WO2018121151A1 - 用于识别拉曼谱图的方法和电子设备 - Google Patents

用于识别拉曼谱图的方法和电子设备 Download PDF

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WO2018121151A1
WO2018121151A1 PCT/CN2017/112915 CN2017112915W WO2018121151A1 WO 2018121151 A1 WO2018121151 A1 WO 2018121151A1 CN 2017112915 W CN2017112915 W CN 2017112915W WO 2018121151 A1 WO2018121151 A1 WO 2018121151A1
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spectrum
peak
measured
standard
measured spectrum
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PCT/CN2017/112915
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English (en)
French (fr)
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苟巍
左佳倩
范锐
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同方威视技术股份有限公司
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Priority to US16/473,505 priority Critical patent/US10948417B2/en
Priority to EP17888822.8A priority patent/EP3561718A4/en
Publication of WO2018121151A1 publication Critical patent/WO2018121151A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/02—Details
    • G01J3/0205—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows
    • G01J3/0208—Optical elements not provided otherwise, e.g. optical manifolds, diffusers, windows using focussing or collimating elements, e.g. lenses or mirrors; performing aberration correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/44—Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching
    • G06F2218/14—Classification; Matching by matching peak patterns

Definitions

  • the present invention generally relates to the field of spectral analysis processing techniques, and more particularly to a method and an electronic device for identifying a Raman spectrum.
  • 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 existing Raman spectral modeling method usually directly selects the data information of the spectrum for modeling, and then matches the Raman spectrum of the tested substance with the standard Raman spectrum in the standard library, not only the recognition rate. Not high, and the operation time is long, can not reflect the advantages of Raman recognition.
  • the present invention has been made in order to overcome or eliminate at least one of the problems and disadvantages of the prior art.
  • At least one object of the present invention is to provide a method and an electronic device for identifying a Raman spectrum that can efficiently and quickly match a Raman spectrum to a standard spectrum.
  • a method for identifying a Raman spectrum comprising the steps of:
  • the measured spectrum obtaining step measuring the Raman spectrum of the substance to be measured to obtain a measured spectrum, the measured spectrum comprising a series of data;
  • Peak information extraction step extracting peak information of the measured spectrum, the peak information including peak intensity, peak position and peak region;
  • the first identifying step comprises:
  • Sorting step sorting the peaks of the measured spectrum and the peaks of the standard spectrum according to the order of peak intensity from large to small, respectively, to select the peaks of the top N names of the measured spectrum and the standard spectrum;
  • Comparison step comparing the peak position information of the top N peaks of the measured spectrum and the standard spectrum.
  • the first matching step determining whether the peak information of the measured spectrum and the standard spectrum match according to the comparison result obtained by the comparing step.
  • the step of comparing the peak position information of the peak of the top N name of the measured spectrum and the standard spectrum in the comparing step specifically includes:
  • N is a predetermined number of comparative peaks, and N is a natural number greater than or equal to 3;
  • i, j respectively represent the sequence numbers of the sorted peaks in the standard spectrum and the measured spectrum, and i and j are integers greater than or equal to 0 and less than or equal to N;
  • P1[i].fPos represents the peak position of the ith peak after sorting in the standard spectrum
  • P2[j].fPos represents the peak position of the jth peak after sorting in the measured spectrum
  • pD represents the absolute difference of the peak positions.
  • the first matching step comprises:
  • p1[i].fWidth represents the peak width of the ith peak after sorting in the standard spectrum
  • p2[j].fWidth represents the peak width of the jth peak after sorting in the measured spectrum.
  • the first identifying step further comprises:
  • Peak matching weight calculation step establishing a punitive function according to the following formula (2) to calculate a peak matching weight
  • a second matching step when the peak matching weight is greater than or equal to the preset weight threshold, determining that the measured spectrum matches the peak information of the standard spectrum; and when the peak matching weight is less than the weight threshold, determining the measured spectrum Does not match the peak information of the standard spectrum,
  • h represents the peak matching weight
  • i, j respectively represent the sequence numbers of the sorted peaks in the standard spectrum and the measured spectrum, and i and j are integers greater than or equal to 0 and less than or equal to N;
  • N is a predetermined number of comparative peaks, and N is a natural number greater than or equal to 3;
  • pD represents the absolute difference of the peak positions
  • P1[i].fWidth represents the peak width of the ith peak after sorting in the standard spectrum
  • P2[j].fWidth represents the peak width of the jth peak after sorting in the measured spectrum.
  • the peak matching weight calculation step and the second matching step are performed.
  • N is a natural number greater than or equal to 3 and less than or equal to 5.
  • the second identifying step comprises:
  • the step of correlating the data of the measured spectrum with the data of the pre-stored standard spectrum comprises:
  • Calculating a correlation coefficient between the data of the measured spectrum and the data of the pre-stored standard spectrum When the calculated correlation coefficient is greater than or equal to a preset correlation threshold, determining that the measured spectrum matches the standard spectrum; When the correlation coefficient is less than the correlation threshold, it is determined that the measured spectrum does not match the standard spectrum.
  • the step of performing correlation comparison between the data of the measured spectrum and the data of the pre-stored standard spectrum is performed in a union interval of peak regions of all peaks of the measured spectrum and the standard spectrum.
  • the method further comprises the steps of:
  • Normalization step normalizing the measured spectrum and the standard spectrum
  • the normalization step is performed before the peak information extraction step.
  • an electronic device including:
  • a memory for storing executable instructions
  • a processor for executing executable instructions stored in a memory to perform the methods described in any aspect or embodiment of the present invention.
  • any one of the above technical solutions of the present invention first performs "peak” by utilizing peak information of the spectrum
  • the comparison of the local features, the preliminary screening of the measured spectrum and the standard spectrum, after the initial screening, the global comparison of the spectral data not only can greatly shorten the matching recognition time, but also improve the accuracy of matching identification. rate.
  • FIG. 1 schematically shows a flow chart of a method for identifying a Raman spectrum according to an embodiment of the present invention
  • Figure 2 is a schematic representation of a peak of a Raman spectrum of a substance
  • FIG. 3 illustrates a flow chart for identifying a matched measured spectrum and a standard spectrum using a method for identifying a Raman spectrum according to an embodiment of the present invention
  • FIG. 4 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.
  • FIG. 1 schematically illustrates a method for identifying a Raman spectrum, in accordance with an exemplary embodiment of the present invention. As shown in FIG. 1, the method may include the following steps:
  • the measured spectrum obtaining step measuring the Raman spectrum of the substance to be measured to obtain a measured spectrum, the measured spectrum comprising a series of data;
  • Peak information extraction step extracting peak information of the measured spectrum, the peak information including peak intensity, peak position and peak region;
  • the second identifying step before comparing the data of the measured spectrum with the data of the pre-stored standard spectrum, that is, before the second identifying step, performing a first identifying step of comparing the peak information of the measured spectrum And peak information for pre-stored standard spectra.
  • the second identifying step is performed only if it is recognized in the first identifying step that the measured spectrum matches the peak information of the standard spectrum; if the measured spectrum and the standard spectrum are identified in the first identifying step If the peak information does not match, it can be directly determined that the measured spectrum does not match the standard spectrum without performing the second identification step. In this way, through the comparison of the peak information, it can play a preliminary screening role.
  • the amount of peak information is much smaller than that of the full interval of the Raman spectrum, the amount of comparison of the peak information is correspondingly much smaller. Therefore, by setting the first recognition step, the amount of calculation can be greatly reduced, thereby improving the recognition speed and improving the recognition accuracy.
  • the above method may further include: a normalization step: normalizing the measured spectrum and the standard spectrum.
  • the normalization step is performed prior to the peak information extraction step described above.
  • the measured spectrum does not correspond to the abscissa of the standard spectrum, that is, the measured spectrum and the standard spectrum include intensity data that are not at the same Raman shift position
  • the measured spectrum and the standard spectrum include intensity data that are not at the same Raman shift position
  • the raw data of the standard spectrum is calculated by interpolation to calculate the intensity data at other Raman shift positions, thereby converting the measured spectrum and the standard spectrum to the same Raman shift position.
  • 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 measured spectrum and the standard spectrum of the raw data do not include these Raman
  • the intensity data at the frequency shift position, then the intensity data at these Raman shift positions can be calculated by interpolation calculation according to the original data of the measured spectrum and the standard spectrum, so that the measured spectrum and the standard spectrum are obtained.
  • the measured spectrum A' may be Switch to the standard spectrum B'.
  • the maximum values max(A'), max(B') of the ordinates of the measured spectra 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 measured spectrum A' is multiplied by the normalization coefficient to convert the measured spectrum A' into the standard spectrum B'.
  • the standard spectrum can be kept unchanged, and the measured spectrum can be directly normalized to the standard spectrum.
  • Figure 2 shows schematically a peak of a Raman spectrum of a substance.
  • the peak information of the Raman spectrum may include a peak region, a peak position, a start and end point of the peak, a peak width w, a peak intensity, and the like.
  • the abscissa of the Raman spectrum represents the Raman shift or wave number (in cm -1 ) and the ordinate represents the intensity of the Raman spectrum (dimensionless or expressed in au).
  • the Raman spectrum can be regarded as a set of discrete data points, as shown by the black circle in Figure 2, the abscissa of the data point can be called the wave number, and the ordinate can be called the intensity value.
  • the Raman spectrum includes a series of data, and each of the data may include a wave number and an intensity value corresponding to the wave number.
  • the peak position may be the position of the highest point P of the peak, that is, the wave number corresponding to P; the start and end points of the peak may be the wave numbers corresponding to the start point S and the end point E of the peak, respectively;
  • the peak width w may be The width defined by the start point S and the end point E of the peak, that is, the difference between the wave number of the end point E of the peak and the wave number of the start point S of the peak;
  • the peak region includes the interval defined by the start point S and the end point E of the peak; the peak intensity can be determined by the peak The intensity value of the highest point P is expressed.
  • the measured spectrum obtaining step measuring the Raman spectrum of the substance to be measured to obtain a measured spectrum, the measured spectrum comprising a series of data;
  • Peak information extraction step extracting peak information of the measured spectrum, the peak information including peak intensity, peak position and peak region;
  • a plurality of standard spectra are pre-stored in the standard spectrum library, for example, standard spectrum 1, standard spectrum 2, ... standard spectrum n, and correspondingly, each standard spectrum includes respective peak information 1 , peak information 2, ... peak information n.
  • the first identifying step may further include a "peak position matching" step, and the "peak position matching” step may include the following steps:
  • Sorting step sorting the peaks of the measured spectrum and the peaks of the standard spectrum according to the order of peak intensity from large to small, respectively, to select the peaks of the top N names of the measured spectrum and the standard spectrum;
  • Comparison step comparing the peak position information of the top N peaks of the measured spectrum and the standard spectrum.
  • the first matching step determining whether the peak information of the measured spectrum and the standard spectrum match according to the comparison result obtained by the comparing step.
  • the step of comparing the peak of the measured spectrum and the standard spectrum in the comparison step in the peak position information of the peak of the top N name may specifically include:
  • N is a predetermined number of comparative peaks, and N is a natural number greater than or equal to 3;
  • i, j respectively represent the serial number of the sorted peak in the standard spectrum and the measured spectrum, and i and j are integers greater than or equal to 0 and less than or equal to N, that is, the serial number of the peak starts from 0 and is marked as 0.
  • the peak indicates the highest peak of the spectrum, the peak indicated as 1 indicates the secondary peak of the spectrum, and so on;
  • P1[i].fPos represents the peak position of the ith peak after sorting in the standard spectrum
  • P2[j].fPos represents the peak position of the jth peak after sorting in the measured spectrum
  • pD represents the absolute difference of the peak positions.
  • the first matching step may specifically include:
  • the condition (1) is: pD ⁇ p2[j].fWidth/3 and pD ⁇ p1[i].fWidth/3,
  • p1[i].fWidth represents the peak width of the ith peak after sorting in the standard spectrum
  • p2[j].fWidth represents the peak width of the jth peak after sorting in the measured spectrum.
  • N is a natural number greater than or equal to 3 and less than or equal to 5.
  • N is a natural number greater than or equal to 3 and less than or equal to 5.
  • the value of N is small, for example, less than 3, the number of peaks to be compared is too small, which is disadvantageous for screening out the standard spectrum matching the peak information of the measured spectrum, that is, it is not conducive to the validity of the identification;
  • the value of N is too large, the calculation amount of the comparison peak information is increased, which may affect the calculation speed of the peak information comparison.
  • N takes a natural number greater than or equal to 3 and less than or equal to 5
  • both the validity and the calculation speed using the peak information identification can be achieved.
  • the first identifying step may further include a "filter” step, which The "filter” step may specifically include:
  • Peak matching weight calculation step establishing a punitive function according to the following formula (2) to calculate a peak matching weight
  • a second matching step when the peak matching weight is greater than or equal to the preset weight threshold, determining that the measured spectrum matches the standard spectrum; when the peak matching weight is less than the weight threshold, determining that the measured spectrum and the standard spectrum are not match,
  • min(p1[i].fWidth,p2[j].fWidth) means taking the smaller values in p1[i].fWidth and p2[j].fWidth;
  • the peak matching weight calculation step and the second matching step are performed. That is, in the embodiment of the present invention, the calculation of the punitive function and the comparison of the peak matching weights are performed only when it is judged that the absolute difference of the peaks satisfies the requirement in the first matching step. Similarly, since the calculation amount of the absolute difference of the calculated peak is smaller than the calculation amount of the calculation of the punitive function, preliminary calculation by calculating the absolute difference of the peak before calculating the punitive function can greatly reduce the calculation amount, thereby improving the recognition. Speed and improve recognition accuracy.
  • the second identifying step may include: performing correlation comparison between the data of the measured spectrum and the data of the pre-stored standard spectrum, that is, the "correlation calculation" step as shown in FIG. .
  • the step of correlating the data of the measured spectrum with the data of the pre-stored standard spectrum comprises:
  • Calculating a correlation coefficient between the data of the measured spectrum and the data of the pre-stored standard spectrum When the calculated correlation coefficient is greater than or equal to a preset correlation threshold, determining that the measured spectrum matches the standard spectrum; When the correlation coefficient is less than the correlation threshold, it is determined that the measured spectrum does not match the standard spectrum.
  • the correlation coefficient is the amount of linear correlation between the variables, and is a measure of the relationship between vectors.
  • a feature vector X(x 1 , x 2 , . . . , x n ), Y(y 1 , y 2 , . . . , y n ) is provided, and the correlation coefficient r of the two can be defined as follows:
  • correlation between the data of the measured spectrum and the data of the pre-stored standard spectrum is performed in a union interval of peak regions of all peaks of the measured spectrum and the standard spectrum.
  • the steps of the correlation of the data of the standard spectrum represents an interval composed of the peak regions of all the peaks of the measured spectrum and the standard spectrum.
  • the peak spectrum information of the spectrum is first used to compare the local features of the “peak”, and the measured spectrum and the standard spectrum are initially screened, and the spectral data is performed after the preliminary screening is passed.
  • the global comparison not only greatly shortens the matching recognition time, but also improves the accuracy of matching recognition.
  • the matching process can be terminated immediately without performing a subsequent identification matching process. It can greatly improve the calculation speed when determining the mismatch between the two. Tests have shown that the matching recognition time is shortened to about 5%, and the accuracy of matching recognition is increased by about 10%.
  • FIG. 4 is a block diagram showing an example hardware arrangement of the electronic device 400.
  • the electronic device 400 includes a processor 406 (eg, a microprocessor ( ⁇ P), a digital signal processor (DSP), etc.).
  • processor 406 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 400 may also include an input unit 402 for receiving signals from other entities, and an output unit 404 for providing signals to other entities.
  • Input unit 402 and output unit 404 can be arranged as a single entity or as separate entities.
  • electronic device 400 can include at least one computer readable storage medium 408 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 408 includes a computer program 410 that includes code/computer readable instructions that, when executed by the processor 406 in the electronic device 400, cause the electronic device 400 and can perform, for example, as described above in connection with the above-described embodiments. Process and any of Deformation.
  • Computer program 410 can be configured as computer program code having architectures such as computer program modules 410A-410C.
  • the computer program module can substantially perform various acts or steps in the processes described in the above embodiments to simulate a device. In other words, when different computer program modules are executed in processor 406, they may correspond to the different units described above in the device.
  • code means in the embodiment disclosed above in connection with FIG. 4 is implemented as a computer program module that, when executed in processor 406, causes hardware arrangement 400 to perform the actions described above in connection with the above-described embodiments, however in an alternative embodiment 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.
  • a processor can include a general purpose microprocessor, an instruction set processor, and/or a related chipset and/or a special purpose 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

一种用于识别拉曼谱图的方法和电子设备。该方法包括以下步骤：对待测物质的拉曼光谱进行测量，以获得被测谱图，该被测谱图包括一系列数据；提取出被测谱图的峰信息，该峰信息包括峰强、峰位和峰区；比较被测谱图的峰信息和预存的标准谱图的峰信息，以识别被测谱图与标准谱图的峰信息是否匹配；和在第一识别步骤中识别出被测谱图与标准谱图的峰信息匹配的情况下，比较被测谱图的数据和预存的标准谱图的数据，以进一步识别被测谱图与标准谱图是否匹配。

Description

用于识别拉曼谱图的方法和电子设备 技术领域
本发明一般地涉及谱图分析处理技术领域，尤其涉及一种用于识别拉曼谱图的方法和电子设备。
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。
在对拉曼光谱的谱图进行分析处理时，经常面临的一个问题是如何有效且快速地识别被测物质的拉曼谱图与标准谱图是否匹配。
现有的拉曼谱图建模方法通常直接选择谱图的数据信息用于建模，然后，将被测物质的拉曼谱图与标准库中的标准拉曼谱图进行匹配，不仅识别率不高，而且运算时间较长，不能体现出拉曼识别的优点。
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识别拉曼谱图的方法和电子设备，其能够有效且快速地匹配拉曼谱图与标准谱图。
根据本发明的一个方面，提供一种用于识别拉曼谱图的方法，包括以下步骤：
被测谱图获得步骤：对待测物质的拉曼光谱进行测量，以获得被测谱图，该被测谱图包括一系列数据；
峰信息提取步骤：提取出被测谱图的峰信息，该峰信息包括峰强、峰位和峰区；
第一识别步骤：比较被测谱图的峰信息和预存的标准谱图的峰信息，以识别被测谱图与标准谱图的峰信息是否匹配；和
第二识别步骤：在第一识别步骤中识别出被测谱图与标准谱图的峰信息匹配的情况下，比较被测谱图的数据和预存的标准谱图的数据，以进一步识别被测谱图与标准谱图是否匹配。
根据一些实施例，所述第一识别步骤包括：
排序步骤：按照峰强从大到小的顺序，对被测谱图的峰和标准谱图的峰分别进行排序，以选择出被测谱图和标准谱图的排序在前N名的峰；
比较步骤：比较被测谱图和标准谱图的排序在前N名的峰的峰位信息；和
第一匹配步骤：根据比较步骤得到的比较结果，确定被测谱图与标准谱图的峰信息是否匹配。
根据一些实施例，所述比较步骤中比较被测谱图和标准谱图的排序在前N名的峰的峰位信息的步骤具体包括：
按照下述公式(1)依次计算被测谱图和标准谱图的排序在前N名的各个峰的峰位的绝对差：pD＝|p2[j].fPos-p1[i].fPos|  公式(1)，
其中，N为预定的比较峰数，N为大于等于3的自然数；
i，j分别表示标准谱图和被测谱图中的排序后的峰的序号，i和j均为大于等于0且小于等于N的整数；
p1[i].fPos表示标准谱图中排序后的第i个峰的峰位；
p2[j].fPos表示被测谱图中排序后的第j个峰的峰位；
pD表示峰位的绝对差。
根据一些实施例，所述第一匹配步骤包括：
当计算出的峰位的绝对差满足下述条件(1)时，确定被测谱图与标准谱图的峰信息匹配；当计算出的峰位的绝对差不满足下述条件(1)时，确定被测谱图与标准谱图的峰信息不匹配，
pD＜p2[j].fWidth/3且pD＜p1[i].fWidth/3  条件(1)，
其中，p1[i].fWidth表示标准谱图中排序后的第i个峰的峰宽；p2[j].fWidth表示被测谱图中排序后的第j个峰的峰宽。
根据一些实施例，所述第一识别步骤还包括：
峰匹配权重计算步骤：根据下述公式(2)建立惩罚性函数，以计算峰匹配权重；和
第二匹配步骤：当峰匹配权重大于等于预设的权重阈值时，确定被测谱图与标准谱图的峰信息匹配；当峰匹配权重小于所述权重阈值时，确定被测谱图 与标准谱图的峰信息不匹配，
h＝(1-2*|j-i|/10)*(0.5/(i+1))*exp(-pD*2/min(p1[i].fWidth，p2[j].fWidth))  公式(2)，
其中：
h表示峰匹配权重；
i，j分别表示标准谱图和被测谱图中的排序后的峰的序号，i和j均为大于等于0且小于等于N的整数；
N为预定的比较峰数，N为大于等于3的自然数；
pD表示峰位的绝对差；
p1[i].fWidth表示标准谱图中排序后的第i个峰的峰宽；
p2[j].fWidth表示被测谱图中排序后的第j个峰的峰宽。
根据一些实施例，在所述第一匹配步骤中确定被测谱图与标准谱图的峰信息匹配的情况下，执行所述峰匹配权重计算步骤和所述第二匹配步骤。
根据一些实施例，N为大于等于3且小于等于5的自然数。
根据一些实施例，所述第二识别步骤包括：
对被测谱图的数据和预存的标准谱图的数据进行相关性比较。
根据一些实施例，所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤包括：
计算被测谱图的数据和预存的标准谱图的数据的相关系数，当计算出的相关系数大于等于预设的相关性阈值时，确定被测谱图与标准谱图匹配；当计算出的相关系数小于所述相关性阈值时，确定被测谱图与标准谱图不匹配。
根据一些实施例，在被测谱图和标准谱图的所有峰的峰区的并集区间内，执行所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤。
根据一些实施例，所述方法还包括如下步骤：
归一化步骤：归一化所述被测谱图和所述标准谱图；
其中，所述归一化步骤在所述峰信息提取步骤之前执行。
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通过利用谱图的峰信息首先进行“峰” 这个局部特征的比较，对被测谱图和标准谱图进行初步筛选，在初步筛选通过之后，才进行谱图数据的全局比较，不仅能够大大的缩短匹配识别时间，而且能够提高匹配识别的准确率。
附图说明
图1示意性地示出根据本发明的实施例的用于识别拉曼谱图的方法的流程图；
图2示意性示出了一种物质的拉曼谱图的一个峰；
图3示出了使用根据本发明实施例的用于识别拉曼谱图的方法识别匹配被测谱图与标准谱图的流程图；和
图4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。
图1示意性地示出根据本发明的一个示例性实施例的用于识别拉曼谱图的方法。如图1所示，该方法可以包括以下步骤：
被测谱图获得步骤：对待测物质的拉曼光谱进行测量，以获得被测谱图，该被测谱图包括一系列数据；
峰信息提取步骤：提取出被测谱图的峰信息，该峰信息包括峰强、峰位和峰区；
第一识别步骤：比较被测谱图的峰信息和预存的标准谱图的峰信息，以识别被测谱图与标准谱图的峰信息是否匹配；和
第二识别步骤：在第一识别步骤中识别出被测谱图与标准谱图的峰信息匹配的情况下，比较被测谱图的数据和预存的标准谱图的数据，以进一步识别被测谱图与标准谱图是否匹配。
在本发明的实施例中，在比较被测谱图的数据和预存的标准谱图的数据之前，即在第二识别步骤之前，还执行第一识别步骤，即比较被测谱图的峰信息和预存的标准谱图的峰信息。只有在第一识别步骤中识别出被测谱图与标准谱图的峰信息匹配的情况下，才执行第二识别步骤；如果在第一识别步骤中识别出被测谱图与标准谱图的峰信息不匹配，那么可以直接确定被测谱图与标准谱图不匹配，而无需再执行第二识别步骤。这样，通过峰信息的比较，可以起到初步筛选的作用。而且，由于与拉曼谱图的全区间的数据相比，峰信息的数据量小得多，相应地，比较峰信息的计算量也会小得多。因此，通过设置第一识别步骤，可以大大减少计算量，从而提高识别速度，并且提高识别准确率。
进一步地，上述方法还可以包括：归一化步骤：归一化被测谱图和标准谱图。在一个实施例中，该归一化步骤在上述峰信息提取步骤之前执行。
具体地，如果被测谱图与标准谱图的横坐标不对应，即被测谱图与标准谱图包括不处于相同拉曼频移位置处的强度数据，那么首先可以根据被测谱图、标准谱图的原始数据，通过插值计算，计算出其它拉曼频移位置处的强度数据，从而将被测谱图、标准谱图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，如果被测谱图、标准谱图的原始数据中不包括这些拉曼频移位置处的强度数据，那么可以根据被测谱图、标准谱图的原始数据，通过插值计算，计算出这些拉曼频移位置处的强度数据，从而将被测谱图、标准谱图转换到相同的拉曼频移位置[350：2：2800]cm-1处，将转换后的被测谱图、标准谱图分别计为被测谱图A’、B’。并且，如果被测谱图与标准谱图的纵坐标不对应，即被测谱图与标准谱图的强度数据不位于同一参考系中或以不同的量程测量，可以将被测谱图A’转换到标准谱图B’中。例如，在一个示例中，可以分别选取被测谱图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，使被测谱图A’的离散数据分别乘以该归一化系数，以将被测谱图A’转换到标准谱图B’中。在其它实施例中，也可以保持标准谱图不 变，直接将被测谱图归一化到标准谱图中。
下面，在进一步描述根据本发明实施例的用于识别拉曼谱图的方法之前，结合附图详细地说明拉曼谱图的峰信息。
图2示意性示出了一种物质的拉曼谱图的一个峰。拉曼谱图的峰信息可以包括峰区、峰位、峰的起点和终点、峰宽w、峰强等。通常，拉曼谱图的横坐标表示拉曼频移或波数(单位为cm-1)，纵坐标表示拉曼光谱的强度(无量纲或以a.u.表示)。在进行数学计算时，拉曼谱图可以看作是一组离散的数据点，如图2中的黑圆点所示，该数据点的横坐标可以称作波数，纵坐标可以称作强度值，即，拉曼谱图包括一系列数据，每一个数据可以包括波数和与该波数对应的强度值。这样，如图2所示，峰位可以为该峰的最高点P的位置，即P对应的波数；峰的起点和终点可以分别为峰的起点S和终点E对应的波数；峰宽w可以为峰的起点S和终点E限定的宽度，即峰的终点E的波数与峰的起点S的波数之差；峰区包括由峰的起点S和终点E限定的区间；峰强可以由峰的最高点P的强度值表示。
下面，结合附图3更详细描述根据本发明实施例的用于识别拉曼谱图的方法。
根据本发明实施例的一种用于识别拉曼谱图的方法可以包括以下步骤：
被测谱图获得步骤：对待测物质的拉曼光谱进行测量，以获得被测谱图，该被测谱图包括一系列数据；
峰信息提取步骤：提取出被测谱图的峰信息，该峰信息包括峰强、峰位和峰区；
第一识别步骤：比较被测谱图的峰信息和预存的标准谱图的峰信息，以识别被测谱图与标准谱图的峰信息是否匹配；和
第二识别步骤：在第一识别步骤中识别出被测谱图与标准谱图的峰信息匹配的情况下，比较被测谱图的数据和预存的标准谱图的数据，以进一步识别被测谱图与标准谱图是否匹配。
如图3所示，标准谱图库中预存有多个标准谱图，例如，标准谱图1、标准谱图2、……标准谱图n，相应地，各个标准谱图包括各自的峰信息1、峰信息2、……峰信息n。
根据本发明的一个实施例，如图3所示，所述第一识别步骤可以进一步包括“峰位匹配”步骤，该“峰位匹配”步骤可以包括如下步骤：
排序步骤：按照峰强从大到小的顺序，对被测谱图的峰和标准谱图的峰分别进行排序，以选择出被测谱图和标准谱图的排序在前N名的峰；
比较步骤：比较被测谱图和标准谱图的排序在前N名的峰的峰位信息；和
第一匹配步骤：根据比较步骤得到的比较结果，确定被测谱图与标准谱图的峰信息是否匹配。
根据一些实施例，所述比较步骤中的比较被测谱图和标准谱图的排序在前N名的峰的峰位信息步骤具体可以包括：
按照下述公式(1)依次计算被测谱图和标准谱图的排序在前N名的各个峰的峰位的绝对差(即，峰位之差的绝对值)，
公式(1)为：pD＝|p2[j].fPos-p1[i].fPos|，
其中，N为预定的比较峰数，N为大于等于3的自然数；
i，j分别表示标准谱图和被测谱图中的排序后的峰的序号，i和j均为大于等于0且小于等于N的整数，即，峰的序号从0开始，标示为0的峰表示该谱图的最高峰，表示为1的峰表示该谱图的次高峰，以此类推；
p1[i].fPos表示标准谱图中排序后的第i个峰的峰位；
p2[j].fPos表示被测谱图中排序后的第j个峰的峰位；
pD表示峰位的绝对差。
根据一些实施例，所述第一匹配步骤具体可以包括：
当计算出的峰位的绝对差满足下述条件(1)时，确定被测谱图与标准谱图的峰信息匹配；当计算出的峰位的绝对差不满足下述条件(1)时，确定被测谱图与标准谱图的峰信息不匹配，
条件(1)为：pD＜p2[j].fWidth/3且pD＜p1[i].fWidth/3，
其中，p1[i].fWidth表示标准谱图中排序后的第i个峰的峰宽；p2[j].fWidth表示被测谱图中排序后的第j个峰的峰宽。
在一个实施例中，N为大于等于3且小于等于5的自然数。当N的取值较小，例如，小于3时，被比较的峰的数量过少，不利于筛选出与被测谱图的峰信息匹配的标准谱图，即，不利于识别的有效性；当N的取值过大时，会增加比较峰信息的计算量，从而可能会影响峰信息比较的计算速度。在N取值大于等于3且小于等于5的自然数的情况下，可以兼顾利用峰信息识别的有效性和计算速度。
进一步地，如图3所示，所述第一识别步骤还可以包括“筛选器”步骤，该 “筛选器”步骤具体可以包括：
峰匹配权重计算步骤：根据下述公式(2)建立惩罚性函数，以计算峰匹配权重；和
第二匹配步骤：当峰匹配权重大于等于预设的权重阈值时，确定被测谱图与标准谱图匹配；当峰匹配权重小于所述权重阈值时，确定被测谱图与标准谱图不匹配，
其中，公式(2)为：
h＝(1-2*|j-i|/10)*(0.5/(i+1))*exp(-pD*2/min(p1[i].fWidth，p2[j].fWidth))，
其中，h表示峰匹配权重；
“min(p1[i].fWidth，p2[j].fWidth)”表示取p1[i].fWidth和p2[j].fWidth中的较小值；
“exp”表示以自然对数e为底的幂函数。
在本发明的实施例中，在所述第一匹配步骤中确定被测谱图与标准谱图的峰信息匹配的情况下，执行所述峰匹配权重计算步骤和所述第二匹配步骤。也就是说，在本发明的实施例中，只有在第一匹配步骤中判断峰的绝对差满足要求时，才进行惩罚性函数的计算和峰匹配权重的比较。同样地，由于计算峰的绝对差的计算量比计算惩罚性函数的计算量小，所以，在计算惩罚性函数之前，通过计算峰的绝对差进行初步筛选，可以大大减少计算量，从而提高识别速度，并且提高识别准确率。
根据本发明的实施例，所述第二识别步骤可以包括：对被测谱图的数据和预存的标准谱图的数据进行相关性比较，即，如图3所示的“相关性计算”步骤。
在一个实施例中，所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤包括：
计算被测谱图的数据和预存的标准谱图的数据的相关系数，当计算出的相关系数大于等于预设的相关性阈值时，确定被测谱图与标准谱图匹配；当计算出的相关系数小于所述相关性阈值时，确定被测谱图与标准谱图不匹配。
具体地，相关系数是研究变量间线性相关程度的量，是一种衡量向量间相互关系的方法。例如，设有特征向量X(x1，x2，…，xn)，Y(y1，y2，…，yn)，二者的相关系数r可以定义如下：
Figure PCTCN2017112915-appb-000001
其中，
Figure PCTCN2017112915-appb-000002
分别表示向量X、Y的均值，i表示向量的第i个数据。
根据本发明的实施例，在被测谱图和标准谱图的所有峰的峰区的并集区间内，执行所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤。也就是说，并不是在谱图的全区间内，而仅在被测谱图和标准谱图的所有峰的峰区的并集区间内，执行所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤。此处的“被测谱图和标准谱图的所有峰的峰区的并集区间”表示由被测谱图和标准谱图的所有峰的峰区组成的区间。这样，可以进一步减少需要进行相关性比较的数据量，从而进一步提高运算速度，并且保证计算的准确性。
在本发明的实施例中，通过利用谱图的峰信息首先进行“峰”这个局部特征的比较，对被测谱图和标准谱图进行初步筛选，在初步筛选通过之后，才进行谱图数据的全局比较，不仅能够大大的缩短匹配识别时间，而且能够提高匹配识别的准确率。而且，在图3所示的“峰位匹配”和“筛选器”步骤中，如果识别出被测谱图与标准谱图不匹配，可以立即终止匹配过程，而无需进行后续的识别匹配过程，可以大大提高确定二者不匹配时的计算速度。试验证明，匹配识别时间缩短为原来的约5％，并且匹配识别的准确率提高了约10％。
根据本发明的又一实施例，还提供一种电子设备，图4是示出了该电子设备400的示例硬件布置的框图。电子设备400包括处理器406(例如，微处理器(μP)、数字信号处理器(DSP)等)。处理器406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400还可以包括用于从其他实体接收信号的输入单元402、以及用于向其他实体提供信号的输出单元404。输入单元402和输出单元404可以被布置为单一实体或者是分离的实体。
此外，电子设备400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408，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408包括计算机程序410，该计算机程序410包括代码/计算机可读指令，其在由电子设备400中的处理器406执行时使得电子设备400和可以执行例如上面结合上述实施例所描述的流程及其任何 变形。
计算机程序410可被配置为具有例如计算机程序模块410A～410C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上述实施例中所描述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406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4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406中执行时使得硬件布置400执行上面结合上述实施例所描述的动作，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。
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 (12)

  1. 一种用于识别拉曼谱图的方法，包括以下步骤：
    被测谱图获得步骤：对待测物质的拉曼光谱进行测量，以获得被测谱图，该被测谱图包括一系列数据；
    峰信息提取步骤：提取出被测谱图的峰信息，该峰信息包括峰强、峰位和峰区；
    第一识别步骤：比较被测谱图的峰信息和预存的标准谱图的峰信息，以识别被测谱图与标准谱图的峰信息是否匹配；和
    第二识别步骤：在第一识别步骤中识别出被测谱图与标准谱图的峰信息匹配的情况下，比较被测谱图的数据和预存的标准谱图的数据，以进一步识别被测谱图与标准谱图是否匹配。
  2. 根据权利要求1所述的方法，其中，所述第一识别步骤包括：
    排序步骤：按照峰强从大到小的顺序，对被测谱图的峰和标准谱图的峰分别进行排序，以选择出被测谱图和标准谱图的排序在前N名的峰；
    比较步骤：比较被测谱图和标准谱图的排序在前N名的峰的峰位信息；和
    第一匹配步骤：根据比较步骤得到的比较结果，确定被测谱图与标准谱图的峰信息是否匹配。
  3. 根据权利要求2所述的方法，其中，所述比较步骤包括：
    按照下述公式(1)依次计算被测谱图和标准谱图的排序在前N名的各个峰的峰位的绝对差：pD＝|p2[j].fPos-p1[i].fPos|         公式(1)，
    其中，N为预定的比较峰数，N为大于等于3的自然数；
    i，j分别表示标准谱图和被测谱图中的排序后的峰的序号，i和j均为大于等于0且小于等于N的整数；
    p1[i].fPos表示标准谱图中排序后的第i个峰的峰位；
    p2[j].fPos表示被测谱图中排序后的第j个峰的峰位；
    pD表示峰位的绝对差。
  4. 根据权利要求3所述的方法，其中，所述第一匹配步骤包括：
    当计算出的峰位的绝对差满足下述条件(1)时，确定被测谱图与标准谱图的峰信息匹配；当计算出的峰位的绝对差不满足下述条件(1)时，确定被测谱图与标准谱图的峰信息不匹配：
    pD＜p2[j].fWidth/3且pD＜p1[i].fWidth/3         条件(1)，
    其中，p1[i].fWidth表示标准谱图中排序后的第i个峰的峰宽；p2[j].fWidth表示被测谱图中排序后的第j个峰的峰宽。
  5. 根据权利要求2-4中任一项所述的方法，其中，所述第一识别步骤还包括：
    峰匹配权重计算步骤：根据下述公式(2)建立惩罚性函数，以计算峰匹配权重；和
    第二匹配步骤：当峰匹配权重大于等于预设的权重阈值时，确定被测谱图与标准谱图的峰信息匹配；当峰匹配权重小于所述权重阈值时，确定被测谱图与标准谱图的峰信息不匹配，
    h＝(1-2*[j-i|/10)*(0.5/(i+1))*exp(-pD*2/min(p1[i].fWidth，p2[j].fWidth))   公式(2)，
    其中：
    h表示峰匹配权重；
    i，j分别表示标准谱图和被测谱图中的排序后的峰的序号，i和j均为大于等于0且小于等于N的整数；
    N为预定的比较峰数，N为大于等于3的自然数；
    pD表示峰位的绝对差；
    p1[i].fWidth表示标准谱图中排序后的第i个峰的峰宽；
    p2[j].fWidth表示被测谱图中排序后的第j个峰的峰宽。
  6. 根据权利要求5所述的方法，其中，在所述第一匹配步骤中确定被测谱图与标准谱图的峰信息匹配的情况下，执行所述峰匹配权重计算步骤和所述第二匹配步骤。
  7. 根据权利要求2-6中任一项所述的方法，其中，N为大于等于3且小于等于5的自然数。
  8. 根据权利要求1-7中任一项所述的方法，其中，所述第二识别步骤包括：
    对被测谱图的数据和预存的标准谱图的数据进行相关性比较。
  9. 根据权利要求8所述的方法，其中，所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤包括：
    计算被测谱图的数据和预存的标准谱图的数据的相关系数，当计算出的相关系数大于等于预设的相关性阈值时，确定被测谱图与标准谱图匹配；当计算出的相关系数小于所述相关性阈值时，确定被测谱图与标准谱图不匹配。
  10. 根据权利要求8或9所述的方法，其中，在被测谱图和标准谱图的所有峰的峰区的并集区间内，执行所述对被测谱图的数据和预存的标准谱图的数据进行相关性比较的步骤。
  11. 根据权利要求1-10中任一项所述的方法，还包括如下步骤：
    归一化步骤：归一化所述被测谱图和所述标准谱图；
    其中，所述归一化步骤在所述峰信息提取步骤之前执行。
  12. 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1-11中任一项所述的方法。
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