CN117053923A - Dark noise calibration method of spectrometer - Google Patents
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- YZCKVEUIGOORGS-OUBTZVSYSA-N Deuterium Chemical compound [2H] YZCKVEUIGOORGS-OUBTZVSYSA-N 0.000 description 4
- 238000000354 decomposition reaction Methods 0.000 description 4
- 229910052805 deuterium Inorganic materials 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 description 4
- 229910052721 tungsten Inorganic materials 0.000 description 4
- 239000010937 tungsten Substances 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
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- 238000012067 mathematical method Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011410 subtraction method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/02—Details
- G01J3/0297—Constructional arrangements for removing other types of optical noise or for performing calibration
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/2803—Investigating the spectrum using photoelectric array detector
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J3/2803—Investigating the spectrum using photoelectric array detector
- G01J2003/282—Modified CCD or like
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J2003/283—Investigating the spectrum computer-interfaced
- G01J2003/2843—Processing for eliminating interfering spectra
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
- G01J3/28—Investigating the spectrum
- G01J2003/2866—Markers; Calibrating of scan
Abstract
The application discloses a dark noise calibration method of a spectrometer, which comprises the following steps: the method comprises the following steps: s1: scanning the spectrometer device; s2: adjusting the integration time; s3: recording the saturation maximum integration time; s4: setting a minimum integration time, a maximum integration time and a time frame number; s5: obtaining a dark spectrum; s6: linear fitting; storing fitting coefficients; s7: and acquiring a bright spectrum, calculating a fitting dark spectrum, and deducting the fitting dark spectrum from the bright spectrum to obtain a signal spectrum. The application uses least square method to linearly fit, subtracts the fitted dark spectrum from the original dark spectrum, and the absolute value of the difference is not more than 50 in 1s integration time under the condition of-2 ℃.
Description
Technical Field
The application relates to the technical field of spectrometers, in particular to a dark noise calibration method of a spectrometer.
Background
Background dark noise subtraction method ()'s of raman spectrometer employing grating array detector in existing patent document cn2019104044792. X; the SVD singular value decomposition method is used, and the SVD method has the defects that the calculation complexity of singular value decomposition is higher in high-dimensional data, and the singular value decomposition needs to occupy more storage space for a larger data matrix. In addition, singular value decomposition may have numerical stability problems, i.e., when the size of the data matrix is large, large errors may occur in the calculation process. These problems limit the application of SVD methods in some scenarios.
Methods of collection, dark correction and reporting of spectra from array detector spectrometers published by the prior patent document CN 201680031806.4; if the data is processed with only summation, average values, some details and outliers in the data may be ignored, and thus possible problems or relationships in the data cannot be found. Meanwhile, simple statistical indexes such as summation, average value and the like can only describe certain aspects of the data, and cannot reflect the complex structure and diversity of the data.
Conventional techniques either collect only one dark spectrum and apply it to all future spectra in the experimental or monitoring process; or a new dark spectrum is collected before each signal spectrum. It can be seen that conventional techniques for solving the problem of dark correction are either inefficient in terms of the amount of time required or inaccurate in matching the true dark response at the time of light collection.
Disclosure of Invention
The application aims to provide a dark noise calibration method of a spectrometer, which aims to solve the problems in the prior art.
In order to achieve the above purpose, the present application provides the following technical solutions: a method of calibrating dark noise of a spectrometer, comprising the steps of: s1: scanning the spectrometer device; s2: adjusting the integration time; s3: recording the saturation maximum integration time; s4: setting a minimum integration time, a maximum integration time and a time frame number; s5: obtaining a dark spectrum; s6: linear fitting; storing fitting coefficients; s7: and acquiring a bright spectrum, calculating a fitting dark spectrum, and deducting the fitting dark spectrum from the bright spectrum to obtain a signal spectrum.
Further, the step S1 includes: the spectrometer devices are scanned one by one with software to confirm whether there are available spectrometer devices.
Further, the step S2 includes: after confirming that the spectrometer equipment is ready, the laser is turned off, and the integration time is continuously adjusted to ensure that maxi t As close to 65535 as possible.
Further, the step S3 includes: the integration time t of step S2 is recorded, letting the saturation maximum integration time maxinttime=t.
Further, the step S4 sets a minimum integration time minIntTime, a maximum integration time maxIntTime, and a time frame number frames; the spectrometer integration time Ti is calculated as follows:
T[i]＝(maxIntTime-minIntTime)*i/frames+minIntTime，i∈[0，frames]。
further, the step S5 includes: reading the number pixels of CCD pixels used for dark noise calibration of the spectrometer, dividing an integration time interval into frames equally divided time according to the set minTime and maxIntTime, taking the divided time as the integration time of the spectrometer, taking the average of 10-20 times under each integration time, and taking the average as a sample dark spectrum;
further, the step S6 includes: the collected sample dark spectrum corresponds to the integration time of the sample dark spectrum, the integration time is taken as an X axis, the response intensity of the dark spectrum corresponding to the integration time is taken as a Y axis, the integration time forms a one-dimensional vector X, and the response intensity of the dark spectrum forms a one-dimensional vector Y;
let y=f (X) =wx, wherein,
W＝[w 1 ，w 2 ，...，w d ，b]，X＝[x 1 ，x 2 ，...，x d ，1]；
assuming now a total of m observations (m > d),
substitution into f (X) may constitute m equations:
and (3) the following steps:
y＝[y 1 ，y 2 ，…，y m ] T
the system of equations for linear fitting is then:
the linear model can also be written as:
further, the step S7 includes: assuming that the dark spectral response intensity coordinate at a given integration time and its integration time is (x 1 ，y 1 )，(x 2 ，y 2 )…(x n ，y n )；
Defining error E i of ordinate y as difference between true value and observed value, defining residual error of yFor the difference between the estimated value and the observed value, the formula is as follows:
fitting errors were minimized using least squares:
the form of the objective function is as follows:
wherein,
y＝[y 1 ，y 2 ，…，y m ] T ；
linear fitting solution using least squaresThe fit residual sum is minimally translated into the following objective function:
；
the objective function derives ω and makes it equal to 0, yielding:
and (3) solving to obtain:
ω＝(x T x) -1 x T y；
ω is a linear fitting coefficient, which is stored.
Further, the step S7 further includes: collecting a dark spectrum sequence D, and calculating and fitting a dark spectrum according to omegaOrder theThe 0 sequence is a white noise sequence; acquiring a bright spectrum sequence L, let ∈ ->S is the spectrum of the sample signal.
Further, the light spectrum and dark spectrum collection ratio reaches 1:10 or more.
Compared with the prior art, the application has the beneficial effects that:
1. using least square method to linearly fit, subtracting the fitted dark spectrum from the original dark spectrum, wherein the absolute value of the difference is not more than 50 in 1s integration time under the condition of-2 ℃; within 1min integration time, the absolute value of the difference value is not more than 200; the absolute value of the difference is not more than 1000 in the integration time of 5 min.
2. The dark noise response correction is only needed once for the instrument, the dark noise response is not needed to be acquired later, and the influence of the integration time on the dark noise effect is considered, and the correction coefficient is an expression related to the integration time in the correction process; the pain point that dark noise response cannot be accurately obtained due to overlong integration time limited by the upper limit of the bit number of the A/D converter of the CCD in the traditional acquisition process is avoided, and the upper limit of settable integration time is expanded to a certain extent; the application saves the measuring time, improves the working efficiency and reduces the measuring cost.
3. The method of the application is performed with a theoretical time complexity of 0 ((minIntTime+maxIntTime) ×frames/2+pixels×frames+pixels×features), where features has a value of 2 in the application, so that the theoretical time complexity can be approximated as O (n 2 ) O (n) relative to SVD 3 ) The temporal complexity is reduced and the spatial complexity is O (pixels), which can be approximated as O (n), O (n) relative to SVD 2 ) The space complexity is reduced, and the method is superior to the SVD method in time and space.
4. The summing average method is generally that the pixel and a plurality of pixels at two sides are summed and averaged, and for the pixels on the boundary, the left and right pixels are not present, and the boundary pixels cannot be properly processed; the summing average method only processes the dummy pixels (dummy pixels, also called non-exposure pixels, refers to pixels which cannot generate charges due to defects or other reasons in the manufacturing process in the CCD), and the non-dummy pixels (exposure pixels, namely pixels participating in spectral imaging) also participate in the calibration process, so that the most original spectral data is ensured, and each detail in the data is not ignored.
5. Through testing, the saturation maximum integration time is different for different spectrometer devices, so that each device is calibrated according to actual conditions, and the saturation maximum integration time is shortened for the device with higher sensitivity.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. In the drawings:
FIG. 1 is a flow chart of the present application;
FIG. 2 is a graph of the white noise spectrum after calibration of the TEC set at-2deg.C with integration time set at 5 min;
FIG. 3 is a graph of the spectrum of the deuterium tungsten lamp signal after the calibration of the test equipment, and fi t is a graph of the spectrum of the deuterium tungsten lamp signal after the calibration of the test equipment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to fall within the scope of the present application. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application.
Referring to fig. 1-3, in an embodiment of the present application, a dark noise calibration method of a spectrometer includes the following steps:
step S1: scanning the spectrometer device;
step S2: adjusting the integration time;
step S3: recording the saturation maximum integration time;
step S4: setting a minimum integration time, a maximum integration time and a time frame number N;
step S5: obtaining a dark spectrum;
step S6: linear fitting; storing fitting coefficients;
step S7: and acquiring a bright spectrum, calculating a fitting dark spectrum, and deducting the fitting dark spectrum from the bright spectrum to obtain a signal spectrum.
The specific implementation of each step of the application is as follows:
s1: the software scans the spectrometer device first to confirm whether there is a usable spectrometer device;
s2: after confirming that the spectrometer equipment is ready, the laser is turned off, and the integration time is continuously adjusted to ensure that maxi t As close to 65535 as possible;
s3: recording t of the step S2, and letting maxinttime=t;
s4: setting a minimum integration time minIntTime, setting a maximum integration time maxIntTime, and setting a time frame number frames;
s5: the software reads the number pixels of CCD pixels used for dark noise calibration of the spectrometer, and according to the set minTime, maxIntTime, the software divides the integration time interval into frames equally divided time as the integration time of the spectrometer, and each integration time is acquired for 10-20 times and averaged to obtain a sample dark spectrum;
s6: the collected sample dark spectrum corresponds to the integration time of the sample dark spectrum, the integration time is taken as an X axis, the response intensity of the dark spectrum corresponding to the integration time is taken as a Y axis, the integration time forms a one-dimensional vector X, and the response intensity of the dark spectrum forms a one-dimensional vector Y;
let y=f (X) =wx, wherein,
W＝[w 1 ,w 2 ,…,w d ,b]，X＝[x 1 ,x 2 ,…,x d ,1]
assuming now a total of m observations (m > d),
then the inclusion in f (X) may constitute m equations:
and (3) the following steps:
y＝[y 1 ,y 2 ,…,y m ] T
the system of equations can be written as:
the linear model can also be written as:
s7: assuming that the dark spectral response intensity coordinate at a given integration time and its integration time is (x 1 ,y 1 ),(x 2 ,y 2 )…(x n ,y n )
Error e defining ordinate y i Defining a residual error of y as the difference between the true value and the observed valueFor the difference between the estimated value and the observed value, the formula is as follows:
the purpose of the least squares method is to minimize the fitting error, namely:
the form of the objective function is as follows:
wherein,
y＝[y 1 ,y 2 ,…,y m ] T
linear fitting solution using least squaresSo that the fitting error (residual error and minimum) can be converted into an objective function
The objective function derives ω and makes it equal to 0, yielding:
and (3) solving to obtain:
ω＝(x T x) -1 x T y
ω is the linear fitting coefficient, storing the coefficient to the device;
collecting a dark spectrum sequence D, and calculating and fitting a dark spectrum according to omegaLet->The O sequence is a white noise sequence;
collecting a bright spectrum sequence L to enableS is the spectrum of the sample signal.
The spectrometer integration time:
T[i]＝(maxIntTime–minIntTime)*i/frames+minintTime,i∈[0,frames]。
preferably, the ratio of the acquired light spectrum to the acquired dark spectrum is more than 1:10. For spectrometer equipment with higher sensitivity, the spectrometer equipment is greatly influenced by ambient light, the stability of spectrum signals is poor, and in the actual test process, the acquisition ratio of a bright spectrum and a dark spectrum is found to be set at 1: and 5, the phenomenon of spectrum signal data burr is obvious (namely, the signal intensity difference between adjacent pixels is large). While the ratio is raised to 1: at 10, the frequency of occurrence of the burr phenomenon decreases.
The least squares method (Least Square Method) is a mathematical method for finding the curve or function in a set of data that most represents the characteristics of the data. The basic idea of the least squares method is to minimize the sum of the distances of the data points from the curve or function by adjusting the parameters of the curve or function. This sum of distances is called the sum of squares of residuals, i.e. the objective function of the least squares method. The least square method can be used in the fields of linear regression, nonlinear regression, curve fitting and the like, and has wide application value.
The application field of the least square method is as follows:
linear regression: let the data points obey a linear relationship, i.e. y=kx+b, where k and b are the parameters to be solved for. The goal of the least squares method is to find a straight line such that the sum of the perpendicular distances of all data points to the straight line is minimized. This can be achieved by solving a normal system of equations, i.e., (k, b) = (X) T X) -1 X T y, where X is the data matrix and y is the target vector.
Curve fitting: assume that the data points obey a polynomial relationship, i.e., y=a 0 +a 1 x+a 2 x 2 +…+a n x n Wherein a is 0 ,a 1 ,…,a n Is the parameter to be solved. The goal of the least squares method is to find a polynomial such that the sum of the vertical distances of all data points to the polynomial curve is minimized. This may be achieved by constructing a vandermonde matrix,
then solve the equation set aa=y, where a= [ a ] 0 ,a 1 ,…,a n ] T ，y＝[y 1 ,y 2 ,…,y m ] T 。
The method of the application is performed with theoretical time complexity of O ((mininttime+maxinttime) ×frames/2+pixels×frames+pixels×features), wherein the value of features is 2 in the application, so the theoretical time complexity can be approximated as O (n 2 ) O (n) relative to SVD 3 ) The temporal complexity is reduced and the spatial complexity is O (pixels), which can be approximated as O (n), O (n) relative to SVD 2 ) The space complexity is reduced, and the method is superior to the SVD method in time and space.
The summing average method is generally that the pixel and a plurality of pixels at two sides are summed and averaged, and for the pixels on the boundary, the left and right pixels are not present, and the boundary pixels cannot be properly processed; the summing average method only processes the dummy pixels (dummy pixels, also called non-exposure pixels, refers to pixels which cannot generate charges due to defects or other reasons in the manufacturing process in the CCD), and the non-dummy pixels (exposure pixels, namely pixels participating in spectral imaging) also participate in the calibration process, so that the most original spectral data is ensured, and each detail in the data is not ignored.
Through testing, the saturation maximum integration time is different for different spectrometer devices, so that each device is calibrated according to actual conditions, and the saturation maximum integration time is shortened for the device with higher sensitivity.
FIG. 2 is a graph of the white noise spectrum after calibration for TEC set at-2deg.C with integration time set at 5min, with the effect that the absolute value of the spectral response maximum does not exceed 1000.
In fig. 3, the fact is the deuterium tungsten lamp signal spectrum curve of the test equipment which is not calibrated, the fit is the deuterium tungsten lamp signal spectrum curve of the test equipment which is calibrated, two lines almost coincide from the figure, which shows that the signals before and after calibration are basically consistent, so that the dark spectrum response can be fitted by using the calibrated dark spectrum coefficient, and the effect of replacing the true dark spectrum effect of the equipment is achieved.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present application has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for calibrating dark noise of a spectrometer, comprising the steps of:
step S1: scanning the spectrometer device;
step S2: adjusting the integration time;
step S3: recording the saturation maximum integration time;
step S4: setting a minimum integration time, a maximum integration time and a time frame number;
step S5: obtaining a dark spectrum;
step S6: linear fitting and storing fitting coefficients;
step S7: and acquiring a bright spectrum, calculating a fitting dark spectrum, and deducting the fitting dark spectrum from the bright spectrum to obtain a signal spectrum.
2. The method according to claim 1, wherein the step S1 comprises: the spectrometer devices are scanned one by one with software to confirm whether there are available spectrometer devices.
3. The method according to claim 1, wherein the step S2 comprises: after confirming that the spectrometer equipment is ready, the laser is turned off, and the integration time is continuously adjusted to ensure that maxi t As close to 65535 as possible.
4. The method according to claim 1, wherein the step S3 comprises: the integration time t of step S2 is recorded, letting the saturation maximum integration time maxinttime=t.
5. The method according to claim 4, wherein the step S4 sets a minimum integration time minIntTime, a maximum integration time maxIntTime, and a time frame number frames; the spectrometer integration time Ti is calculated as follows:
T[i]＝(maxIntTime–minIntTime)*i/frames+minIntTime,i∈[0,frames]。
6. the method according to claim 4, wherein the step S5 comprises: reading the number pixels of CCD pixels used for dark noise calibration of the spectrometer, dividing an integration time interval into frames equally divided time according to the set minTime and maxIntTime, taking the divided time as the integration time of the spectrometer, taking the average of the integrated time as a sample dark spectrum after 10-20 times of each integration time.
7. The method according to claim 1, wherein the step S6 comprises: the collected sample dark spectrum corresponds to the integration time of the sample dark spectrum, the integration time is taken as an X axis, the response intensity of the dark spectrum corresponding to the integration time is taken as a Y axis, the integration time forms a one-dimensional vector X, and the response intensity of the dark spectrum forms a one-dimensional vector Y;
let y=f (X) =wx, wherein,
W＝[w 1 ,w 2 ,…,w d ,b]，X＝[x 1 ,x 2 ,…,x d ,1]；
assuming now a total of m observations (m > d),
substitution into f (X) may constitute m equations:
and (3) the following steps:
y＝[y 1 ,y 2 ,…,y m ] T
the system of equations for linear fitting is then:
the linear model can also be written as:
8. the method for calibrating dark noise of a spectrometer according to claim 7, wherein said step S7 comprises: assuming that the dark spectral response intensity coordinate at a given integration time and its integration time is (x 1 ,y 1 ),(x 2 ,y 2 )…(x n ,y n )；
Error e defining ordinate y i Defining a residual error of y as the difference between the true value and the observed valueFor the difference between the estimated value and the observed value, the formula is as follows:
fitting errors were minimized using least squares:
the form of the objective function is as follows:
wherein,
y＝[y 1 ,y 2 ,…,y m ] T ；
linear fitting solution using least squaresThe fit residual sum is minimally translated into the following objective function:
；
the objective function derives ω and makes it equal to 0, yielding:
and (3) solving to obtain:
ω＝(x T x) -1 x T y；
ω is a linear fitting coefficient, which is stored.
9. The method according to claim 1, wherein the step S7 further comprises:
collecting a dark spectrum sequence D, and calculating and fitting a dark spectrum according to omegaLet->The O sequence is a white noise sequence;
collecting a bright spectrum sequence L to enableS isSample signal spectrum.
10. The method for calibrating dark noise of a spectrometer according to claim 9, wherein the ratio of the light spectrum to the dark spectrum is up to 1:10 or more.
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