CN107782706A - A kind of polycyclic aromatic hydrocarbon fluorescence spectrum denoising method based on set empirical mode decomposition - Google Patents
A kind of polycyclic aromatic hydrocarbon fluorescence spectrum denoising method based on set empirical mode decomposition Download PDFInfo
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Abstract
The invention discloses a kind of polycyclic aromatic hydrocarbon fluorescence spectrum denoising method based on set empirical mode decomposition, the three-dimensional fluorescence spectrum figure and contour spectrogram of a kind of polycyclic aromatic hydrocarbon are obtained by XRF；Then characteristic peak analysis is carried out to fluorescence spectra, the fluorescence data of selected characteristic spike section enters row set empirical mode decomposition denoising, and contrasted with traditional 2-d wavelet denoising method, show that self-adaptive solution method set empirical mode decomposition method denoising effect is more preferable.Denoising to the fluorescence spectrum of polycyclic aromatic hydrocarbon can be more preferably realized using empirical mode decomposition is gathered.The inventive method has the advantages that easy to operate, method is simple.
Description
Technical field
The present invention relates to environmental testing, especially a kind of polycyclic aromatic hydrocarbon fluorescence spectrum signal antinoise method.
Background technology
Polycyclic aromatic hydrocarbon is a kind of volatile hydrocarbon being made up of two or more phenyl ring, and solution has one
Determine fluorescence, its cyclic structure is sufficiently stable, it is difficult to degrade, be a kind of global pollutant.The water pollution broken out these years,
The events such as atmosphere pollution, it is all closely bound up with polycyclic aromatic hydrocarbon, and many polycyclic aromatic hydrocarbons all have carcinogenicity, teratogenesis.These
Factor makes polycyclic aromatic hydrocarbon turn into typical persistence organic pollutant, affects the survival and development of the mankind, therefore to more cyclophanes
The research of hydrocarbon is significant.
The method of detection polycyclic aromatic hydrocarbon mainly has gas chromatography, high performance liquid chromatography, gas phase color both at home and abroad at present
Spectrum/MS etc..Fly in Liu's ship, Chen Tong, the patent of invention (application number of Sun Chunyan and Xiao Xiaofeng applications：
201610893350.4) in methods described, after plastic products are crushed, divided with hexane and dichloromethane organic solvent
From purification, then using gas-chromatography tandem mass spectrum, polycyclic aromatic hydrocarbon in 18 in plastics is detected.Cloud tints in office, Liu Guangquan
With the patent of invention (application number of Wang Rongsha applications：201410658017.6) in methods described, using column chromatography to containing greasy dirt
Polycyclic aromatic hydrocarbon in mud is detected.There is certain separating effect with column chromatography, but accurately qualitative and quantitative point can not be carried out
Analysis.Qualitative and quantitative analysis can be carried out simultaneously with gas chromatography, high performance liquid chromatography or mass spectrography, but will to sample
Ask very high, to be pre-processed, it is cumbersome, it is necessary to a large amount of organic reagents, to remove substantial amounts of impurity.
Polycyclic aromatic hydrocarbon has rigid planar structure, in the case where suitable wavelength excites, can produce stronger fluorescence more.Fluorescence
Analytic approach has the advantages that high sensitivity, selectivity is good, method is easy.Therefore three way fluoremetry has been widely used in
The qualitative and quantitative analysis of Polycyclic Aromatic Hydrocarbonat Existing in Environment.But because polycyclic aromatic hydrocarbon to be analyzed is in the matrix of complexity more, and
Species is various, structure is similar, and experimentation light source, the noise jamming of light path, and ambient noise interference be can not
Avoid, it can be seen that, the fluorescence spectrum information of polycyclic aromatic hydrocarbon can be efficiently extracted by choosing correct denoising method, be improved
The accuracy of detection of polycyclic aromatic hydrocarbon.Therefore a kind of examination technology of polycyclic aromatic hydrocarbon content in quick, easy, effective water body is sought
Just seem very necessary.
The content of the invention
Present invention aims at provide a kind of easy to operate, method simply based on the polycyclic of set empirical mode decomposition
Aromatic hydrocarbons fluorescence spectrum denoising method.
To achieve the above object, following technical scheme is employed：The invention mainly comprises XRF, balance, constant volume
Bottle, BaP BaP, methanol solvate, methods described step are as follows：
Step 1, the refrigerator of XRF is opened, tested when temperature is dropped to below -17 DEG C；Prepare
0.004ug/mlBaP solution；Experiment is provided with BaP BaP by China National Measuring Science Research Inst.；Solvent methanol is chromatographically pure；
Fluorescence spectrum instrument parameter is set：Excitation wavelength 250nm~400nm, launch wavelength 300nm~500nm, step-length are
2nm, slit width 2.5nm, the scanning time of integration are 0.1S, and constant experimental temperature is 20 DEG C；Obtained by XRF
BaP three-dimensional fluorescence spectrum figure and contour spectrogram, launch wavelength where choosing photoluminescence peak are 404nm, excitation wavelength
Fluorescence data at 290nm-400nm carries out denoising；
Step 2 is 404nm to launch wavelength, excitation wavelength 290nm-400nm fluorescence data enters row set Empirical Mode
State denoising；Fluorescence spectrum signal is decomposed using Empirical mode decomposition is gathered, obtains the intrinsic mode letter of fluorescence signal
Number component IMF1, IMF2..., IMFn；According to IMFnIMF points of fluorescence signal are filtered out with the cross-correlation coefficient of primary signal
Amount, reconstruct fluorescence spectra；
Step 3,2-d wavelet denoising is carried out to BaP fluorescence spectrum；2-d wavelet signal is chosen, 2D signal is entered
Row wavelet decomposition, obtain each decomposition coefficient；Threshold value quantizing processing is carried out to high frequency coefficient, it is right using Birge-Massart strategies
Threshold value is set；Signal weight is carried out to the low frequency coefficient after wavelet decomposition and the high frequency coefficient after threshold value quantizing is handled
Structure, reconstruct fluorescence spectra.
Further, XRF described in step 1 is FS920 (Edinburgh Instrument, England)
XRF.
Further, in step 2, the process of EEMD algorithms is as follows：
(1) new signal Xi(t) by primary signal x (t) and white Gaussian noise ni(t) it is overlapped to obtain：
Xi(t)=x (t)+ni(t), i=1,2,3 ... i ＜ M (1)
I represents number in formula；M represents that the maximum of number is generally 100；By test of many times
(2) to Xi(t) EMD decomposition is carried out, obtains each rank IMF components ci,s(t), signal has been reconstructed：
C in formulai,s(t) IMF components are represented；S represents IMF number；ri,s(t) residual error is represented；
(3) (1) and (2) is repeatedly carried out, while inserts different white noises, more IMF can be obtained afterwards；
[{c1, s(t) }, { c2, s(t) } ..., { cM, s(t)}] (3)
Primary signal is carried out to decompose resulting IMF components cn(t) it is：
Add the rule that number is obeyed：
N, which is represented, in formula adds white noise number；ε, which is represented, adds white noise acoustic amplitude；εnRepresent reconstruction signal error；By public affairs
Formula obtains, if the amplitude of noise is determination value, N value is bigger, then opposite εnValue will be smaller, reconstruction signal
It is more similar to primary signal.
Further, in step 3, collective's implementation process of two-dimensional wavelet transformation algorithm is as follows：
(1) wavelet decomposition of 2D signal；2-d wavelet signal is chosen, the decomposition level number for determining small echo is j, right
The 2D signal that will be analyzed carries out j layer wavelet decompositions；
H in formula, g represent quadrature mirror filter；cj,kRepresent wavelet scale coefficient；dj,kRepresent what wavelet decomposition obtained
Wavelet coefficient；J represents the wavelet decomposition number of plies；N represents the sampling number of signal；
(2) quantization of wavelet coefficient；Each decomposition coefficient obtained after wavelet decomposition, threshold value quantizing is used to high frequency coefficient
Form handled；For each layer of wavelet decomposition, a suitable threshold value of the numerical value as wavelet coefficient is selected, with this
Threshold value is that standard is handled high frequency coefficient using the method for threshold value quantizing processing；
(3) signal reconstruction of 2-d wavelet；Wavelet reconstruction includes two-part content, after a part is wavelet decomposition
N-th layer low frequency coefficient, another part are the high frequency coefficients after threshold value quantizing is handled；
N layers, which decompose, to be realized using db12 wavelet basis functions to fluorescence spectrum image, it is right using Birge-Massart strategies
Threshold value is set, and BaP fluorescence spectrum is handled using software MATLAB, obtains the fluorescence spectrum image after denoising.
Compared with prior art, the inventive method has the following advantages that：BaP is detected using XRF, obtained
To BaP fluorescence spectrums.Enter row set empirical modal denoising to BaP fluorescence spectrums, eliminate what fluorescence spectrum occurred in experimentation
Noise, improve the qualitative and quantitative analysis ability of polycyclic aromatic hydrocarbon.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is BaP solution fluorescences spectral schematic of the present invention.
Fig. 3 is BaP of the present invention denoised signal schematic diagram.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings：
As shown in figure 1, the method for the invention step is as follows：
Step 1：The refrigerator of XRF is first turned on, is tested when temperature is dropped to below -17 DEG C；Then
Using balance, constant volume bottle etc. prepares 0.004ug/mlBaP solution；Experiment is provided with BaP by China National Measuring Science Research Inst.,
Used for experiment；Solvent methanol is chromatographically pure.XRF FS920 (Edinburgh Instrument, England) parameter
Set：Excitation wavelength 250nm~400nm, launch wavelength 300nm~500nm, step-length 2nm, slit width 2.5nm, sweep
It is 0.1S to retouch the time of integration, and experimental temperature is controlled at constant 20 DEG C；BaP three-dimensional fluorescence spectrum figure such as Fig. 2 is obtained by experiment
(a) and contour spectrogram such as Fig. 2 (b), the fluorescent value that BaP has three positions as can be seen from Figure 2 are higher than other, excitation wave
Length is located at 290nm-300nm, 350nm-370nm, 370nm-390nm respectively, and launch wavelength is at 400-410nm, feature
Wavelength is in same emission wavelength values.We choose launch wavelength 404nm, excitation wavelength 290nm-400nm fluorescence data
Carry out denoising.
Step 2：It is launch wavelength 404nm according to wave band where the fluorescence spectral characteristic peak that step 1 chooses BaP, excites
Wavelength 290nm-400nm fluorescence data enters row set empirical modal denoising；Using gathering Empirical mode decomposition to fluorescence light
Spectrum signal is decomposed, and obtains the intrinsic mode function component IMF of fluorescence signal1, IMF2..., IMFn；According to IMFnWith original
The cross-correlation coefficient of beginning signal filters out the IMF components of fluorescence signal, reconstructs fluorescence spectra.
Collective's implementation process of EEMD algorithms is as follows：
(1) new signal Xi(t) by primary signal x (t) and white Gaussian noise ni(t) it is overlapped to obtain：
Xi(t)=x (t)+ni(t), i=1,2,3 ... i ＜ M (1)
I represents number in formula；M represents the maximum of number, and maximum is 100；
(2) to Xi(t) EMD decomposition is carried out, obtains each rank IMF components ci,s(t), signal has been reconstructed：
C in formulai,s(t) IMF components are represented；S represents IMF number；ri,s(t) residual error is represented；
(3) (1) and (2) is repeatedly carried out, while inserts different white noises, more IMF can be obtained afterwards；
[{c1, s(t) }, { c2, s(t) } ..., { cM, s(t)}] (3)
Because the mean frequency value of white Gaussian noise is zero, then the error in white Gaussian noise time domain, can be eliminated.
Primary signal is carried out to decompose resulting IMF components cn(t) it is：
The white Gaussian noise that certain EEMD is added in decomposable process is not rule unlimited, that addition number is obeyed
Rule：N, which is represented, in formula adds white noise number；ε, which is represented, adds white noise acoustic amplitude；εnRepresent reconstruction signal error；By
Formula obtains, if the amplitude of noise is determination value, N value is bigger, then opposite εnValue will be smaller, reconstruct letter
It is number more similar to primary signal.So to ε value with regard to most important, too low too high can all have an impact.It is general to add number
200 once more than 100, and εnTo be determined according to actual conditions.It is 100 N value regulation by testing for several times, mark
Standard poor 0.2.
Step 3：Fluorescence spectrum is acted in order to contrast self-adaptive solution method and traditional Wavelet noise-eliminating method, it is right
BaP fluorescence spectrum carries out 2-d wavelet denoising；2-d wavelet signal is chosen, wavelet decomposition is carried out to 2D signal, is obtained each
Decomposition coefficient；Threshold value quantizing processing is carried out to high frequency coefficient, threshold value set using Birge-Massart strategies；To small
Low frequency coefficient after Wave Decomposition and the high frequency coefficient after threshold value quantizing is handled carry out signal reconstruction, reconstruct fluorescence spectra.
Collective's implementation process of two-dimensional wavelet transformation algorithm is as follows：
(1) wavelet decomposition of 2D signal.2-d wavelet signal is chosen, the decomposition level number for determining small echo is j, right
The 2D signal that will be analyzed carries out j layer wavelet decompositions.
H in formula, g represent quadrature mirror filter；cj,kRepresent wavelet scale coefficient；dj,kRepresent what wavelet decomposition obtained
Wavelet coefficient；J represents the wavelet decomposition number of plies；N represents the sampling number of signal.
(2) quantization of wavelet coefficient.Each decomposition coefficient obtained after wavelet decomposition, threshold value quantizing is used to high frequency coefficient
Form handled.For each layer of wavelet decomposition, a suitable threshold value of the numerical value as wavelet coefficient is selected, with this
Threshold value is that standard is handled high frequency coefficient using the method for threshold value quantizing processing.
(3) signal reconstruction of 2-d wavelet.Wavelet reconstruction includes two-part content, after a part is wavelet decomposition
N-th layer low frequency coefficient, another part are the high frequency coefficients after threshold value quantizing is handled.
N layers, which decompose, to be realized using db12 wavelet basis functions to fluorescence spectrum image, it is right using Birge-Massart strategies
Threshold value is set, and BaP fluorescence spectrum is handled using software MATLAB, obtains the fluorescence spectrum image after denoising.
Step 4：Fig. 3 is the signal graph obtained by two kinds of denoising methods, respectively primary signal figure such as Fig. 3 (a), collection
Signal graph such as Fig. 3 (b), signal graph such as Fig. 3 (c) of 2-d wavelet denoising of empirical mode decomposition denoising are closed, can be with from Fig. 3
Find out more smooth by the signal for gathering empirical mode decomposition denoising.In order to which two kinds of denoising methods of quantitative analysis are to BaP fluorescence
The influence of spectrum, using root-mean-square error, signal to noise ratio, and waveform similarity factor contrast two kinds of denoising effects.
In formula, f (i, j) is the raw intensity values of Noise；G (i, j) is the fluorescence intensity level after denoising.Root mean square misses
Difference can show the difference between the fluorescence intensity after raw florescent intensity and denoising, and root-mean-square error is got over during real work
The effect of small denoising is more preferable.After mapminmax function normalizations, evaluating calculating is carried out in the range between [- 1,1],
The root-mean-square error of 2-d wavelet denoising 0.094, EEMD denoisings root-mean-square error 0.083.
Signal to noise ratio is more high, filters out that the ability of noise is stronger, and denoising effect is better.The letter being computed after 2-d wavelet denoising
Make an uproar than the signal to noise ratio after 8.4104, EEMD denoisings 24.7191.
Waveform similarity factor is more high, and obtained useful signal is more, and denoising effect is better.2-d wavelet is computed to go
Waveform similarity factor of the waveform similarity factor after 0.9733, EEMD denoisings after making an uproar is 0.9993.In summary, it is of the invention
Methods described can be very good to eliminate noise to polycyclic aromatic hydrocarbon BaP.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the present invention's
Scope is defined, on the premise of design spirit of the present invention is not departed from, technology of the those of ordinary skill in the art to the present invention
The various modifications and improvement that scheme is made, it all should fall into the protection domain of claims of the present invention determination.
Claims (4)
1. it is a kind of based on set empirical mode decomposition polycyclic aromatic hydrocarbon fluorescence spectrum denoising method, mainly including XRF,
Balance, constant volume bottle, BaP BaP, methanol solvate, it is characterised in that methods described step is as follows：
Step 1, the refrigerator of XRF is opened, tested when temperature is dropped to below -17 DEG C；Prepare 0.004ug/
MlBaP solution；Experiment is provided with BaP BaP by China National Measuring Science Research Inst.；Solvent methanol is chromatographically pure；
Fluorescence spectrum instrument parameter is set：Excitation wavelength 250nm~400nm, launch wavelength 300nm~500nm, step-length 2nm are narrow
Slit width degree is 2.5nm, and the scanning time of integration is 0.1S, and constant experimental temperature is 20 DEG C；The three of BaP are obtained by XRF
Fluorescence spectra and contour spectrogram are tieed up, launch wavelength where choosing photoluminescence peak is 404nm, excitation wavelength 290nm-
Fluorescence data at 400nm carries out denoising；
Step 2 is 404nm to launch wavelength, excitation wavelength 290nm-400nm fluorescence data enters row set empirical modal
Make an uproar；Fluorescence spectrum signal is decomposed using Empirical mode decomposition is gathered, obtains the intrinsic mode function point of fluorescence signal
Measure IMF1, IMF2..., IMFn；According to IMFnThe IMF components of fluorescence signal are filtered out with the cross-correlation coefficient of primary signal, weight
Structure fluorescence spectra；
Step 3,2-d wavelet denoising is carried out to BaP fluorescence spectrum；2-d wavelet signal is chosen, 2D signal is carried out small
Wave Decomposition, obtain each decomposition coefficient；Threshold value quantizing processing is carried out to high frequency coefficient, using Birge-Massart strategies to threshold value
Set；Signal reconstruction, weight are carried out to the low frequency coefficient after wavelet decomposition and the high frequency coefficient after threshold value quantizing is handled
Structure fluorescence spectra.
2. a kind of polycyclic aromatic hydrocarbon fluorescence spectrum denoising method based on set empirical mode decomposition according to claim 1,
It is characterized in that：XRF described in step 1 is FS920 (Edinburgh Instrument, England) fluorescence light
Spectrometer.
3. a kind of polycyclic aromatic hydrocarbon fluorescence spectrum denoising method based on set empirical mode decomposition according to claim 1,
Characterized in that, in step 2, the process of EEMD algorithms is as follows：
(1) new signal Xi(t) by primary signal x (t) and white Gaussian noise ni(t) it is overlapped to obtain：
Xi(t)=x (t)+ni(t), i=1,2,3 ... i ＜ M (1)
I represents number in formula；M represents the maximum of number, and maximum is 100；
(2) to Xi(t) EMD decomposition is carried out, obtains each rank IMF components ci,s(t), signal has been reconstructed：
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C in formulai,s(t) IMF components are represented；S represents IMF number；ri,s(t) residual error is represented；
(3) (1) and (2) is repeatedly carried out, while inserts different white noises, more IMF can be obtained afterwards；
[{c1, s(t) }, { c2, s(t) } ..., { cM, s(t)}] (3)
Primary signal is carried out to decompose resulting IMF components cn(t) it is：
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Add the rule that number is obeyed：
N, which is represented, in formula adds white noise number；ε, which is represented, adds white noise acoustic amplitude；εnRepresent reconstruction signal error；Obtained by formula,
If the amplitude of noise is determination value, N value is bigger, then opposite εnValue will be smaller, reconstruction signal and original letter
It is number more similar.
4. a kind of polycyclic aromatic hydrocarbon fluorescence spectrum denoising method based on set empirical mode decomposition according to claim 1,
Characterized in that, in step 3, collective's implementation process of two-dimensional wavelet transformation algorithm is as follows：
(1) wavelet decomposition of 2D signal；2-d wavelet signal is chosen, the decomposition level number for determining small echo is j, to that will divide
The 2D signal of analysis carries out j layer wavelet decompositions；
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H in formula, g represent quadrature mirror filter；cj,kRepresent wavelet scale coefficient；dj,kRepresent the wavelet systems that wavelet decomposition obtains
Number；J represents the wavelet decomposition number of plies；N represents the sampling number of signal；
(2) quantization of wavelet coefficient；Each decomposition coefficient obtained after wavelet decomposition, the form of threshold value quantizing is used to high frequency coefficient
Handled；For each layer of wavelet decomposition, a suitable threshold value of the numerical value as wavelet coefficient is selected, using the threshold value as mark
Standard is handled high frequency coefficient using the method for threshold value quantizing processing；
(3) signal reconstruction of 2-d wavelet；Wavelet reconstruction includes two-part content, and a part is the n-th layer after wavelet decomposition
Low frequency coefficient, another part are the high frequency coefficients after threshold value quantizing is handled；
N layers, which decompose, to be realized using db12 wavelet basis functions to fluorescence spectrum image, threshold value is entered using Birge-Massart strategies
Row setting, is handled BaP fluorescence spectrum using software MATLAB, obtains the fluorescence spectrum image after denoising.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112730712A (en) * | 2020-12-31 | 2021-04-30 | 杭州谱育科技发展有限公司 | Method for improving LC-MS data signal-to-noise ratio |
| CN114624271A (en) * | 2022-03-25 | 2022-06-14 | 电子科技大学 | X-ray fluorescence spectrum background deduction method based on variational modal decomposition |
| CN117405622A (en) * | 2023-12-14 | 2024-01-16 | 广东伊茗药业有限公司 | Intelligent detection method for nitrite content in bird's nest |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102697495A (en) * | 2012-05-23 | 2012-10-03 | 杭州电子科技大学 | Second-generation wavelet electromyographic signal noise eliminating method based on ensemble empirical mode decomposition |
| CN104316317A (en) * | 2014-10-08 | 2015-01-28 | 西北工业大学 | Gear system multi-fault diagnosis method based on COM assemblies |
| CN104636609A (en) * | 2015-01-30 | 2015-05-20 | 电子科技大学 | Signal combined denoising method based on empirical mode decomposition (EMD) and wavelet analysis |
| CN106093701A (en) * | 2016-06-06 | 2016-11-09 | 国家电网公司 | A kind of cable fault signal detecting method based on empirical mode decomposition filtering |
| CN106798554A (en) * | 2017-01-12 | 2017-06-06 | 安徽大学 | A kind of denoising method of noisy IMF components and electrocardiosignal |
-
2017
- 2017-09-05 CN CN201710789475.7A patent/CN107782706A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102697495A (en) * | 2012-05-23 | 2012-10-03 | 杭州电子科技大学 | Second-generation wavelet electromyographic signal noise eliminating method based on ensemble empirical mode decomposition |
| CN104316317A (en) * | 2014-10-08 | 2015-01-28 | 西北工业大学 | Gear system multi-fault diagnosis method based on COM assemblies |
| CN104636609A (en) * | 2015-01-30 | 2015-05-20 | 电子科技大学 | Signal combined denoising method based on empirical mode decomposition (EMD) and wavelet analysis |
| CN106093701A (en) * | 2016-06-06 | 2016-11-09 | 国家电网公司 | A kind of cable fault signal detecting method based on empirical mode decomposition filtering |
| CN106798554A (en) * | 2017-01-12 | 2017-06-06 | 安徽大学 | A kind of denoising method of noisy IMF components and electrocardiosignal |
Non-Patent Citations (3)
| Title |
|---|
| 孙曙光 等: "一种基于新型小波阈值去噪预处理的EEMD谐波检测方法", 《电力系统保护与控制》 * |
| 张旭东 等: "不同信号的小波变换去噪方法", 《石油地球物理勘探》 * |
| 苑媛媛 等: "基于EMD和数学形态学的多环芳烃光谱去噪", 《光学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112730712A (en) * | 2020-12-31 | 2021-04-30 | 杭州谱育科技发展有限公司 | Method for improving LC-MS data signal-to-noise ratio |
| CN112730712B (en) * | 2020-12-31 | 2022-06-07 | 杭州谱育科技发展有限公司 | Method for improving LC-MS data signal-to-noise ratio |
| CN114624271A (en) * | 2022-03-25 | 2022-06-14 | 电子科技大学 | X-ray fluorescence spectrum background deduction method based on variational modal decomposition |
| CN114624271B (en) * | 2022-03-25 | 2023-08-25 | 电子科技大学 | X-ray fluorescence spectrum background subtraction method based on variation modal decomposition |
| CN117405622A (en) * | 2023-12-14 | 2024-01-16 | 广东伊茗药业有限公司 | Intelligent detection method for nitrite content in bird's nest |
| CN117405622B (en) * | 2023-12-14 | 2024-03-22 | 广东伊茗药业有限公司 | Intelligent detection method for nitrite content in bird's nest |
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