WO2018121082A1 - Procédé d'analyse qualitative de type à auto-apprentissage basé sur un spectre raman - Google Patents

Procédé d'analyse qualitative de type à auto-apprentissage basé sur un spectre raman Download PDF

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WO2018121082A1
WO2018121082A1 PCT/CN2017/109712 CN2017109712W WO2018121082A1 WO 2018121082 A1 WO2018121082 A1 WO 2018121082A1 CN 2017109712 W CN2017109712 W CN 2017109712W WO 2018121082 A1 WO2018121082 A1 WO 2018121082A1
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substance
list
self
similarity
learning
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PCT/CN2017/109712
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Chinese (zh)
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赵自然
王红球
杨内
苟巍
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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
    • G01N21/658—Raman scattering enhancement Raman, e.g. surface plasmons
    • 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

Definitions

  • the present disclosure relates to the field of Raman spectroscopy, and in particular to a self-learning qualitative analysis method based on Raman spectroscopy.
  • Raman spectroscopy is a non-contact spectroscopy technique based on the Raman scattering effect of excitation light. It can qualitatively and quantitatively analyze the composition of a substance.
  • Raman spectroscopy is a molecular vibrational spectroscopy that reflects the fingerprint characteristics of a molecule, and the Raman spectrum of each substance is unique. The Raman spectrum obtained by comparison with the known Raman spectrum database of various substances is used to identify the composition of the substance to be tested, and thus can be used for detecting substances, and has been widely used for liquid security. , jewelry testing, explosives testing, drug testing, drug testing and other fields.
  • a conventional Raman spectroscopy detecting device generally performs a qualitative analysis based on a spectral database to perform a qualitative measurement, and finally displays a measurement result
  • the approximate workflow can be summarized as: collecting spectral data; preprocessing the acquired spectral image The pre-processed acquired spectra are compared with the spectral library; the qualitative analysis results are obtained; and the qualitative analysis results are displayed.
  • the Raman spectral similarity of the two species can be quantitatively represented, for example, by a "similarity” parameter, such as the similarity function commonly used to calculate similarity.
  • this conventional Raman spectroscopy method for qualitative analysis generally has a high false alarm rate and a false negative rate for substances of low purity, and is merely exhaustive and mechanically performed with the exhaustivity of the spectral database. Contrast until a consistent alignment result is obtained to complete the qualitative analysis, so that the analysis process takes a long time; and the sample with a small difference between the two components adopts a global simple repeated Raman spectral similarity alignment analysis, which is difficult to be similar Degree calculation result Therefore, the current conventional similarity calculation method and the similarity discrimination threshold also encounter certain difficulties.
  • the purpose of the present disclosure is to address at least one aspect of the above problems and deficiencies existing in the prior art.
  • the embodiments of the present disclosure provide a self-learning qualitative analysis method based on Raman spectroscopy, which combines self-learning and manual comparison to complete Raman spectroscopy, which can reduce false positives due to insufficient material purity in qualitative analysis. And the incidence of underreporting, improve the accuracy of qualitative analysis; shorten the analysis processing time; and shorten the system startup time.
  • an embodiment of the present disclosure provides a method for self-learning qualitative analysis based on Raman spectroscopy, comprising: a Raman spectroscopy acquisition step: collecting Raman of an item to be measured Spectral; feature extraction and comparison steps: extracting the Raman spectral data and comparing the spectral signature database in the spectral library to obtain a list of original identification substance IDs; similarity comparison step: obtaining each substance in the list of original identification substance IDs for Raman spectroscopy calculation The similarity of the IDs to generate a similarity list, and compared with the similarity threshold library in the spectral library; and the substance ID selection step: the similarity super similarity threshold obtained after comparing with the similarity threshold based on the self-learning library
  • the similarity identification substance ID list is subjected to verification detection, including false alarm detection and false negative detection.
  • the false alarm detection is performed. ; when there is no substance ID in the similarity list that exceeds the similarity threshold of the substance ID stored in the similarity threshold library Executive false negative test.
  • the false negative detection is additionally performed after the false alarm detection is performed.
  • any one of the false positive detection and the false negative detection is set to selectively perform three parallel material ID selection methods, including: statistical selection Method: statistically select all false positives or missing material IDs in the self-learning library; feature recognition method: for the false alarms or missing material IDs of the “self-learning type” in the self-learning library The selection of the feature recognition method; and the secondary recognition method: the secondary recognition mode is selected for the false alarm or the missing material ID of the "self-learning type” in the self-learning library.
  • any one of the false positive detection and the false negative detection is set to include a pre-processing step and a post-processing step, the pre-processing step comprising: by listing the identified substance IDs ID and self-learning library for all false positives or missing material IDs, false positives or missing material IDs for "self-learning types" in the self-learning library, and "for self-learning libraries”
  • the self-learning type "false identification or missing material ID” whose value is "secondary recognition” is respectively compared to generate the highest correct substance ID of the statistical selection method, the feature recognition method, and the secondary recognition method, respectively.
  • the post-processing step selectively performing the three substance IDs based on a comparison of the highest correct substance ID number of the statistical selection method, the feature identification method, and the secondary identification method with respective number thresholds Method of choosing.
  • the list of identified substance IDs in the pre-processing step of the false positive detection is selected as the similarity identifying substance ID list.
  • the list of identified substance IDs in the pre-processing step of the missing report detection is selected as the original identification substance ID list.
  • a threshold number of times of the highest correct substance ID number obtained for all false positive or missing material IDs in the self-learning library is set to be larger than "self-learning" for the self-learning library
  • the type "value” is a threshold value of the number of times the highest correct substance ID number obtained by the false positive or missing material ID of one of "feature recognition” and "secondary recognition”.
  • the generated at least two identification substance ID lists are equal, it is confirmed as the list of the identified substance IDs after the verification detection.
  • the intersection is confirmed as the list of the identified substance IDs after the verification detection.
  • the substance ID selection step is performed again for a portion other than the intersection in the generated at least two identification substance ID lists.
  • the substance ID selection step performed again includes enhanced detection by using an additive-to-measurement article and an enhancer to obtain an enhanced Raman spectrum.
  • the post-processing step of the false positive detection is performed only when the number of statistical false positives is greater than the false positive number threshold.
  • the method further includes adding the obtained false positive substance ID list and the missing material ID list to the self-learning library according to the “self-learning type” field after performing the qualitative analysis on the item to be measured.
  • the method prior to performing qualitative analysis on the item to be measured, the method further includes creating a self-learning library using one of an initial self-learning library that performs initial learning and input presets on the self-learning library.
  • the method further comprises selectively identifying the substance using a manual comparison method.
  • an embodiment of the present disclosure further provides an electronic device, including: a memory for storing executable instructions; and a processor for executing executable instructions stored in the memory to perform as before Said method.
  • FIG. 1 shows a schematic diagram of a basic process according to an embodiment of the present disclosure, the illustrated components being two phases of a learning phase and an actual detection phase;
  • FIG. 2 is a schematic diagram showing the overall flow of an actual detection stage according to an embodiment of the present disclosure as shown in FIG. 1;
  • 3(a) and 3(b) respectively show schematic diagrams of Raman spectra before and after the pretreatment step in the overall flow of the actual detection phase shown in FIG. 2;
  • FIG. 4(a) shows an exemplary similarity list obtained in step S31 in the overall flow diagram shown in FIG. 2;
  • FIG. 4(b) shows step S32 in the overall flow diagram shown in FIG.
  • FIG. 4(c) shows an exemplary excess generated after threshold comparison in step S32 in the overall flow diagram shown in FIG. 2.
  • FIG. 4(d) shows schematic content of an exemplary self-learning library generated by step S10 in the overall flow diagram shown in FIG. 2;
  • FIG. 5 shows a basic schematic flow chart of false alarm detection in the actual detection phase as shown in FIG. 2;
  • Figure 6 is a schematic flow chart showing an extension of the "three method election" implementation of false alarm detection in the actual detection phase as shown in Figure 2;
  • Figure 7 is a schematic flow diagram of an extended exemplary embodiment of false alarm detection as shown in Figure 6;
  • Figure 8 is a schematic flow chart of another extended exemplary embodiment of false alarm detection as shown in Figure 6;
  • Figure 9 is a sub-flow diagram of re-false alarm detection performed using enhanced Raman spectroscopy in another extended exemplary embodiment of false alarm detection as shown in Figure 8, showing a re-error as shown in Figure 8.
  • Figure 10 shows a basic schematic flow chart of the false negative detection in the actual detection phase as shown in Figure 2;
  • Figure 11 is a schematic flow chart showing an extension of the "three method election" implementation of the false negative detection in the actual detection phase as shown in Figure 2;
  • Figure 12 is a schematic flow chart of an extended exemplary embodiment of the false negative detection shown in Figure 11;
  • Figure 13 is a schematic flow chart of another extended exemplary embodiment of the false negative detection shown in Figure 11;
  • Figure 14 is a sub-flow diagram of re-missing detection performed using enhanced Raman spectroscopy in another extended exemplary embodiment of the false negative detection shown in Figure 13, showing the re-leakage as shown in Figure 13 An exemplary decomposition step of reporting detection;
  • Figure 15 shows an operational schematic of the method of the embodiment of Figure 1 in accordance with the present disclosure
  • FIG. 16 shows still another flow diagram according to an embodiment of the present disclosure, which is illustrated as being divided into Two phases, the learning phase and the actual testing phase, which show the possible detection methods for simultaneous false positives and false negatives;
  • FIG. 17 is a block diagram showing an example hardware arrangement of an electronic device in accordance with still another embodiment of the present invention.
  • a self-learning qualitative analysis method based on Raman spectroscopy comprising: a Raman spectroscopy acquisition step: acquiring a Raman spectrum of an item to be measured; a feature extraction and comparison step: extracting Raman spectroscopy data Comparing with the spectral feature library in the spectral library, obtaining a list of original identification substance IDs; similarity comparison step: obtaining the similarity degree of each substance ID in the original identification substance ID list for the Raman spectrum calculation to generate a similarity list, and the spectrum Comparing the similarity threshold library in the gallery; and the substance ID selection step: verifying, based on the self-learning library, the similarity identification substance ID list obtained by comparing the similarity super-similarity threshold with the similarity threshold, including the error Report detection and false negative detection.
  • FIG. 1 shows a schematic diagram of a basic process according to an embodiment of the present disclosure. Two stages of the learning phase and the actual testing phase.
  • the main purpose is to establish a Raman spectral self-learning library for the samples used for actual testing.
  • the self-learning library is used, and the actual sample to be tested is detected by combining artificial contrast Raman spectroscopy to obtain the result of the qualitative analysis.
  • the above learning phase can also be equivalently considered as a pre-set or calibration phase of the self-learning library, for example typically comprising the steps of measuring the Raman spectrum of the learning sample, such as by extracting its spectral features and comparing it to the spectral feature library; For example, by comparing the spectral features, the similarity list is obtained and compared with the similarity threshold library; whether there is a substance exceeding the threshold exists, and based on the judgment result, (1) if there is more than the listed in the comparison with the threshold library
  • the substance ID of the similarity threshold performs false positive detection (ie, whether there is a substance that is detected by the current similarity threshold exceeding the similarity threshold and is not substantially included in the current learning sample due to a false alarm), the error
  • the report detection selects the false positive substance ID, for example, by comparing with the false positive substance ID or name in the existing self-learning library, and further selectively adopting different self-learning type methods, and (2) if there is no more than The material ID of the similarity threshold is listed
  • the missing detection is compared, for example, by comparison with a missing substance ID or name in an existing self-learning library, and further selectively using different self-learning types
  • the method selects the missing material ID; then optionally determines whether to perform manual comparison and selectively performs manual comparison based on the judgment result; finally, the substance ID such as the correct identification and the correction identification type (ie, false alarm, false negative)
  • the information is entered into the self-learning library as part of the initial preset value of the self-learning library.
  • the above process can be performed separately for one or more learning samples until the Raman spectra of the new learning samples that are no longer needed require acquisition and qualitative detection.
  • the actual detection phase described above can also be equivalently considered as a stage for qualitative analysis of a test sample based on a generated self-learning library, for example typically comprising the steps of measuring the Raman spectrum of the sample to be measured, such as by extracting its spectral characteristics. And comparing with the spectral feature library; and obtaining the similarity list based on the comparison of the spectral features and comparing with the similarity threshold library; determining whether there is a substance exceeding the threshold, and based on the judgment result, (1) if If there is a substance ID that exceeds the similarity threshold listed in the threshold database, then a false positive detection is performed (ie, it is determined whether there is a false positive in the currently detected substance exceeding the similarity threshold.
  • the false positive detection is compared, for example, by comparison with a false positive substance ID or name in an existing self-learning library, and further selectively adopting different self-learning types.
  • the method selects the missing material ID; then optionally determines whether to perform manual comparison and selectively performs manual comparison based on the determination result; finally displays the identification result of the qualitative analysis; and then the substance ID such as the correct identification and the correction identification type thereof
  • the information ie, false positives, false negatives
  • the conventional Raman spectroscopy method For conventional Raman spectroscopy methods, if only the measured samples are directly tested and judged based on the original Raman spectroscopy data, in some cases the accuracy of the detection is difficult to guarantee for certain samples such as samples of insufficient purity; And if only the artificial comparison method is used, usually based on the experience of the tester, objective and accurate test results cannot be obtained; and the conventional Raman spectroscopy detection method generates at most the initial calibration sample database for direct comparison, and there is no self. Learning ability to adapt to lack of flexibility when performing qualitative analysis, for example, on substances in mixtures of different components. Moreover, the conventional Raman spectroscopy method generally has a problem that the analysis processing time is long.
  • the self-learning qualitative analysis method based on Raman spectroscopy utilizes a combination of self-learning and manual comparison.
  • the measured sample is tested.
  • the self-learning library is continuously supplemented and perfected, such as learning by using learning samples in a pre-staged learning phase, and the results of qualitative analysis of different samples of the substance to be tested in actual use. Learning to improve the accuracy and efficiency of the recognition results, so that the detection efficiency and detection accuracy of qualitative analysis based on Raman spectroscopy can be optimally optimized, especially in the case of insufficient material purity, which cannot be used by conventional Raman detection methods. In case of direct identification.
  • the learning sample may, for example, be selected such that the characteristic peaks in the generated spectrum are clear, the peak position is uniform, A sample of a substance that interferes with small substances. Moreover, it is desirable that the learning samples are selected to have a more uniform peak interval and a certain interval to facilitate more accurate pre-learning.
  • the learning sample is, for example, a liquid or solid sample. And, for example, considering that the sample to be actually tested is a mixture of a plurality of substances, the learning sample is selected, for example, as a mixture of a plurality of components whose single component purity is not absolutely superior to be adapted for comparison in a later measurement. Claim.
  • the Raman spectrum of the learning sample has, for example, at least four characteristic peaks.
  • the greater number of characteristic peaks is beneficial to the accuracy of the initial learning to improve the accuracy of subsequent qualitative detection operations based on the self-learning library.
  • this is not essential, and the learning sample can also have, for example, two or three characteristic peaks.
  • an initial self-learning library may be established using representative learning sample items; on the other hand, the above learning phase is not necessary.
  • the operator can perform a qualitative analysis of the sample material to be measured using a self-learning library that is input in advance rather than a newly generated self-learning library.
  • the above-mentioned pre-self-learning phase does not have to be performed for a long time before the actual detection, for example, instead of self-learning while detecting the measured sample substance at the inspection site, the newly added test sample is accumulated during use. Add substances to the self-learning library.
  • the overall spectral library in the usual Raman detection is subdivided into a plurality of sub-libraries: a spectral feature library, such as by Some basic features such as peak number, peak position, and peak intensity of the graph are extracted to generate the spectral feature library for use in algorithm comparison and identification, and are loaded at software startup; (similarity) threshold library, including recognition spectrum The similarity threshold, material ID, library number and other information are used for display processing and loaded at software startup; the substance name library includes material ID, name, alias and other information for use in software display processing.
  • a spectral feature library such as by Some basic features such as peak number, peak position, and peak intensity of the graph are extracted to generate the spectral feature library for use in algorithm comparison and identification, and are loaded at software startup
  • (similarity) threshold library including recognition spectrum The similarity threshold, material ID, library number and other information are used for display processing and loaded at software startup
  • the substance name library includes material ID, name, alias and other information for use in software display processing.
  • the sub-libraries of the respective subdivisions are respectively loaded for comparison at the respective detection steps, and it is not necessary to always load the complete spectral library as a whole or multiple times, thereby shortening the response time of each step and improving the response time. Detection speed.
  • the actual detection phase includes, for example:
  • Step S0 start;
  • Step S1 generating a Raman spectrum to be detected and extracting Raman spectrum data
  • Step S2 comparing the extracted Raman spectral data with a spectral feature library
  • Step S3 using a similarity calculation and a similarity threshold comparison to generate a preliminary determined substance list
  • Step S4 Determine whether there is a substance exceeding the threshold?
  • Step S5 further performing false alarm detection for the case where it is determined that there is a substance exceeding the threshold;
  • Step S6 further performing false negative detection for the case where it is determined that there is no substance exceeding the threshold
  • Step S7 generating a list of substances confirmed by a false positive (or missing report) test
  • Step S8 Manually comparing the detection of Raman spectroscopy
  • Step S9 generating a list of substances for final detection confirmation, and finding a substance name from the substance library;
  • Step S10 all the test results of the current time are written into the self-learning library
  • step S11 displaying the detection result of the qualitative analysis, and the current detection process is terminated.
  • step S1 specifically includes:
  • Step S11 collecting a Raman spectrum, which can be obtained, for example, by a known process such as beam emission, collection, and splitting;
  • Step S12 pre-processing the collected Raman spectrum to obtain a raw Raman spectrum to be tested
  • Step S13 extracting spectral data from the original Raman spectrum to be tested.
  • the measured raw spectral data needs to be preprocessed as shown in step S12 above to facilitate the extraction of subsequent valid information.
  • the pre-processed spectral pre-processing of the above step S12 generally includes interpolation, de-noising, baseline correction, normalization processing, etc., in particular, the main purpose is to perform smooth denoising processing on the input spectrogram signal. Spectral signals before and after pre-processing are shown in Figures 3(a) and 3(b), respectively.
  • the collected original spectrum generally needs to be pre-processed, and for brevity, it will not be described below.
  • step S3 includes, for example, specifically:
  • Step S31 calculating a list of acquired similarities
  • Step S32 The similarity list is compared with the similarity threshold library, and a substance list exceeding the threshold is acquired.
  • FIG. 4(a) shows an exemplary similarity list obtained in step S31 in the overall flow diagram shown in FIG. 2;
  • FIG. 4(b) shows FIG. An exemplary threshold library for threshold comparison included in the Raman spectral spectrum library in step S32 is shown in the overall flow diagram;
  • FIG. 4(c) shows step S32 in the overall flow diagram shown in FIG. An exemplary over-threshold substance list generated after threshold comparison;
  • FIG. 4(d) shows schematic content of an exemplary self-learning library generated in step S10 in the overall flow diagram shown in FIG. 2.
  • the qualitative analysis of the Raman spectrum of the sample to be measured is still based on the typical idea of Raman spectroscopy, that is, the comparison with the reference Raman spectrum, that is, the measured Raman spectrum and the reference pull of the sample to be measured Whether the error of the spectroscopy is within a predetermined range, for example, by calculating the similarity between the two.
  • the calculation of the similarity in the above step S31 is, for example, a plurality of methods, for example, calculating the similarity based on the Euclidean distance algorithm as an industry standard algorithm for spectral search; more specifically, as an example, assuming The reference Raman spectrum curve of the sample that has been studied is A(x), and the measured Raman spectrum curve of the sample to be measured is B(x).
  • the maximum likelihood algorithm is used, based on the Euclidean distance algorithm.
  • the similarity between the two can be calculated by equation (1):
  • Corr represents the similarity between the reference Raman spectrum of the sample that has been studied and the measured Raman spectrum of the sample to be measured, and " ⁇ " indicates the dot product operation.
  • the similarity is calculated in an algorithm similar to that described above, but the average of the spectra is subtracted prior to execution of the algorithm.
  • A(x) and B(x) may be sampled separately to obtain n sampling points, respectively denoted as A 1 , A 2 , . . . , A n and B 1 , B 2 , . . . , B n .
  • the similarity of the learned reference Raman spectrum and the measured Raman spectrum of the sample to be measured Corr can be calculated according to formula (2):
  • ⁇ also represents a dot product operation.
  • A(x) and B(x) may also be sampled separately to obtain n sample points, denoted as A 1 , A 2 , . . . , A n and B 1 , B 2 , respectively. ..., B n , the similarity of the learned reference Raman spectrum and the measured Raman spectrum of the sample to be measured Corr can be calculated according to formula (3):
  • the above similarity calculation may be performed for the entire Raman spectrum, or may be performed only for the portion having the characteristic portion in the Raman spectrum. The closer the similarity value is to 1, the higher the degree of similarity.
  • the threshold of the similarity may be set to 0.9, 0.8, and the like.
  • the similarity threshold is given, for example, by more actual detection sensitivity, accuracy of the detection instrument, and the like.
  • the term "characteristic portion" refers to a key portion of a Raman spectrum curve of a sample to be tested that differs from other samples in a Raman spectrum curve.
  • the feature portion may be one or more feature peaks, feature valleys, phase inflection points, and the like.
  • the above similarity may be weighted based on the peak position, the peak width, and/or the peak height of the characteristic peak.
  • the feature peaks may also be searched and sorted prior to calculating the similarity.
  • the Raman spectrum of each substance is a reflection of the molecular structure of the substance, it has unique structural and mode characteristics.
  • a Raman spectral spectrum can be expressed as a pattern vector in the pattern space, and the analysis of the similarity between the N maps is transformed into the computational pattern space. The similarity of N pattern vectors.
  • the similarity calculation such as the angle cosine method or the Jakedian similarity coefficient method based on the Jachard distance is used, so that the method for calculating the HQI value is simple and fast, and the calculated value is also based on the above-mentioned Euclidean
  • the similarity calculation of the distance algorithm similarly has a fixed interval range between 0 and 1, which is easy to measure. Further, an adjusted cosine similarity algorithm can also be selectively employed.
  • determining whether the error of the Raman spectrum and the reference Raman spectrum of the sample to be measured is within a predetermined range or directly passing peak intensity detection (amplitude detection) and peak position detection (Phase detection or inflection detection) to extract the information of the characteristic peaks, thereby directly comparing the measured Raman spectrum with the information of the characteristic peaks in the reference Raman spectrum.
  • peak intensity detection amplitude detection
  • peak position detection Phase detection or inflection detection
  • the Raman spectrum is biased due to the difference in sample uniformity, instrument noise, fluorescence background, etc., and in the spectral processing process, denoising, baseline correction, etc. will also produce errors.
  • the accuracy of the substance recognition using only the similarity in the recognition process is not high. Therefore, in the embodiment of the present disclosure, the object to be inspected is further qualitatively analyzed, for example, by introducing a combination of the self-learning recognition method and the manual contrast recognition method.
  • Fig. 5 shows a basic schematic flow chart of the false alarm detecting step S5 in the actual detecting phase as shown in Fig. 2.
  • the false alarm detection step S5 is further performed, which is performed.
  • the report detecting step S5 includes two stages: a false positive check pre-processing step S50, S50' and S50"; and a false positive detection post-processing step S51.
  • the false positive detection pre-processing steps S50, S50', and S50" are three logically parallel sub-flows, respectively corresponding to subsequent post-processing steps.
  • S50 corresponds to the first substance ID selection method, that is, the statistical method is used to verify one by one, which is also called “statistical selection” method
  • S50′ corresponds to the second substance ID selection method, that is, the preset “features are called” Corresponding algorithm for identifying the interface to select the verified substance ID, also referred to as the "feature recognition” method
  • S50" corresponds to the third substance ID selection method, that is, calling the corresponding algorithm of the preset "secondary identification interface”
  • the verified substance ID is also referred to as a "secondary recognition” method.
  • S50 is also referred to as a pre-processing step of "statistical selection”
  • S50' is also called For the pre-processing step of "feature recognition”
  • S50 is also referred to as the pre-processing step of "secondary recognition”.
  • the above-described three pre-processing steps S50, S50' and S50" are logically parallel to mean performing independently of each other, for example, substantially simultaneously, or sequentially, or temporally independent of each other.
  • the pre-reporting pre-processing step that is, the pre-processing step S50 of "statistical selection", the pre-processing step S50' of "feature recognition”, and the pre-processing step S50 of "secondary recognition" "For example:
  • Step S500, S500', S500" The false positive check subroutine starts.
  • Step S501, S501', S501" the substance IDs in the list of identification substance IDs (hereinafter referred to as "threshold identification list") of the similarity super-threshold acquired after the threshold comparison are sequentially and (in the corresponding/or corresponding) in the self-learning library A single) "false positive substance ID” field is compared.
  • step S501 is to sequentially compare the IDs in the threshold-valued threshold identification list with the "false positive substance ID” field in the entire self-learning library; step S501 'Comparing the IDs in the threshold-identified threshold identification list with the "false positive substance ID” field in the case where the "self-learning type” field in the self-learning library takes the value of "feature identification”; and the steps S501" is to compare the IDs in the threshold-identified threshold identification list with the "false positive substance ID” field in the case where the "self-learning type” field in the self-learning library takes the value of "secondary recognition";
  • Steps S502, S502', S502" determine whether the same ID is matched (ie, is it recognized that the false positive substance ID exists?).
  • Steps S503, S503', S503" If the same substance ID is matched, it is equivalent to finding a false positive substance ID, and the false alarm count counter is incremented by one.
  • Steps S504, S504', S504" If the same substance ID is not matched, the current substance ID is not a false alarm but is actually considered to exist, and the correct substance ID number is counted. Add 1 to the device.
  • Steps S505, S505', S505" determining whether the comparison of the identification substance ID list is completed. If the comparison is not completed, the process proceeds to step S501, S501', S501" is executed cyclically; if the comparison is completed, the process proceeds to the next step S506, S506', S506. ".
  • Step S506, S506', S506" determining whether the number of false positives is greater than 10. If the number of false positives is less than or equal to 10, it is considered that the number of false positives is insufficient to ensure the smooth progress of the self-learning detection, thereby jumping to manual contrast recognition; If the number is greater than 10, the assignment step of the "Maximum correct substance ID times" field is entered.
  • the number of false positives is set to 10 is an empirical value.
  • the number of false positives exceeds the value, it is determined that the number of false positives generated is sufficient to generate a sufficiently large set of substance IDs to be verified.
  • Subsequent post-processing step S51 performs material ID selection.
  • Steps S507, S507', S507” assigning respective current "correct substance ID times counters" to the corresponding "highest correct substance ID times” field MaxRightIDNum(n), respectively, as a post-processing step S51 to determine whether or not to perform subsequent correspondence.
  • the criterion for the nth substance ID selection method is the criterion for the nth substance ID selection method.
  • the post-false alarm detection post-processing step S51 includes, for example:
  • S511 It is judged that for the above three component flows S50, S50' and S50", the comparison formula "field MaxRightIDNum(n)> corresponding threshold THR(n)? Whether it is established for at least two groups. This judgment is a criterion for dividing whether the highest correct substance ID number is sufficient to ensure the execution of the corresponding substance ID selection method, and if satisfied, at least two substance ID selection methods are available for acquiring at least two The group material ID list is used to jointly verify the existence of the substance ID that can be identified in a program-controlled manner.
  • S514 The same at least two substance lists are used as a list of identification substances that are respectively recognized and jointly confirmed by the corresponding at least two substance ID selection methods.
  • the respective thresholds THR(n) of the field MaxRightIDNum(n) are respectively set, for example, as thresholds for the "statistical selection” method, the "feature recognition” method, and the “secondary recognition” method, respectively.
  • the “feature recognition” method is a dimensionality reduction method used in pattern recognition to remove uncorrelated or redundant features from the original feature set
  • the “secondary recognition” method is used to estimate the mean and covariance matrix, for example, after feature extraction.
  • the classifier is trained to be classified and identified, so that the two can achieve the purpose of reducing the number of features, improving the detection accuracy, and reducing the running time; and the "statistical selection” method is inconsistently comparing and confirming one by one, thereby “statistics”
  • the reliability of the selection method is smaller than the "feature recognition” method or the "secondary recognition” method using pattern recognition, and accordingly, the first threshold THR(1) is set to be compared to the second threshold THR (2).
  • the third threshold THR(3) is larger.
  • the "feature recognition" method is a dimensionality reduction method in pattern recognition for rejecting irrelevant or redundant features from the original feature set, for example in
  • the embodiment of the present disclosure is implemented by calling a plurality of feature recognition interfaces preset in the “feature identification interface” field of the self-learning library, and may be selected as at least one of the following:
  • Filter/Filter which characterizes the importance of each feature by selecting an indicator, and then sorts the features based on the index values of the features, such as by setting thresholds and removing them Feature selection is not performed by the characteristics of the threshold, or by setting the number of features to be selected and selecting the top N or sorting to a certain percentage of the top.
  • the weights represent the importance of the dimension features and are then sorted by weight.
  • the usual filtering method uses the characteristics of the training set to screen out the feature subsets. Generally, the independence of the features or the relationship with the dependent variables, such as chi-square test, information gain, correlation coefficient, etc., are considered.
  • the parcel/encapsulation method essentially considers the selection of feature subsets as a search optimization problem, and generates different combinations (feature subsets) by packaging, and evaluates the combinations and compares them with other combinations, for example.
  • the accuracy of the classification is used as a measure of how good or bad the feature subset is. Therefore, the selection of subsets is regarded as an optimization problem, for example, it can be solved by many optimization algorithms, especially heuristic optimization algorithms, such as genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, artificial bee colony algorithm and so on. Parcel/encapsulation methods such as recursive feature elimination algorithms.
  • Embedded It uses some machine learning algorithms and models to train, obtains the weight coefficients of each feature, and then selects features according to the weight coefficients from large to small. Similar to the Filter method, but through training to determine the pros and cons of the feature, that is, to learn the best attributes to improve the accuracy of the model in the case of the model. Specifically, in the process of establishing the model, it is important to select the characteristics that are important for the training of the model (for example, the greatest contribution to improving the accuracy). The most common Embedded methods are the regularization methods.
  • the "secondary recognition" method is implemented, for example, by calling a plurality of secondary recognition interfaces preset in a "feature recognition interface” field of the self-learning library, and For example, it is constructed in such a manner as to use a quadratic discriminant equation QDF classifier commonly used in pattern recognition, an MQDF improved quadratic discriminant equation classifier, etc., and the classifier is trained by estimating the mean and covariance matrix, and the covariance matrix reflects the feature. The spread between the two, the greater the covariance, the more information is included, the more accurate the final classification.
  • the obtained statistical selection substance list ID1 is selected statistically from the "false positive substance ID" field of the entire self-learning library; if the field MaxRightIDNum(2)>5
  • the feature recognition interface is called to obtain the feature recognition substance.
  • List ID2 if the field MaxRightIDNum(3)>6 is established, the secondary recognition interface is called to obtain the secondary identification substance list ID3.
  • the substance identification verification is performed independently by using at least two sets of substance ID selection methods, and then the confirmed substance ID list is compared. Once the same, it means that based on the similarity judgment, further A list of identified substance IDs is co-confirmed using at least two independent methods, thereby obtaining a more accurate self-learning substance identification ID than conventional Raman spectroscopy based only on similarity judgment and manually performed Raman spectroscopy. List.
  • the jump to S7 generates a substance list confirmed by the false positive check.
  • Fig. 10 shows a basic schematic flow chart of the false negative detection in the actual detection phase as shown in Fig. 2.
  • the missing report detection step 71 is further performed.
  • the missing report detecting step S6 includes two stages: a missing pre-test pre-processing step S60, S60' and S60"; and a missing-post detection post-processing step S61.
  • the pre-missing detection pre-processing steps S60, S60', and S60" are three logically parallel sub-flows, respectively corresponding to subsequent post-processing steps.
  • the nth (n 1, 2, 3) substance ID selection method to be used in S61: S60 corresponds to the first type, that is, the aforementioned "statistical selection” method; and S60' corresponds to the second type, that is, the aforementioned "feature recognition" The method; and S60” corresponds to the third, ie, the aforementioned "secondary recognition" method.
  • S60 is also referred to as a pre-processing step of "statistical selection”
  • S60' is also referred to as a pre-processing step of "feature recognition”
  • S60" is also referred to as " Pre-processing steps of secondary recognition.
  • the above three pre-processing steps S60, S60' and S60" are logically parallel to mean that they are executed independently of each other, for example, substantially simultaneously, or sequentially, or temporally independent of each other in time. Execution.
  • the pre-reporting pre-processing step that is, the pre-processing step S60 of "statistical selection", "feature recognition”
  • the pre-processing step S60' and the "secondary recognition” pre-processing step S60" include, for example:
  • Steps S600, S600', S600" The underreporting test subroutine begins.
  • Steps S601, S601', S601" The substance IDs in the original identification substance ID list are sequentially compared with the (whole/or corresponding single) "missing substance ID" field in the self-learning library.
  • step S601 is to sequentially compare the IDs in the original identification substance ID list with the "false negative substance ID” field in the entire self-learning library;
  • step S601' is The IDs in the original identification substance ID list are sequentially compared with the "false negative substance ID” field in the case where the "self-learning type” field in the self-learning library takes the value of "feature recognition”;
  • step S601" is the original identification substance The IDs in the ID list are sequentially compared with the "Reporting Substance ID” field in the case where the "Self-learning Type” field in the self-learning library takes the value of "Secondary Recognition";
  • Step S602, S602', S602" It is judged whether or not the same ID is matched (ie, is it recognized that the missing substance ID exists?).
  • Steps S603, S603', S603" If the same substance ID is matched, it is equivalent to finding the missing substance ID once, and the counter of the correct substance ID number (here, equivalent to the number of missing substance IDs) is incremented by one.
  • Steps S604, S604', S604" determining whether the comparison of the identification substance ID list is completed. If the comparison is not completed, the process proceeds to step S601, S601', S601" is cyclically executed; if the comparison is completed, the process proceeds to the next step S605, S605', S605. ".
  • Steps S605, S605', S605" assigning respective current "correct substance ID times counters" to the corresponding "highest correct substance ID times” field MaxRightIDNum(n), respectively, as a post-processing step S61 to determine whether or not to perform subsequent correspondence.
  • the criterion for the nth substance ID selection method is the criterion for the nth substance ID selection method.
  • the missing report detection post-processing step S61 includes, for example:
  • S611 It is judged that for the above three component flows S60, S60' and S60", the comparison formula "field MaxRightIDNum(n)> corresponding threshold value THR(n)'? Whether it is established for at least two groups. This judgment is a criterion for dividing whether the highest correct substance ID number is sufficient to ensure the execution of the corresponding substance ID selection method, and if satisfied, at least two substance ID selection methods are available for acquiring at least two Group material ID list to jointly verify substances that can be identified by program control The existence of the ID.
  • S614 The same at least two substance lists are used as a list of identification substances that are respectively recognized and jointly confirmed by the corresponding at least two substance ID selection methods.
  • the selection and setting of the corresponding threshold THR(n)' of the field MaxRightIDNum(n) is the same as or similar to the false positive detection.
  • the first threshold THR(1)' is set to be larger than the second threshold THR(2) and the third threshold THR(3).
  • the "feature recognition" method and the "secondary recognition” method are also the same or similar, and are respectively executed by calling a plurality of different "feature recognition interfaces" and a plurality of "secondary recognition interfaces".
  • the obtained statistical selection substance list ID1' is selected statistically from the "missing substance ID" field of the entire self-learning library; if the field MaxRightIDNum (2)
  • the feature recognition interface is called to obtain the feature recognition substance list ID2'; if the field MaxRightIDNum(3)'>6 is established, the secondary recognition interface is called to obtain the secondary identification substance list ID3'.
  • step S614 at least two sets of substance ID selection methods are used to independently perform substance identification verification, and then the confirmed substance ID list is compared. Once the same, it means that based on the similarity judgment, further Use at least two separate methods The list of identification substance IDs is collectively confirmed, thereby obtaining a more accurate list of self-learning substance identification IDs than conventional Raman spectroscopy tests based on similarity judgment and manually performed Raman spectroscopy.
  • the jump to S7 generates a substance list confirmed by the false negative check.
  • FIG. 15 shows a schematic diagram of an operation for detecting a Raman spectrum of a test sample using a method according to an embodiment of the present disclosure.
  • the main processes in this example include:
  • Figure 6 shows a schematic flow chart of an extension of the "three method elections" implementation of false positive detection in the actual detection phase as shown in Figure 2.
  • the difference between the false positive detection flow S5 in the example of FIG. 6 and the false positive detection flow S5 in the example of FIG. 5 mainly lies in, as shown in FIG. 6, for example, based on "at least two (by various substance IDs)
  • the false positive detection post-processing step S51 additionally includes an optional step S515, that is, a further "three-method election" based on "intersection". For the sake of brevity, the remaining sub-steps will not be described again.
  • FIG. 7 is a schematic flow chart of a substantially extended exemplary embodiment of false alarm detection as shown in FIG. 6.
  • the difference between the false positive detection flow S5 in the example of FIG. 7 and the false positive detection flow S5 in the example of FIG. 15 is mainly that, as shown in FIG. 7, for example, the optional step of the post-false positive detection processing step S51 S515 specifically includes:
  • Step S5150 It is judged that there is an intersection of at least two of the generated substance lists ID1, ID2, and ID3. If yes, proceed to step S5150, that is, there is an overlap portion of the list of substance IDs respectively selected by using at least two independent methods, and the overlapping portion can be used to generate a list of commonly recognized identification substance IDs; otherwise, jump Go to manual contrast recognition.
  • Step S5151 In the case where step S5150 is established, the intersection is assigned to the first identification list.
  • the first identification list is directly used as a list of substances confirmed after the false positive check in the subsequent step S7.
  • the extended flowchart of the false positive detection S5 shown in FIG. 7 further utilizes at least two independent methods after jointly identifying the identification substance ID list based on the similarity recognition and the determination of the same result using at least two independent methods.
  • Figure 8 is a schematic flow diagram of another further expanded exemplary embodiment of false alarm detection as shown in Figure 6.
  • the difference between the false positive detection flow S5 in the example of FIG. 8 and the false positive detection flow S5 in the example of FIG. 7 is mainly that, as shown in FIG. 8, for example, the optional step of the false positive detection post-processing step S51 S515 additionally includes a list of substance IDs selected for each of at least two independent methods, in addition to confirming the intersection portion, further verifying the non-intersection portion.
  • the optional step S515 of the post-false positive detection processing step S51 additionally includes:
  • S5152 Subtract the intersection of the at least two substance lists ID1, ID2, and ID3 to obtain a list of substances to be rechecked.
  • step S5154 It is judged whether there is a newly confirmed substance list generation after re-incrementing the false alarm detection. If yes, proceed to step S5155, otherwise, go to step S5156.
  • FIG. 9 is a sub-flow diagram of re-false alarm detection S5153 performed using enhanced Raman spectroscopy in another extended exemplary embodiment of false alarm detection as shown in FIG.
  • the re-false alarm detects an exemplary decomposition step of S5153.
  • the re-false alarm detection S5153 includes, for example:
  • S51531 Acquire enhanced Raman spectroscopy by mixing the sample to be tested and the enhancer.
  • S51532 Perform false alarm detection. Specifically, for example, based on enhanced Raman spectroscopy, embedded The sleeve utilizes the aforementioned step S5.
  • S51533 (for example, human confirmation) to determine whether to jump to manual comparison.
  • S51535 Generate a list of substances that are confirmed to be present by re-execution of false alarm detection using enhanced Raman spectroscopy.
  • the mixture of the sample to be tested and the enhancer may be directly mixed by the sample to be tested and the enhancer or by the sample to be tested.
  • the aqueous solution or organic solution is mixed with the reinforcing agent.
  • the mixture of the measured substance sample and the enhancer is formed by directly mixing the sample of the measured substance with the enhancer or by mixing an aqueous solution of the sample of the test substance or an organic solution with the enhancer.
  • the enhancer may comprise any one of metal nanoparticle materials, metal nanowires, metal nanoclusters, carbon nanotubes, and carbon nanoparticles, or a combination thereof.
  • the enhancer may comprise a metal nanomaterial, or may also contain a chloride nanoparticle, a bromide ion, a sodium ion, a potassium ion, or a sulfate ion.
  • the metal may include, for example, any one of gold, silver, copper, magnesium, aluminum, iron, cobalt, nickel, palladium, or platinum, or a combination thereof.
  • Figure 11 shows a schematic flow chart of an extension of the "three method elections" implementation of false negative detection in the actual detection phase as shown in Figure 2.
  • the difference between the missing report detection flow S6 in the example of FIG. 11 and the missing report detection flow S6 in the preferred embodiment of FIG. 10 is mainly as shown in FIG. 11, for example, based on "at least two (by various After the substance ID selection method separately identifies the identified substance ID list, the missing report detection processing step S61 additionally includes an optional step S615, that is, a further "three method election" based on "intersection". For the sake of brevity, the remaining sub-steps will not be described again.
  • FIG. 12 is a schematic flow chart of a substantially expanded exemplary embodiment of the false negative detection shown in FIG.
  • the optional step S615 of the post-report detection post-processing step S61 specifically includes:
  • Step S6150 It is judged that there is an intersection of at least two of the generated substance lists ID1', ID2', ID3'? If yes, proceed to step S6150, that is, there is an overlapped portion of the list of substance IDs respectively selected by using at least two independent methods, and the overlapping portion can be used to generate a list of commonly recognized identification substance IDs; otherwise, jump Go to manual contrast recognition.
  • Step S6151 In the case where step S6150 is established, the intersection is assigned to the first identification list.
  • the first identification list is directly used as a list of substances confirmed by the missing report test in the subsequent step S7.
  • the extended flowchart of the false negative detection S6 shown in FIG. 12 further utilizes at least two independent methods after jointly identifying the identification substance ID list based on the similarity recognition and the determination of the same result using at least two independent methods.
  • FIG. 13 is a schematic flow chart of another further extended exemplary embodiment of the false negative detection shown in FIG.
  • the difference between the missing report detection flow S6 in the example of FIG. 13 and the missing report detection flow S6 in the example of FIG. 12 is mainly that, as shown in FIG. 13, for example, the optional step of the missing report detection post-processing step S61 S615 additionally includes a list of substance IDs selected for each of the at least two independent methods, and further verifying the non-intersection portion in addition to confirming the intersection portion.
  • the optional step S615 of the post-report detection post-processing step S61 additionally includes:
  • S6152 Subtract the intersection of the at least two substance lists ID1, ID2, and ID3 to obtain a list of substances to be re-examined.
  • step S6154 It is judged whether or not a newly confirmed substance list is generated after the enhanced false negative detection is performed again. If yes, proceed to step S6155, otherwise, go to step S6156.
  • the missing report detection S6 of FIG. 13 is substantially on the basis of the example shown in FIG. 12, and is substantially a portion of the "additions other than the intersection" that cannot be confirmed after the "intersection judgment". Perform further analysis and verification. The specific steps are explained in detail below.
  • FIG. 14 is a sub-flowchart of re-false negative detection S6153 performed using enhanced Raman spectroscopy in another extended exemplary embodiment of the false negative detection shown in FIG. 13, which is shown in FIG.
  • the re-missing detection detects an exemplary decomposition step of S6153.
  • the re-missing detection S6153 includes, for example:
  • step S61532 Perform a false negative detection. Specifically, for example, based on the enhanced Raman spectrum, the above-described step S6 is nested.
  • S61533 (for example, human confirmation) to determine whether to jump to manual comparison.
  • S61535 Generate a list of substances that are confirmed to be present by re-executing the false negative detection using the enhanced Raman spectrum.
  • the above specific operation flow has strict logic and can avoid the abnormal operation of the user.
  • the self-learning described above is also replaced, for example, by using a self-learning mixture analysis method.
  • FIG. 16 shows a further flow diagram in accordance with an embodiment of the present disclosure, illustrated as being divided into two phases, a learning phase and an actual detection phase, in which a detection manner regarding the simultaneous presence of false positives and false negatives is shown.
  • FIG. 17 is a block diagram showing an example hardware arrangement 100 of the electronic device.
  • the hardware arrangement 100 includes a processor 106 (eg, a microprocessor ( ⁇ P), a digital signal processor (DSP), etc.).
  • processor 106 may be a single processing unit or a plurality of processing units for performing different acts of the method steps described herein.
  • the arrangement 100 may also include an input unit 102 for receiving signals from other entities, and an output unit 104 for providing signals to other entities.
  • Input unit 102 and output unit 104 may be arranged as a single entity or as separate entities.
  • arrangement 100 can include at least one readable storage medium 108 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 108 includes a computer program 110 that includes code/computer readable instructions that, when executed by the processor 106 in the arrangement 100, cause the hardware arrangement 100 and/or the device including the hardware arrangement 100 to The flow described above in connection with the above embodiments and any variations thereof are performed.
  • Computer program 110 can be configured as computer program code having a computer program module 110A-110C architecture, for example.
  • the code in the computer program of arrangement 100 includes a plurality of modules, including but not limited to, for example, illustrated modules 110A, 110B, and 110C, the plurality of modules Respectively configured to perform different determinations or operational steps, such as any of the processes, sub-processes, sub-processes, and/or steps performed in the previous Figures 1-2, and 5-16 .
  • the computer program module can substantially perform the various actions in the flow described in the above embodiments to simulate the device.
  • different computer program modules when executed in processor 106, they may correspond to the different units described above in the device.
  • code means in the embodiment disclosed above in connection with FIG. 17 is implemented as a computer program module that, when executed in processor 106, causes hardware arrangement 100 to perform the actions described above in connection with the above-described embodiments, in alternative embodiments 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)).
  • ASIC application specific integrated circuit
  • the processor can 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 flash memory, random access memory (RAM), read only memory (ROM), EEPROM, and the computer program modules described above can be distributed to different computers in the form of memory within the UE in alternative embodiments. In the program product.
  • the present disclosure has at least the following advantages: it can make full use of the similarity method, the self-learning method, and the combination with the optional manual recognition method to achieve efficient and rapid spectral processing of substance recognition.

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Abstract

L'invention concerne un procédé pour effectuer une analyse qualitative de type à auto-apprentissage sur la base d'un spectre Raman. Le procédé comprend : une étape d'acquisition de spectre Raman pour acquérir un spectre Raman ; une étape d'extraction et de comparaison d'éléments pour extraire des données de spectre et les comparer à une bibliothèque d'éléments de spectre d'une bibliothèque de spectrogramme de façon à obtenir une liste d'ID de substance de reconnaissance d'origine ; une étape de comparaison de similarité pour obtenir, au moyen d'un calcul, la similarité entre les ID de substance dans la liste d'ID de substance de reconnaissance d'origine pour le spectre Raman de façon à générer une liste de similarités, et de comparaison de celle-ci à une bibliothèque de valeurs de seuil de similarité dans la bibliothèque de spectrogramme ; et une étape de sélection d'ID de substance pour vérifier, sur la base d'une bibliothèque d'auto-apprentissage, une liste, obtenue après comparaison avec une valeur de seuil de similarité, d'ID de substance de reconnaissance de similarité, dont la similarité dépasse une valeur seuil, comprenant lorsqu'il y a, dans la liste de similarité, un ID de substance dépassant une valeur de seuil correspondant à un ID de substance dans la bibliothèque de valeurs de seuil de similarité, l'exécution d'une détection de faux positifs ; et lorsqu'il n'y a pas, dans la liste de similarité, d'ID de substance dépassant une valeur de seuil correspondant à un ID de substance dans la bibliothèque de valeurs de seuil de similarité, l'exécution d'une détection de faux négatifs.
PCT/CN2017/109712 2016-12-26 2017-11-07 Procédé d'analyse qualitative de type à auto-apprentissage basé sur un spectre raman WO2018121082A1 (fr)

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CN112395803A (zh) * 2020-09-11 2021-02-23 北京工商大学 基于粒子群算法的icp-aes多峰谱线分离方法
CN112395803B (zh) * 2020-09-11 2023-10-13 北京工商大学 基于粒子群算法的icp-aes多峰谱线分离方法
CN113466206A (zh) * 2021-06-23 2021-10-01 上海仪电（集团）有限公司中央研究院 一种基于大数据的拉曼光谱分析系统
CN114814593A (zh) * 2022-04-29 2022-07-29 哈尔滨工业大学（威海） 基于闵氏距离和两步检测策略的电池组多故障诊断方法

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