A method for detecting hydrogen sulfide gas

Abstract

The present invention discloses a method for detecting hydrogen sulfide gas. The method comprises the following steps: passing a gas to be tested into a sample chamber, emitting infrared light from an infrared light source, detecting spectral data of the infrared light passing through the gas to be tested and spectral data of the infrared light passing through a reference optical fiber; calculating the difference between the two spectral data to obtain a spectral data set L3, and calculating an intensity wavenumber ratio data set T; inputting the data in the intensity wavenumber ratio data set T into a nonlinear resonance model to plot a characteristic signal-to-noise ratio curve; calculating the envelope area corresponding to each trough on the characteristic signal-to-noise ratio curve except the last trough; marking the points formed by each trough number and its corresponding envelope area in a second rectangular coordinate system, and performing linear fitting to obtain the formula y=kx+D; determining whether the gas to be tested is hydrogen sulfide gas based on k, and calculating the concentration of the hydrogen sulfide gas based on D. The method can quickly and accurately detect hydrogen sulfide gas and its concentration, with good stability.

Description

Technical Field

[0001] The present invention relates to the technical field of gas detection, and in particular to a hydrogen sulfide gas detection method. Background Art

[0002] Hydrogen sulfide is an inorganic compound. Under standard conditions, it is a flammable, acidic gas that is colorless and has a rotten egg odor at low concentrations. At extremely low concentrations, it has a sulfurous odor and is highly toxic. Currently, gas sensors made from highly sensitive gas-sensitive materials are commonly used to detect hydrogen sulfide. However, these materials typically undergo an adsorption-desorption cycle. If desorption is incomplete, the sensor element cannot return to its initial state, meaning the detection signal cannot return to its baseline. This results in poor detection repeatability and affects the accuracy of hydrogen sulfide gas detection. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a hydrogen sulfide gas detection method, which can quickly and accurately detect hydrogen sulfide gas and its concentration, and has good detection repeatability and stability.

[0004] In order to solve the above problems, the present invention adopts the following technical solutions:

[0005] A method for detecting hydrogen sulfide gas according to the present invention is characterized in that it comprises the following steps:

[0006] S1: The gas to be tested is introduced into the sample chamber until the sample chamber is filled with the gas to be tested and the pressure in the sample chamber reaches 2 atmospheres;

[0007] S2: The infrared light source is activated to emit infrared light. The infrared light passes through the gas to be tested and is detected by the first infrared detection module. The first infrared detection module sends the detected spectral data set D1 to the central processing unit. The spectral data set D1 contains n spectral data. The infrared light passes through the reference optical fiber and is detected by the second infrared detection module. The second infrared detection module sends the detected spectral data set D2 to the central processing unit. The spectral data set D2 contains n spectral data. Each spectral data consists of a wave number wn and a corresponding spectral intensity sp.

[0008] S3: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to wavenumber to obtain a spectral data set L1, and arranges the spectral data in the spectral data set D2 from large to small according to wavenumber to obtain a spectral data set L2. The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain a spectral data set L3;

[0009] S4: The central processing unit calculates the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3, and obtains the intensity wavenumber ratio data set T, where T = {tr(1), tr(2)…tr(n)}, tr(i) is the intensity wavenumber ratio corresponding to the i-th spectral data G3(i) in the spectral data set L3, and 1≤i≤n;

[0010] S5: The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model, calculates the characteristic signal-to-noise ratio SNR using the nonlinear resonance model, and establishes a first rectangular coordinate system with the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis, and draws a characteristic signal-to-noise ratio curve in the first rectangular coordinate system;

[0011] S6: Draw an auxiliary line perpendicular to the Y axis from the point with the maximum signal-to-noise ratio on the characteristic signal-to-noise ratio curve to the Y axis;

[0012] From left to right, number the troughs on the characteristic signal-to-noise ratio curve as 1, 2, ..., m, where m is the number of troughs on the characteristic signal-to-noise ratio curve. Select the first m-1 troughs on the characteristic signal-to-noise ratio curve, and draw a first connecting line from each trough through the adjacent peak on its left, and a second connecting line from each trough through the adjacent peak on its right. Both the first and second connecting lines intersect with the auxiliary lines. The first, second, and auxiliary lines, starting from each trough, enclose an envelope region corresponding to each trough. Calculate the area of ​​the envelope region corresponding to each trough.

[0013] S7: The central processing unit establishes a second rectangular coordinate system with the trough number as the x-axis and the envelope area as the y-axis. Each trough number and its corresponding envelope area are marked in the second rectangular coordinate system. The linear fitting formula y=kx+D is obtained. If g1≤k≤g2, it means that the gas to be measured is hydrogen sulfide gas, and the concentration of hydrogen sulfide gas is

[0014] In this solution, first, the sample chamber is filled with the gas to be measured at 2 atmospheres. Then, the infrared light source is activated to emit light. The first infrared detection module detects the spectral data of the infrared light passing through the gas to be measured, and the second infrared detection module detects the spectral data of the infrared light passing through the reference optical fiber. The two are subtracted to obtain the spectral data set L3 reflecting the gas to be measured. Then, the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3 is calculated to obtain the intensity wavenumber ratio data set T. The data in the intensity wavenumber ratio data set T is input into the nonlinear resonance model, and the characteristic signal-to-noise ratio SNR is calculated using the nonlinear resonance model. The central processing unit takes the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis to establish the first rectangular coordinate system, plots the characteristic signal-to-noise ratio curve in the first rectangular coordinate system, draws the envelope region corresponding to each trough according to the peaks and troughs of the characteristic signal-to-noise ratio curve, calculates the area of the envelope region corresponding to each trough, and linearly fits the points composed of each trough number and the corresponding envelope region area in the second rectangular coordinate system to obtain the formula y = kx + D. If g1 ≤ k ≤ g2, it means the gas to be measured is hydrogen sulfide gas, and the concentration of hydrogen sulfide gas is If k < g1 or k > g2, it means the gas to be measured is not hydrogen sulfide gas.

[0015] This solution uses a continuous "trough + adjacent peak" to determine the envelope region, calculates the area of the envelope region, linearly fits the vector composed of the areas of the envelope regions, and judges whether the gas to be measured is hydrogen sulfide gas according to the slope of the linear fitting line, and calculates the concentration of hydrogen sulfide gas according to the intercept, which has better stability than the direct infrared spectroscopy analysis method. This solution can quickly and accurately detect hydrogen sulfide gas and its concentration, with good detection repeatability and stability.

[0016] Preferably, the step S1 includes the following steps: The gas to be measured is introduced into the sample chamber, all the air in the sample chamber is exhausted, the air outlet is sealed, and the gas to be measured is continuously introduced into the sample chamber. When the air pressure in the sample chamber reaches 2 atmospheres, the introduction of the gas to be measured into the sample chamber is stopped, and the air inlet is sealed.

[0017] Preferably, the step S3 includes the following steps:

[0018] S31: The central processing unit arranges the spectral data in the spectral data set D1 in descending order of wavenumber to obtain the spectral data set L1, L1 = {G1(1), G1(2)…G1(n)}. The i-th spectral data G1(i) in the spectral data set L1 = (wn1(i), sp1(i)), 1 ≤ i ≤ n, sp1(i) is the spectral intensity in the spectral data G1(i), and wn1(i) is the wavenumber in the spectral data G1(i);

[0019] S32: The central processing unit arranges the spectral data in the spectral data set D2 from large to small according to the wave number to obtain a spectral data set L2, L2 = {G2(1), G2(2) ... G2(n)}, the i-th spectral data G2(i) in the spectral data set L2 = (wn2(i), sp2(i)), sp2(i) is the spectral intensity in the spectral data G2(i), wn2(i) is the wave number in the spectral data G2(i);

[0020] S33: The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain the spectral data set L3, L3 = {G3(1), G3(2) ... G3(n)}, the i-th spectral data G3(i) in the spectral data set L3 = (wn3(i), sp3(i)),

[0021] Wherein, sp3(i)=sp1(i)-sp2(i), wn3(i)=wn1(i)=wn2(i), sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i).

[0022] Preferably, step S5 includes the following steps:

[0023] The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model:

[0024]

[0025] Where x is the position of the virtual particle in the nonlinear resonance model, V(x) is the nonlinear symmetric potential function, A is the input signal intensity, f0 is the modulation signal frequency, is the initial phase, D is the excitation noise intensity, a and b are coefficients, ζ(i) is the i-th Gaussian white noise, and its autocorrelation function is: E[ξ(i)ξ(0)]=2Dδ(i), δ(i) is the impulse function;

[0026] When D=D1, the nonlinear resonance model generates resonance and obtains the characteristic signal-to-noise ratio SNR.

[0027]

[0028] Where V0 is the potential barrier height;

[0029] The central processing unit establishes a first rectangular coordinate system with the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis, and draws a characteristic signal-to-noise ratio curve in the first rectangular coordinate system.

[0030] Preferably, the calculation formula of the intensity wavenumber ratio tr(i) corresponding to the i-th spectral data G3(i) in the spectral data set L3 in step S4 is as follows:

[0031] Wherein sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i).

[0032] The beneficial effects of the present invention are: being able to quickly and accurately detect hydrogen sulfide gas and its concentration, with good detection repeatability and good stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a flow chart of an embodiment;

[0034] Figure 2 is a schematic diagram of the characteristic signal-to-noise ratio curve;

[0035] Figure 3 is a schematic diagram of linear fitting;

[0036] Figure 4 It is a structural diagram of the sample chamber.

[0037] In the figure: 1. Sample chamber, 2. Air inlet, 3. Air outlet, 4. Infrared light source, 5. Infrared detection device, 6. Reference optical fiber. DETAILED DESCRIPTION

[0038] The technical solution of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0039] Example: A method for detecting hydrogen sulfide gas in this embodiment, such as Figure 1 As shown, the following steps are included:

[0040] S1: Pass the gas to be tested into the sample chamber, exhaust all the air in the sample chamber, seal the air outlet, and continue to pass the gas to be tested into the sample chamber. When the air pressure in the sample chamber reaches 2 atmospheres, stop passing the gas to be tested into the sample chamber and seal the air inlet;

[0041] S2: The infrared light source is activated to emit infrared light. The infrared light passes through the gas to be tested and is detected by the first infrared detection module. The first infrared detection module sends the detected spectral data set D1 to the central processing unit. The spectral data set D1 contains n spectral data. The infrared light passes through the reference optical fiber and is detected by the second infrared detection module. The second infrared detection module sends the detected spectral data set D2 to the central processing unit. The spectral data set D2 contains n spectral data. Each spectral data consists of a wave number wn and a corresponding spectral intensity sp.

[0042] S3: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to the wave number to obtain the spectral data set L1, L1 = {G1(1), G1(2)…G1(n)}, the i-th spectral data G1(i) in the spectral data set L1 = (wn1(i), sp1(i)), 1≤i≤n, sp1(i) is the spectral intensity in the spectral data G1(i), wn1(i) is the wave number in the spectral data G1(i);

[0043] The central processing unit arranges the spectral data in the spectral data set D2 from large to small according to the wave number to obtain the spectral data set L2, L2 = {G2(1), G2(2) ... G2(n)}, the i-th spectral data G2(i) in the spectral data set L2 = (wn2(i), sp2(i)), sp2(i) is the spectral intensity in the spectral data G2(i), wn2(i) is the wave number in the spectral data G2(i);

[0044] The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain the spectral data set L3, L3 = {G3(1), G3(2) ... G3(n)}, the i-th spectral data G3(i) in the spectral data set L3 = (wn3(i), sp3(i)),

[0045] Wherein, sp3(i)=sp1(i)-sp2(i), wn3(i)=wn1(i)=wn2(i), sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i);

[0046] S4: The central processing unit calculates the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3, and obtains the intensity wavenumber ratio data set T, T = {tr(1), tr(2) ... tr(n)}, tr(i) is the intensity wavenumber ratio corresponding to the i-th spectral data G3(i) in the spectral data set L3,

[0047] S5: The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model, calculates the characteristic signal-to-noise ratio SNR using the nonlinear resonance model, and establishes a first rectangular coordinate system with the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis, and draws a characteristic signal-to-noise ratio curve in the first rectangular coordinate system;

[0048] S6: Draw an auxiliary line perpendicular to the Y axis from the point with the maximum signal-to-noise ratio on the characteristic signal-to-noise ratio curve to the Y axis;

[0049] From left to right, number the troughs on the characteristic signal-to-noise ratio curve as 1, 2, ..., m, where m is the number of troughs on the characteristic signal-to-noise ratio curve. Select the first m-1 troughs on the characteristic signal-to-noise ratio curve, and draw a first connecting line from each trough through the adjacent peak on its left, and a second connecting line from each trough through the adjacent peak on its right. Both the first and second connecting lines intersect with the auxiliary lines. The first, second, and auxiliary lines, starting from each trough, enclose an envelope region corresponding to each trough. Calculate the area of ​​the envelope region corresponding to each trough.

[0050] S7: The central processing unit establishes a second rectangular coordinate system with the trough number as the x-axis and the envelope area as the y-axis. Each point consisting of the trough number and its corresponding envelope area is marked in the second rectangular coordinate system. The linear fitting formula y=kx+D is obtained. If 2.4≤k≤2.7, it means that the gas to be measured is hydrogen sulfide gas, and the concentration of hydrogen sulfide gas is If k<2.4 or k>2.7, it means that the gas to be measured is not hydrogen sulfide gas.

[0051] Step S5 includes the following steps:

[0052] The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model:

[0053]

[0054] Where x is the position of the virtual particle in the nonlinear resonance model, V(x) is the nonlinear symmetric potential function, A is the input signal intensity, f0 is the modulation signal frequency, is the initial phase, D is the excitation noise intensity, a and b are coefficients, ζ(i) is the i-th Gaussian white noise, and its autocorrelation function is: E[ξ(i)ξ(0)]=2Dδ(i), δ(i) is the impulse function;

[0055] When D=D1, the nonlinear resonance model generates resonance and obtains the characteristic signal-to-noise ratio SNR.

[0056]

[0057] Where V0 is the potential barrier height;

[0058] The central processing unit establishes a first rectangular coordinate system with the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis, and draws a characteristic signal-to-noise ratio curve in the first rectangular coordinate system.

[0059] like Figure 4As shown, the sample chamber 1 is provided with an air inlet 2 and an air outlet 3. An infrared light source 4 and an infrared detection device 5 are symmetrically provided on the left and right sides of the sample chamber 1. The infrared detection device 5 includes a first infrared detection module and a second infrared detection module. A reference optical fiber 6 is also provided in the sample chamber 1. The two ends of the reference optical fiber 6 are respectively connected to the infrared light source 4 and the second infrared detection module. The first infrared detection module is used to detect infrared light passing through the gas to be tested, and the second infrared detection module is used to detect infrared light passing through the reference optical fiber.

[0060] In this solution, the sample chamber is first filled with the gas to be tested at 2 atmospheres of pressure, and then the infrared light source is started to emit light. The first infrared detection module detects the spectral data of the infrared light passing through the gas to be tested, and the second infrared detection module detects the spectral data of the infrared light passing through the reference optical fiber. The two are subtracted to obtain the spectral data set L3 reflecting the gas to be tested. Then, the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3 is calculated to obtain the intensity wavenumber ratio data set T. The data in the intensity wavenumber ratio data set T are input into the nonlinear resonance model, and the nonlinear resonance model is used to calculate the intensity wavenumber ratio data set T. To the characteristic signal-to-noise ratio SNR, the central processing unit establishes a first rectangular coordinate system with the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis. The characteristic signal-to-noise ratio curve is drawn in the first rectangular coordinate system. The envelope area corresponding to each trough is drawn according to the peaks and troughs of the characteristic signal-to-noise ratio curve. The area of ​​the envelope area corresponding to each trough is calculated. The points consisting of each trough number and its corresponding envelope area are linearly fitted in the second rectangular coordinate system to obtain the formula y=kx+D. If 2.4≤k≤2.7, it means that the gas to be measured is hydrogen sulfide gas, and the concentration of hydrogen sulfide gas is If k<2.4 or k>2.7, it means that the gas to be measured is not hydrogen sulfide gas.

[0061] After detecting a gas to be tested, the embodiment marks each trough number and the point formed by the corresponding envelope area in the second rectangular coordinate system, such as Figure 3 As shown, the linear fitting formula y=2.55x+26.08 is obtained. Since k=2.55, the gas to be measured is hydrogen sulfide gas, and the concentration of hydrogen sulfide gas is 5.139 ppm.

[0062] After plotting the characteristic signal-to-noise ratio curve in the first rectangular coordinate system, the envelope area corresponding to each trough is plotted as follows:

[0063] Figure 2 The characteristic signal-to-noise ratio curve drawn by the central processing unit in the first rectangular coordinate system when detecting a gas to be tested is: Figure 2The trough numbered 1 on the characteristic signal-to-noise ratio curve is point o, the adjacent peak to its left is point p, and the adjacent peak to its right is point q. Draw a first connecting line from point o through point p, and a second connecting line from point o through point q. The first connecting line intersects the auxiliary line at point m, and the second connecting line intersects the auxiliary line at point n. The envelope area corresponding to trough numbered 1 is triangle omn. Continue drawing the envelope area for each trough from left to right, following the above method, until you reach the penultimate trough.

[0064] This solution uses a continuous "trough + adjacent peak" method to determine the envelope region and calculate its area. A linear fit is then performed on the vector formed by this area. The slope of the linear fit line is used to determine whether the gas being measured is hydrogen sulfide. The intercept is used to calculate the concentration of hydrogen sulfide. This solution offers greater stability than direct infrared spectroscopy analysis. This solution can quickly and accurately detect hydrogen sulfide and its concentration, with excellent repeatability and stability.

Claims

1. A method for detecting hydrogen sulfide gas, characterized in that: The following steps are involved: S1: The gas to be tested is introduced into the sample chamber until the sample chamber is filled with the gas to be tested and the pressure in the sample chamber reaches 2 atmospheres; S2: The infrared light source is activated to emit infrared light. The infrared light passes through the gas to be tested and is detected by the first infrared detection module. The first infrared detection module sends the detected spectral data set D1 to the central processing unit. The spectral data set D1 contains n spectral data. The infrared light passes through the reference optical fiber and is detected by the second infrared detection module. The second infrared detection module sends the detected spectral data set D2 to the central processing unit. The spectral data set D2 contains n spectral data. Each spectral data consists of a wave number wn and a corresponding spectral intensity sp. S3: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to wavenumber to obtain a spectral data set L1, and arranges the spectral data in the spectral data set D2 from large to small according to wavenumber to obtain a spectral data set L2. The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain a spectral data set L3; S4: The central processing unit calculates the intensity wavenumber ratio tr corresponding to each spectral data in the spectral data set L3, and obtains the intensity wavenumber ratio data set T, where T = {tr(1), tr(2)…tr(n)}, tr(i) is the intensity wavenumber ratio corresponding to the i-th spectral data G3(i) in the spectral data set L3, and 1≤i≤n; S5: The central processing unit inputs the data in the intensity wavenumber ratio data set T into the nonlinear resonance model, calculates the characteristic signal-to-noise ratio SNR using the nonlinear resonance model, and establishes a first rectangular coordinate system with the excitation noise intensity as the X-axis and the signal-to-noise ratio value as the Y-axis, and draws a characteristic signal-to-noise ratio curve in the first rectangular coordinate system; S6: Draw an auxiliary line perpendicular to the Y axis from the point with the maximum signal-to-noise ratio on the characteristic signal-to-noise ratio curve to the Y axis; From left to right, number the troughs on the characteristic signal-to-noise ratio curve as 1, 2, ..., m, where m is the number of troughs on the characteristic signal-to-noise ratio curve. Select the first m-1 troughs on the characteristic signal-to-noise ratio curve, and draw a first connecting line from each trough through the adjacent peak on its left, and a second connecting line from each trough through the adjacent peak on its right. Both the first and second connecting lines intersect with the auxiliary lines. The first, second, and auxiliary lines, starting from each trough, enclose an envelope region corresponding to each trough. Calculate the area of ​​the envelope region corresponding to each trough. S7: The central processing unit establishes a second rectangular coordinate system with the trough number as the x-axis and the envelope area as the y-axis. Each trough number and its corresponding envelope area are marked in the second rectangular coordinate system. The linear fitting formula y=kx+D is obtained. If g1≤k≤g2, it means that the gas to be measured is hydrogen sulfide gas, and the concentration of hydrogen sulfide gas is 2. A hydrogen sulfide gas detection method according to claim 1, characterized in that: The step S1 includes the following steps: introducing the gas to be tested into the sample chamber, exhausting all the air in the sample chamber, sealing the air outlet, and continuing to introduce the gas to be tested into the sample chamber. When the air pressure in the sample chamber reaches 2 atmospheres, stopping introducing the gas to be tested into the sample chamber and sealing the air inlet.

3. A hydrogen sulfide gas detection method according to claim 1, characterized in that: The step S3 comprises the following steps: S31: The central processing unit arranges the spectral data in the spectral data set D1 from large to small according to the wave number to obtain the spectral data set L1, L1 = {G1(1), G1(2) ... G1(n)}, the i-th spectral data G1(i) in the spectral data set L1 = (wn1(i), sp1(i)), 1≤i≤n, sp1(i) is the spectral intensity in the spectral data G1(i), wn1(i) is the wave number in the spectral data G1(i); S32: The central processing unit arranges the spectral data in the spectral data set D2 from large to small according to the wave number to obtain a spectral data set L2, L2 = {G2(1), G2(2) ... G2(n)}, the i-th spectral data G2(i) in the spectral data set L2 = (wn2(i), sp2(i)), sp2(i) is the spectral intensity in the spectral data G2(i), wn2(i) is the wave number in the spectral data G2(i); S33: The central processing unit subtracts the spectral intensity of the spectral data in the spectral data set L1 from the spectral intensity of the corresponding spectral data in the spectral data set L2 to obtain the spectral data set L3, L3 = {G3(1), G3(2) ... G3(n)}, the i-th spectral data G3(i) in the spectral data set L3 = (wn3(i), sp3(i)), Wherein, sp3(i)=sp1(i)-sp2(i), wn3(i)=wn1(i)=wn2(i), sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i).

4. A method for detecting hydrogen sulfide gas according to claim 1, characterized in that: The calculation formula of the intensity wavenumber ratio tr(i) corresponding to the i-th spectral data G3(i) in the spectral data set L3 in step S4 is as follows: Wherein sp3(i) is the spectral intensity in the spectral data G3(i), and wn3(i) is the wave number in the spectral data G3(i).

Images