CN110082319B - Calibration data correction method and electronic device thereof - Google Patents
Calibration data correction method and electronic device thereof Download PDFInfo
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Abstract
The embodiment of the invention relates to a calibration data correction method and electronic equipment thereof. The calibration data correction method includes: receiving an original data set consisting of a plurality of reaction data; performing primary screening on the original data set through linear fitting to obtain a primary screening data set; and carrying out secondary screening on the primary screening data set through nonlinear fitting to obtain a corrected data set. The method sequentially carries out coarse screening and fine screening on the original reaction curve data set, and iteratively removes and rejects abnormal data which do not accord with the reaction trend and the normal reaction rule by using two fitting modes, so that the accuracy of the final calibration result is ensured.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of data screening, in particular to a calibration data correction method and electronic equipment thereof.
[ background of the invention ]
Turbidimetry is a very widely used method for the quantitative determination of antigens/antibodies. The antigen/antibody quantitative determination is realized by utilizing the characteristic that in a specific time period of antigen-antibody reaction (namely, the combination between sample antigen/antibody and reagent antibody/antigen added in a reaction cup forms a specific combination), the reaction speed is in direct proportion to the concentration first power of a measured object, and the increase or decrease of the scattered light intensity (absorbance) in the time period is in direct proportion to the concentration of the measured object.
For example, a light source is used to irradiate a reaction cup, and the scattered light intensity of each sampling point in a fixed measurement time is recorded to calculate the variation of the scattered light intensity (i.e., the reaction amplitude) at two specific time points in the time period. By measuring the reaction amplitude of reactants (samples) with different concentrations, a two-dimensional curve of the concentration and the reaction amplitude can be drawn, and the calibration process is completed. The reaction amplitude of the sample is obtained by using the calibration measurement, and the concentration of the sample can be calculated by using the calibration relation.
From the measurement principle of turbidimetry, it can be seen that the calibration process is crucial to the accuracy of concentration measurement and plays a decisive role. The calibration accuracy depends on the corresponding reaction amplitudes of the samples with different concentrations.
However, in the actual measurement process, the light intensity of scattered light cannot reflect the reaction rule, and the phenomenon is particularly obvious in the measurement of low-concentration samples, which is abnormal due to the influence of factors such as bubbles, contaminating particles, and non-specific precipitates generated when the sample and the reagent are uniformly mixed.
In order to improve the reliability of the calibration process, some data screening methods are provided for eliminating abnormal data of the reaction curve. However, these methods have different limitations in practical applications, such as being unable to adapt to a noisy environment or being prone to misfitting abnormal data. How to reliably identify and reject abnormal data in a high-noise environment and improve the detection effect of a turbidimetry on low-concentration samples is a problem which needs to be solved urgently in the prior art.
[ summary of the invention ]
In order to solve the above technical problems, embodiments of the present invention provide a calibration data correction method and an electronic device thereof, which can reliably identify and reject abnormal points of a response curve.
In order to solve the above technical problems, embodiments of the present invention provide the following technical solutions: a calibration data correction method.
The calibration data correction method includes: receiving an original data set consisting of a plurality of reaction data; performing primary screening on the original data set through linear fitting to obtain a primary screening data set; and carrying out secondary screening on the primary screening data set through nonlinear fitting to obtain a corrected data set.
Optionally, the primary screening is performed on the original data set through linear fitting to obtain a primary screening data set, which specifically includes:
performing linear fitting on input reaction data to obtain a corresponding linear equation;
determining a deviation between each of said reaction data and said line equation;
and screening one or more reaction data which do not accord with the reaction rule according to the deviation.
Optionally, the screening out, according to the deviation, one or more first reaction data of the unreachable rule, specifically includes:
judging whether the first screening termination standard is met or not;
if so, finishing primary screening of the original data set;
if not, removing the reaction data with the maximum deviation and performing linear fitting again.
Optionally, the performing linear fitting on the input reaction data to obtain a corresponding linear equation specifically includes:
fitting to obtain a linear equation with an independent variable as a measuring moment and a dependent variable as scattered light intensity by a linear least square method; the reaction data is the actual scattered light intensity sampled at the selected measurement instant.
Optionally, the determining a deviation between each of the reaction data and the linear equation specifically includes:
obtaining first theoretical scattered light intensity at different measurement moments through the linear equation;
calculating an absolute value between a first theoretical scattered light intensity and an actual scattered light intensity at the same measurement time as a deviation between the reaction data and the linear equation.
Optionally, the first screening termination criteria comprises:
the ratio of the maximum deviation to the actual scattered light intensity variation range of the raw data set is smaller than a preset first threshold value, an
And the ratio of the maximum deviation to the standard deviation of the deviations of all the reaction data in the original data set is smaller than a preset second threshold value.
Optionally, the secondary screening is performed on the primary screening data set through nonlinear fitting to obtain a corrected data set, and the method specifically includes:
performing curve fitting on the reaction data in the primary screening data set by using a function consistent with a reaction rule to obtain a corresponding fitting curve;
judging whether the fitting curve meets a preset second screening termination standard or not;
if so, finishing the secondary screening of the primary screening data set;
and if not, removing the abnormal reaction data in the primary screening data set and performing curve fitting again.
Optionally, the removing abnormal reaction data in the preliminary screening data set specifically includes:
obtaining second theoretical scattered light intensity at different measurement moments through the fitting curve;
calculating the absolute value of the difference between the second theoretical scattered light intensity and the actual scattered light intensity at the same measurement moment, wherein the reaction data is the actual scattered light intensity obtained by sampling at the selected measurement moment;
removing the reaction data with the maximum absolute value.
Optionally, the second screening termination criterion is that a correlation coefficient between the second theoretical scattered light intensity and the actual scattered light intensity is greater than a preset coefficient threshold.
The embodiment of the invention also provides the following technical scheme: an electronic device is provided. The electronic device includes: a processor and a memory; the memory stores computer-executable program instructions to cause the processor, when invoked, to perform a calibration data correction method as described above, to remove one or more reaction data from an input original data set to obtain a corrected data set, and to complete a calibration process for turbidimetry based on the corrected data set.
Compared with the prior art, the calibration data correction method provided by the embodiment of the invention sequentially carries out coarse screening and fine screening on the original reaction curve data set, and uses two fitting modes to iteratively remove and reject abnormal data which do not accord with the reaction trend and the normal reaction rule, thereby avoiding the condition of curve misfitting and well ensuring the accuracy of the calibration result. Even under the condition that the number of the abnormal points is large, the screening effect can be ensured, and correct data conforming to the reaction rule are reserved.
[ description of the drawings ]
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.
FIG. 1 is a schematic diagram of a scaling process provided by an embodiment of the present invention;
FIG. 2 is a block diagram of an electronic computing platform according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for correcting calibration data according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for primary screening according to an embodiment of the present invention;
FIG. 5 is a flowchart of a method of secondary screening provided by an embodiment of the present invention;
FIG. 6 is a flowchart of a method for correcting calibration data according to another embodiment of the present invention;
FIG. 7 is a diagram of an original data set provided in embodiment 1 of the present invention;
FIG. 8 is a graph showing the fitting result of curve fitting provided in example 1 of the present invention;
FIG. 9 is a graph showing the fitting results of the linear fitting provided in example 2 of the present invention;
FIG. 10 is a diagram illustrating an original data set provided in embodiment 3 of the present invention;
FIG. 11 is a graph showing the fitting results of the primary screening provided in example 3 of the present invention;
fig. 12 is a schematic diagram of a fitting result of the secondary screening provided in embodiment 3 of the present invention.
[ detailed description ] embodiments
In order to facilitate an understanding of the invention, the invention is described in more detail below with reference to the accompanying drawings and specific examples. It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present. As used in this specification, the terms "upper," "lower," "inner," "outer," "bottom," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention and simplicity in description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are not to be considered limiting of the invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Furthermore, the technical features mentioned in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.
The calibration process of the turbidimetry method is to collect a series of reaction data by a measuring device in a set sampling period in a specific reaction time period after a calibration sample with a known concentration is added in a reaction cup. Finally, based on the reaction data, a reaction curve corresponding to the calibration sample is obtained by fitting.
Conventionally, the reaction data may be absorbance intensity or scattered light intensity, or the like. Depending on the type of measurement data of the measuring device. For convenience of description, the scattered light intensity is described as an example in the present embodiment. It will be understood by those skilled in the art that the reaction data may be replaced by any other different measurement data, including but not limited to absorbance intensity, etc., according to the actual application.
Fig. 1 is a schematic diagram of a calibration process according to an embodiment of the present invention. As shown in fig. 1, the general process includes:
first, the upper computer 40 controls the laser transmitter 10 to continuously emit laser light having a specific intensity to pass through the cuvette 20 in which the antigen-antibody reaction is being performed, and during a selected reaction time period, the upper computer 40 controls the laser receiver 30 to sample at a set sampling period, thereby obtaining a series of reaction data. The acquired series of reaction data can be transmitted and represented in the form of a data set.
The reaction data is recorded with the scattered light intensity with the time of measurement. That is, the obtained scattered light intensity is measured at different measurement times. As shown in fig. 7 and 10, in a coordinate system with the abscissa as the measurement time and the ordinate as the scattered light intensity, these reaction data are represented as corresponding "coordinate points". Thus, in the present embodiment, the term "reaction point" is used to indicate the reaction data acquired at a specific measurement time.
Then, the data screening process is performed on the reaction data by the upper computer 40. Generally, the data collected directly during the reaction process may be referred to as raw data. Due to the influence of various factors, these raw data do not correctly reflect the antigen-antibody reaction performed in the cuvette. Therefore, it is necessary to analyze and process the raw reaction data to eliminate the interference of various influencing factors to obtain better calibration results.
During the data screening process, the skilled person may specifically use a number of different strategies to implement. The performance or performance of the data screening method has an important influence on the calibration accuracy.
One of the classic data screening processing methods is: firstly, a function adaptive to a reaction rule is selected, and the function is used for fitting a reaction curve to calculate the deviation between the fitted theoretical scattered light intensity and the actually measured scattered light intensity. Then, sorting is carried out according to the deviation magnitude, and the first few reaction points with the largest deviation amount are removed. And finally, re-fitting the reaction point set with a plurality of points deviated from the larger points removed to obtain a final reaction curve.
Although the above correction method has a good effect when the degree of noise deviation is small. However, when the noise is large (i.e. the abnormal point is far from the normal response curve), the fitting curve may be deviated from the normal response curve seriously, and the normal response point and the severely deformed fitting curve may be deviated too much and may be rejected erroneously.
There are also some iterative methods of deleting abnormal data one by one, which first select a function adapted to the reaction law, and fit the function to the reaction curve to calculate the deviation between the fitted theoretical scattered light intensity and the actually measured scattered light intensity. Then, after a reaction point with the largest deviation is searched and removed, fitting is carried out by reusing the removed data until certain precision conditions are met.
The correction method only deletes one abnormal point every time, and the situation that the normal reaction point is deleted by mistake is not easy to occur. However, in some special cases, for example, when the number of abnormal points that do not meet the reaction trend is large, the selected function is too strong in fitting ability, and the abnormal points are better fitted, so that the normal reaction points are mistakenly removed.
And finally, fitting the reaction data after the data screening processing through the upper computer 40 to obtain a reaction curve 50 of the calibration sample with known concentration.
The final goal of the calibration process is to obtain a response curve. The reaction curve may be stored in any suitable type of storage device for later use as a basis for turbidimetric concentration measurements.
In the actual concentration measurement process, the reaction amplitude is obtained by processing the reaction curve of the sample to be measured. Then, the corresponding reaction amplitude is searched in a reaction curve (i.e. a correlation graph of the sample concentration and the reaction amplitude) formed by the calibration sample with the known concentration, and the concentration of the sample to be measured can be determined through corresponding calculation.
In an actual application scenario, the upper computer may be any type of electronic device or an electronic computing platform formed by combining a plurality of electronic devices, including but not limited to a personal computer, a server, a portable mobile computer, and the like, and only needs to be able to meet the requirements of computing capability, memory, and the like.
Fig. 2 is a block diagram of an electronic computing platform according to an embodiment of the present invention. As shown in fig. 2, the electronic computing platform comprises: a processor 21, a memory 22, an interaction device 23, and input/output ports 24.
Fig. 2 takes a bus connection as an example, and establishes communication connections between any two of the processor 21, the memory 22 and the interaction device 23.
The processor 21 may be any type of single-threaded or multi-threaded processor. The processor 21 may have one or more processing cores as a control hub for acquiring data, performing logical operation functions, issuing operation processing results, and the like.
The memory 22 is a non-volatile computer-readable storage medium such as at least one magnetic disk storage device, flash memory device, distributed storage device remotely located from the processor 21, or other non-volatile solid state storage device. Which has a program storage area for storing non-volatile software programs, non-volatile computer-executable programs, and modules.
These computer-executable programs and functional modules may be called by the processor 21 to cause the processor 21 to perform one or more method steps. The memory 22 may also have a data storage area for storing the operation processing result issued by the processor 21.
The interactive device 23 is any type of device for collecting user instructions or presenting or feeding back information to the user, including but not limited to: keys, a display screen, a touch screen, a speaker, etc. The interactive device 33 may collect user instructions and present the corresponding interactive interface to the user. For example, the processing condition of the reaction data can be displayed to the user through a display screen, or the curve fitting condition of the reaction data in a time-scattering light intensity coordinate system can be shown.
The input/output port 24 is any type of interface for receiving and outputting data information. It may specifically be an air communication interface or a wired connection interface, such as a GPIO interface or a UART interface. The input/output ports 24 may be provided in a suitable number as required by the actual situation and employ a corresponding communication protocol to enable interaction between the electronic computing platform and external devices.
In the embodiment of the present invention, fig. 1 is only an example of an upper computer. In other embodiments, a plurality of different electronic devices may cooperate to perform the functions to be performed by the upper computer shown in fig. 1. The upper computer can be internally provided with corresponding functional modules to execute a data screening method consisting of one or more steps.
Although only the nephelometry is taken as an example in the embodiment shown in fig. 1, based on the same inventive concept, those skilled in the art can further adjust, change or integrate the calibration data correction method provided by the embodiment of the present invention to apply to the data screening process of other test methods, and is not limited to the immune nephelometry.
Fig. 3 is a schematic diagram of a calibration data correction method according to an embodiment of the present invention. The method can be executed by the electronic computing platform or the electronic equipment, overcomes the defect that the normal reaction points are deleted by mistake in the conventional correction method, and effectively improves the reliability of the drawn reaction curve.
As shown in fig. 3, the method includes:
301. a raw data set comprised of a number of reaction data is received.
In the present embodiment, for convenience of description and subsequent calculation, all the reaction data are combined into a raw data set, and the elements in the set are reaction data detected at different measurement times.
As described above, the reaction data may be data such as absorbance or scattered light intensity depending on the actual application. In addition, each reaction data record has a corresponding measurement time, and is represented as a specific "reaction point" in the coordinate system.
302. And carrying out primary screening on the original data set through linear fitting to obtain a primary screening data set.
The primary screening is a coarse screening process, which adopts a linear fitting mode with low fitting capability to screen, and eliminates abnormal reaction data which do not accord with the reaction trend with low computational cost. In addition, the linear fitting mode with low fitting capability is not easy to generate overfitting, so that the problem that the fitting curve deviates from the normal reaction rule is caused.
In some embodiments, the primary screening process may be implemented using an iterative culling method as shown in fig. 4. As shown in fig. 4, the method includes:
3021. and performing linear fitting on the input reaction data to obtain a corresponding linear equation.
Intuitively, a reaction datum in a coordinate system with the measurement time being an x-axis and the scattered light intensity being a y-axis is a specific coordinate point. Based on the known plurality of coordinate points, a corresponding straight-line equation can be obtained by using linear least squares fitting.
3022. Determining a deviation between each of the reaction data and the linear equation.
The linear equation can be calculated to obtain theoretical reaction data (namely, predicted values obtained by calculation of the linear equation) at different measurement moments as a result of fitting.
The "deviation" is used to measure the difference between the actual response data and the theoretical response data calculated by the linear equation, and may be expressed by any suitable form of data or statistical result, depending on the actual calculation requirement.
For example, the deviation between the reaction data and the linear equation can be determined by calculating:
first, from the equation of a straight line, first theoretical scattered light intensities at different measurement instants are obtained. Then, the absolute value between the first theoretical scattered light intensity and the actual scattered light intensity at the same measurement instant is calculated as the deviation between the reaction data and the linear equation.
3023. And judging whether the first screening termination standard is met or not. If yes, go to step 3024; if not, go to step 3025.
The first screening termination criterion is an index used to measure whether the primary screening is completed. Depending on the needs of the actual situation or the characteristics of the reaction data, a combination of one or more judgment conditions may be employed.
In a preferred embodiment, the first screening termination criteria may include the following two conditions:
1) the ratio of the maximum deviation to the actual scattered light intensity variation range of the raw data set is smaller than a preset first threshold value.
The actual scattered light intensity variation range is a range composed of the lowest value and the highest value of the actual scattered light intensity obtained by actual measurement in the raw data set.
The first threshold is an empirical value that can be adjusted or set by a technician as needed for practical situations, which can determine the extent of the primary screening.
It can be understood that the variation range of the scattered light intensity is actually closely related to the concentration of the test sample, and in the reaction curve of the low-concentration sample, the variation range of the scattered light intensity is smaller, while the variation range of the reaction curve of the high-concentration sample is larger.
Since the noise level present during the reaction is relatively constant, it does not change with the sample concentration. Therefore, by providing a proper first threshold value, enough coarse screening can be ensured when processing a reaction curve of a low-concentration sample, abnormal reaction data (namely abnormal points) which do not meet the reaction trend can be eliminated as much as possible, and the problem that when the abnormal points of a curve with stronger fitting capability are too many, the abnormal points are mistakenly referred to for fitting, so that the normal reaction points are removed and the abnormal points are reserved is solved.
2) And the ratio of the maximum deviation to the standard deviation of the deviations of all the reaction data in the original data set is smaller than a preset second threshold value.
For a high concentration sample, condition 1) is relatively easy to satisfy (the intensity variation range is large). Therefore, in order to avoid early withdrawal of the primary screening when the data processing is performed on the high-concentration sample, the condition 2) may be used in combination to ensure that the primary screening can be sufficiently performed.
It can be understood that the computational power and memory consumed by the primary screening and the secondary screening are significantly different, and more resource consumption is required by using curve fitting with stronger fitting capability (especially when the secondary screening uses a fitting algorithm with higher requirements on computational power and memory, such as a gauss-newton iteration method).
Therefore, more rejecting works are handed over to the primary screening to be completed, the sufficient primary screening is ensured, the workload of secondary screening can be effectively reduced, and the effects of saving computing power and memory of a computer are achieved.
3024. And finishing primary screening of the original data set.
After the primary screening is completed, the remaining data may be subjected to a "secondary screening" step for further screening and fitting.
3025. The most deviating reaction data were removed.
After removing the reaction data with the maximum deviation, the process may return to step 3021, and perform linear fitting again using the remaining reaction data until the deviation condition of the remaining reaction data meets the preset first screening termination criterion.
The above-mentioned reaction data elimination operation performed iteratively can identify and eliminate abnormal reaction data which do not conform to the reaction trend. Moreover, the data elimination process is carried out one by one, only one abnormal reaction data is deleted or eliminated in each linear fitting process, and the problem of mistakenly eliminating normal data is not easy to occur.
303. And carrying out secondary screening on the primary screening data set through nonlinear fitting to obtain a corrected data set.
"Secondary screening" is a more elaborate screening process than "primary screening". A nonlinear fitting method with stronger fitting capability is used, a reaction curve more consistent with a reaction rule can be obtained, and the calibration requirement is met.
The 'correction data set' refers to the reaction data which are left after the original data set is subjected to two screening operations and the abnormal reaction data are removed. The method has higher assurance of normal reaction data, and can output the reaction data used for subsequent reaction curve fitting to finish the calibration process.
In some embodiments, secondary screening may also employ a culling-by-culling strategy similar to primary screening. Fig. 5 is a flowchart of a secondary screening method according to an embodiment of the present invention. As shown in fig. 5, the secondary screening method includes:
3031. and performing curve fitting on the reaction data in the preliminary screening data set by using a function conforming to the reaction rule to obtain a corresponding fitting curve.
3032. And judging whether the fitted curve meets a preset second screening termination standard or not. If yes, go to step 3033; if not, go to step 3034.
The second screening termination criteria is a criterion used to measure whether the remaining response data can accurately express the response law. Which may employ any suitable combination of one or more conditions.
Specifically, the preset second screening termination criterion may be that a correlation coefficient between the second theoretical scattered light intensity and the actual scattered light intensity is greater than a preset coefficient threshold.
The second theoretical scattered light intensity is the scattered light intensity calculated from the fitted curve in the case of a defined measurement instant. Which is a theoretical value determined based on the curve calculation done by fitting.
The "correlation coefficient" is a coefficient calculated using a suitable statistical method, indicating the degree of correlation between the two variables of the second theoretical scattered light intensity and the actual scattered light intensity. Which may be based on different objects and achieve the object in any suitable way.
3033. And finishing the secondary screening of the primary screening data set.
3034. And removing abnormal reaction data in the primary screening data set.
The abnormal response data refers to one or more abnormal response data that do not significantly conform to or deviate from the theoretical prediction of the fitted curve.
Specifically, abnormal reaction data may be determined and removed as follows: first, a second theoretical scattered light intensity at different measurement instants is obtained from the fitted curve. Then, the absolute value between the first theoretical scattered light intensity and the actual scattered light intensity at the same measurement instant is calculated, and the reaction data is the actual scattered light intensity sampled at the selected measurement instant. And finally, determining the reaction data with the maximum absolute value as abnormal reaction data and removing the abnormal reaction data.
3035. And updating the primary screening data set.
"update" refers to the process of taking the remaining data as a new prescreened data set after removing at least one abnormal reaction data deviating from the normal reaction law. Similar to the primary screening, after step 3035, returning to step 3031, a curve fit is performed using the updated primary screening data set until the fitted curve meets the second screening termination criteria.
The following describes the response data correction method and the application effect thereof in detail with reference to specific examples and drawings in the specification. Fig. 6 is a flowchart illustrating a complete implementation of the calibration data modification method according to an embodiment of the present invention. As shown in fig. 6, the method includes:
s601) mixing the reactants and reagents, and then irradiating the cuvette with a light source (as shown in fig. 1, a laser light source of constant intensity is used). If the scene used is nephelometry, the intensity of the scattered light is measured at an angle (e.g. 17 °) to the incident light. If the scene used is transmission turbidimetry, the transmitted light intensity (i.e., absorbance) at an angle of 0 ° to the incident light is measured. For convenience of presentation, the present embodiment will be described by using nephelometry as an example.
S602) the scattered light intensity is measured at regular time intervals within a fixed time period after the reaction starts, and a plurality of consecutive scattered light intensity values can be obtained (for example, 150 consecutive scattered light intensity values can be obtained by sampling once every 1S interval within a fixed time period of 150 seconds).
Let the scattered light intensity sequence obtained by successive measurements be RjThe corresponding measurement time sequence is TjThe scattered light intensity at the i-th measurement time is
j is the number of the abnormal data which are removed and do not accord with the reaction rule.
As shown in FIG. 7, in the coordinate system for measuring the time-scattered light intensity value, the scattered light intensity measured at the i-th time point can be represented as a reaction point
All the reaction points obtained by measurement are combined into a point set, which can be referred to as the raw data set (i.e. when j takes a value of 0).
S603) calculating a scattered light intensity variation range of the raw data set by the following equation (1):
Range_R＝max(R0)-min(R0)(1)
wherein Range _ R is the variation Range of the scattered light intensity, max is the operation of taking the maximum value, and min is the operation of taking the minimum value.
S604) time sequence TjAs independent variable, the sequence of scattered light intensities RjAs a dependent variable, a linear least squares fit is performed to obtain a fitted straight line equation as shown in equation (2):
r＝kjt+bj,(j≥0,j<n-2)(2)
wherein t is the measurement time, r is the scattered light intensity, and n is the total number of reaction points k in the original data setjAnd bjRespectively the slope of the line and the intercept of the line.
S605) time sequence TjEach measurement time is used as an independent variable and is input into a linear equation shown in formula (2), and a corresponding first theoretical scattered light intensity sequence Linefit _ R is obtained through calculationj。
S606) calculating the absolute value of the difference value between the first theoretical scattered light intensity and the actually measured scattered light intensity at the same measuring time to obtain a deviation sequence delta of the first theoretical scattered light intensity and the measured scattered light intensityj。
S607) calculating the maximum value and standard deviation of the deviation series.
S608) judging whether the condition is satisfied
And is
(i.e., the first screening end criteria). If yes, the primary screening is ended, and step S610 is executed. If not, go to step S609.
Wherein Th1 is the first threshold, Th2 is the second threshold,
for the maximum value of the deviation sequence, std _ deltajIs the standard deviation of the sequence of deviations.
S609) removing the reaction point corresponding to the maximum value of the deviation sequence
To update the scattered light intensity sequence and the corresponding measurement time sequence (let j ═ j +1), and returns to step S604.
S610) selecting a function f (x) according with a reaction rule, and performing curve fitting by using the scattered light intensity sequence and the corresponding measurement time sequence to obtain a fitting function shown as the following formula (3):
f(x|θj)(3)
wherein, thetajFor the fitting parameters, j represents the input as a set of points with j reaction points removed.
In this embodiment, the function is specifically selected as f (x) ax3+bx2+ cx + d. Methods of function fitting include, but are not limited to, nonlinear least squares or gauss-newton iteration. The reaction curve of the high-concentration sample is stable and can reflect the reaction rule better. Therefore, in the data selection, the reaction data of the high-concentration sample can be selected as input quantity of the fitting curve as much as possible.
S611) time series TjEach measured time is used as an independent variable and input into a curve fitting function shown in formula (3) to obtainCorresponding second theoretical scattered light intensity sequence CurveFit _ Rj。
S612) calculating a second theoretical scattered light intensity sequence and a measured scattered light intensity sequence R by the following equation (4)jCorrelation coefficient between:
wherein CorjE is the desired operation and D is the variance operation for the correlation coefficient.
S613) judging whether the correlation coefficient is larger than a preset third threshold value.
If yes, ending the secondary screening process, and executing step S614; if not, go to step S615.
S614) outputting the fitting function of the formula (3) and the residual reaction point data as the reaction curve and the reaction data of the sample with the known concentration in the calibration process.
S615) calculating the absolute value of the difference value between the second theoretical scattered light intensity and the actually measured scattered light intensity at the same measuring time to obtain a deviation sequence epsilon of the second theoretical scattered light intensity and the measured scattered light intensityj。
S616) removing the reaction point corresponding to the maximum value of the deviation sequence
To update the scattered light intensity sequence and the corresponding measurement time sequence (let j ═ j +1), and returns to step S610 to perform fitting again.
Example 1:
in this embodiment 1, the steps (S603 to S609) related to the primary screening shown in fig. 6 are skipped, and curve fitting is directly performed on the original data set. Fig. 7 is a schematic diagram of an original data set acquired by executing step S601 in embodiment 1 of the present invention. Fig. 8 is a schematic diagram of the fitting result of skipping the primary screening and directly performing curve fitting on the raw data set shown in fig. 7.
As shown in fig. 7 and 8, there are many abnormal points that do not conform to the reaction trend in example 1, and many abnormal points that do not conform to the reaction law are incorrectly identified as normal points by the fitting curve, so that the curve obtained by fitting cannot reflect the correct reaction law, and the calibration result is not ideal.
Example 2:
in the present embodiment 2, the primary screening step (S603 to S609) and the secondary screening step shown in fig. 6 are performed to obtain a final fitting curve and a corrected data set, also based on the original data set shown in fig. 7. FIG. 9 is a schematic diagram of the primary screening step.
As shown in fig. 9, most of the abnormal points in fig. 7 that do not conform to the reaction rule can be effectively identified by the straight line fitting method of the primary screening. The mode of carrying out curve fitting after deleting the identified abnormal points can well avoid the problem that the abnormal points are mistaken as normal points during curve fitting.
Example 3:
in this embodiment 3, the data correction method shown in fig. 6 is performed to screen and eliminate the original data set, so as to obtain an accurate response curve and eliminate the interference factors.
Fig. 10 is a schematic diagram of an original data set acquired by executing step S601 in embodiment 3 of the present invention. FIG. 11 is a graph showing the results of fitting the primary screening to the raw data set shown in FIG. 10. FIG. 12 is a graph showing the fitting results of the secondary screening.
As shown in FIG. 11, the initial filtering first identifies and deletes most outliers in the original data set of FIG. 10. As shown in fig. 12, based on the fitting result of fig. 11, a more accurate reaction curve more suitable for the reaction trend can be provided by a fitting curve having a stronger fitting ability.
It can be seen from the comparison between the embodiment 1 and the embodiment 2 that most abnormal points can be effectively identified and eliminated through the coarse screening effect of the primary screening under the conditions that the number of the abnormal points in the original data set is large and the data noise is very large, so that the problem of curve misfitting is avoided.
It can be seen from embodiment 3 that, under the conditions that the quality of the original data set is good and the number of abnormal points is low, good fitting effect can be provided by the primary screening and the secondary screening, and an accurate fitting curve can be obtained. Moreover, the mode of using the coarse screening for the primary screening can achieve the effect of reducing the workload of the secondary screening, and reduce the computing resources (such as computing power, memory and the like) consumed by the electronic computing equipment when data correction is performed.
It will be further appreciated by those of skill in the art that the various steps of the exemplary calibration data modification methods described in connection with the embodiments disclosed herein can be embodied in electronic hardware, computer software, or combinations of both, and that the exemplary components and steps have been described above generally in terms of their functionality for the purpose of clearly illustrating the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation.
Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. The computer software may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A method for correcting calibration data of a turbidimetric method, comprising:
receiving an original data set consisting of a plurality of reaction data;
performing primary screening on the original data set through linear fitting to obtain a primary screening data set;
carrying out secondary screening on the primary screening data set through nonlinear fitting to obtain a corrected data set;
the linear fit has a lower fitting power than the non-linear fit;
the secondary screening is performed on the primary screening data set through nonlinear fitting to obtain a corrected data set, and the method specifically comprises the following steps:
performing curve fitting on the reaction data in the primary screening data set by using a function consistent with a reaction rule to obtain a corresponding fitting curve;
judging whether the fitting curve meets a preset second screening termination standard or not;
if so, finishing the secondary screening of the primary screening data set; if not, removing abnormal reaction data in the preliminary screening data set and performing curve fitting again;
the primary screening is carried out on the original data set through linear fitting to obtain a primary screening data set, and the method specifically comprises the following steps:
performing linear fitting on input reaction data to obtain a corresponding linear equation;
determining a deviation between each of said reaction data and said line equation;
screening one or more reaction data which do not accord with the reaction rule according to the deviation;
according to the deviation, screening out one or more first reaction data of the law which cannot be reacted specifically comprises:
judging whether the first screening termination standard is met or not;
if so, finishing primary screening of the original data set; if not, removing the reaction data with the maximum deviation and performing linear fitting again.
2. The method according to claim 1, wherein the linear fitting of the input reaction data to obtain the corresponding line equation specifically comprises:
fitting to obtain a linear equation with an independent variable as a measuring moment and a dependent variable as scattered light intensity by a linear least square method; the reaction data is the actual scattered light intensity sampled at the selected measurement instant.
3. The method of claim 2, wherein determining the deviation between each of the reaction data and the line equation comprises:
obtaining first theoretical scattered light intensity at different measurement moments through the linear equation;
calculating an absolute value of a difference between a first theoretical scattered light intensity and an actual scattered light intensity at the same measurement time as a deviation between the reaction data and the linear equation.
4. The method of claim 1, wherein the first screening termination criteria comprises:
the ratio of the maximum deviation to the actual scattered light intensity variation range of the raw data set is less than a preset first threshold, an
The ratio of the maximum deviation to the standard deviation of the deviations of all the reaction data in the original data set is smaller than a preset second threshold value;
the maximum deviation is: a maximum in a deviation between the reaction data and the linear equation.
5. The method according to claim 1, wherein the removing abnormal reaction data in the preliminary screening data set specifically comprises:
obtaining second theoretical scattered light intensity at different measurement moments through the fitting curve;
calculating the absolute value of the difference between the second theoretical scattered light intensity and the actual scattered light intensity at the same measurement moment, wherein the reaction data is the actual scattered light intensity obtained by sampling at the selected measurement moment;
removing the reaction data with the maximum absolute value.
6. The method of claim 5, wherein the second screening termination criterion is that a correlation coefficient between the second theoretical scattered light intensity and the actual scattered light intensity is greater than a preset coefficient threshold.
7. An electronic device, comprising: a processor and a memory; the memory stores computer-executable program instructions to cause the processor, when invoked, to perform the calibration data correction method of any of claims 1-6, to remove one or more reaction data from an input original data set to obtain a corrected data set, and to complete a calibration process for turbidimetry based on the corrected data set.
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| CN115560878B (en) * | 2022-12-05 | 2023-03-10 | 中科声龙科技发展（北京）有限公司 | Equipment temperature detection method, device, equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103559337A (en) * | 2013-10-18 | 2014-02-05 | 中冶集团武汉勘察研究院有限公司 | Method for building fine grain tailing project property index estimation empirical formula based on linear regression |
| CN103777628A (en) * | 2005-04-04 | 2014-05-07 | 费舍-柔斯芒特系统股份有限公司 | Statistical processing method used in abnormal situation detection |
| CN105044298A (en) * | 2015-07-13 | 2015-11-11 | 常熟理工学院 | Method for detecting crab freshness grade based on machine olfaction |
| CN106708783A (en) * | 2016-12-28 | 2017-05-24 | 深圳市瑞凌实业股份有限公司 | Calculation method of linear fitting function and device |
| CN107092772A (en) * | 2017-03-01 | 2017-08-25 | 深圳怡化电脑股份有限公司 | The determination method and its device of a kind of sensor characteristic curve |
| CN108318436A (en) * | 2018-02-06 | 2018-07-24 | 迈克医疗电子有限公司 | Response curve generation method, device and Systems for optical inspection |
| CN108981780A (en) * | 2017-05-31 | 2018-12-11 | 王在彬 | A kind of Calibration of measuring equipment calibration method |
| CN109557227A (en) * | 2018-12-04 | 2019-04-02 | 北京工商大学 | A method of frying oil quality is predicted based on fitting of a polynomial combination physical and chemical index |
| CN109580549A (en) * | 2018-11-14 | 2019-04-05 | 深圳市理邦精密仪器股份有限公司 | Calculating, calibrating method and the device of content of material |
-
2019
- 2019-04-22 CN CN201910325446.4A patent/CN110082319B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103777628A (en) * | 2005-04-04 | 2014-05-07 | 费舍-柔斯芒特系统股份有限公司 | Statistical processing method used in abnormal situation detection |
| CN103559337A (en) * | 2013-10-18 | 2014-02-05 | 中冶集团武汉勘察研究院有限公司 | Method for building fine grain tailing project property index estimation empirical formula based on linear regression |
| CN105044298A (en) * | 2015-07-13 | 2015-11-11 | 常熟理工学院 | Method for detecting crab freshness grade based on machine olfaction |
| CN106708783A (en) * | 2016-12-28 | 2017-05-24 | 深圳市瑞凌实业股份有限公司 | Calculation method of linear fitting function and device |
| CN107092772A (en) * | 2017-03-01 | 2017-08-25 | 深圳怡化电脑股份有限公司 | The determination method and its device of a kind of sensor characteristic curve |
| CN108981780A (en) * | 2017-05-31 | 2018-12-11 | 王在彬 | A kind of Calibration of measuring equipment calibration method |
| CN108318436A (en) * | 2018-02-06 | 2018-07-24 | 迈克医疗电子有限公司 | Response curve generation method, device and Systems for optical inspection |
| CN109580549A (en) * | 2018-11-14 | 2019-04-05 | 深圳市理邦精密仪器股份有限公司 | Calculating, calibrating method and the device of content of material |
| CN109557227A (en) * | 2018-12-04 | 2019-04-02 | 北京工商大学 | A method of frying oil quality is predicted based on fitting of a polynomial combination physical and chemical index |
Non-Patent Citations (1)
| Title |
|---|
| 利用Origin对数据异常值的剔除方法进行比较;张璇等;《实验科学与技术》;20120229;第10卷(第1期);第74-76以及118页 * |
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