] Compare the main characteristic peak position deviation value with a preset main characteristic peak reference deviation threshold value:

[0024] When the main characteristic peak position deviation value exceeds twice the preset main characteristic peak reference deviation threshold value, generate a first grade evaluation parameter in combination with all secondary characteristic peak position points;

[0025] When the main characteristic peak position deviation value exceeds the preset main characteristic peak reference deviation threshold value but is less than twice the value, generate a second grade evaluation parameter in combination with half of the secondary characteristic peak position points;

[0026] When the main characteristic peak position deviation value does not exceed the preset main characteristic peak reference deviation threshold value, generate a third grade evaluation parameter only according to the main characteristic peak position deviation value.

[0027] As a preferred embodiment, the specific way in which the detection accuracy correction module corrects the position coordinates is as follows:

[0028] Taking the glucan quality grade evaluation parameter as a reference, when the first grade evaluation parameter is generated, increase the position coordinates of all spectrum collection position points by a preset position compensation amount;

[0029] When the second grade evaluation parameter is generated, only increase the position coordinates of the intermediate collection position points by the preset position compensation amount;

[0030] When the third grade evaluation parameter is generated, keep the original position coordinates unchanged.

[0031] As a preferred embodiment, the system further comprises a parameter optimization module for generating an adaptive feedback control factor according to the difference between new and old glucan quality grade evaluation parameters, and the specific way is as follows:

[0032] A difference value of the glucan quality grade evaluation parameters before and after the calculation correction is calculated, and when the difference value exceeds a preset feedback threshold, a glucan quality grade evaluation parameter meeting a preset screening condition in historical detection is extracted;

[0033] The current difference value is fused and calculated with a weighted mean value of the historical parameters to generate an adaptive feedback control factor.

[0034] As a preference, the system further comprises a calibration update module configured to update the preset glucan standard spectral profile based on the adaptive feedback control factor, in particular by:

[0035] The adaptive feedback control factor is multiplied by the peak vertex coordinates of the preset glucan standard spectral profile, and the obtained product is used as new peak vertex coordinates, based on which a perpendicular line is regenerated to update the preset glucan standard spectral profile.

[0036] As a preference, the feature peak identification module identifies the secondary feature peak position points in particular by:

[0037] The spectral intensity value sequence is divided into multiple sub-datasets according to timestamps, each sub-dataset is assigned an encoding label pointing to its collection period, and the secondary feature peak position points are identified based on the encoding label and associated to the corresponding spectral collection section.

[0038] As a preference, the present application further comprises a glucan quality spectral detection and analysis method based on the above-mentioned glucan quality spectral detection and analysis system.

[0039] The present application has the following advantages: in the aspect of spectral data processing and feature peak utilization, the feature peak identification module set by the system can not only identify the primary feature peak position points, but also effectively capture the secondary feature peak position points. The glucan quality related information contained in the secondary feature peaks is complementary to the primary feature peaks, and the combination of the two can obtain more comprehensive information when the system performs quality analysis, breaking the limitation of the existing system which only relies on the primary feature peaks for quality evaluation, enriching the dimension of quality analysis, making the quality evaluation no longer limited to a single information source, and reflecting the molecular structure, purity and other key quality attributes of glucan from more angles, so that the quality evaluation result is more in line with the actual quality status of glucan.

[0040] At the level of main characteristic peak positioning and standardization, the characteristic peak position calibration module calibrates the main characteristic peak position point according to the preset dextran standard spectrum profile, and generates a main characteristic peak calibrated position point. This calibration process provides a unified and standardized reference benchmark for the main characteristic peak position under different detection scenarios, eliminates the problem of poor comparability of main characteristic peak position data caused by the lack of standard calibration, enables the main characteristic peak position data of different batches and different samples to be compared under a unified standard, creates conditions for establishing a stable and unified dextran quality evaluation system, and helps to form a standardized quality detection process, thereby improving the reliability and universality of the detection data.

[0041] At the level of deviation analysis and detection stability improvement, the deviation value calculation module generates a main characteristic peak position deviation value by measuring and analyzing the offset between the main characteristic peak calibrated position point and the actual main characteristic peak position point multiple times. The multiple measurement mode can effectively avoid the influence of accidental errors in single measurement, accurately capture the real fluctuation of the main characteristic peak position, and clearly identify the source of deviation, thereby providing targeted basis for subsequent quality evaluation and precision correction. Based on the deviation value, the quality parameter evaluation module can fully consider the deviation of the main characteristic peak position when generating the dextran quality grade evaluation parameter in combination with the secondary characteristic peak position point, reduce the interference of the deviation on the quality evaluation result, make the quality grade evaluation parameter more truly reflect the dextran quality, improve the reliability of the quality evaluation, and avoid misjudgment caused by the deviation.

[0042] At the level of detection precision dynamic optimization and closed-loop improvement, the detection precision correction module corrects the position coordinates of the preset spectrum acquisition position point according to the dextran quality grade evaluation parameter, thereby changing the existing system mode of using fixed correction parameters. Different quality grades of dextran samples have differences in spectral characteristics, and targeted position coordinate correction based on the actual quality grade evaluation parameter can make the spectrum acquisition position more suitable for the spectral characteristic requirements of the current sample, improve the accuracy of spectrum data acquisition, and make the subsequently obtained spectrum data more truly reflect the real quality information of the sample. The dynamic feedback module reacquires spectrum data and calculates new quality grade evaluation parameters after correction, thereby constructing a closed-loop optimization mechanism for the detection process. Through reacquisition and analysis, the correction effect can be verified, and if the new evaluation parameter still has deviation, the correction strategy can be further adjusted to continuously optimize the detection precision, thereby realizing continuous improvement of the detection process, enabling the system to adapt to the detection requirements of dextran samples with different quality conditions, improving the adaptability of the system and the stability of the detection results, and better meeting the high requirements for quality detection in the production, processing and application processes of dextran. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 a timing diagram of the dextran quality spectrum detection and analysis system described in the present application;

[0044] Figure 2 A work principle flow chart for a spectral data set construction module to acquire continuous acquisition position points;

[0045] Figure 3 A work principle flow chart for a characteristic peak position calibration module to generate main characteristic peak calibration position points.

[0046] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0047] It should be understood that the specific embodiments described herein merely exemplify the application and are not intended to limit the application.

[0048] As Figure 1 shown, the present application provides a dextran quality spectrum detection analysis system and method, including a spectrum intensity value sequence of multiple continuous acquisition position points. A characteristic peak identification module processes the spectrum data to identify main characteristic peak position points and secondary characteristic peak position points. A characteristic peak position calibration module calibrates the position of the main characteristic peak position points according to a preset dextran standard spectrum profile line to generate main characteristic peak calibration position points. A deviation value calculation module generates main characteristic peak position deviation values by analyzing the offset between the main characteristic peak calibration position points and the actual main characteristic peak position points through multiple measurements. A quality parameter evaluation module generates dextran quality grade evaluation parameters in combination with the main characteristic peak position deviation values and the secondary characteristic peak position points. A detection precision correction module corrects the position coordinates of the preset spectrum acquisition position points according to the evaluation parameters. A dynamic feedback module reacquires spectrum data after correction and calculates new quality grade evaluation parameters to form a closed loop control.

[0049] In one embodiment, embodiment 1: refer to Figure 2 and Figure 3, the spectral dataset construction module adopts a systematic position identification method in the process of acquiring continuous acquisition position points, sets the starting point of spectral acquisition as the first position point and assigns it the number P1, and sets the termination point as the last position point and assigns it the number Pn. For all intermediate acquisition position points between the starting point and the termination point, the numbering process is carried out in strict accordance with the order of the spectral acquisition device, forming a continuous numbering sequence of P2 to Pn-1. This sequential numbering mechanism assigns each acquisition point a unique identifier, establishing a clear correspondence between the spectral data and the spatial position. Adjacent position points automatically form independent spectral acquisition segments, each of which is defined by the numbers of its two end position points, for example, the position points numbered P1 and P2 form the acquisition segment S1-2, and the position points numbered P2 and P3 form the acquisition segment S2-3, and so on until Sn-1-n. Each spectral acquisition segment not only contains the spectral intensity data of the interval, but also associates the spatial coordinate information of its boundary position points. The intermediate acquisition position points have special significance because they belong to both the previous and subsequent acquisition segments. The acquisition segment data associated with these position points will be used as the main input source for the feature peak identification module. During data transmission, the system extracts the acquisition segment data adjacent to each intermediate position point and transmits it to the subsequent processing module, ensuring the continuity and integrity of the spectral data.

[0050] The feature peak position calibration module first calls the preset standard glucan spectral profile line data when performing the main feature peak calibration position point generation operation. This profile line is usually stored in the system's standard database and contains spectral waveform information that has been standardized. The module automatically locates the peak vertex in the profile line using a peak detection algorithm. This vertex has clear position coordinates and intensity coordinates in a two-dimensional coordinate system. Taking this peak vertex as the reference point, the system draws a perpendicular line perpendicular to the spectral intensity axis in the direction of the position axis. This perpendicular line appears as a straight line parallel to the intensity axis in the spectral graph, and its intersection with the position axis is the theoretical ideal feature peak position. The reference point is recorded as the endpoint of the perpendicular line, and its intensity coordinate value is recorded for subsequent comparison. In specific implementation, the generation of the perpendicular line uses the principle of geometric drawing, forming a straight line by fixing the position coordinate value and varying the intensity coordinate between zero and the maximum value. This main feature peak calibration position line becomes the reference for subsequent actual detection results, and all actual acquired spectral data will be compared with this reference line. The calibration process uses digital processing to convert the perpendicular line into a dataset containing a series of discrete points, each point containing position coordinates and intensity coordinates, facilitating subsequent offset calculation and comparison analysis by the computer. The output of this module is a set of standardized coordinate data describing the geometric characteristics and position attributes of the main feature peak calibration position line.

[0051] During the spectral data acquisition process, the acquisition device moves at a constant speed along the preset path, and each acquisition position point corresponds to a timestamp and a spatial coordinate. The position numbering operation is automatically performed after the data acquisition is completed. The system sorts and numbers the position points according to the acquisition time sequence to ensure that the numbering sequence is completely consistent with the actual spatial position sequence. Each number is not only an identifier, but also implies the relative position information of the position point in the entire acquisition path. For example, a position point with a smaller number is usually located at the front of the acquisition path, and a position point with a larger number is located at the rear of the acquisition path. This numbering scheme allows the approximate relative position relationship to be inferred through the number even without directly obtaining the spatial coordinate. The division of the acquisition section is based on the concept of adjacent position points, and each acquisition section represents a continuous data interval containing the spectral intensity data of all acquisition points in the interval. These acquisition sections are sent to the feature peak identification module as basic processing units, and the peak detection and feature extraction operations are performed in the module. The intermediate acquisition position points have transitional data characteristics because they connect two acquisition sections, and special attention needs to be paid to the processing of these points because the feature peaks may appear exactly in these boundary regions.

[0052] The peak vertex detection of the standard spectral profile line uses an automatic optimization algorithm. The system searches for the maximum intensity point within the preset peak range, and determines the accurate peak position by comparing the intensity value changes of adjacent points. After finding the peak vertex, the system records the position coordinate and intensity value of the point as the reference for the entire calibration process. When generating the perpendicular line, the system keeps the position coordinate unchanged and only changes the intensity value to generate a series of points, which form a straight line perpendicular to the position axis. This straight line represents the position where the main feature peak should appear in the ideal case on the spectral graph. Comparing the position of the main feature peak actually detected with the position of the main feature peak can evaluate the quality of the sample. The storage of the calibration position line uses a parameterized form, and the system only records the reference point coordinate and the perpendicular line direction information. When needed, the specific point set data is generated in real time. This storage method saves memory space and improves processing efficiency. When comparison is needed, the system recalculates the coordinates of the points on the perpendicular line and performs superimposed analysis with the actual spectral data.

[0053] The transmission of the data acquisition section adopts a pipeline manner, each acquisition section is sent to the characteristic peak identification module for processing immediately after completing the numbering, and the acquisition sections associated with the intermediate acquisition position points are preferentially processed because these sections may contain important characteristic information. The system establishes metadata records for each acquisition section, including its start and end position point number, acquisition time interval, average intensity, and other basic information. These metadata help the subsequent module quickly understand the basic characteristics of the data section. During transmission, data is in compressed format to reduce transmission time, but decompression operation is performed before processing to ensure data integrity. After receiving the acquisition section data, the characteristic peak identification module first checks the data integrity and quality, and only data that passes the quality check will enter the subsequent processing flow to ensure the reliability of the analysis results.

[0054] The determination of the peak vertex needs to consider local features and global features. The system slides a window on the standard spectral profile line, calculates the intensity statistical features in each window, and comprehensively judges the peak position combined with the derivative change information. Once the peak vertex is determined, the system will perform verification checks to ensure that the point is indeed a local maximum point and meets the peak width requirements. During the vertical line generation process, the system uses interpolation methods to increase the density of data points, making the generated vertical line smoother and more continuous, facilitating accurate comparison with actual data. The point spacing on the vertical line is adjustable according to the accuracy requirements, usually set to the same resolution as the actual acquisition data to ensure that points can be one-to-one corresponding when compared. All generated data has a timestamp and version information to track the history and changes of the calibration process.

[0055] The position numbering system also supports an exception handling mechanism. When the data quality of a certain position point is unqualified, the system will automatically exclude it from the numbering sequence and adjust the numbering relationship of adjacent position points to ensure the continuity of the numbering sequence is not affected. Each acquisition section is marked with a status identifier indicating whether the data is complete and usable. The characteristic peak identification module will decide whether to process the data based on the status identifier. The standard spectral profile line is regularly calibrated and updated to adapt to changes in instrument state and environmental conditions, ensuring the accuracy and reliability of the calibration reference. Each calibration operation records an operation log, including calibration time, operator information, and parameter settings used. These logs are used for quality traceability and problem troubleshooting. The system also provides visualization tools to display the superimposition of the generated main characteristic peak calibration position line and actual spectral data, making it easy for operators to intuitively understand the calibration effect and adjust parameter settings.

[0056] In one embodiment, embodiment 2: the bias value calculation module adopts a multi-step measurement and statistical analysis method in the process of generating the main characteristic peak position bias value, randomly selects one from the intermediate collection position points as the target position point, the random selection algorithm generates a random number seed based on the system timestamp, ensuring the randomness and unbiasedness of the selection process. After obtaining the spectrum collection section associated with the target position point as the target collection section, the system starts the multi-measurement process, each measurement is carried out under slightly different instrument parameter settings, such as fine-tuning the light source intensity of the spectrometer or the integration time of the detector, to obtain independent measurement data sets. In each measurement, the system calculates the offset angle between the main characteristic peak calibration position line and the actual main characteristic peak position line in the target collection section, the angle is obtained by comparing the difference in inclination of the two straight lines, and the straight line fitting algorithm in digital image processing is used for accurate calculation. The measurement of the offset angle is based on the spatial relationship of the two characteristic lines, the main characteristic peak calibration position line remains fixed as the reference benchmark, and the actual main characteristic peak position line is obtained by linear regression analysis of the peak region in the target collection section. The system identifies the highest intensity continuous point set in the target collection section as the peak region, and performs straight line fitting on the data points in the region to obtain the mathematical expression of the actual main characteristic peak position line. By calculating the angle between the fitting line and the calibration position line, the offset angle value of a single measurement is obtained. A group of offset angle data is generated by multiple measurements, and these data may have abnormal values due to noise or measurement errors.

[0057] The system performs screening processing on the obtained offset angle data set, and a preset fluctuation threshold is determined according to statistical characteristics of historical measurement data, and is usually set as a multiple of the standard deviation of the angle value. The screening process excludes values that deviate too much from the group characteristics, and retains measurement results within a reasonable fluctuation range. When the number of values retained after screening exceeds a preset quantity threshold, the system calculates the arithmetic mean of these valid values as the main characteristic peak position deviation value of the target position point. The preset quantity threshold is usually set as a certain percentage of the total number of measurements, to ensure that a sufficient number of reliable data participate in the average calculation. If the number of values after screening is insufficient for the preset quantity threshold, the mean of the extreme values of the offset angle is used to calculate the main characteristic peak position deviation value. This processing method can still provide a reasonable estimated value when the data quantity is insufficient. After completing the deviation value calculation of a target position point, the system automatically traverses all intermediate collection position points, and repeats the above measurement and analysis process for each position point. The traversal process is performed in order of position number, starting from the position point with a smaller number and gradually processing to the position point with a larger number, to ensure that each intermediate collection position point obtains a corresponding deviation value. The calculation process of each position point is independent of each other, and the same number of measurements and parameter settings are used to ensure the comparability of the results. All calculated main characteristic peak position deviation values are stored in a special data structure, which records the number of each intermediate collection position point, the corresponding deviation value, and the parameter information used in the calculation.

[0058] In actual operation, the process of randomly selecting a target position point uses a uniform distribution random number generation algorithm to ensure that each intermediate collection position point has an equal probability of being selected. Once a position point is selected as the target, the system immediately retrieves its associated spectral collection segment data, which contains the complete spectral information of the interval. During multiple measurements, the system automatically adjusts some adjustable parameters of the spectrometer, but keeps the core measurement conditions unchanged, to ensure that data variation mainly comes from random errors rather than systematic errors. The calculation of the offset angle uses vector analysis method, regarding the two characteristic lines as vectors in two-dimensional space, and obtaining the angle value by calculating the vector angle. The setting of the preset fluctuation threshold considers the measurement accuracy requirement and the actual application scenario, and is usually determined based on long-term accumulated quality control data. The screening algorithm uses statistical outlier detection technology to identify and exclude values that deviate significantly from the trend in the data set. The arithmetic mean calculation uses a weighted average method, giving higher weight to recent measurement data to improve the timeliness of the estimated value. The extreme value mean calculation method is used as a backup scheme to provide a relatively robust estimated result when the data quality is poor. During the traversal process, the system displays the processing progress and preliminary results in real time, allowing the operator to monitor the calculation process.

[0059] The data storage adopts a hierarchical structure, and the deviation values of each intermediate collection position point are stored together with its metadata, including measurement time, operator identification, instrument status, and other information. These additional information helps subsequent data tracing and quality analysis. The system also provides data visualization functions to display the deviation value distribution of all intermediate collection position points in chart form, intuitively showing the trend of deviation value changes along the collection path. When some position points have abnormal deviation values, the system triggers an early warning mechanism to prompt the operator to check or re-measure. The entire calculation process is automated, reducing subjective errors caused by human intervention. The system has built-in self-checking functions that automatically verify instrument status and parameter settings before each measurement to ensure that the measurement conditions meet the requirements. After calculation, a detailed report is generated, including the deviation value of each position point, statistical characteristic value, and data quality evaluation index. These data provide accurate input information for subsequent quality level evaluation, supporting the system to make reliable quality judgments.

[0060] In one embodiment, the quality parameter evaluation module generates a glucan quality level evaluation parameter based on a multi-level judgment logic. The main characteristic peak position deviation value is systematically compared with the preset main characteristic peak reference deviation threshold value. The preset main characteristic peak reference deviation threshold value is derived from the statistical results of a large number of standard sample test data, reflecting the allowable fluctuation range of the main characteristic peak position under ideal measurement conditions. The comparison process uses a hierarchical judgment mechanism. When the main characteristic peak position deviation value exceeds twice the preset main characteristic peak reference deviation threshold value, the system starts the full evaluation mode and calls all secondary characteristic peak position data for calculation. The selection criteria for secondary characteristic peak position points include intensity threshold and peak shape integrity requirements. Only secondary characteristic peaks that meet the criteria are included in the calculation range. The system calculates the relative distance ratio and intensity ratio of each secondary characteristic peak to the main characteristic peak, compares these ratios with standard values, and generates a set of feature vectors. This feature vector, together with the main characteristic peak deviation value, forms the first level evaluation parameter.

[0061] When the main characteristic peak position deviation value exceeds the preset main characteristic peak reference deviation threshold value but is less than twice the value, the system uses a partial data evaluation strategy and randomly selects half the number of secondary characteristic peak position points for calculation. The random selection algorithm uses equidistant sampling to select characteristic peaks from the secondary characteristic peak list at fixed intervals, ensuring representativeness. For the selected secondary characteristic peaks, the system calculates their position relationship and intensity relationship with the main characteristic peak, generating a simplified version of the feature vector. This feature vector, combined with the main characteristic peak deviation value, forms the second level evaluation parameter, which has a reduced data volume but retains the main characteristic information.

[0062] When the main characteristic peak position deviation value does not exceed the preset main characteristic peak reference deviation threshold value, the system only generates a third level evaluation parameter according to the main characteristic peak position deviation value. The evaluation parameter is a single numerical type, directly reflects the degree of shift of the main characteristic peak, and the calculation process is simple and efficient. The detection accuracy correction module performs corresponding position coordinate correction operations according to the quality level evaluation parameter. The correction operation is based on a preset position compensation amount. The position compensation amount is determined through instrument calibration experiments and reflects the small position adjustment value required by the system. When the system generates the first level evaluation parameter, it indicates that the measurement deviation is large, and the position coordinates of all spectral acquisition position points need to be compensated and corrected. During the correction process, the horizontal coordinate value of each position point is increased by a fixed preset position compensation amount, and the vertical coordinate remains unchanged. This global correction method ensures that all acquisition points are uniformly adjusted and eliminates systematic position deviation. When the second level evaluation parameter is generated, the system only increases the position coordinates of the intermediate acquisition position points by the preset position compensation amount. The intermediate acquisition position points are the main data source for feature analysis, and their position accuracy has a greater impact on the measurement results. The correction operation is performed on these key points, which improves data quality and reduces computational complexity. The position coordinate correction uses the following mathematical relationship:

[0063]

[0064] wherein: represents the corrected position coordinates, represents the original position coordinates, represents the preset position compensation amount. The compensation amount is determined according to the instrument accuracy requirements and historical calibration data, and is usually a small adjustment value in microns or nanometers.

[0065] When the third level evaluation parameter is generated, the system keeps the original position coordinates unchanged, as the deviation value is within the allowable range and no position correction is needed. This hierarchical correction strategy achieves a balance between accurate adjustment and computational efficiency, avoiding unnecessary operation waste. The generation of the quality level evaluation parameter needs to consider multiple factors. The first level evaluation parameter contains the most abundant information dimension, stored in the form of a multi-dimensional vector, and the vector elements include the main characteristic peak deviation value and the characteristic values of each characteristic peak. The data structure of the second level evaluation parameter is relatively simplified, containing only the main characteristic peak deviation value and part of the characteristic values of the secondary characteristic peaks. The third level evaluation parameter adopts a simple scalar form, directly recording the main characteristic peak position deviation value. All evaluation parameters are accompanied by a quality identifier, indicating the reliability and confidence level of the parameter.

[0066] The detection accuracy correction module records detailed correction logs when performing coordinate correction, including pre-correction coordinate values, post-correction coordinate values, compensation amount used, and correction time, etc. These log data are used for subsequent quality traceability and system performance analysis. The corrected coordinate values are immediately updated to the system's coordinate database, ensuring that the latest coordinate parameters are used for subsequent data acquisition and processing. The system also provides a correction effect verification function, which evaluates the effectiveness of the correction operation by comparing the data quality indicators before and after correction. The selection and processing of secondary characteristic peaks need to follow strict standards, only those characteristic peaks with intensity exceeding a certain proportion of the global maximum intensity and complete peak shape are included in the calculation. Each secondary characteristic peak is subjected to quality inspection, including symmetry check, peak width test and signal-to-noise ratio evaluation, to ensure the reliability of the data. In calculating the relative distance ratio and intensity ratio, the system uses standardized processing methods to eliminate the influence of sample size and measurement conditions, making the results comparable. The determination of position compensation amount needs to consider factors such as instrument accuracy, sample characteristics and measurement requirements. Through a large number of calibration experiments and data analysis, the system establishes a scientific compensation amount determination method. Different instrument models and measurement conditions may correspond to different compensation amount values, and the system will automatically select the most appropriate compensation amount value according to the actual situation. The compensation operation adopts a gradual adjustment strategy to avoid data distortion caused by one-time large adjustment.

[0067] In one embodiment, embodiment 4: the parameter optimization module starts the difference calculation process when generating adaptive feedback control factors, and the system obtains the dextran quality grade evaluation parameters before and after correction and calculates their value difference. The evaluation parameter adopts a multi-dimensional vector form, and the difference calculation involves the operation process of taking the modulus length after the component-by-component difference of the corresponding vector elements. When the calculated difference exceeds the preset feedback threshold, the module automatically triggers the historical data retrieval function to extract the historical evaluation parameter records that meet the specific screening conditions from the system database. The screening conditions include data time range, sample type identification and measurement environment parameters, etc., to ensure that the retrieved historical data have comparability and reference value.

[0068] The processing of historical data adopts a time-weighted average algorithm, with higher weight coefficients for recent data and gradually decreasing weight coefficients for historical data. The system fuses the current difference value with the weighted average of historical parameters, taking into account the numerical value and trend of both. Through a fusion function, the final adaptive feedback control factor is generated. This control factor is a real number between 0 and 1, reflecting the required adjustment intensity. After receiving the adaptive feedback control factor, the calibration update module immediately starts the standard spectral profile update program. This module reads the currently used preset dextran standard spectral profile data and locates the peak vertex coordinate information. The update calculation multiplies the adaptive feedback control factor by the peak vertex coordinate to generate new vertex coordinate data. Based on the new vertex coordinate, the system regenerates a perpendicular line perpendicular to the spectral intensity axis and updates the original standard spectral profile with this new perpendicular line. Refer to Table 1, the historical evaluation parameter data table, which shows the data types and formats used by the system when calculating the weighted average.

[0069] Table 1: Historical Dextran Quality Grade Evaluation Parameter Record

[0070]

[0071] In actual operation, the parameter optimization module first calculates the difference between the quality grade evaluation parameter generated in the current measurement period and the value in the previous period. Suppose the current evaluation parameter value is 0.892 and the previous period is 0.765, the difference is 0.127. The system presets a feedback threshold of 0.100, and the current difference exceeds the threshold, triggering historical data query. The query condition is set to measurement records within the last 3 hours, the same sample batch, and an environmental temperature between 22.0-23.0°C.

[0072] The system retrieves historical records that meet the conditions from the database, including 6 evaluation parameter values at different time points. The weight coefficients are dynamically assigned according to the interval between the record time and the current time, with higher weights for closer times. The weighted average calculation process is: 0.856x0.15 + 0.841x0.18 + 0.632x0.12 + 0.823x0.20 + 0.451x0.10 + 0.675x0.25 = 0.738. The current difference 0.127 and the historical weighted average 0.738 are input into the fusion function, and the adaptive feedback control factor 0.93 is output. The calibration update module uses this control factor to update the standard spectral profile, assuming the original peak vertex coordinate is (125, 3850), the updated coordinate is (125x0.93, 3850x0.93) = (116.25, 3580.5). The system regenerates a perpendicular line based on the new coordinate and updates the entire standard spectral profile database.

[0073] The whole updating process records detailed operation log, including original coordinate value, new coordinate value, control factor size, operation time and other information. The newly generated standard spectral profile line takes effect immediately and is used for subsequent detection and analysis work. The system also retains historical versions of standard profile line data to support data backtracking and comparative analysis when needed. The difference calculation of the parameter optimization module uses a periodic execution mechanism, which automatically starts the calculation process after each measurement period ends. The historical data retrieval range can be configured according to actual needs, usually set to the last 24 hours or the last 100 records. The weight coefficient allocation uses an exponential decay model, with the weight of records closer to the current time being higher, ensuring that the system can timely reflect the recent trend of change.

[0074] The fusion calculation process uses a nonlinear function, and the fusion ratio of the current difference and the weighted average of historical data is dynamically adjusted according to the difference size. When the difference is large, the current difference dominates in the fusion; when the difference is small, the influence of historical data is more significant. This design enables the system to respond to significant changes in a timely manner while maintaining long-term stability. The calibration updating module performs data validity test when performing coordinate updating to ensure that the new coordinates are within a reasonable range. If the calculated new coordinates exceed the preset valid range, the system will automatically adjust the control factor size to make the new coordinates within the valid range. After updating, the system automatically runs a verification test to verify the accuracy of the new profile line using standard samples. The management of historical data uses a rolling update strategy, with new measurement records continuously added to the database and old records gradually archived according to time sequence. The database uses an optimized index structure to support fast query and statistical calculation. All data operations have complete transaction logs to ensure data consistency and traceability. The system provides a visualization tool to display the change history of the standard spectral profile line, helping operators understand the adaptive adjustment process of the system. At the same time, it provides a manual adjustment interface to allow experienced operators to intervene in the adjustment process when necessary, ensuring that the system is always in the best working state.

[0075] In one embodiment, embodiment 5: the feature peak identification module adopts a time segmentation processing strategy when processing the sequence of spectral intensity values, dividing the continuously collected spectral intensity values into multiple sub-datasets according to the timestamp information. Each sub-dataset contains spectral intensity readings within a fixed time period, and the time interval is usually set to the order of milliseconds to ensure the continuity of the data segment. The division process is based on the time synchronization signal of the acquisition device, and the start and end time points of each sub-dataset are accurately recorded with timestamps. The system assigns a unique coded label to each sub-dataset, which contains date and time information, sampling frequency identification and serial number, and can clearly point to its corresponding acquisition period. The coded label is stored in a structured data format, facilitating fast retrieval and matching operations.

[0076] Based on the assigned encoding label, the system performs the identification operation of the secondary feature peak position points within each sub-data set. The identification process adopts a sliding window peak detection algorithm to find local extreme points that meet the intensity threshold and peak width requirements in each sub-data set. Each identified secondary feature peak position point carries the encoding label of its belonging sub-data set, through which the association with the spectral acquisition segment is established. The association process is achieved by querying the time metadata of the spectral acquisition segment, the system matches the timestamp of the secondary feature peak with the time range of the acquisition segment to determine the spectral acquisition segment to which each secondary feature peak belongs. In the specific implementation process, assuming the total spectral acquisition time is 120 seconds and the sampling frequency is 1000 Hz, the system divides the data into 240 sub-data sets, each containing 500 data points, corresponding to an acquisition time of 0.5 seconds. Each sub-data set obtains an encoding label in the format of "20240916-1030-025-500Hz", which contains date, time, serial number and sampling frequency information. The peak detection algorithm runs in each 0.5 second data segment to find local extreme values whose intensity value exceeds 15% of the global maximum value and half-peak width is greater than 3 sampling points. The identified secondary feature peak position points record their time offset and intensity value, while carrying the encoding label of the sub-data set in which they are located. The system associates each secondary feature peak with the corresponding spectral acquisition segment through database query, and the query condition is based on timestamp matching. A multi-level verification mechanism is adopted in the data processing process, and each identified secondary feature peak needs to pass the peak shape test and signal-to-noise ratio test. The peak shape test requires the slope change on both sides of the peak to meet the typical feature peak shape, and the signal-to-noise ratio test requires the peak value intensity to be at least 3 times the background noise intensity. The secondary feature peaks that pass the test are included in the formal data set, and the peaks that do not pass the test are recorded as suspected peaks for subsequent analysis.

[0077] The design of the coding mark supports fast indexing and retrieval. The front time and date information helps to locate the specific time period of data acquisition, the middle sequence number indicates the sequential position of data in the acquisition stream, and the tail sampling frequency information ensures the correct parameter settings during data processing. This marking method enables the system to quickly locate the spectral data at any time point and its corresponding acquisition segment information. The association between the secondary characteristic peaks and the acquisition segments is stored in a specially designed mapping table, which records the unique identifier of each secondary characteristic peak, the acquisition segment number it belongs to, the time offset, intensity value, and other detailed information. The mapping table establishes a bidirectional index, allowing the secondary characteristic peaks to be queried by their acquisition segments, and vice versa. This design supports flexible data query and analysis requirements. During system implementation, the complexity of the actual acquisition environment is considered, and a high-precision clock source is used for the timestamp synchronization mechanism to ensure the time consistency between the acquisition devices. The division of sub-datasets uses an overlapping sliding window approach, with a 10% data overlap between adjacent sub-datasets, avoiding the segmentation of characteristic peaks in different sub-datasets. This processing method ensures the integrity of the characteristic peak identification and reduces errors caused by boundary effects. The generation of the coding mark uses an automated process, with the system automatically generating a basic timestamp based on the start time of acquisition, and then assigning a sequence number to each sub-dataset in order. The sampling frequency information is read from the device configuration file to ensure the accuracy of the mark information. The entire marking process is executed immediately after data acquisition is complete, preparing for the subsequent processing stage.

[0078] The secondary characteristic peak identification algorithm uses an adaptive threshold technique, dynamically adjusting the intensity threshold based on the noise level of each sub-dataset. Peak detection not only considers intensity values, but also examines morphological features such as peak symmetry and steepness. Each identified peak is given a quality score, taking into account factors such as signal-to-noise ratio, completeness, and separation. Peaks with lower scores are marked for review. The association establishment process uses a batch processing approach, with the system first completing the identification of secondary characteristic peaks for all sub-datasets, and then uniformly establishing the association with the acquisition segments. The association algorithm is based on the time matching principle, calculating the inclusion relationship between the timestamp of each secondary characteristic peak and the time range of each acquisition segment. A fault-tolerant mechanism is used in the matching process, allowing for minor time deviations to ensure correct association even when there are minor errors in clock synchronization. The data storage uses an optimized structure, with secondary characteristic peak information and raw spectral data stored separately but linked through index pointers. This design improves data retrieval efficiency while maintaining data integrity. The system regularly checks the consistency of the associated data to ensure the accuracy of the correspondence between secondary characteristic peaks and acquisition segments. The entire processing flow is fully automated, from data acquisition to secondary characteristic peak identification and association establishment, without the need for human intervention. The system provides a visual monitoring interface that displays the processing progress and intermediate results in real-time, making it easy for operators to understand the processing status. All processing steps are logged in detail, supporting fault diagnosis and performance analysis.

[0079] The above merely provides the preferred embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which is made according to the contents of the present application specification and drawings, shall be included in the patent protection scope of the present application.

Claims

1. A dextran-based mass spectrometry detection and analysis system, characterized in that, include: A spectral dataset construction module is used to acquire spectral data of dextran samples, wherein the spectral data contains a sequence of spectral intensity values ​​from multiple consecutive acquisition locations; The feature peak identification module is used to identify the location points of feature peaks in the spectral data, including the location points of main feature peaks and the location points of secondary feature peaks. The characteristic peak position calibration module is used to calibrate the position of the main characteristic peak according to the preset standard spectral contour of dextran and generate the main characteristic peak calibration position point; The deviation value calculation module is used to measure and analyze the offset between the calibration position point of the main characteristic peak and the actual position point of the main characteristic peak in the spectral data multiple times, and generate the main characteristic peak position deviation value. The quality parameter evaluation module is used to generate dextran quality grade evaluation parameters by combining the main characteristic peak position deviation value and the secondary characteristic peak position point; The detection accuracy correction module is used to correct the position coordinates of the preset spectral acquisition point according to the dextran quality grade evaluation parameters; The dynamic feedback module is used to reacquire spectral data after correction and calculate new dextran quality grade evaluation parameters; The specific method by which the quality parameter evaluation module generates dextran quality grade evaluation parameters is as follows: The position deviation value of the main characteristic peak is compared with the preset reference deviation threshold value of the main characteristic peak: When the position deviation of the main feature peak exceeds twice the preset main feature peak reference deviation threshold, the first-level evaluation parameters are generated by combining all the position points of the secondary feature peaks. When the position deviation of the main feature peak exceeds the preset main feature peak reference deviation threshold but is less than twice, the second-level evaluation parameters are generated by combining the position points of the half-number of feature peaks. When the position deviation of the main feature peak does not exceed the preset reference deviation threshold of the main feature peak, the third-level evaluation parameters are generated only based on the position deviation of the main feature peak. The first-level evaluation parameters are composed of the feature vector generated by comparing the relative distance ratio and intensity ratio of each secondary feature peak to the main feature peak with the standard value, and the deviation value of the main feature peak. The second-level evaluation parameters are formed by combining a simplified feature vector generated based on the positional and intensity relationships between the selected secondary and primary feature peaks with the deviation value of the primary feature peak. The third-level evaluation parameter is a single numerical type, which directly reflects the degree of shift of the main characteristic peak.

2. The dextran-based mass spectrometry detection and analysis system according to claim 1, characterized in that, The specific method by which the spectral dataset construction module obtains continuously acquired location points is as follows: The starting point of spectral acquisition is taken as the first position point, and the ending point is taken as the last position point. The starting point, ending point and intermediate acquisition position points are numbered according to the acquisition sequence. Spectral acquisition segments are formed between adjacent position points. Each spectral acquisition segment is associated with the number of the position points at its two ends. The spectral acquisition segment associated with the intermediate acquisition position point is used as the input of the feature peak identification module.

3. The dextran-based mass spectrometry detection and analysis system according to claim 2, characterized in that, The specific method by which the characteristic peak position calibration module generates the main characteristic peak calibration position points is as follows: The peak vertices of the preset dextran standard spectral profile are extracted as reference points. A perpendicular line is drawn from the reference point to the spectral intensity axis. This perpendicular line is used as the main characteristic peak calibration line, and the reference point is used as the endpoint of the main characteristic peak calibration line.

4. The dextran-based mass spectrometry detection and analysis system according to claim 3, characterized in that, The specific method by which the deviation calculation module generates the main characteristic peak position deviation value is as follows: Randomly select an intermediate acquisition point as the target location point, obtain the spectral acquisition segment associated with the target location point as the target acquisition segment, and measure the offset angle between the main characteristic peak calibration position line and the actual main characteristic peak position line in the target acquisition segment multiple times. The values ​​of the offset angles that meet the preset fluctuation threshold are selected. When the number of selected values ​​exceeds the preset threshold, the average value is taken as the position deviation value of the main feature peak of the target location point. Otherwise, the average value of the extreme values ​​of the offset angles is taken as the position deviation value of the main feature peak. Iterate through all intermediate acquisition points to generate the corresponding main feature peak position deviation value.

5. The dextran-based mass spectrometry detection and analysis system according to claim 1, characterized in that, The specific method by which the detection accuracy correction module corrects the position coordinates is as follows: Based on the dextran quality grade evaluation parameters, when generating the first grade evaluation parameters, the position coordinates of all spectral acquisition points are increased by a preset position compensation amount. When generating the second-level evaluation parameters, only the position coordinates of the intermediate acquisition points are increased by the preset position compensation amount; When generating the third-level evaluation parameters, keep the original position coordinates unchanged.

6. The dextran-based mass spectrometry detection and analysis system according to claim 1, characterized in that: The system also includes a parameter optimization module, used to generate an adaptive feedback control factor based on the differences in the quality grade evaluation parameters of the new and old dextran, specifically in the following manner: Calculate the difference in the quality grade evaluation parameters of dextran before and after correction. When the difference exceeds the preset feedback threshold, extract the quality grade evaluation parameters of dextran that meet the preset screening conditions from historical tests. The current difference is combined with the weighted average of historical parameters to generate an adaptive feedback control factor.

7. The dextran-based mass spectrometry detection and analysis system according to claim 6, characterized in that: The system further includes a calibration update module, used to update the preset dextran standard spectral profile based on the adaptive feedback control factor, specifically in the following manner: The adaptive feedback control factor is multiplied by the peak coordinates of the preset dextran standard spectral profile, and the product is used as the new peak coordinates. Based on these coordinates, a vertical line is regenerated to update the preset dextran standard spectral profile.

8. The dextran-based mass spectrometry detection and analysis system according to claim 1, characterized in that, The specific method by which the feature peak identification module identifies the location points of secondary feature peaks is as follows: The spectral intensity value sequence is divided into multiple subsets according to timestamps. Each subset is assigned a coded marker pointing to its acquisition period. The location of the secondary characteristic peak is identified based on the coded marker and associated with the corresponding spectral acquisition segment.

9. A method for mass spectrometry detection and analysis based on dextran, characterized in that, It includes all modules and method flows of the dextran-based mass spectrometry detection and analysis system as described in any one of claims 1 to 8.

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