WO2018103541A1 - 用于去除溶剂干扰的拉曼光谱检测方法和电子设备 - Google Patents

用于去除溶剂干扰的拉曼光谱检测方法和电子设备 Download PDF

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WO2018103541A1
WO2018103541A1 PCT/CN2017/112916 CN2017112916W WO2018103541A1 WO 2018103541 A1 WO2018103541 A1 WO 2018103541A1 CN 2017112916 W CN2017112916 W CN 2017112916W WO 2018103541 A1 WO2018103541 A1 WO 2018103541A1
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solvent
raman
raman spectrum
signal
solution
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French (fr)
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王红球
苟巍
陈卓
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同方威视技术股份有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering

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  • the present invention generally relates to the field of Raman spectroscopy, and more particularly to a Raman spectroscopy method and an electronic device for removing solvent interference.
  • Raman spectroscopy is a molecular vibrational spectroscopy that reflects the fingerprint characteristics of molecules and can be used to detect substances. Raman spectroscopy detects and identifies a substance by detecting a Raman spectrum produced by the Raman scattering effect of the analyte on the excitation light. Raman spectroscopy has been widely used in liquid security, jewelry testing, explosives testing, drug testing, drug testing, pesticide residue testing and other fields.
  • the present invention has been made in order to overcome or eliminate at least one of the problems and disadvantages of the prior art.
  • a Raman spectroscopy method for removing solvent interference comprising the steps of:
  • the Raman spectrum signal of the solvent is gradually subtracted from the Raman spectrum signal of the solution to obtain a series of Raman spectrum signals for removing solvent interference;
  • the solvent-interfering Raman spectral signal corresponding to the maximum information entropy is used as an optimized solvent-removing Raman spectral signal.
  • the step of gradually subtracting the Raman spectrum signal of the solvent from the Raman spectrum signal of the solution to obtain a series of solvent-interfering Raman spectrum signals comprises:
  • the product of the proportionality factor and the Raman spectral signal of the solvent is subtracted from the Raman spectral signal of the solution, and the proportionality factor is gradually changed to gradually subtract the Raman spectral signal of the solvent from the Raman spectral signal of the solution.
  • the Raman spectral signal of the solution is a discrete data sequence A
  • the Raman spectral signal of the solvent is a discrete data sequence B
  • the Raman spectrum of the solvent is subtracted from the Raman spectral signal of the solution by the following formula The product of the signal:
  • C is a discrete data sequence representing a Raman spectral signal from which solvent interference is removed, j is sequentially taken as 1, 2, 3, ... N and j is a natural number, N is a preset number of calculations, and K is a preset removal proportion.
  • the Raman spectroscopy detecting method further comprises the steps of: determining a position of a characteristic peak of a Raman spectrum of the solvent, and taking a section including a position of a characteristic peak of a Raman spectrum of the solvent as a calculation section;
  • the step of gradually subtracting the Raman spectrum signal of the solvent from the Raman spectrum signal of the solution to obtain a series of solvent-interfering Raman spectrum signals includes: Raman spectrum signal from the solution in the calculation interval The Raman spectral signal of the solvent is gradually subtracted to obtain a series of Raman spectral signals for solvent interference removal.
  • the calculating of each of the series of solvent-interfering Raman spectral signals is performed
  • the steps of an information entropy include:
  • i represents the i-th wave number of the Raman spectral signal
  • n represents the signal length of the Raman spectral signal
  • x i represents the intensity corresponding to the i-th wave number
  • p(x i ) represents the intensity x in the Raman spectral signal. The probability of i .
  • the Raman spectroscopy method further comprises the step of: Raman spectroscopy of the solvent and Raman spectroscopy of the solution before gradually subtracting the Raman spectrum signal of the solvent from the Raman spectrum signal of the solution.
  • the signal is normalized.
  • the value of K ranges from 0.005 to 0.03, and the value of N ranges from 200 to 600.
  • a Raman spectroscopy method for removing solvent interference comprising the steps of:
  • the product of the normalized solvent Raman spectrum signal and the optimized scale factor is subtracted from the Raman spectrum signal of the normalized solution to obtain an optimized solvent-removed Raman spectrum signal.
  • an electronic device including:
  • a memory for storing executable instructions
  • a processor for executing executable instructions stored in a memory to perform a method as described in any aspect or embodiment of the invention.
  • any one of the above technical solutions of the present invention can be dissolved from the mixed information by calculating the maximum information entropy
  • the interference spectrum information caused by the solvent is removed from the Raman spectrum signal of the sample spectrum, so that the Raman spectrum signal reflecting the properties of the sample itself can be correctly obtained.
  • This scheme can accurately detect the Raman spectrum of the sample in solution, thereby effectively identifying the sample to be tested.
  • FIG. 1 schematically shows a flow chart of a Raman spectroscopy detection method for removing solvent interference according to an embodiment of the present invention
  • FIG. 2 is a flow chart schematically showing a Raman spectroscopy detection method for removing solvent interference according to another embodiment of the present invention
  • FIG. 3 is a flow chart schematically showing a Raman spectroscopy detection method for removing acetonitrile interference according to an embodiment of the present invention
  • Figure 4 is a schematic representation of the Raman spectrum of an imipenem acetonitrile solution
  • Figure 5 schematically shows the Raman spectrum of an acetonitrile solvent
  • FIG. 6 is a view schematically showing an information entropy graph calculated by a method according to an embodiment of the present invention.
  • Figure 7 is a view schematically showing a Raman spectrum of a solution obtained by removing acetonitrile interference from an imipenem acetonitrile solution by using a Raman spectroscopy method of an embodiment of the present invention
  • FIG. 8 is a block diagram showing an example hardware arrangement of an electronic device for performing a method in accordance with an embodiment of the present invention.
  • the sample to be tested When the sample is detected by Raman spectroscopy, the sample to be tested sometimes needs to be dissolved in a solvent, for example, when detecting a pesticide residue, the sample to be tested is dissolved in a solvent such as acetonitrile. Therefore, the detection of the sample may require the excitation light for detection to be simultaneously irradiated onto the sample and the solvent, and the solvent may also A Raman scattering effect is generated on the excitation light, in which case the solvent interferes with the Raman spectral signal of the sample itself. Removing such interference is very important for accurate and efficient detection and identification of samples.
  • FIG. 1 schematically illustrates a flow chart of a Raman spectroscopy detection method for removing solvent interference, in accordance with an embodiment of the present invention.
  • the method includes:
  • a Raman spectrum signal obtaining step of the solution measuring a Raman spectrum of the solution containing the solvent and the sample to obtain a Raman spectrum signal A of the solution;
  • a Raman spectrum signal obtaining step of the solvent measuring the Raman spectrum of the solvent to obtain a Raman spectrum signal B of the solvent;
  • Removing the solvent interference Raman spectrum signal obtaining step gradually subtracting the Raman spectrum signal B of the solvent from the Raman spectrum signal A of the solution to obtain a series of Raman spectrum signals C for removing solvent interference;
  • Information entropy calculation step calculating the information entropy of each of the series of solvent-interfering Raman spectral signals C;
  • Maximum information entropy selection step selecting the maximum information entropy of the calculated series of information entropies
  • the optimized solvent removal interference Raman spectrum signal obtaining step the solvent-interfering Raman spectrum signal corresponding to the maximum information entropy is used as an optimized solvent-removing Raman spectrum signal.
  • the Raman spectral signal A of the obtained solution is a discrete data sequence, denoted as A i , wherein i is sequentially taken as 1, 2, 3, ... ...n and i is a natural number, and n represents the total number of data points of the data sequence.
  • the discrete data sequence A i can be a vector or matrix of peak intensity data from a set of discrete solutions of Raman spectral signals.
  • the Raman spectral signal B of the obtained solvent is also a discrete data sequence, denoted as B i , for example, the peak intensity of the Raman spectral signal from a set of discrete solvents.
  • B i a discrete data sequence
  • the step of removing the solvent-interfering Raman spectral signal specifically includes: gradually subtracting the “Raman spectral signal B of the solvent of j*K* solvent” from the Raman spectral signal A of the solution to obtain a The series removes the solvent-interfering Raman spectral signal C j .
  • j is sequentially taken as 1, 2, 3, ... N and j is a natural number
  • N is a preset number of calculations
  • the series of solvent-interfering Raman spectral signals C include C 1 , C 2 , C 3 , ..., C N . It should be understood that each of the solvent-interfering Raman spectral signals Cj is also a discrete data sequence that also includes n data points.
  • the information entropy calculation step comprises: calculating an information entropy of each Cj of a series of solvent-interfering Raman spectral signals according to the following information entropy calculation formula:
  • i represents the i-th wavenumber of the Raman spectral signal
  • n represents the signal length of the Raman spectral signal
  • x i represents the intensity corresponding to the i-th wavenumber
  • p(x i ) represents the Raman spectral signal The probability of intensity x i .
  • the information entropy evaluates that the random variable (for example, X) is equal to the average information amount of each value, that is, the uncertainty indicating the random variable X.
  • the uniform distribution should be the most uncertain.
  • the process of removing the Raman spectrum signal of the solvent from the Raman spectrum signal containing the solvent and the sample is to remove some characteristic peaks.
  • the process that is, the process of smoothing the Raman spectral signal; further, if the solvent interference information is excessively removed, the Raman spectrum is increased by some inverse peaks, that is, the Raman spectral signal is again made uneven.
  • the gradual Raman spectral signal corresponds to a relatively uniform discrete distribution, which corresponds to the maximum information entropy.
  • the above-described Raman spectroscopy method for removing solvent interference may further include the steps of normalizing the Raman spectroscopy signal B of the solvent and the Raman spectroscopy signal A of the solution. Normalization is to limit the data to be processed by a normalization algorithm to a predetermined range, which allows data in two different reference frames to be compared, calculated, etc., to facilitate subsequent data processing. And speed up the convergence of subsequent calculations.
  • the Raman spectral signal B of the solvent can be normalized to the Raman spectral signal A of the solution to facilitate the stepwise subtraction of the solvent from the Raman spectral signal A of the solution performed in a subsequent step. Calculation of the Raman spectral signal B.
  • only the characteristic peak of the Raman spectral signal of the solvent may be The above-described information entropy calculation step is performed in the interval to reduce the amount of calculation, thereby speeding up the detection.
  • 2 is a flow chart schematically showing a Raman spectroscopy detecting method for removing solvent interference according to another embodiment of the present invention, such that the Raman spectroscopy detecting method for removing solvent interference may include the following steps:
  • a Raman spectrum signal obtaining step of the solution measuring a Raman spectrum of the solution containing the solvent and the sample to obtain a Raman spectrum signal A of the solution;
  • a Raman spectrum signal obtaining step of the solvent measuring the Raman spectrum of the solvent to obtain a Raman spectrum signal B of the solvent;
  • Normalization step normalized solvent Raman spectral signal B and solution Raman spectral signal A;
  • a calculation section determining step of determining a position of a characteristic peak of a Raman spectrum of the solvent, and taking a section including a position of a characteristic peak of a Raman spectrum of the solvent as a calculation section;
  • Step of removing the solvent interference of the Raman spectral signal acquisition step in the calculation interval, gradually subtracting the Raman spectrum signal B of the normalized solvent from the Raman spectrum signal A of the normalized solution and the preset The product of the proportional coefficient, by gradually changing the proportional coefficient, to obtain a series of solvent-interfering interval Raman spectral signals C';
  • Information entropy calculation step calculating an information entropy of each of a series of interval Raman spectral signals C' from which solvent interference is removed;
  • An optimized proportional coefficient determining step selecting a maximum information entropy of the calculated series of information entropies, and selecting a scaling coefficient corresponding to the maximum information entropy as the optimized scaling factor;
  • Optimized solvent-interfering Raman spectral signal acquisition step subtracting the product of the normalized solvent Raman spectral signal B from the optimized proportional coefficient from the Raman spectral signal A of the normalized solution, Obtain optimized Raman spectroscopy signals that remove solvent interference.
  • the method may include the following steps:
  • Raman spectroscopy signal obtaining step of the imipenem acetonitrile solution measuring the Raman spectrum of the imipenem acetonitrile solution to obtain the Raman spectrum signal A of the imipenem acetonitrile solution, as shown in FIG.
  • the abscissa represents the Raman frequency shift or the wave number (cm -1 )
  • the ordinate represents the intensity (dimensionless), so that the Raman spectral signal A can include several discrete maps.
  • the Raman spectrum signal obtaining step of acetonitrile measuring the Raman spectrum of acetonitrile itself to obtain the Raman spectrum signal B of acetonitrile, as shown in FIG. 5, the meanings of the horizontal and vertical coordinates in FIG. 5 are the same as those in FIG.
  • the Raman spectral signal B may be a vector including a plurality of discrete intensity data represented by the ordinate shown in FIG. 5, or a Raman frequency shift including a plurality of discrete abscissa representations shown in FIG. 5. And a matrix of intensity data represented by the corresponding ordinate.
  • Normalization step normalized Raman spectroscopy signal B of acetonitrile and Raman spectral signal A of imipenem acetonitrile solution. Specifically, if the Raman spectral signal B does not correspond to the abscissa of the Raman spectral signal A, that is, the Raman spectral signal B and the Raman spectral signal A include intensity data not at the same Raman shift position, then first According to the raw data of the Raman spectral signals A and B, the intensity data at the other Raman shift positions are calculated by interpolation calculation, thereby converting the Raman spectral signals A and B to the same Raman shift position.
  • the Raman shift position [350: 2: 2800] cm -1 may be set for the Raman shift position [350: 2: 2800] cm -1, wherein, 350cm -1, 2800cm -1 respectively represent the minimum, maximum Raman shift position, 2 represents a spacer
  • the step size that is, the respective Raman shift positions are 350, 352, 354, 356 cm -1 , and so on, up to 2800 cm -1 , and then, if the Raman spectral signals A, B do not include these Raman frequencies in the raw data
  • the intensity data at these Raman shift positions can be calculated from the original data of the Raman spectral signals A and B by interpolation, thereby converting the Raman spectral signals A and B to the same
  • the converted Raman spectral signals A and B are respectively counted as Raman spectral signals A' and B'.
  • the Raman spectral signal B does not correspond to the ordinate of the Raman spectral signal A, that is, the intensity data of the Raman spectral signal B and the Raman spectral signal A are not in the same reference frame or measured in different ranges,
  • the Raman spectral signal B' is converted to the Raman spectral signal A'.
  • the maximum values max(A'), max(B') of the ordinates of the Raman spectral signals A', B' can be selected separately, and then max(A') and max(B') The ratio is used as the normalization coefficient of the ordinate, and the discrete data of the Raman spectral signal B' is multiplied by the normalization coefficient to convert the Raman spectral signal B' into the Raman spectral signal A', which will be converted.
  • the latter Raman spectral signal B' is denoted as Raman spectral signal B".
  • the Raman spectral signal A can also be kept unchanged, and the Raman spectral signal B can be directly normalized to the Raman spectral signal A. in.
  • the calculation interval determining step determining the position of the characteristic peak of the acetonitrile, and determining the interval including the position of the characteristic peak based on the position of the characteristic peak of the acetonitrile as the calculation interval.
  • the position of the characteristic peak of acetonitrile is mainly in the interval of 900-950 cm -1 and 2200-2300 cm -1 , so the interval of 900-950 cm -1 and 2200-2300 cm - 1 is determined as the calculation interval.
  • Interval Raman spectroscopy signal removal step for removing acetonitrile interference in the calculation interval 900-950 cm -1 and 2200-2300 cm -1 , the Raman spectral signal A' of the normalized imipenem acetonitrile solution is gradually reduced. The product of the normalized acetonitrile Raman spectral signal B" and the preset proportional coefficient is gradually changed by a proportional coefficient to obtain a series of interval Raman spectral signals C for removing acetonitrile interference.
  • Information entropy calculation step calculating a series of information entropy of each of the interval Raman spectral signals C for removing acetonitrile interference;
  • N is a preset number of calculations
  • K is a preset removal ratio.
  • j*K constitutes a gradually changing scale factor KK.
  • the value of K ranges from 0.005 to 0.03, and the value of N ranges from 200 to 600.
  • N is set to 300 and K is set to 0.01.
  • the Raman spectral signal B" of acetonitrile can be gradually subtracted from the Raman spectral signal A' of the imipenem acetonitrile solution in steps of 0.01, thereby obtaining 300 acetonitrile removal signals.
  • the information entropy of the interval Raman spectral signal C j for removing each acetonitrile interference is calculated according to the following information entropy calculation formula. Since there are 300 interval Raman spectral signals C for removing acetonitrile interference, 300 corresponding calculations can be calculated.
  • the information entropy shows a change graph of the information entropy calculated by using the Raman spectral signal shown in FIGS. 4 and 5 as the original data and calculated by the above calculation method.
  • the abscissa Indicates the sequence number of the Raman spectrum signal, and the ordinate indicates the calculated information entropy.
  • i represents the i-th wavenumber of the Raman spectral signal
  • n represents the signal length of the Raman spectral signal
  • x i represents the intensity corresponding to the i-th wavenumber
  • p(x i ) represents the Raman spectral signal The probability of intensity x i .
  • the amount of calculation can be greatly reduced, thereby speeding up the detection speed.
  • Optimized removal of acetonitrile interference Raman spectroscopy signal acquisition step subtracting the product of the acetonitrile Raman spectral signal B" from the Raman spectral signal A' of the imipenem acetonitrile solution with the optimized proportionality factor to obtain an optimized Removal of acetonitrile interference Raman spectroscopy signal C optimization .
  • C optimization A-60*0.01*B, by substituting the data corresponding to Fig. 4 and Fig. 5 into the formula, optimized acetonitrile removal can be obtained.
  • Interfering Raman spectral signal C optimized data according to the data, the optimized Raman spectrum for removing acetonitrile interference is shown in Fig. 7. Comparing Fig. 4 and Fig. 7, it can be seen that the Raman spectral signal C is optimized.
  • the acetonitrile interference signal is preferably removed.
  • FIG. 8 is a block diagram showing an example hardware arrangement of the electronic device 800.
  • Electronic device 800 includes a processor 806 (eg, a microprocessor ( ⁇ P), a digital signal processor (DSP), etc.).
  • processor 806 can be or include a single processing unit or a plurality of processing units for performing different acts of the method steps described herein.
  • the electronic device 800 may also include an input unit 802 for receiving signals from other entities, and an output unit 804 for providing signals to other entities.
  • Input unit 802 and output unit 804 can be arranged as a single entity or as separate entities.
  • electronic device 800 can include at least one computer readable storage medium 808 in the form of a non-volatile or volatile memory, such as an electrically erasable programmable read only memory (EEPROM), flash memory, and/or a hard drive.
  • the readable storage medium 808 includes a computer program 810 that includes code/computer readable instructions that, when executed by the processor 806 in the electronic device 800, cause the electronic device 800 to perform, for example, as described above in connection with Figures 1-3. Process and any variations thereof.
  • Computer program 810 can be configured as computer program code having architectures such as computer program modules 810A-810C.
  • the computer program modules can essentially perform various acts or steps in the flows illustrated in Figures 1-3 to simulate a device.
  • processor 806 when different computer program modules are executed in processor 806, they may correspond to the different units described above in the device.
  • code means in the embodiment disclosed above in connection with FIG. 8 is implemented as a computer program module that, when executed in processor 806, causes electronic device 800 to perform the actions described above in connection with FIGS. 1-3, however, in an alternate implementation In an example, at least one of the code means can be implemented at least partially as a hardware circuit.
  • the processor may be a single CPU (Central Processing Unit), but may also include two or more processing units.
  • the processor can include a general purpose microprocessor, an instruction set processor, and/or a related chipset And/or a dedicated microprocessor (eg, an application specific integrated circuit (ASIC)).
  • the processor may also include an onboard memory for caching purposes.
  • the computer program can be carried by a computer program product connected to the processor.
  • the computer program product can comprise a computer readable medium having stored thereon a computer program.
  • the computer program product can be a flash memory, a random access memory (RAM), a read only memory (ROM), an EEPROM, and the computer program modules described above can be distributed to different computer program products in the form of memory in alternative embodiments. .

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Abstract

一种用于去除溶剂干扰的拉曼光谱检测方法和电子设备（800）。检测方法包括以下步骤：对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号；对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号；从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号；计算该系列去除溶剂干扰的拉曼光谱信号中的每一个的信息熵；选择所计算出的一系列的信息熵中的最大信息熵；和将与该最大信息熵对应的去除溶剂干扰的拉曼光谱信号作为优化的去除溶剂干扰的拉曼光谱信号。

Description

用于去除溶剂干扰的拉曼光谱检测方法和电子设备 技术领域
本发明一般地涉及拉曼光谱检测领域，尤其涉及一种用于去除溶剂干扰的拉曼光谱检测方法和电子设备。
背景技术
拉曼光谱是一种分子振动光谱，它可以反映分子的指纹特征，可用于对物质的检测。拉曼光谱检测通过检测待测物对于激发光的拉曼散射效应所产生的拉曼光谱来检测和识别物质。拉曼光谱检测方法已经广泛应用于液体安检、珠宝检测、爆炸物检测、毒品检测、药品检测、农药残留检测等领域。
拉曼光谱检测方法在物质检测中遇到的一个问题是对于溶于溶剂中的样品的检测。由于待测样品有时需要溶于溶剂中，例如在对农药残留进行检测时，因此，不可避免地会使检测受到溶剂的干扰。对于溶于溶剂中的样品，要想采集到它的拉曼光谱，激发光必须同时激发溶剂和样品，而溶剂被光激发后自身会产生一些信号，这会导致采集到的光谱和被测样品本身的拉曼光谱有很大差异，进而导致很多物质无法准确识别。目前对于溶剂的干扰，通常通过主观判断给出一个参数作为去除溶剂干扰的比例系数，任何包含溶剂的光谱信号在去除溶剂干扰时都设定相同的参数，不能很好地适应光谱信号的变化，从而也不能准确地去除溶剂干扰。
因此，如何在拉曼光谱检测中去除溶剂干扰，以准确获得待测样品的拉曼光谱，进而实现对待测物的准确识别，对提高拉曼光谱检测方法在各个应用领域检测的准确性具有重要意义。
发明内容
为了克服或消除现有技术存在的问题和缺陷中的至少一种，提出了本发明。
本发明的至少一个目的是提供用于去除溶剂干扰的拉曼光谱检测方法和电子设备，其能够有效地去除溶剂的拉曼光谱对于样品的拉曼光谱信号的干扰，以准确地对样品进行检测和识别。
根据本发明的一个方面，提供一种用于去除溶剂干扰的拉曼光谱检测方法，包括以下步骤：
对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号；
对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号；
从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号；
计算该系列去除溶剂干扰的拉曼光谱信号中的每一个的信息熵；
选择所计算出的一系列的信息熵中的最大信息熵；和
将与该最大信息熵对应的去除溶剂干扰的拉曼光谱信号作为优化的去除溶剂干扰的拉曼光谱信号。
根据一些实施例，所述的从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号的步骤包括：
从溶液的拉曼光谱信号中减去比例系数与溶剂的拉曼光谱信号的乘积，并且逐步改变比例系数，以从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号。
根据一些实施例，溶液的拉曼光谱信号为离散数据序列A，溶剂的拉曼光谱信号为离散数据序列B，采用下式从溶液的拉曼光谱信号中减去比例系数与溶剂的拉曼光谱信号的乘积：
C＝A-j*K*B；
C为表示去除溶剂干扰的拉曼光谱信号的离散数据序列，j依次取值为1，2，3，……N且j为自然数，N为预设的计算次数，且K为预设的去除比例。
根据一些实施例，所述拉曼光谱检测方法还包括如下步骤：确定溶剂的拉曼光谱的特征峰的位置，并且将包括溶剂的拉曼光谱的特征峰的位置的区间作为计算区间；
所述从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号的步骤包括：在所述计算区间内，从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号。
根据一些实施例，所述的计算该系列去除溶剂干扰的拉曼光谱信号中的每 一个的信息熵的步骤包括：
根据下面的信息熵计算公式计算一系列去除溶剂干扰的拉曼光谱信号C中的每一个的信息熵：
Figure PCTCN2017112916-appb-000001
i表示该拉曼光谱信号的第i个波数，n表示该拉曼光谱信号的信号长度，xi表示第i个波数对应的强度，p(xi)表示该拉曼光谱信号中取强度xi的概率。
根据一些实施例，所述拉曼光谱检测方法还包括如下步骤：在从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号之前，对溶剂的拉曼光谱信号与溶液的拉曼光谱信号进行归一化处理。
根据一些实施例，K的取值范围为0.005～0.03，且N的取值范围为200～600。
根据本发明的另一方面，还提供一种用于去除溶剂干扰的拉曼光谱检测方法，包括如下步骤：
对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号；
对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号；
对溶剂的拉曼光谱信号和溶液的拉曼光谱信号作归一化处理；
确定溶剂的拉曼光谱的特征峰的位置，并且将包括溶剂的拉曼光谱的特征峰的位置的区间作为计算区间；
在计算区间内，从归一化后的溶液的拉曼光谱信号中逐步减去归一化后的溶剂的拉曼光谱信号与比例系数的乘积，通过逐渐改变比例系数，以获得一系列去除溶剂干扰的区间拉曼光谱信号；
计算一系列去除溶剂干扰的区间拉曼光谱信号中的每一个的信息熵；
选择所计算出的一系列的信息熵中的最大信息熵，并且选择与最大信息熵对应的比例系数作为优化的比例系数；和
从归一化后的溶液的拉曼光谱信号中减去归一化后的溶剂的拉曼光谱信号与优化的比例系数的乘积，以获得优化的去除溶剂干扰的拉曼光谱信号。
根据本发明的又一方面，还提供一种电子设备，包括：
存储器，用于存储可执行指令；以及
处理器，用于执行存储器中存储的可执行指令，以执行如本发明的任一方面或实施例中所述的方法。
本发明的上述技术方案中的任何一个能够借助计算最大信息熵，从混有溶 剂光谱的样品拉曼光谱信号中去除溶剂所导致的干扰信息，从而能够正确获得反映样品自身的属性的拉曼光谱信号。这种方案可以对溶液中的样品的拉曼光谱进行准确的检测，从而有效地识别待测样品。
附图说明
图1示意性地示出根据本发明的实施例的用于去除溶剂干扰的拉曼光谱检测方法的流程图；
图2示意性地示出根据本发明的另一实施例的用于去除溶剂干扰的拉曼光谱检测方法的流程图；
图3示意性地示出将根据本发明的实施例的拉曼光谱检测方法用于去除乙腈干扰的流程图；
图4示意性示出亚胺硫磷乙腈溶液的拉曼光谱；
图5示意性示出乙腈溶剂的拉曼光谱；
图6示意性示出根据本发明实施例的方法计算出的信息熵曲线图；
图7示意性示出通过使用本发明的实施例的拉曼光谱检测方法从亚胺硫磷乙腈溶液中去除乙腈干扰后的溶液的拉曼光谱；和
图8是示出了用于执行根据本发明的实施例的方法的电子设备的示例硬件布置的框图。
具体实施方式
下面通过实施例，并结合附图，对本发明的技术方案作进一步具体的说明。在说明书中，相同或相似的附图标号表示相同或相似的部件。下述参照附图对本发明实施方式的说明旨在对本发明的总体发明构思进行解释，而不应当理解为对本发明的一种限制。
在本文中，为了描述方便，使用“第一、第二”、“A、B、C”等表述描述方法的步骤，但是，除非有特别说明，这样的表述不应理解为对步骤执行顺序的限制。
在利用拉曼光谱对样品进行检测时，待测样品有时需要溶于溶剂中，例如在检测农药残留时，将待测样品溶于例如乙腈的溶剂中。因而，对于样品的检测可能需要用于检测的激发光同时照射到样品和溶剂上来进行，而溶剂也可能 对激发光产生拉曼散射效应，在此情况下，溶剂对样品自身的拉曼光谱信号会产生干扰。而去除这种干扰对于准确有效地检测和识别样品是非常重要的。
图1示意性地示出根据本发明的实施例的用于去除溶剂干扰的拉曼光谱检测方法的流程图。该方法包括：
溶液的拉曼光谱信号获得步骤：对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号A；
溶剂的拉曼光谱信号获得步骤：对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号B；
去除溶剂干扰的拉曼光谱信号获得步骤：从溶液的拉曼光谱信号A中逐步减去溶剂的拉曼光谱信号B，以获得一系列去除溶剂干扰的拉曼光谱信号C；
信息熵计算步骤：计算该系列去除溶剂干扰的拉曼光谱信号C中的每一个的信息熵；
最大信息熵选取步骤：选择所计算出的一系列的信息熵中的最大信息熵；和
优化的去除溶剂干扰的拉曼光谱信号获得步骤：将与该最大信息熵对应的去除溶剂干扰的拉曼光谱信号作为优化的去除溶剂干扰的拉曼光谱信号。
具体地，在所述溶液的拉曼光谱信号获得步骤中，获得的溶液的拉曼光谱信号A是一个离散数据序列，记为Ai，其中，i依次取值为1，2，3，……n且i为自然数，n表示该数据序列的数据点总数。例如，离散数据序列Ai可以是由一组离散的溶液的拉曼光谱信号的峰强度数据构成的向量或矩阵。同样地，在溶剂的拉曼光谱信号获得步骤中，获得的溶剂的拉曼光谱信号B也是一个离散数据序列，记为Bi，例如，由一组离散的溶剂的拉曼光谱信号的峰强度数据构成的向量或矩阵。
在一个示例中，所述的去除溶剂干扰的拉曼光谱信号获得步骤具体包括：从溶液的拉曼光谱信号A中逐步减去“j*K*溶剂的拉曼光谱信号B”，以获得一系列去除溶剂干扰的拉曼光谱信号Cj。其中，j依次取值为1，2，3，……N且j为自然数，N为预设的计算次数，且K为预设的比例系数。即，Cj＝A-j*K*B，j依次取值为1，2，3，……N且j为自然数。相应地，所述一系列去除溶剂干扰的拉曼光谱信号C包括C1、C2、C3、……，CN。应该理解，每一个去除溶剂干扰的拉曼光谱信号Cj也是一个离散数据序列，其也包括n个数据点。
根据本发明的实施例，所述的信息熵计算步骤包括：根据下面的信息熵计 算公式计算一系列去除溶剂干扰的拉曼光谱信号中的每一个Cj的信息熵：
Figure PCTCN2017112916-appb-000002
其中，i表示该拉曼光谱信号的第i个波数，n表示该拉曼光谱信号的信号长度，xi表示第i个波数对应的强度，p(xi)表示该拉曼光谱信号中取强度xi的概率。
根据信息熵理论，信息熵评价的是随机变量(例如X)等于各个值的平均信息量，即表示随机变量X的不确定度。而对于离散数据序列而言，因为离散情况的均匀分布没有任何的偏向，所以均匀分布应是最不确定的。例如，考虑二元的情况，就是随机变量X只能有X＝a或者X＝b两种情况，如果p(X＝a)概率很大，接近于1，同时p(X＝b)概率很小，那么在此情况下，由于比较肯定X＝a的发生，所以X的不确定度很小是明显的。因此，应该理解，二元在发生概率相同下应该是最混乱的状态。也就是说，随机变量X在均匀的离散分布下信息熵最大。
具体地，在去除溶剂干扰信息时，由于溶剂本身的拉曼光谱信号具有一些特征峰，所以从包含溶剂和样品的拉曼光谱信号中去除溶剂的拉曼光谱信号的过程是去除一些特征峰的过程，即，使拉曼光谱信号变平缓的过程；进一步地，如果过度去除溶剂干扰信息，那么会使得拉曼光谱增加了一些反向峰，即，使拉曼光谱信号又变得不平缓。而根据上述理论可知，平缓的拉曼光谱信号对应比较均匀的离散分布，也就对应最大信息熵。因此，在逐步去除溶剂干扰的过程中，当获得最大信息熵时，意味着此时恰好去除掉溶剂干扰信息，即，此时对应优化的从包含溶剂和样品的拉曼光谱信号中去除溶剂的拉曼光谱信号的情形。
在一些实施例中，上述的用于去除溶剂干扰的拉曼光谱检测方法还可以包括如下步骤：对溶剂的拉曼光谱信号B和溶液的拉曼光谱信号A进行归一化处理。归一化化是将需要处理的数据通过某种归一化算法处理后限制在预定的范围内，其使得处于两个不同参考系下的数据可以进行比较、计算等，以便于后续数据处理，并且加快后续计算的收敛。例如，在一个示例中，可以将溶剂的拉曼光谱信号B归一化到溶液的拉曼光谱信号A中，以便于后续步骤中进行的从溶液的拉曼光谱信号A中逐步减去溶剂的拉曼光谱信号B的计算。
根据本发明的一些实施例，可以仅在溶剂的拉曼光谱信号的特征峰所在的 区间内进行上述的信息熵计算步骤，以减少计算量，从而加快检测速度。图2示意性地示出根据本发明的另一实施例的用于去除溶剂干扰的拉曼光谱检测方法的流程图，这样，该用于去除溶剂干扰的拉曼光谱检测方法可以包括如下步骤：
溶液的拉曼光谱信号获得步骤：对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号A；
溶剂的拉曼光谱信号获得步骤：对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号B；
归一化步骤：归一化溶剂的拉曼光谱信号B和溶液的拉曼光谱信号A；
计算区间确定步骤：确定溶剂的拉曼光谱的特征峰的位置，并且将包括溶剂的拉曼光谱的特征峰的位置的区间作为计算区间；
去除溶剂干扰的区间拉曼光谱信号获得步骤：在计算区间内，从归一化后的溶液的拉曼光谱信号A中逐步减去归一化后的溶剂的拉曼光谱信号B与预设的比例系数的乘积，通过逐渐改变比例系数，以获得一系列去除溶剂干扰的区间拉曼光谱信号C’；
信息熵计算步骤：计算一系列去除溶剂干扰的区间拉曼光谱信号C’中的每一个的信息熵；
优化的比例系数确定步骤：选择所计算出的一系列的信息熵中的最大信息熵，并且选择与最大信息熵对应的比例系数作为优化的比例系数；和
优化的去除溶剂干扰的拉曼光谱信号获得步骤：从归一化后的溶液的拉曼光谱信号A中减去归一化后的溶剂的拉曼光谱信号B与优化的比例系数的乘积，以获得优化的去除溶剂干扰的拉曼光谱信号。
下面，以溶剂为乙腈并且样品为亚胺硫磷为示例，结合图3-7，详细描述根据本发明实施例的用于去除溶剂干扰的拉曼光谱检测方法。如图3所示，该方法可以包括如下步骤：
亚胺硫磷乙腈溶液的拉曼光谱信号获得步骤：对亚胺硫磷乙腈溶液的拉曼光谱进行测量，以获得亚胺硫磷乙腈溶液的拉曼光谱信号A，如图4所示，在图4所示的拉曼光谱图中，横坐标表示拉曼频移或波数(cm-1)，纵坐标表示强度(无量纲)，这样，拉曼光谱信号A可以是包括若干个离散的图4中示出的纵坐标表示的强度数据的向量，或是包括若干个离散的图4中示出的横坐标表示的拉曼频移和对应的纵坐标表示的强度数据的矩阵。
乙腈的拉曼光谱信号获得步骤：对乙腈本身的拉曼光谱进行测量，以获得乙腈的拉曼光谱信号B，如图5所示，图5中横、纵坐标的含义与图4相同，同样地，拉曼光谱信号B可以是包括若干个离散的图5中示出的纵坐标表示的强度数据的向量，或是包括若干个离散的图5中示出的横坐标表示的拉曼频移和对应的纵坐标表示的强度数据的矩阵。
归一化步骤：归一化乙腈的拉曼光谱信号B和亚胺硫磷乙腈溶液的拉曼光谱信号A。具体地，如果拉曼光谱信号B与拉曼光谱信号A的横坐标不对应，即拉曼光谱信号B与拉曼光谱信号A包括不处于相同拉曼频移位置处的强度数据，那么首先可以根据拉曼光谱信号A、B的原始数据，通过插值计算，计算出其它拉曼频移位置处的强度数据，从而将拉曼光谱信号A、B转换到相同的拉曼频移位置处。例如，在一个示例中，可以设定拉曼频移位置为[350：2：2800]cm-1，其中，350cm-1、2800cm-1分别表示最小、最大拉曼频移位置，2表示间隔步长，即各个拉曼频移位置为350、352、354、356cm-1，以此类推，直至2800cm-1，然后，如果拉曼光谱信号A、B的原始数据中不包括这些拉曼频移位置处的强度数据，那么可以根据拉曼光谱信号A、B的原始数据，通过插值计算，计算出这些拉曼频移位置处的强度数据，从而将拉曼光谱信号A、B转换到相同的拉曼频移位置[350：2：2800]cm-1处，将转换后的拉曼光谱信号A、B分别计为拉曼光谱信号A’、B’。并且，如果拉曼光谱信号B与拉曼光谱信号A的纵坐标不对应，即拉曼光谱信号B与拉曼光谱信号A的强度数据不位于同一参考系中或以不同的量程测量，可以将拉曼光谱信号B’转换到拉曼光谱信号A’中。例如，在一个示例中，可以分别选取拉曼光谱信号A’、B’的纵坐标的最大值max(A’)、max(B’)，然后将max(A’)与max(B’)的比值作为纵坐标的归一化系数，使拉曼光谱信号B’的离散数据分别乘以该归一化系数，以将拉曼光谱信号B’转换到拉曼光谱信号A’中，将转换后的拉曼光谱信号B’记为拉曼光谱信号B”。在其它实施例中，也可以保持拉曼光谱信号A不变，直接将拉曼光谱信号B归一化到拉曼光谱信号A中。
计算区间确定步骤：确定乙腈的特征峰的位置，并根据乙腈的特征峰的位置确定包括这些特征峰的位置的区间，以作为计算区间。在图5所示的实施例中，可以确定乙腈的特征峰的位置主要处在900-950cm-1和2200-2300cm-1的区间内，所以可以将区间900-950cm-1和2200-2300cm-1确定为计算区间。
去除乙腈干扰的区间拉曼光谱信号获得步骤：在计算区间900-950cm-1和 2200-2300cm-1内，从归一化后的亚胺硫磷乙腈溶液的拉曼光谱信号A’中逐步减去归一化后的乙腈的拉曼光谱信号B”与预设的比例系数的乘积，通过逐渐改变比例系数，以获得一系列去除乙腈干扰的区间拉曼光谱信号C。
信息熵计算步骤：计算一系列去除乙腈干扰的区间拉曼光谱信号C中的每一个的信息熵；
具体地，上述两个步骤的计算过程可以用下列公式表示：
Cj＝A-j*K*B
其中，j依次取值为1，2，3，……N且j为自然数，N为预设的计算次数，且K为预设的除去比例，通过逐步改变j，就可以实现j*K的逐步改变，即j*K构成了被逐步改变的比例系数KK。在一些实施例中，K的取值范围为0.005～0.03，且N的取值范围为200～600。优选地，在一个示例中，N设为300，K设为0.01，当N、K取该数值时，能够较快地计算出最大信息熵，又不至于过度增加计算时间。这样，通过依次改变j的取值，可以以0.01为步长，逐步从亚胺硫磷乙腈溶液的拉曼光谱信号A’中减去乙腈的拉曼光谱信号B”，从而获得300个去除乙腈干扰的区间拉曼光谱信号C。
然后，根据下面的信息熵计算公式计算每一个去除乙腈干扰的区间拉曼光谱信号Cj的信息熵，由于存在300个去除乙腈干扰的区间拉曼光谱信号C，所以对应地可以计算出300个信息熵，如图6所示，其示出了以图4、图5所示的拉曼光谱信号为原始数据并通过上述计算方法计算出的信息熵的变化图，在图6中，横坐标表示拉曼光谱信号的序号，纵坐标表示计算出的信息熵。
Figure PCTCN2017112916-appb-000003
其中，i表示该拉曼光谱信号的第i个波数，n表示该拉曼光谱信号的信号长度，xi表示第i个波数对应的强度，p(xi)表示该拉曼光谱信号中取强度xi的概率。
优化的比例系数确定步骤：选择所计算出的300个信息熵中的最大信息熵，并且选择与该最大信息熵对应的比例系数作为优化的比例系数，将该优化的比例系数记为KK优化；如图6所示，最大信息熵出现在j＝60的位置处，那么KK优化＝0.01*60。
在上面的计算过程中，通过将计算区间设定为包括乙腈的特征峰的位置的区间，可以极大减少计算量，从而加快检测速度。
优化的去除乙腈干扰的拉曼光谱信号获得步骤：从亚胺硫磷乙腈溶液的拉曼光谱信号A’中减去乙腈的拉曼光谱信号B”与优化的比例系数的乘积，以获得优化的去除乙腈干扰的拉曼光谱信号C优化。图示的实施例中，C优化＝A-60*0.01*B，通过将图4、图5对应的数据代入该公式中，可以得到优化的去除乙腈干扰的拉曼光谱信号C优化的数据，根据该数据绘制的优化的去除乙腈干扰的拉曼谱图如图7所示，比较图4和图7，可以看出，该拉曼光谱信号C优化较好去除了乙腈干扰信号。
根据本发明的又一实施例，还提供一种电子设备，图8是示出了该电子设备800的示例硬件布置的框图。电子设备800包括处理器806(例如，微处理器(μP)、数字信号处理器(DSP)等)。处理器806可以是或包括用于执行本文描述的方法步骤的不同动作的单一处理单元或者是多个处理单元。电子设备800还可以包括用于从其他实体接收信号的输入单元802、以及用于向其他实体提供信号的输出单元804。输入单元802和输出单元804可以被布置为单一实体或者是分离的实体。
此外，电子设备800可以包括具有非易失性或易失性存储器形式的至少一个计算机可读存储介质808，例如是电可擦除可编程只读存储器(EEPROM)、闪存、和/或硬盘驱动器。可读存储介质808包括计算机程序810，该计算机程序810包括代码/计算机可读指令，其在由电子设备800中的处理器806执行时使得电子设备800可以执行例如上面结合图1-3所描述的流程及其任何变形。
计算机程序810可被配置为具有例如计算机程序模块810A～810C等架构的计算机程序代码。
计算机程序模块实质上可以执行图1-3中所示出的流程中的各个动作或步骤，以模拟设备。换言之，当在处理器806中执行不同计算机程序模块时，它们可以对应于设备中的上述不同单元。
尽管上面结合图8所公开的实施例中的代码手段被实现为计算机程序模块，其在处理器806中执行时使得电子设备800执行上面结合图1～3所描述的动作，然而在备选实施例中，该代码手段中的至少一项可以至少被部分地实现为硬件电路。
处理器可以是单个CPU(中央处理单元)，但也可以包括两个或更多个处理单元。例如，处理器可以包括通用微处理器、指令集处理器和/或相关芯片组 和/或专用微处理器(例如，专用集成电路(ASIC))。处理器还可以包括用于缓存用途的板载存储器。计算机程序可以由连接到处理器的计算机程序产品来承载。计算机程序产品可以包括其上存储有计算机程序的计算机可读介质。例如，计算机程序产品可以是闪存、随机存取存储器(RAM)、只读存储器(ROM)、EEPROM，且上述计算机程序模块在备选实施例中可以用存储器的形式被分布到不同计算机程序产品中。
本领域技术人员应当理解，在本发明的一些实施例中，虽然以亚胺硫磷乙腈溶液为示例详细说明了本发明的技术构思，但是本发明不局限于去除乙腈干扰。
虽然结合附图对本发明进行了说明，但是附图中公开的实施例旨在对本发明优选实施方式进行示例性说明，而不能理解为对本发明的一种限制。
虽然本发明总体构思的一些实施例已被显示和说明，本领域普通技术人员将理解，在不背离本总体发明构思的原则和精神的情况下，可对这些实施例做出改变，本发明的范围以权利要求和它们的等同物限定。

Claims (9)

  1. 一种用于去除溶剂干扰的拉曼光谱检测方法，包括以下步骤：
    对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号；
    对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号；
    从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号；
    计算该系列去除溶剂干扰的拉曼光谱信号中的每一个的信息熵；
    选择所计算出的一系列的信息熵中的最大信息熵；和
    将与该最大信息熵对应的去除溶剂干扰的拉曼光谱信号作为优化的去除溶剂干扰的拉曼光谱信号。
  2. 根据权利要求1所述的拉曼光谱检测方法，其中，所述的从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号的步骤包括：
    从溶液的拉曼光谱信号中减去比例系数与溶剂的拉曼光谱信号的乘积，并且逐步改变比例系数，以从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号。
  3. 根据权利要求2所述的拉曼光谱检测方法，其中，溶液的拉曼光谱信号为离散数据序列A，溶剂的拉曼光谱信号为离散数据序列B，采用下式从溶液的拉曼光谱信号中减去比例系数与溶剂的拉曼光谱信号的乘积：
    C＝A-j*K*B；
    其中，C为表示去除溶剂干扰的拉曼光谱信号的离散数据序列，j依次取值为1，2，3，……N且j为自然数，N为预设的计算次数，且K为预设的去除比例。
  4. 根据权利要求1-3中任一项所述的拉曼光谱检测方法，还包括如下步骤：确定溶剂的拉曼光谱的特征峰的位置，并且将包括溶剂的拉曼光谱的特征峰的位置的区间作为计算区间；
    其中，所述从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号的步骤包括：在所述计算区间内，从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号，以获得一系列去除溶剂干扰的拉曼光谱信号。
  5. 根据权利要求1-4中任一项所述的拉曼光谱检测方法，其中，所述的计算该系列去除溶剂干扰的拉曼光谱信号中的每一个的信息熵的步骤包括：
    根据下面的信息熵计算公式计算一系列去除溶剂干扰的拉曼光谱信号C中的每一个的信息熵：
    Figure PCTCN2017112916-appb-100001
    其中，i表示该拉曼光谱信号的第i个波数，n表示该拉曼光谱信号的信号长度，xi表示第i个波数对应的强度，p(xi)表示该拉曼光谱信号中取强度xi的概率。
  6. 根据权利要求1-3中任一项所述的拉曼光谱检测方法，还包括如下步骤：
    在从溶液的拉曼光谱信号中逐步减去溶剂的拉曼光谱信号之前，对溶剂的拉曼光谱信号与溶液的拉曼光谱信号进行归一化处理。
  7. 根据权利要求3所述的拉曼光谱检测方法，其中，K的取值范围为0.005～0.03，且N的取值范围为200～600。
  8. 一种用于去除溶剂干扰的拉曼光谱检测方法，包括如下步骤：
    对包含溶剂和样品的溶液的拉曼光谱进行测量，以获得溶液的拉曼光谱信号；
    对溶剂的拉曼光谱进行测量，以获得溶剂的拉曼光谱信号；
    对溶剂的拉曼光谱信号和溶液的拉曼光谱信号作归一化处理；
    确定溶剂的拉曼光谱的特征峰的位置，并且将包括溶剂的拉曼光谱的特征峰的位置的区间作为计算区间；
    在计算区间内，从归一化后的溶液的拉曼光谱信号中逐步减去归一化后的溶剂的拉曼光谱信号与比例系数的乘积，通过逐渐改变比例系数，以获得一系 列去除溶剂干扰的区间拉曼光谱信号；
    计算一系列去除溶剂干扰的区间拉曼光谱信号中的每一个的信息熵；
    选择所计算出的一系列的信息熵中的最大信息熵，并且选择与最大信息熵对应的比例系数作为优化的比例系数；和
    从归一化后的溶液的拉曼光谱信号中减去归一化后的溶剂的拉曼光谱信号与优化的比例系数的乘积，以获得优化的去除溶剂干扰的拉曼光谱信号。
  9. 一种电子设备，包括：
    存储器，用于存储可执行指令；以及
    处理器，用于执行存储器中存储的可执行指令，以执行如权利要求1-8中任一项所述的方法。
PCT/CN2017/112916 2016-12-08 2017-11-24 用于去除溶剂干扰的拉曼光谱检测方法和电子设备 WO2018103541A1 (zh)

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