CN117892061B

CN117892061B - One-dimensional spectrogram data processing method, system, terminal and medium for qualitative and quantitative analysis

Info

Publication number: CN117892061B
Application number: CN202311831287.8A
Authority: CN
Inventors: 产天龙; 韩峻松; 徐祎春; 袁箐; 丁岩汀; 何志远; 纪留芸
Original assignee: SHANGHAI BIOCHIP CO Ltd
Current assignee: SHANGHAI BIOCHIP CO Ltd
Priority date: 2023-12-27
Filing date: 2023-12-27
Publication date: 2024-06-28
Anticipated expiration: 2043-12-27
Also published as: CN117892061A

Abstract

The application provides a one-dimensional spectrogram data processing method, a system, a terminal and a medium for qualitative and quantitative analysis, which are used for carrying out qualitative analysis and/or quantitative analysis on a sample to be tested by carrying out smooth denoising, baseline correction, peak finding fitting and other treatments on the preprocessed one-dimensional spectrogram data of the sample to be tested and combining a model equation of a constructed standard product, thereby realizing automatic, simple, quick, accurate, effective and qualitative analysis and/or quantitative analysis on the sample to be tested.

Description

One-dimensional spectrogram data processing method, system, terminal and medium for qualitative and quantitative analysis

Technical Field

The application relates to the technical field of substance analysis, in particular to a one-dimensional spectrogram data processing method, a system, a terminal and a medium for qualitative and quantitative analysis.

Background

A one-dimensional spectrogram refers to a graph representing data in one dimension, typically used to express the intensity or distribution of a signal at different locations, points in time, or frequencies. In biomedical research applications and industrial applications, one-dimensional spectrograms are widely used in a variety of fields including capillary gel electrophoresis, biosensor data, electrocardiography, electroencephalography, industrial vibration analysis, environmental monitoring, signal processing, spectroscopy, mass spectrometry, nuclear magnetic resonance, chromatography, and the like. In a one-dimensional spectrogram, the horizontal axis generally represents a single dimension, such as time, wavelength, mass-to-charge ratio, location, etc., and the vertical axis represents signal intensity in that dimension. The curve, peak, position or other graphical feature in the spectrogram represents the distribution of the signal in this dimension. Features such as shape of spectrogram, position, area and height of peak can provide clues about properties, composition, structure or other information of the sample, thus laying foundation for deeper study and application. In the early stages of experiments and observations, rational analysis of one-dimensional spectra is a key step to ensure data quality and to provide powerful support for subsequent studies.

The one-dimensional spectrogram type data analysis has a plurality of unique characteristics, including the characteristics of signal peaks, high noise background, data preprocessing requirement, various data types, nonlinear relation, field specialty and the like. One-dimensional spectrogram type data analysis, while a very useful tool in many fields, has drawbacks that can affect the accuracy of the data and the reliability of interpretation. Noise and baseline drift are common problems in one-dimensional spectra, and can affect signal sharpness; achieving accurate quantitative analysis requires overcoming various complexities, including variations in peak shape, nonlinear response, etc.; the position and the intensity of the calibration peak can be influenced by errors, and the inaccuracy of the calibration can influence the quantitative and qualitative analysis results of the spectrogram; data processing typically requires complex preprocessing steps that can have some impact on the results.

Disclosure of Invention

In view of the above-mentioned drawbacks of the prior art, an object of the present application is to provide a one-dimensional spectrogram data processing method, system, terminal and medium for qualitative and quantitative analysis, which are used for solving the above problems in the prior art.

To achieve the above and other related objects, a first aspect of the present application provides a one-dimensional spectrogram data processing method for qualitative and quantitative analysis, which performs preprocessing on acquired raw one-dimensional spectrogram data of a sample to be tested; performing spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaping data; based on the peak information obtained from the spectrogram shaping data, according to the obtained target attribute information, the properties of the sample to be tested are qualitatively and/or quantitatively analyzed in a mode of correcting and/or assisting in analysis by using a constructed standard model equation so as to obtain an analysis result of the sample to be tested.

In some embodiments of the first aspect of the present application, the preprocessing of the obtained raw one-dimensional spectrogram data of the sample to be measured includes: judging whether abnormal signal value data exist in the original one-dimensional spectrogram data; if the abnormal signal value data exists in the original one-dimensional spectrogram data, deleting the abnormal signal value data, and carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data with the abnormal signal value data deleted so as to obtain preprocessed one-dimensional spectrogram data; if the original one-dimensional spectrogram data is judged to have no abnormal signal value data, carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data to obtain preprocessed one-dimensional spectrogram data.

In some embodiments of the first aspect of the present application, performing a spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaping data comprises: smoothing and denoising the preprocessed one-dimensional spectrogram data, and correcting a baseline of the smoothed and denoised one-dimensional spectrogram data to obtain preliminary shaping data; and identifying and deleting abnormal signal peak data in the primary shaping data, and filling corresponding missing values by adopting a base line level value of a deleting position to obtain spectrogram shaping data.

In some embodiments of the first aspect of the present application, the means for obtaining peak information from the spectrogram shaping data comprises: carrying out peak searching operation based on the spectrogram shaping data to obtain peak searching result information; and carrying out peak type fitting on partial peaks in the spectrogram shaping data according to the peak searching result information so as to obtain peak information.

In some embodiments of the first aspect of the present application, the method further comprises: in the case where only a plurality of peak-to-peak coordinate information is obtained after the peak searching operation is completed, the range of the corresponding peak is inferred based on each obtained peak-to-peak coordinate information to obtain peak information.

In some embodiments of the first aspect of the present application, based on the peak information obtained from the spectrogram shaping data, the means for qualitatively and/or quantitatively analyzing the property of the sample to be measured by performing correction and/or auxiliary analysis using the constructed standard model equation according to the obtained target attribute information, for obtaining the analysis result of the sample to be measured includes: distinguishing peak information obtained from the spectrogram shaping data according to the obtained target attribute information; correcting the first-class peak information confirmed to be in accordance with the target attribute and/or performing auxiliary analysis on the second-class peak information confirmed to be in non-compliance with the target attribute by using the constructed standard model equation so as to obtain an analysis result of the sample to be tested; the standard model equation is obtained by modeling the obtained one-dimensional spectrogram data of the standard and the target attribute.

In some embodiments of the first aspect of the present application, the method further comprises: and after the analysis result of the sample to be tested is obtained, a corresponding analysis report generated based on the analysis result of the sample to be tested is visually displayed.

To achieve the above and other related objects, a second aspect of the present application provides a one-dimensional spectrogram data processing system for qualitative and quantitative analysis, comprising: the preprocessing module is used for preprocessing the acquired original one-dimensional spectrogram data of the sample to be detected; the spectrogram shaping module is connected with the preprocessing module and is used for executing spectrogram shaping operation on the preprocessed one-dimensional spectrogram data so as to obtain spectrogram shaping data; the analysis module is connected with the spectrogram shaping module and is used for carrying out qualitative analysis and/or quantitative analysis on the properties of the sample to be detected by utilizing a constructed standard model equation in a mode of correcting and/or assisting in analysis according to the obtained target attribute information based on the peak information obtained from the spectrogram shaping data so as to obtain an analysis result of the sample to be detected.

To achieve the above and other related objects, a third aspect of the present application provides a terminal comprising: a processor and a memory; the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory so as to enable the terminal to execute the one-dimensional spectrogram data processing method for qualitative and quantitative analysis.

To achieve the above and other related objects, a fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the one-dimensional spectrogram data processing method for qualitative and quantitative analysis.

As described above, the one-dimensional spectrogram data processing method, system, terminal and medium for qualitative and quantitative analysis of the application have the following beneficial effects:

the one-dimensional spectrogram data of the sample to be detected after pretreatment is subjected to the treatment of smoothing denoising, baseline correction, peak finding fitting and the like, and qualitative analysis and/or quantitative analysis are carried out on the sample to be detected by combining with a model equation of a constructed standard substance, so that the automatic, simple, quick, accurate, effective and qualitative analysis and/or quantitative analysis on the sample to be detected is realized.

Drawings

FIG. 1 is a flow chart of a one-dimensional spectrogram data processing method for qualitative and quantitative analysis according to an embodiment of the application.

FIG. 2 is a diagram of raw one-dimensional spectrogram data in an embodiment of the application.

FIG. 3 is a schematic diagram of filling missing values using interpolation in an embodiment of the application.

FIG. 4 is a schematic diagram of an adaptive threshold truncation method for converting negative values to non-negative values according to an embodiment of the present application.

FIG. 5 is a diagram showing the comparison of the original one-dimensional spectrogram data with the preprocessed one-dimensional spectrogram data according to an embodiment of the present application.

FIG. 6 is a diagram showing a comparison of one-dimensional spectrogram data after preprocessing and one-dimensional spectrogram data after spectrogram shaping processing according to an embodiment of the present application.

FIG. 7 is a schematic diagram showing the extraction of peak regions by gradient method according to an embodiment of the present application.

FIG. 8 is a schematic diagram showing the fitting of peaks to normal distribution peaks in an embodiment of the application.

FIG. 9 is a diagram showing modeling of the abscissa position and molecular weight size of one-dimensional spectra of 16 standards in an embodiment of the application.

FIG. 10 is a schematic diagram of an evaluation of standard model equations established in an embodiment of the present application.

FIG. 11 is a schematic diagram of an analysis report generated in an embodiment of the application.

Fig. 12 is a schematic diagram showing one-dimensional spectrogram data, preprocessed one-dimensional spectrogram data and spectrogram shaping data marked with analysis results according to an embodiment of the present application.

FIG. 13 is a schematic diagram of a one-dimensional spectrogram data processing system for qualitative and quantitative analysis according to an embodiment of the present application.

Fig. 14 is a schematic diagram of a structure of a terminal according to an embodiment of the application.

Detailed Description

Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.

In the following description, reference is made to the accompanying drawings, which illustrate several embodiments of the application. It is to be understood that other embodiments may be utilized and that mechanical, structural, electrical, and operational changes may be made without departing from the spirit and scope of the present application. The following detailed description is not to be taken in a limiting sense, and the scope of embodiments of the present application is defined only by the claims of the issued patent. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Spatially relative terms, such as "upper," "lower," "left," "right," "lower," "upper," and the like, may be used herein to facilitate a description of one element or feature as illustrated in the figures as being related to another element or feature.

In the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," "held," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.

Furthermore, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," "includes," and/or "including" specify the presence of stated features, operations, elements, components, items, categories, and/or groups, but do not preclude the presence, presence or addition of one or more other features, operations, elements, components, items, categories, and/or groups. It will be further understood that the terms "or" and/or "as used herein are to be interpreted as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and/or C" means "any of the following: a, A is as follows; b, a step of preparing a composite material; c, performing operation; a and B; a and C; b and C; A. b and C). An exception to this definition will occur only when a combination of elements, functions or operations are in some way inherently mutually exclusive.

In order to make the objects, technical solutions and advantages of the present invention more apparent, further detailed description of the technical solutions in the embodiments of the present invention will be given by the following examples with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Fig. 1 is a schematic flow chart of a one-dimensional spectrogram data processing method for qualitative and quantitative analysis in an embodiment of the invention.

The one-dimensional spectrogram data processing method for qualitative and quantitative analysis comprises the following steps:

Step S101: and preprocessing the obtained original one-dimensional spectrogram data of the sample to be detected.

In one embodiment, the preprocessing method for the raw one-dimensional spectrogram data of the acquired sample to be detected includes: judging whether abnormal signal value data exist in the original one-dimensional spectrogram data; if the abnormal signal value data exists in the original one-dimensional spectrogram data, deleting the abnormal signal value data, and carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data with the abnormal signal value data deleted so as to obtain preprocessed one-dimensional spectrogram data; if the original one-dimensional spectrogram data is judged to have no abnormal signal value data, carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data to obtain preprocessed one-dimensional spectrogram data.

The process of pretreatment will be explained in detail below with reference to examples and drawings:

The first step of preprocessing is to judge whether abnormal signal value data exists in the original one-dimensional spectrogram data of the sample to be detected.

Judging whether the original one-dimensional spectrogram data of the sample to be tested has abnormal signal value data or not, wherein the field where the sample to be tested is needed to be combined is located.

For example, in the fields of molecular biology, optics, etc., peaks do not occur in certain ranges. If a peak appears in the range of the original one-dimensional spectrogram data of the sample to be detected, the data of the peak can be identified as abnormal signal value data. As another example, some outlier data may be identified directly, such as the downward peak as shown in fig. 2.

The second step is to carry out missing value filling and negative data conversion according to the judgment result of the first step, and specifically comprises the following steps:

if the original one-dimensional spectrogram data of the sample to be detected does not have abnormal signal value data, filling missing values and converting negative data of the original one-dimensional spectrogram data of the sample to be detected to obtain preprocessed one-dimensional spectrogram data;

If the abnormal signal value data exists in the original one-dimensional spectrogram data of the sample to be detected, deleting the abnormal signal value data. And then, carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data from which the abnormal signal value data are deleted, so as to obtain the preprocessed one-dimensional spectrogram data.

It should be noted that, the missing value in the original one-dimensional spectrogram data may be caused by external reasons such as an instrument, etc. not only by deleting the abnormal signal value data, but also by deleting the missing value in the original one-dimensional spectrogram data.

In this embodiment, missing value filling is performed by any one of the estimated baseline filling, interpolation filling, filling based on the minimum value, and window sliding filling. It should be noted that, those skilled in the art may also use other filling modes according to actual requirements, and the present invention is not limited thereto.

The estimated baseline filling method specifically comprises the following steps: and filling the baseline value in the one-dimensional spectrogram by adopting a global baseline value equation for estimating the one-dimensional spectrogram or a baseline value equation for estimating the horizontal axis and the vertical axis in the fitting one-dimensional spectrogram.

The baseline value refers to a statistic that may describe the expected value of the data distribution, such as the mean value and median value of each vertical axis value or the vertical axis value measured by the instrument when the signal is 0 and the influence of internal and external factors such as instrument offset and noise pollution.

The interpolation filling method is to perform filling interpolation by using a machine learning method, a linear interpolation method, a spline interpolation method, a Lagrange interpolation method and the like.

The minimum filling method is based on the method that the minimum value of the vertical axis of the one-dimensional spectrogram is obtained and is filled with a value a slightly smaller than the minimum value, and further, the value a is the difference between the two previous 50 percentiles of spectrogram data subtracted from the minimum value, such as the difference between the 15 percentiles and the 1 percentile.

The window sliding filling method is to estimate the missing value by gradually sliding a window in the spectrogram data and selecting an appropriate interpolation method using the information in the window.

In this embodiment, the negative data conversion is to convert negative data in the original one-dimensional spectrogram data into non-negative real data; and performing negative data conversion by adopting any one mode of estimated baseline positive regression, adaptive threshold truncation and sliding window positive regression. It should be noted that, those skilled in the art may also adopt other transformation modes according to actual requirements, and the present invention is not limited thereto.

The estimated baseline positive regression refers to fitting a change curve of the estimated baseline, and the spectrogram data is correspondingly adjusted according to a curve equation to be converted into a non-negative real number.

The self-adaptive threshold value cut-off refers to self-adaptive adjustment of a threshold value in a variant form by adopting a normal distribution 3 sigma principle, a Gaussian method, a gradient method, a self-adaptive Niblack and other methods according to the characteristics of all or partial spectrogram data, wherein all or partial spectrogram data smaller than the corresponding threshold value are converted into 0, and the rest of the spectrogram data are kept unchanged.

The sliding window positive regression means that a window is gradually slid in spectrogram data, local maximum values are searched or peak information is reserved by using a peak detection algorithm, a base line in the window is estimated, the base line is converted into a value 0 by using nonlinear transformation, and spectrogram data is adjusted on the basis that the spectrogram data is converted into non-negative real numbers and reserved peaks are overlapped.

To better illustrate the preprocessing process, a specific embodiment is provided below in conjunction with fig. 2,3,4, and 5.

Embodiment one: a preprocessing method for original one-dimensional spectrogram data of a sample to be detected.

As shown in fig. 2, raw one-dimensional spectrogram data is shown as a sample to be measured. As can be seen from fig. 2, the original one-dimensional spectrogram data has serious noise, baseline drift, abnormal signal value data and the like, but has more obvious peaks.

There is a downward peak near the exit of abscissa 1100 and the signal value is negative. The peak is judged as an abnormal signal value and needs to be deleted. After deleting the peak at this point, the missing value filling was performed by the lagrangian method. As shown in fig. 3, in the vicinity of the abnormal peak, the signal value of the original peak (broken line) and the signal value of the lagrangian-filled data (solid line) are shown.

And for negative data in the original one-dimensional spectrogram data, adopting an adaptive threshold value truncation method to convert the negative data into non-negative values. For the one-dimensional spectrogram data shown in fig. 3, a data distribution histogram (divided into 300 bins) shown in fig. 4 is plotted. For one-dimensional spectrogram data processed as in fig. 3, a data distribution histogram (divided into 300 bins) is drawn. The histogram shows that the data distribution approximates a normal distribution (the small tail at the right is affected by the peak area data), which is fitted to a curve of gaussian distribution, calculated as 0.748959 for gaussian distribution μ and 20.597862 for σ. And judging mu-3 sigma= -61.34883218789747 as a threshold value according to the 3 sigma principle of normal distribution. The spectrogram is shifted upwards 61.34883218789747, and on the basis, the negative number (the signal value smaller than the threshold value part) is uniformly assigned to be 0. After the pretreatment is finished, the comparison between the pretreated one-dimensional spectrogram data and the original one-dimensional spectrogram data is shown in fig. 5, and the problem of missing values and the problem of negative data can be seen from fig. 5.

Step S102: and performing spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaping data.

In one embodiment, performing a spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaped data comprises: smoothing and denoising the preprocessed one-dimensional spectrogram data, and correcting a baseline of the smoothed and denoised one-dimensional spectrogram data to obtain preliminary shaping data; and identifying and deleting abnormal signal peak data in the primary shaping data, and adopting a base line level value of a deleting position to carry out corresponding filling to obtain spectrogram shaping data.

The spectrogram shaping operation will be explained in detail below with reference to specific embodiments and accompanying drawings:

The first step of the spectrogram shaping operation is to perform smoothing denoising processing on the preprocessed one-dimensional spectrogram data obtained in step S101, and perform baseline correction on the smoothed one-dimensional spectrogram data to obtain preliminary shaping data.

It should be noted that, the smoothing denoising processing is performed on the preprocessed one-dimensional spectrogram data to remove background noise pollution, improve signal to noise ratio, eliminate random or systematic fluctuation and highlight signal characteristics; the baseline correction is carried out on the one-dimensional spectrogram data after the smoothing denoising treatment to remove instrument drift and baseline drift, improve signal to noise ratio, highlight peak characteristics and accurately position peak information.

In this embodiment, any one of a window sliding method, a Savitzky-Golay filtering method, a gaussian filtering method, a Kaiser window method, a wavelet analysis method and a Lowess method is adopted to perform smoothing denoising processing on the preprocessed one-dimensional spectrogram data. It should be noted that, those skilled in the art may adopt other smoothing denoising methods according to actual needs, and the present invention is not limited thereto.

For example, the noise of the preprocessed one-dimensional spectrogram data is preliminarily judged to conform to the normal distribution from fig. 3, and the wavelet analysis method is adopted for smooth noise reduction. Wavelet analysis achieves denoising by thresholding the wavelet coefficients. In the wavelet domain of a signal, noise typically appears as a high frequency component of small amplitude. The preprocessing one-dimensional spectrogram data is subjected to wavelet decomposition, the wavelet coefficients with larger amplitudes are mostly useful signals, and the coefficients with smaller amplitudes are generally noise, namely the wavelet transform coefficients of the useful signals can be considered to be larger than the wavelet transform coefficients of the noise. By setting a threshold, the wavelet coefficients smaller than the threshold are set to zero, and the coefficients larger than the threshold are reserved. This process can effectively remove noise components. The thresholded wavelet coefficients are synthesized by inverse transformation into denoised signals. The denoised signal is shown in fig. 6, and the noise is significantly more effectively resolved than before denoising.

In this embodiment, any one of a monotonic minimum method, a parametric fitting method, a piecewise fitting method, a polynomial fitting method, a Whittaker smoothing method, an iterative peak reduction method, and a wavelet function method is used to perform baseline correction on the one-dimensional spectrogram data after the smoothing denoising. It should be noted that, those skilled in the art may use other baseline correction manners according to actual needs, which the present invention is not limited to.

The monotonic minimum method is to obtain the first derivative of a one-dimensional spectrogram, slide from head to tail, and slide all the time when the first derivative is negative and the first derivative is positive, and the baseline of the section area is the signal value; the first derivative is positive and slides all the time when the signal value is larger than the left endpoint, and the baseline of the section area is the signal value of the left endpoint.

The parametric fitting method is to divide a one-dimensional spectrogram into a plurality of small segments, and construct a probability density equation of a preset distribution (such as the sum of normal distribution and uniform distribution) of signal values for each segment region. Judging a peak area and a base line area according to the fitting parameters of the probability density equation, and calculating the base line value of the peak area according to the fitting curve fitting value of the preset distribution. And (3) iterating for a plurality of times, and changing the region division each time until the estimated baseline residual error of the iteration is smaller than a preset threshold value or reaches the preset iteration times.

The segmentation fitting method is to divide a one-dimensional spectrogram into a plurality of small segments, filter a high signal value (such as assigning a signal value greater than 10 percentiles to 10 percentiles) by a self-adaptive threshold value for each segment region, and perform low-order polynomial regression on the processed signal value to calculate a baseline value. The replacement area is divided a plurality of times, and the baseline value is recalculated. Taking the average of multiple calculations.

Polynomial fitting means that a polynomial curve is fitted iteratively and the peak area value is gradually reduced, so that a baseline is extracted. And continuously iterating, replacing the peak area value with the fitting curve value, and calculating the residual error until the residual error change is smaller than a preset condition (such as a preset threshold value or the residual error change rate is smaller than 5% for a plurality of times).

The Whittaker smoothing method is to iterate Whittaker smoothing and dynamic weight adjustment to reduce the influence of peak areas, thereby extracting a baseline between peaks. And flexibly updating the weight vector according to the positive and negative of the residual error, setting the weight of the peak area to zero so as to ignore the peak effect, and adjusting the weights of other areas according to the absolute value of the residual error so as to adaptively adjust the data area concerned by the algorithm. And iterating continuously until the sum of absolute values of the residual errors is reduced below a preset threshold value or reaches preset iteration times.

The iterative peak reduction method is to extract a baseline from one-dimensional spectrogram data in a way of gradually reducing local peaks through an iterative process. After data evolution and log conversion, each iteration gradually reduces the range, selects a subsequence in the middle of peak value data, horizontally translates the sequence for a plurality of times, takes the translated average value, and updates the subsequence in the middle of the peak value data to be the minimum value of the average value and the original value.

Wavelet function is an idea of using multi-scale analysis of wavelet transforms and local weighted averaging, combined with median filtering to estimate the baseline and attenuate the effect of peaks to some extent. Obtaining a wavelet coefficient matrix by utilizing wavelet transformation; local weighted average of wavelet coefficients is achieved using convolution operations; and performing related filtering processing such as median filtering on the smoothed wavelet coefficient to obtain an estimated baseline. Further, one-dimensional spectrogram data after baseline correction is obtained according to the obtained baseline and the input signal value.

For example, after the smooth noise reduction is completed, a segmentation fitting method is selected to perform baseline correction on the one-dimensional spectrogram data after the smooth noise reduction. As can be seen from fig. 6, the baseline wander problem is effectively solved after the baseline correction is completed. And the second step of spectrogram shaping operation is to identify and delete abnormal signal peak data in the preliminary shaping data after the smoothing denoising and baseline correction processing, and to fill corresponding missing values by adopting the baseline level value of the deleted position.

Specifically, after the abnormal signal peak data in the preliminary shaping data is identified, the abnormal signal peak data is deleted. The deleted value will appear after deletion, and the position where the deleted value appears is the deleted position. After this, the corresponding filling is performed with the baseline level value of the deleted location.

The abnormal signal peak data in this embodiment is different from the abnormal signal value data in the above embodiment. The abnormal signal value is not supposed to exist in theory, and once the abnormal signal value appears, the abnormal signal value is judged to be an impurity or an abnormal signal. While the abnormal signal peak is actually present. Abnormal signal peaks include, but are not limited to: peaks that do not match the target (e.g., capillary gel electrophoresis, some of which have signal values above 80000), peaks that do not match the peak (e.g., chromatographic mass spectrometry, severe tailing of the peak, or too small a peak area identified), and matrix peaks.

Step S103: based on the peak information obtained from the spectrogram shaping data, according to the obtained target attribute information, the properties of the sample to be tested are qualitatively and/or quantitatively analyzed in a mode of correcting and/or assisting in analysis by using a constructed standard model equation so as to obtain an analysis result of the sample to be tested.

Specifically, the invention can perform three kinds of analysis on the sample to be detected, namely qualitative analysis, quantitative analysis and qualitative and quantitative analysis. And correspondingly obtaining a qualitative analysis result, a quantitative analysis result and qualitative and quantitative analysis results. The qualitative and/or quantitative analysis results are correlated with the target property. For example, if the target attribute is molecular weight, the obtained analysis result is the information about the molecular weight of the sample to be measured.

In one embodiment, the means for obtaining peak information from the spectrogram shaping data comprises: carrying out peak searching operation based on the spectrogram shaping data to obtain peak searching result information; and carrying out peak type fitting on partial peaks in the spectrogram shaping data according to the peak searching result information so as to obtain peak information.

In one embodiment, the method further comprises: in the case where only a plurality of peak-to-peak coordinate information is obtained after the peak searching operation is completed, the range of the corresponding peak is inferred based on each obtained peak-to-peak coordinate information to obtain peak information.

The manner in which the peak information is obtained will be explained in detail below with reference to the accompanying drawings:

in this embodiment, any one of a gradient method, a continuous wavelet transform method, a window sliding method, a second derivative method, a first derivative method, and a normalized spectrogram method is used for peak searching operation.

The gradient method is to determine possible peak positions by searching rising and falling edges of gradient change by using gradient information of signals, and filter out uninteresting peaks by set conditions (such as peak height, peak width and the like).

The continuous wavelet transform method refers to analyzing frequency domain information of a signal using continuous wavelet transform, and determining peak positions in the signal by finding spectral peaks at different scales and positions.

The window sliding method is to set a threshold in a one-dimensional signal, traverse signal elements, alternately find local maxima and minima, and judge whether the current signal is likely to be a new peak or valley by comparing the difference between the current signal and the last local extremum, and simultaneously meet the requirement of being the maximum or minimum in a preset window. The peak positions satisfying the condition are recorded.

The second derivative method is to find zero crossing points with negative second derivative, find the starting point of positive slope forward from the point for each zero crossing point, find the end point of negative slope backward, determine the peak range, and keep the peak meeting the preset peak width threshold. It is determined whether the same peak (peak of the preventive repeat search) is the most distant starting point. By retaining the peaks with a greater height, it is ensured that the distance between adjacent peaks exceeds a certain threshold.

The first derivative method is to find zero crossing points in the first derivative as alternatives, and at the same time, the absolute value of the first derivative is required to be larger than a preset threshold value within a preset window size by more than a certain proportion. And meanwhile, obtaining an adaptive threshold according to the distribution condition of surrounding first-order derivatives, and determining the range of the peak according to the adaptive threshold and the first-order derivatives.

Normalized spectrogram refers to calculating the root mean square of spectrogram data, dividing each point data by the root mean square and squaring, and selecting local maxima and being greater than a preset threshold (e.g., 0.3).

For example, as shown in fig. 7, a peak searching operation is performed by a gradient method, in which a peak area (width is a peak area width and height is 3/4 of a peak height) is marked with gray color bars, and peak peaks are marked with digital coordinates. Note that fig. 7 shows only the labeling information of the first five peaks of the peak height order.

After the peak searching operation is completed, obtaining peak searching result information; wherein, peak finding result information includes: peak top point coordinate information, peak width information, and peak left and right end point information of each peak in the spectrogram shaping data.

And after peak searching result information is obtained, carrying out peak type fitting on partial peaks in the spectrogram shaping data according to the peak searching result information. Wherein, partial peaks subjected to peak type fitting are irregular or specific-shaped peaks. The reason that the peaks need to be fitted is that, in combination with the technical knowledge of the field, some peaks can be directly confirmed to have errors (such as that some peaks have slight tailing), and the one-dimensional spectrogram data measured by the instrument are discrete data in nature, however, some analyses need to acquire information such as peak areas, the discrete information can be converted into continuous information by peak fitting, and the information such as peak areas can be obtained after conversion.

For example, in combination with the technical knowledge, it is determined that the peak type of a certain peak in the spectrogram shaping data in fig. 7 should be gaussian, and it is necessary to fit the gaussian distribution. The fitted peaks are shown in fig. 8, and the solid line is spectrogram shaping data; each point of the peak area is marked by a large dot; the dashed line is the fitted gaussian distribution peak shape, the boundaries of the fitted peak curve are determined on the 3 sigma principle and are shown in semi-dashed lines. The mean, standard deviation, baseline value of the region, peak height, peak area, peak range of the fitted peak are shown in text on the left.

In addition, peak point coordinate information of each peak can only be extracted by adopting certain algorithms for peak searching operation, and if other relevant information of the peak, such as peak area and the like, is needed for qualitative and quantitative analysis of the sample to be detected, the range of the corresponding peak is deduced based on the obtained peak point coordinate information so as to obtain the peak information.

In this embodiment, the peak range can be estimated in two ways:

The first way is: and (3) taking the peak top point coordinate of a peak of a range to be inferred as the center, respectively expanding and searching the coordinate of a first valley bottom a1 to two sides, and further taking the first valley bottom coordinate as a base point, and continuously searching the coordinate of a second valley bottom a2 forwards. If the distance between a1 and a2 is smaller than the preset distance threshold value, comparing the difference value of the signal value of a1 minus the signal value of a2, and setting a1 as a2 when the difference value is larger than the preset intensity threshold value, and continuously searching for a2; otherwise, the peak end point is set to a1 or the intermediate value of a1 and a 2. If the distance between a1 and a2 is greater than another preset distance threshold, estimating the noise level b between a1 and a2, obtaining correction signal values (such as Gaussian average) for each point of a1 and a2 by using a rolling window method, selecting the point with the correction signal value smaller than the noise level b and closest to a1 as the peak endpoint. And if the distance between the points a1 and a2 is in the middle of two preset thresholds, performing unitary curve fitting for the points a1 and a2 for multiple times, and selecting the bottom of the curve and the point closest to the point a1 as the peak end point. Still further, the noise level b should be set to be not less than the minimum between a1 and a2, such as by selecting a value that is 10% higher than the maximum of the 10 percentile of the entire one-dimensional spectrogram and the 30 percentile of the a1, a2 range.

The second way is: for the fitted peaks fitted to a specific curve or statistical distribution, the peak range is determined by using the characteristics and parameters of the relevant distribution (for example, normal distribution, and the peak range can be determined according to the 3 sigma principle).

The peak information finally obtained includes, but is not limited to, peak height, half-width, left and right boundaries, peak top point coordinates, and peak area.

In an embodiment, based on the peak information obtained from the spectrogram shaping data, according to the obtained target attribute information, performing qualitative analysis and/or quantitative analysis on the property of the sample to be tested by using a constructed standard model equation to perform correction and/or auxiliary analysis, so as to obtain a qualitative analysis result and/or a quantitative analysis result of the sample to be tested, where the method includes: distinguishing peak information obtained from the spectrogram shaping data according to the obtained target attribute information; correcting the first-class peak information confirmed to be in accordance with the target attribute and/or performing auxiliary analysis on the second-class peak information confirmed to be in non-compliance with the target attribute by using the constructed standard model equation so as to obtain a qualitative analysis result and/or a quantitative analysis result of the sample to be tested; the standard model equation is obtained by modeling the obtained one-dimensional spectrogram data of the standard and the target attribute.

In one embodiment, the target attribute information may be concentration, molecular weight, etc., which is not intended to be exhaustive.

Specifically, the standard substance is required to be tested on the same instrument as the sample to be tested to obtain one-dimensional spectrogram data of the standard substance, and then the one-dimensional spectrogram data of the standard substance is modeled based on target attribute information to obtain a model equation of the standard substance. After obtaining the standard model equation, the overall performance of the equation is evaluated. The evaluation process comprises the steps of obtaining coefficients of an equation, and checking confidence intervals and P values of the measured coefficients, R ² values of the measured equation and P values of F test by T; correlation coefficient of predicted value and true value and confidence interval thereof, and corresponding p value.

For example, one-dimensional spectrogram data of 16 standards are acquired, a unitary five-time equation is established based on the abscissa thereof and the corresponding concentration magnitude, and 6 coefficients are obtained. After this, the overall performance was evaluated for this unitary quintic equation. As shown in fig. 9, the points are 16 standards, the line is a fitted unitary quintic curve, and the R ² value for the lower right text display equation is 0.99999. The p-value of model F test is much less than 0.001, with excellent significance. The upper left text shows the result of pearson correlation analysis of the predicted value and the actual value of the equation, the correlation coefficient reaches 0.99999, the p value is far less than 0.001, and the prediction performance is excellent. As can be seen, the overall performance of the equation is superior. As shown in fig. 10, the detailed information for modeling the model includes the correlation between the coefficient and its confidence interval and the p value, the model F test result, and the model predicted value and the actual value.

After a standard model equation is constructed, peak information obtained from the spectrogram shaping data is distinguished according to the obtained target attribute information.

The peak information has two types, namely, first-type peak information and second-type peak information. The obtained peak information has three cases, including only the first-type peak information, only the second-type peak information, and both the first-type peak information and the second-type peak information, respectively. When the obtained peak information only comprises the first type of peak information, correcting the first type of peak information by using a constructed standard product model equation so as to obtain an analysis result of the sample to be detected; when the obtained peak information only comprises the second type of peak information, carrying out auxiliary analysis on the second type of peak information by using a constructed standard product model equation so as to obtain an analysis result of the sample to be detected; when the obtained peak information comprises the first type peak information and the second type peak information, correcting the first type peak information and performing auxiliary analysis on the second type peak information by using a constructed standard model equation so as to obtain an analysis result of the sample to be detected.

The analysis result of the sample to be tested (for example, for mutual comparison between the concentrations of the same substance in different treatment groups, when the peak area is the relative concentration of the substance) can be directly obtained based on the first type peak information, and the first type peak information needs to be corrected by using a standard model equation. For example, the absolute concentration of the sample to be measured can be measured by using a standard model equation; the analysis result of the sample to be detected cannot be directly obtained based on the second kind of peak information, and at this time, auxiliary analysis is needed by using a standard model equation, specifically, the abscissa information of the second kind of peak information is converted. For example, the abscissa of the peak of the fitted curve is brought into the constructed standard model equation, and peak information and analysis results are obtained. As shown in fig. 11, information for 4 peaks is presented, including: automatically searching the abscissa of the peak top point, the left and right end points and the peak width of the peak; the mean value, standard deviation, baseline value, peak height, peak area, peak range and peak width of the fitting peak obtained by fitting the curve; predicting a resulting target property (i.e., molecular weight size); the method comprises the steps of automatically searching an original signal value of a peak top point of a peak, a signal value after a preprocessing module, a signal value after smooth noise reduction and a signal value after baseline correction.

In one embodiment, the method further comprises: after the analysis result of the sample to be tested is obtained, a corresponding analysis report generated based on the analysis result of the sample to be tested is visually displayed; the visualization is to mark the analysis result in the one-dimensional spectrogram data of the sample to be detected.

For example, as shown in fig. 12, the labeled one-dimensional spectrogram data simultaneously shows the original signal value, the signal value after the preprocessing module, the signal value after the smoothing noise reduction and the signal value after the baseline correction on the one-dimensional spectrogram, and the mean value, standard deviation, baseline value of the area, peak height, peak area, peak range of the peak fitting curve and the predicted analysis result (i.e. molecular weight size) are marked. For clarity of illustration, only 4 more distinct peaks are shown in fig. 12.

Similar to the above embodiment, the present invention also provides a one-dimensional spectrogram data processing system for qualitative and quantitative analysis.

Specific embodiments are provided below with reference to the accompanying drawings:

Fig. 13 is a schematic structural diagram of a one-dimensional spectrogram data processing system for qualitative and quantitative analysis according to an embodiment of the present invention.

The one-dimensional spectrogram data processing system for qualitative and quantitative analysis comprises:

The preprocessing module 131 is configured to preprocess the obtained raw one-dimensional spectrogram data of the sample to be detected;

a spectrogram shaping module 132, connected to the preprocessing module 131, for performing a spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaping data;

The analysis module 133 is connected to the spectrum shaping module 132, and is configured to perform qualitative analysis and/or quantitative analysis on the properties of the sample to be tested by using a constructed standard model equation to perform correction and/or auxiliary analysis according to the obtained target attribute information based on the peak information obtained from the spectrum shaping data, so as to obtain an analysis result of the sample to be tested.

Virtual device description:

It should be noted that, the modules provided in this embodiment are similar to the methods provided above, and therefore, the description thereof is omitted. It should be further noted that, it should be understood that the division of each module of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into one physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the preprocessing module 131 may be a processing element that is set up separately, may be implemented in a chip of the above apparatus, or may be stored in a memory of the above apparatus in the form of a program code, and the function of the preprocessing module 131 may be called and executed by a processing element of the above apparatus. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.

For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more Application SPECIFIC INTEGRATED Circuits (ASIC), or one or more microprocessors (DIGITAL SIGNAL processor, DSP), or one or more field programmable gate arrays (Field Programmable GATE ARRAY, FPGA), etc. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

In one embodiment, the preprocessing method for the raw one-dimensional spectrogram data of the acquired sample to be detected includes: judging whether abnormal signal value data exist in the original one-dimensional spectrogram data; if the abnormal signal value data exists in the original one-dimensional spectrogram data, deleting the abnormal signal value data, and carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data with the abnormal signal value data deleted so as to obtain preprocessed one-dimensional spectrogram data; if the original one-dimensional spectrogram data is judged to have no abnormal signal value data, carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data to obtain preprocessed one-dimensional spectrogram data. In one embodiment, performing a spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaped data comprises: smoothing and denoising the preprocessed one-dimensional spectrogram data, and correcting a baseline of the smoothed and denoised one-dimensional spectrogram data to obtain preliminary shaping data; and identifying and deleting abnormal signal peak data in the primary shaping data, and filling corresponding missing values by adopting a base line level value of a deleting position to obtain spectrogram shaping data.

In one embodiment, as shown in fig. 13, the system further comprises: and a peak-to-peak coordinate estimation peak range unit configured to, in a case where only a plurality of peak-to-peak coordinate information is obtained after the completion of the peak searching operation, infer a range of the corresponding peak based on each obtained peak-to-peak coordinate information to obtain peak information.

In an embodiment, based on the peak information obtained from the spectrogram shaping data, according to the obtained target attribute information, performing qualitative analysis and/or quantitative analysis on the properties of the sample to be tested by using the constructed standard model equation to perform correction and/or auxiliary analysis, so as to obtain an analysis result of the sample to be tested, where the method includes: distinguishing peak information obtained from the spectrogram shaping data according to the obtained target attribute information; correcting the first-class peak information confirmed to be in accordance with the target attribute and/or performing auxiliary analysis on the second-class peak information confirmed to be in non-compliance with the target attribute by using the constructed standard model equation so as to obtain an analysis result of the sample to be tested; the standard model equation is obtained by modeling the obtained one-dimensional spectrogram data of the standard and the target attribute.

In one embodiment, as shown in fig. 13, the system further comprises: and the report generating module 134 is configured to, after obtaining the analysis result of the sample to be tested, generate and visually display a corresponding analysis report based on the analysis result of the sample to be tested.

Referring to fig. 14, an optional hardware structure schematic diagram of a terminal 14 provided in the embodiment of the present invention may be shown, where the terminal 14 may be a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc. The terminal 14 includes: at least one processor 141, memory 142, at least one network interface 144, and a user interface 146. The various components in the device are coupled together by a bus system 145. It is understood that bus system 145 is used to enable connected communication between these components. The bus system 145 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus systems in fig. 14.

The user interface 146 may include, among other things, a display, keyboard, mouse, trackball, click gun, keys, buttons, touch pad, or touch screen, etc.

It will be appreciated that memory 142 may be volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read Only Memory (ROM), a programmable Read Only Memory (PROM, programmable Read-Only Memory), which serves as an external cache, among others. By way of example, and not limitation, many forms of RAM are available, such as static random Access Memory (SRAM, staticRandom Access Memory), synchronous static random Access Memory (SSRAM, synchronous Static RandomAccess Memory). The memory described by embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.

The memory 142 in embodiments of the present invention is used to store various categories of data to support the operation of the terminal 14. Examples of such data include: any executable programs for operating on the terminal 14, such as an operating system 1421 and application programs 1422; the operating system 1421 contains various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application programs 1422 may include various application programs such as a media player (MediaPlayer), a Browser (Browser), etc. for implementing various application services. The one-dimensional spectrogram data processing method for qualitative and quantitative analysis provided by the embodiment of the invention can be included in the application 1422.

The method disclosed in the above embodiment of the present invention may be applied to the processor 141 or implemented by the processor 141. Processor 141 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 401 or by instructions in the form of software. The Processor 141 described above may be a general purpose Processor, a digital signal Processor (DSP, digital Signal Processor), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Processor 141 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. The general purpose processor 141 may be a microprocessor or any conventional processor or the like. The steps of the accessory optimization method provided by the embodiment of the invention can be directly embodied as the execution completion of the hardware decoding processor or the execution completion of the hardware and software module combination execution in the decoding processor. The software modules may be located in a storage medium having memory and a processor reading information from the memory and performing the steps of the method in combination with hardware.

In an exemplary embodiment, the terminal 14 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, programmable logic devices (PLDs, programmable Logic Device), complex programmable logic devices (CPLDs, complex Programmable LogicDevice) for performing the aforementioned methods.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.

In the embodiments provided herein, the computer-readable storage medium may include read-only memory, random-access memory, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, U-disk, removable hard disk, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer. In addition, any connection is properly termed a computer-readable medium. For example, if the instructions are transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. It should be understood, however, that computer-readable and data storage media do not include connections, carrier waves, signals, or other transitory media, but are intended to be directed to non-transitory, tangible storage media. Disk and disc, as used herein, includes Compact Disc (CD), laser disc, optical disc, digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

In summary, the one-dimensional spectrogram data processing method, the system, the terminal and the medium for qualitative and quantitative analysis are provided by the application, and the automatic, simple, rapid, accurate, effective and qualitative analysis and/or quantitative analysis of the sample to be tested is realized by carrying out the processes of smoothing and denoising, baseline correction, peak finding fitting and the like on the preprocessed one-dimensional spectrogram data of the sample to be tested and carrying out qualitative analysis and/or quantitative analysis on the sample to be tested by combining with the constructed model equation of the standard. Therefore, the application effectively overcomes various defects in the prior art and has high industrial utilization value.

The above embodiments are merely illustrative of the principles of the present application and its effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the application. Accordingly, it is intended that all equivalent modifications and variations of the application be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.

Claims

1. A one-dimensional spectrogram data processing method for qualitative and quantitative analysis, comprising:

Preprocessing the obtained original one-dimensional spectrogram data of the sample to be detected;

The method for preprocessing the acquired original one-dimensional spectrogram data of the sample to be detected comprises the following steps:

judging whether abnormal signal value data exist in the original one-dimensional spectrogram data;

if the abnormal signal value data exists in the original one-dimensional spectrogram data, deleting the abnormal signal value data, and carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data with the abnormal signal value data deleted so as to obtain preprocessed one-dimensional spectrogram data;

if the original one-dimensional spectrogram data is judged to have no abnormal signal value data, carrying out missing value filling and negative data conversion on the original one-dimensional spectrogram data to obtain preprocessed one-dimensional spectrogram data;

performing spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaping data;

Based on peak information obtained from the spectrogram shaping data, carrying out qualitative analysis and/or quantitative analysis on the properties of the sample to be detected by utilizing a constructed standard model equation in a mode of correction and/or auxiliary analysis according to the obtained target attribute information so as to obtain an analysis result of the sample to be detected; wherein, its concrete mode includes:

distinguishing peak information obtained from the spectrogram shaping data according to the obtained target attribute information;

Correcting the first-class peak information confirmed to be in accordance with the target attribute and/or performing auxiliary analysis on the second-class peak information confirmed to be in non-compliance with the target attribute by using the constructed standard model equation so as to obtain an analysis result of the sample to be tested; the standard model equation is obtained by modeling the obtained one-dimensional spectrogram data of the standard and the target attribute.

2. The one-dimensional spectrogram data processing method for qualitative and quantitative analysis according to claim 1, wherein performing a spectrogram shaping operation on the preprocessed one-dimensional spectrogram data to obtain spectrogram shaping data comprises:

Smoothing and denoising the preprocessed one-dimensional spectrogram data, and correcting a baseline of the smoothed and denoised one-dimensional spectrogram data to obtain preliminary shaping data;

And identifying and deleting abnormal signal peak data in the primary shaping data, and filling corresponding missing values by adopting a base line level value of a deleting position to obtain spectrogram shaping data.

3. The one-dimensional spectrogram data processing method for qualitative and quantitative analysis according to claim 2, wherein the manner of obtaining peak information from the spectrogram shaping data comprises:

carrying out peak searching operation based on the spectrogram shaping data to obtain peak searching result information;

And carrying out peak type fitting on partial peaks in the spectrogram shaping data according to the peak searching result information so as to obtain peak information.

4. A one-dimensional spectrogram data processing method for qualitative and quantitative analysis according to claim 3, characterized in that the method further comprises: in the case where only a plurality of peak-to-peak coordinate information is obtained after the peak searching operation is completed, the range of the corresponding peak is inferred based on each obtained peak-to-peak coordinate information to obtain peak information.

5. The one-dimensional spectrogram data processing method for qualitative and quantitative analysis according to claim 1, further comprising: and after the analysis result of the sample to be tested is obtained, a corresponding analysis report generated based on the analysis result of the sample to be tested is visually displayed.

6. A one-dimensional spectrogram data processing system for qualitative and quantitative analysis, comprising:

The preprocessing module is used for preprocessing the acquired original one-dimensional spectrogram data of the sample to be detected;

the spectrogram shaping module is connected with the preprocessing module and is used for executing spectrogram shaping operation on the preprocessed one-dimensional spectrogram data so as to obtain spectrogram shaping data;

The analysis module is connected with the spectrogram shaping module and is used for carrying out qualitative analysis and/or quantitative analysis on the properties of the sample to be detected by utilizing a constructed standard model equation in a mode of correction and/or auxiliary analysis according to the obtained target attribute information based on the peak information obtained from the spectrogram shaping data so as to obtain an analysis result of the sample to be detected; wherein, its concrete mode includes:

7. A terminal, comprising: a processor and a memory;

The memory is used for storing a computer program;

the processor is configured to execute the computer program stored in the memory, so as to cause the terminal to perform the method according to any one of claims 1 to 5.

8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any one of claims 1 to 5.