WO2015043417A1 - 用于毒品检测的拉曼光谱测量方法 - Google Patents
用于毒品检测的拉曼光谱测量方法 Download PDFInfo
- Publication number
- WO2015043417A1 WO2015043417A1 PCT/CN2014/086851 CN2014086851W WO2015043417A1 WO 2015043417 A1 WO2015043417 A1 WO 2015043417A1 CN 2014086851 W CN2014086851 W CN 2014086851W WO 2015043417 A1 WO2015043417 A1 WO 2015043417A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- sample
- drug
- tested
- curve
- original
- Prior art date
Links
- 239000003814 drug Substances 0.000 title claims abstract description 136
- 229940079593 drug Drugs 0.000 title claims abstract description 136
- 238000001237 Raman spectrum Methods 0.000 title claims abstract description 90
- 238000001514 detection method Methods 0.000 title claims abstract description 43
- 238000000691 measurement method Methods 0.000 title abstract description 4
- 238000000034 method Methods 0.000 claims abstract description 35
- 239000000203 mixture Substances 0.000 claims abstract description 15
- 239000012744 reinforcing agent Substances 0.000 claims abstract description 11
- 238000001069 Raman spectroscopy Methods 0.000 claims description 101
- 230000003595 spectral effect Effects 0.000 claims description 56
- 239000003623 enhancer Substances 0.000 claims description 28
- 229910052751 metal Inorganic materials 0.000 claims description 12
- 239000002184 metal Substances 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 11
- 239000000243 solution Substances 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 7
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 6
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 claims description 6
- KDLHZDBZIXYQEI-UHFFFAOYSA-N Palladium Chemical compound [Pd] KDLHZDBZIXYQEI-UHFFFAOYSA-N 0.000 claims description 6
- 239000007864 aqueous solution Substances 0.000 claims description 6
- BASFCYQUMIYNBI-UHFFFAOYSA-N platinum Chemical compound [Pt] BASFCYQUMIYNBI-UHFFFAOYSA-N 0.000 claims description 6
- 239000000463 material Substances 0.000 claims description 5
- CPELXLSAUQHCOX-UHFFFAOYSA-M Bromide Chemical compound [Br-] CPELXLSAUQHCOX-UHFFFAOYSA-M 0.000 claims description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 3
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 claims description 3
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims description 3
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 claims description 3
- BQCADISMDOOEFD-UHFFFAOYSA-N Silver Chemical compound [Ag] BQCADISMDOOEFD-UHFFFAOYSA-N 0.000 claims description 3
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 claims description 3
- 229910052782 aluminium Inorganic materials 0.000 claims description 3
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 claims description 3
- 239000011852 carbon nanoparticle Substances 0.000 claims description 3
- 229910017052 cobalt Inorganic materials 0.000 claims description 3
- 239000010941 cobalt Substances 0.000 claims description 3
- GUTLYIVDDKVIGB-UHFFFAOYSA-N cobalt atom Chemical compound [Co] GUTLYIVDDKVIGB-UHFFFAOYSA-N 0.000 claims description 3
- 229910052802 copper Inorganic materials 0.000 claims description 3
- 239000010949 copper Substances 0.000 claims description 3
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 claims description 3
- 229910052737 gold Inorganic materials 0.000 claims description 3
- 239000010931 gold Substances 0.000 claims description 3
- 229910052742 iron Inorganic materials 0.000 claims description 3
- 229910052749 magnesium Inorganic materials 0.000 claims description 3
- 239000011777 magnesium Substances 0.000 claims description 3
- 239000002082 metal nanoparticle Substances 0.000 claims description 3
- 239000002086 nanomaterial Substances 0.000 claims description 3
- 239000002070 nanowire Substances 0.000 claims description 3
- 229910052759 nickel Inorganic materials 0.000 claims description 3
- 229910052763 palladium Inorganic materials 0.000 claims description 3
- 229910052697 platinum Inorganic materials 0.000 claims description 3
- 229910001414 potassium ion Inorganic materials 0.000 claims description 3
- 229910052709 silver Inorganic materials 0.000 claims description 3
- 239000004332 silver Substances 0.000 claims description 3
- 229910001415 sodium ion Inorganic materials 0.000 claims description 3
- 239000004081 narcotic agent Substances 0.000 claims description 2
- 229910052799 carbon Inorganic materials 0.000 claims 1
- 239000002071 nanotube Substances 0.000 claims 1
- 238000003255 drug test Methods 0.000 description 5
- 238000002360 preparation method Methods 0.000 description 5
- 238000007476 Maximum Likelihood Methods 0.000 description 2
- 239000002041 carbon nanotube Substances 0.000 description 2
- 229910021393 carbon nanotube Inorganic materials 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- NPYPAHLBTDXSSS-UHFFFAOYSA-N Potassium ion Chemical compound [K+] NPYPAHLBTDXSSS-UHFFFAOYSA-N 0.000 description 1
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 229940006460 bromide ion Drugs 0.000 description 1
- 238000013098 chemical test method Methods 0.000 description 1
- 238000004587 chromatography analysis Methods 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004949 mass spectrometry Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 239000002105 nanoparticle Substances 0.000 description 1
- 239000002547 new drug Substances 0.000 description 1
- 239000003960 organic solvent Substances 0.000 description 1
- 239000002574 poison Substances 0.000 description 1
- 231100000614 poison Toxicity 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000004611 spectroscopical analysis Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- 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
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/47—Scattering, i.e. diffuse reflection
-
- 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
- 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
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/15—Medicinal preparations ; Physical properties thereof, e.g. dissolubility
Definitions
- the invention relates to the technical field of safety detection, in particular to a method for detecting smuggling drugs by using Raman spectroscopy technology.
- the Customs Anti-smuggling Bureau usually uses the method of screening and re-confirmation to check the poison.
- the test is mainly carried out by using a kit or a test strip.
- the advantages of these methods are quick and simple, and the cost is low, but the method is specific. If you do not know the type of the sample to be tested, you need to take the reagent or test paper one by one. Especially when testing smuggled drugs, because smuggling drugs often contain a variety of doping components, the reliability of testing with reagents or test paper is poor, which may lead to false positive results.
- a Raman spectroscopy method for drug detection comprising the steps of:
- the method may further include the following steps before the step (a):
- the comparison between the original Raman spectrum curve of the sample to be tested and the original Raman spectrum database of the drug can be obtained by calculating the original Raman spectrum curve of the sample to be tested and the original of the drug sample.
- the similarity of the Raman spectrum curve is performed, and the enhanced Raman spectrum curve of the sample to be tested is compared with the enhanced Raman spectrum database of the drug by calculating the enhanced Raman spectrum curve of the sample to be tested and the enhancement of the drug sample.
- the similarity of the Raman spectral curve is performed if the original Raman spectral curve of the sample to be tested and the original Raman spectral curve of the drug exceed the first threshold or the enhanced Raman spectral curve of the sample to be tested and the enhanced pull of the drug If the similarity of the spectroscopy curve exceeds the second threshold, it is determined that the drug is contained in the sample to be tested.
- the feature portion may be one or more feature peaks that are weighted based on peak positions, peak widths, and/or peak heights of the feature peaks.
- the mixture of the sample to be tested and the enhancer may be formed by directly mixing the sample to be tested and the enhancer or by mixing an aqueous solution or an organic solution of the sample to be tested with a reinforcing agent, the drug sample and the enhancer.
- Mixtures can be made from drug samples and enhancers It is directly mixed or mixed with an aqueous solution or an organic solution of a drug sample and a reinforcing agent.
- the enhancer may comprise any one or a combination of metal nanoparticle materials, metal nanowires, metal nanoclusters, carbon nanotubes, and carbon nanoparticles having a scale in the range of 1-1000 nm.
- the enhancer comprises a metal nanomaterial.
- the enhancer may further comprise chloride ions, bromide ions, sodium ions, potassium ions or sulfate ions.
- the metal may include any one of gold, silver, copper, magnesium, aluminum, iron, cobalt, nickel, palladium or platinum or a combination thereof.
- At least one of the above-described technical solutions of the present invention can detect a sample to be tested by combining an original Raman spectrum and an enhanced Raman spectrum. This approach balances and optimizes the accuracy of drug testing with improved detection efficiency for fast, efficient and accurate drug testing.
- FIG. 1 shows a schematic flow chart of a Raman spectroscopy measurement method for drug detection according to an embodiment of the present invention
- FIG. 2 is a view schematically showing an original Raman spectrum curve of a first drug detected by a Raman spectroscopy method according to an embodiment of the present invention
- FIG. 3a schematically illustrates an original Raman spectrum curve of a second drug detected by a Raman spectroscopy method according to an embodiment of the present invention.
- Figure 3b schematically illustrates an enhanced Raman spectral curve of a second drug detected by a Raman spectroscopy method in accordance with an embodiment of the present invention.
- FIG. 1 schematically illustrates a pull for drug detection in accordance with an embodiment of the present invention.
- Flow chart of the Mann spectrum measurement method The method can be divided into two phases, a preparation phase and an actual detection phase.
- the preparation phase the main purpose is to establish a Raman spectral database of drugs for actual testing.
- the actual detection stage the actual sample to be tested is tested and compared with the drug Raman spectrum database to obtain the result.
- the preparation phase may include the steps of: measuring a Raman spectrum of the drug sample to obtain a raw Raman spectrum curve of the drug sample; and determining whether the original Raman spectrum curve of the drug sample has a characteristic portion if the original pull The manner spectral curve has a characteristic portion, and the original Raman spectroscopy database of the drug is established based on the original Raman spectroscopy curve, and if the original Raman spectroscopy curve has no characteristic portion, the mixture of the drug sample and the enhancer is Measurements were taken to obtain an enhanced Raman spectral curve of the drug sample and a database of enhanced Raman spectra of the drug was established based on the enhanced Raman spectral curve.
- the above process can be performed separately for one or more drug samples until the original Raman spectroscopy or enhanced Raman spectroscopy of the desired new drug sample is no longer needed.
- the actual detection phase may include the following steps: measuring the Raman spectrum of the sample to be tested to obtain a raw Raman spectrum curve of the sample to be tested; determining whether the original Raman spectrum curve of the sample to be tested has a characteristic portion, if the test is to be performed If the original Raman spectrum curve of the sample does not have a characteristic portion, the mixture of the sample to be tested and the enhancer is measured to obtain an enhanced Raman spectrum curve of the sample to be tested; and if the original Raman spectrum curve of the sample to be tested has The characteristic part compares the original Raman spectrum curve of the sample to be tested with the original Raman spectrum database of the drug to determine whether the drug is contained in the sample to be tested, and if the original Raman spectrum curve of the sample to be tested is not Having a characteristic portion, the enhanced Raman spectroscopy curve of the sample to be tested is compared with a drug-enhanced Raman spectroscopy database to determine whether the drug is contained in the sample to be tested.
- the Raman spectroscopy method utilizes a combination of original Raman spectroscopy data and enhanced Raman spectroscopy data to detect drugs.
- This method can optimize the detection efficiency and detection accuracy of drugs.
- Drug testing has specific requirements compared to general chemical testing. On the one hand, because drug testing often involves the identification of criminal behavior, the detection of drugs must be very accurate; on the other hand, because drug testing is often At the airport, customs, etc. The entry point is carried out. Therefore, the detection of drugs must have high detection efficiency. Some complicated detection methods are difficult to apply to rapid detection in the field, but can only be used for subsequent further determination.
- the method according to the present invention can ensure the accuracy of drug detection and improve the detection efficiency as much as possible by adopting a combination of detecting the original Raman spectrum and the enhanced Raman spectrum of the sample to be tested.
- the above preparation phase is not essential, for example, the operator can utilize the original Raman spectroscopy data of the existing drug or the enhanced Raman spectroscopy data and the actual The test results of the samples were compared.
- the above preparation phase is not necessarily performed long before the actual sample is detected.
- the drug sample can be detected at the site where the sample to be tested is detected. To obtain raw Raman spectral data or enhanced Raman spectral data for drugs.
- the comparison of the original Raman spectral curve of the sample to be tested with the original Raman spectral database of the drug can be performed by calculating the similarity between the original Raman spectral curve of the sample to be tested and the original Raman spectral curve of the drug sample.
- the comparison of the enhanced Raman spectral curve of the sample to be tested with the enhanced Raman spectral database of the drug is performed by calculating the similarity of the enhanced Raman spectral curve of the sample to be tested to the enhanced Raman spectral curve of the drug sample.
- Corr represents the similarity between the original Raman spectral curve of the drug and the original Raman spectral curve of the sample to be tested, and " ⁇ " represents the dot product operation.
- A(x) and B(x) can be sampled separately to obtain n sample points, denoted as A 1 , A 2 , . . . , A n and B 1 , B, respectively. 2 ,...,B n
- the similarity of the original Raman spectral curve of the drug to the original Raman spectral curve of the sample to be tested Corr can be calculated according to formula (2):
- ⁇ also represents a dot product operation.
- an absolute value algorithm may also be employed, and A(x) and B(x) may be separately sampled 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 original Raman spectral curve of the drug to the original Raman spectral curve of the sample to be tested Corr can be calculated according to formula (3):
- the above similarity calculation may be performed for the entire Raman spectrum curve, or may be performed only for the portion having the characteristic portion in the Raman spectrum curve.
- the calculation of the similarity between the enhanced Raman spectrum curve of the sample to be tested and the enhanced Raman spectrum curve of the drug is basically the same. , will not repeat them here.
- the above is merely an example of some similarity calculations, and some other similarity calculation methods known to those skilled in the art are also feasible.
- the similarity between the original Raman spectrum curve of the sample to be tested and the original Raman spectrum curve of the drug if it exceeds the first threshold, it can be determined that the sample to be tested contains the drug. Similarly, if the similarity between the enhanced Raman spectrum curve of the sample to be tested and the enhanced Raman spectrum curve of the drug exceeds the second threshold, it is determined that the drug is contained in the sample to be tested.
- the first threshold and the second threshold may or may not be equal. The first threshold and the second threshold may be given according to actual detection requirements, accuracy of the detection instrument, and the like.
- the term "characteristic portion” refers to a key portion of a Raman spectrum curve of a drug or a sample to be tested that is different from other drugs or samples to be tested.
- 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, peak width, and/or peak height of the characteristic peak.
- the feature peaks may also be searched and sorted prior to calculating the similarity.
- the similarity calculation can be simplified even to the original Raman spectral curve or the enhanced Raman spectral curve of the sample to be tested. Whether or not there is a characteristic peak corresponding to the original Raman spectral curve of the drug or the characteristic peak of the enhanced Raman spectral curve at one or more positions is directly determined.
- 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 an aqueous solution or an organic solution of the sample to be tested. It is mixed with a reinforcing agent.
- the mixture of the drug sample and the enhancer is directly mixed with the drug sample and the enhancer or is formed by mixing an aqueous solution or an organic solution of the drug sample with the enhancer.
- the enhancer may comprise any one or a combination of metal nanoparticle materials, metal nanowires, metal nanoclusters, carbon nanotubes, and carbon nanoparticles having a scale in the range of 1-1000 nm.
- 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.
- the drug molecules attach to the surface of the enhancer material, and the electromagnetic field on the surface of the enhancer material enhances the Raman spectral signal of the drug sample.
- the acquisition of Raman spectroscopy data can be irradiated by laser using a laser
- the sample or the sample to be tested, and the Raman scattered light generated by the laser irradiation of the drug sample or the sample to be tested is extracted and subjected to spectral analysis to obtain a Raman spectrum curve.
- Figures 2, 3a and 3b show examples of detecting drugs using Raman spectroscopy according to the present invention.
- Original Raman spectrum graph of Figure 2 illustrates a first drug, can be seen from Figure 2, a first Raman spectral profile of the original drugs have significant nearby peaks at 850cm -1 and 1000cm -1.
- For the detection of the drug it is only necessary to obtain the original Raman spectrum curve of the sample to be tested and compare it with the original Raman spectrum curve of the drug without using the enhanced Raman spectrum data.
- Figures 3a and 3b show the original Raman spectral curve and the enhanced Raman spectral curve of the second drug, respectively.
- the original Raman spectral curve of the second drug has no distinct characteristic peaks, and thus, if directly Detection using raw Raman spectroscopy data may affect the accuracy of the detection.
- the enhanced Raman spectral curve of the drug obtained by dissolving the second drug sample in water or an organic solvent and mixing the reinforcing agent has a distinct characteristic peak, as shown in Fig. 3b.
- Enhanced Raman spectral curve of a second drug in the nearby 530cm -1 and 630cm -1 significant peaks.
- the enhanced Raman spectrum curve of the sample to be tested should be measured and collected, and compared with the enhanced Raman spectrum curve of the drug to determine whether there is a second in the sample to be tested.
- kind of drugs such as may be provided by or enhancers Solvent generation
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- Pathology (AREA)
- General Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Immunology (AREA)
- Biochemistry (AREA)
- Engineering & Computer Science (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Molecular Biology (AREA)
- Pharmacology & Pharmacy (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Biophysics (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
一种用于毒品检测的拉曼光谱测量方法,包括以下步骤:对待测样品的拉曼光谱进行测量以获得待测样品的原始拉曼光谱曲线;判断待测样品的原始拉曼光谱曲线是否具有特征部分,如果待测样品的原始拉曼光谱曲线不具有特征部分,则对待测样品与增强剂的混合物进行测量以获得待测样本的增强拉曼光谱曲线;如果待测样品的原始拉曼光谱曲线具有特征部分,则将待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品,而如果待测样品的原始拉曼光谱曲线不具有特征部分,则将待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品。
Description
本发明涉及安全检测技术领域,尤其涉及一种利用拉曼光谱技术来对走私毒品进行检测的方法。
目前海关缉私局通常采用先筛查、再确证的方法查毒。在筛查时主要采用试剂盒或试纸条进行测试,这些方法的优点是快速简便、成本低,但该法特异性强,如果不知道待测样品的类型,需要拿试剂或试纸逐个去试,尤其是在对走私毒品进行检测时,由于走私毒品中往往包含多种掺杂成分,用试剂或试纸进行检测时可靠性较差,容易导致假阳性结果。色谱、质谱等方法是确证过程中采用的主要方法,但是这些方法操作复杂,耗材昂贵,难以在现场快速检测中得到应用。因此,需要一种简单快捷、准确可靠的方法解决走私毒品检测的难题。
发明内容
本发明的目的是提供一种用于毒品检测的拉曼光谱测量方法,其能够快速、高效、准确地确定待测样品中是否包含毒品。
为了实现上述发明目的,本发明的技术方案通过以下方式来实现:
根据本发明的第一方面,提供一种用于毒品检测的拉曼光谱测量方法,包括以下步骤:
(a)对待测样品的拉曼光谱进行测量以获得待测样品的原始拉曼光谱曲线;
(b)判断所述待测样品的原始拉曼光谱曲线是否具有特征部分,如果待测样品的原始拉曼光谱曲线不具有特征部分,则对所述待测样品与增强剂的混合物进行测量以获得待测样本的增强拉曼光
谱曲线;
(c)如果待测样品的原始拉曼光谱曲线具有特征部分,则将所述待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品,而如果待测样品的原始拉曼光谱曲线不具有特征部分,则将所述待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品。
进一步地,所述方法在上述步骤(a)之前还可以包括以下步骤:
(o1)对毒品样本的拉曼光谱进行测量以获得毒品样本的原始拉曼光谱;和
(o2)判断所述毒品样本的原始拉曼光谱曲线是否具有特征部分,如果原始拉曼光谱曲线具有特征部分,则基于所述毒品样本的原始拉曼光谱曲线建立毒品的原始拉曼光谱数据库,而如果所述原始拉曼光谱曲线不具有特征部分,则对所述毒品样本与增强剂的混合物进行测量以获得毒品样本的增强拉曼光谱曲线并基于所述毒品样本的增强拉曼光谱曲线建立毒品的增强拉曼光谱数据库。
进一步地,在所述步骤(c)中,所述待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库的对比可以通过计算待测样品的原始拉曼光谱曲线与毒品样本的原始拉曼光谱曲线的相似度来进行,所述待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库的对比可以通过计算所述待测样品的增强拉曼光谱曲线与毒品样本的增强拉曼光谱曲线的相似度来进行,如果待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱曲线的相似度超过第一阈值或待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱曲线的相似度超过第二阈值,则判定待测样品中含有所述毒品。
更进一步地,所述特征部分可以是一个或更多个特征峰,所述相似度基于所述特征峰的峰位、峰宽和/或峰高来进行加权计算。
进一步地,所述待测样品与增强剂的混合物可以由待测样品与增强剂直接混合而成或由待测样品的水溶液或有机溶液与增强剂混合而成,所述毒品样本与增强剂的混合物可以由毒品样本与增强剂
直接混合而成或由毒品样本的水溶液或有机溶液与增强剂混合而成。
进一步地,所述增强剂可以包含尺度在1-1000nm范围内的金属纳米颗粒材料、金属纳米线、金属纳米团簇、碳纳米管和碳纳米颗粒中任一种或它们的组合。
进一步地,所述增强剂包含金属纳米材料。
更进一步地,所述增强剂还可包含氯离子、溴离子、钠离子、钾离子或硫酸根离子。
具体地,所述金属可以包括金、银、铜、镁、铝、铁、钴、镍、钯或铂中的任一种或它们的组合。
本发明的上述技术方案中的至少一个方面能够通过结合原始拉曼光谱和增强拉曼光谱对待测样品进行检测。这种方案可以兼顾和优化毒品检测的准确性与提高检测效率的平衡,从而实现快速、高效和准确的毒品检测。
图1示出根据本发明的实施例的用于毒品检测的拉曼光谱测量方法的示意性流程图;
图2示意性地示出根据本发明一实施例的拉曼光谱测量方法检测的第一种毒品的原始拉曼光谱曲线;
图3a示意性地示出根据本发明一实施例的拉曼光谱测量方法检测的第二种毒品的原始拉曼光谱曲线;和
图3b示意性地示出根据本发明一实施例的拉曼光谱测量方法检测的第二种毒品的增强拉曼光谱曲线。
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。
图1示意性地示出根据本发明的一实施例的用于毒品检测的拉
曼光谱测量方法的流程图。该方法可以分成两个阶段,即准备阶段和实际检测阶段。在准备阶段,主要目的是建立供实际检测使用的毒品的拉曼光谱数据库。在实际检测阶段,则是对实际的待测样品进行检测,并与毒品的拉曼光谱数据库进行比对以得出结果。
该准备阶段可以包括以下步骤:对毒品样本的拉曼光谱进行测量以获得毒品样本的原始拉曼光谱曲线;和判断所述毒品样本的原始拉曼光谱曲线是否具有特征部分,如果所述原始拉曼光谱曲线具有特征部分,则基于所述原始拉曼光谱曲线建立毒品的原始拉曼光谱数据库,而如果所述原始拉曼光谱曲线不具有特征部分,则对所述毒品样本与增强剂的混合物进行测量以获得毒品样本的增强拉曼光谱曲线并基于所述增强拉曼光谱曲线建立毒品的增强拉曼光谱数据库。上述过程可以对于一种或多种毒品样本分别实施,直至不再有所需的新的毒品样本的原始拉曼光谱或增强拉曼光谱需要采集为止。
该实际检测阶段可以包括以下步骤:对待测样品的拉曼光谱进行测量以获得待测样品的原始拉曼光谱曲线;判断所述待测样品的原始拉曼光谱曲线是否具有特征部分,如果待测样品的原始拉曼光谱曲线不具有特征部分,则对所述待测样品与增强剂的混合物进行测量以获得待测样本的增强拉曼光谱曲线;而如果待测样品的原始拉曼光谱曲线具有特征部分,则将所述待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品,而如果待测样品的原始拉曼光谱曲线不具有特征部分,则将所述待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品。
从上述可知,根据本发明的实施例的拉曼光谱测量方法利用了原始拉曼光谱数据和增强拉曼光谱数据相结合的方式对毒品进行检测。这种方式能够将对毒品的检测效率和检测准确性进行最佳的优化。毒品检测与一般的化学品检测相比具有其特定的要求,一方面,由于毒品检测往往涉及对犯罪行为的认定,因此,对毒品的检测必须非常准确;而另一方面,由于毒品检测往往是在机场、海关等出
入境地点进行,因此,对毒品的检测必须具有很高的检测效率,一些流程复杂的检测方法尽管检测精度很高也难以应用在现场快速检测中而只能用于后续的进一步判定。
对于拉曼光谱测量方法而言,如果仅采用直接对待测样品进行检测并根据原始拉曼光谱数据进行判定,在一些情况下检测的准确性对于某些毒品难以保证;而如果仅采用对待测样品和增强剂的混合物进行检测并根据增强拉曼光谱数据进行判定,则可能会导致检测过程出现不必要的复杂化而降低检测效率。而根据本发明的方法由于采用了检测待测样品的原始拉曼光谱和增强拉曼光谱相结合的方式,既能够保证毒品检测的准确性,又能够尽可能地提高检测效率。
在根据本发明的用于毒品检测的拉曼光谱测量方法中,上述准备阶段并不是必须的,例如,操作者可以利用已有的毒品的原始拉曼光谱数据或增强拉曼光谱数据与对实际样品的检测结果进行比对。另一方面,上述准备阶段也不一定在进行实际样品的检测之前很久进行,例如,为了保证检测的准确性或校准拉曼光谱检测设备,可以在对待测样品进行检测的现场对毒品样本进行检测来获得毒品的原始拉曼光谱数据或增强拉曼光谱数据。
在一示例中,待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库的对比可以通过计算待测样品的原始拉曼光谱曲线与毒品样本的原始拉曼光谱曲线的相似度来进行。同样,待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库的对比通过计算所述待测样品的增强拉曼光谱曲线与毒品样本的增强拉曼光谱曲线的相似度来进行。
相似度的计算有多种方法,比如相关算法、最大似然法、绝对值算法等等。例如,假定毒品的原始拉曼光谱曲线为A(x),待测样品的原始拉曼光谱曲线为B(x),在一示例中,采用最大似然算法,可以通过式(1)对两者的相似度进行计算:
其中Corr表示毒品的原始拉曼光谱曲线和待测样品的原始拉曼光谱曲线的相似度,“·”表示点积运算。
在另一示例中,采用相关算法,可以对A(x)和B(x)分别进行采样以各获得n个采样点,分别表示为A1,A2,…,An以及B1,B2,…,Bn,毒品的原始拉曼光谱曲线和待测样品的原始拉曼光谱曲线的相似度Corr可以根据式(2)进行计算:
其中,“·”也表示点积运算。
在另一示例中,还可以采用绝对值算法,亦可以对A(x)和B(x)分别进行采样以各获得n个采样点,分别表示为A1,A2,…,An以及B1,B2,…,Bn,毒品的原始拉曼光谱曲线和待测样品的原始拉曼光谱曲线的相似度Corr可以根据式(3)进行计算:
上述相似度计算可以针对整个拉曼光谱曲线进行,也可以仅针对于拉曼光谱曲线中具有特征部分的局部进行。对于待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱曲线的相似度的计算,与上述待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱曲线的相似度的计算基本相同,在此不再赘述。以上仅是给出了一些相似度计算的示例,本领域技术人员所知的一些其他的相似度计算方法也是可行的。
对于待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱曲线的相似度,如果其超过第一阈值,则可以判定待测样品中含有所述
毒品。同样,如果待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱曲线的相似度超过第二阈值,则判定待测样品中含有所述毒品。第一阈值和第二阈值可以相等,也可以不相等。第一阈值和第二阈值可以根据实际的检测需要、检测仪器的精度等因素来给出。
在本发明中,术语“特征部分”是指某种毒品或待测样品的拉曼光谱曲线中有别于其它的毒品或待测样品的拉曼光谱曲线的关键部分。例如,所述特征部分可以是一个或更多个特征峰、特征谷、相位拐点等等。
在毒品的原始拉曼光谱曲线包括特征峰的情况下,上述相似度可以基于所述特征峰的峰位、峰宽和/或峰高来进行加权计算。在一示例中,在计算所述相似度之前,还可以对所述特征峰进行搜索和排序。在毒品的原始拉曼光谱曲线或增强拉曼光谱曲线的特征峰比较明显的情况下,在实际中,相似度计算甚至可以简化成搜索待测样品的原始拉曼光谱曲线或增强拉曼光谱曲线中是否在某一个或多个位置上存在与毒品的原始拉曼光谱曲线或增强拉曼光谱曲线的特征峰相对应的特征峰来直接进行确定。
在一示例中,在利用毒品的增强拉曼光谱数据进行检测时,所述待测样品与增强剂的混合物可以由待测样品与增强剂直接混合而成或由待测样品的水溶液或有机溶液与增强剂混合而成。同样,所述毒品样本与增强剂的混合物由毒品样本与增强剂直接混合而成或由毒品样本的水溶液或有机溶液与增强剂混合而成。作为示例,增强剂可以包含尺度在1-1000nm范围内的金属纳米颗粒材料、金属纳米线、金属纳米团簇、碳纳米管和碳纳米颗粒中任一种或它们的组合。在另一示例中,增强剂可以包含金属纳米材料,也可在包含金属纳米材料的同时还包含氯离子、溴离子、钠离子、钾离子或硫酸根离子。所述金属例如可以包括金、银、铜、镁、铝、铁、钴、镍、钯或铂中的任一种或它们的组合。在毒品样本与增强剂的混合物中,毒品分子会附着于增强剂材料的表面,而增强剂材料表面的电磁场会使得毒品样本的拉曼光谱信号得到增强。
拉曼光谱数据的获取,可以通过利用激光器发出的激光照射毒
品样本或待测样品,并对由激光照射毒品样本或待测样品产生的拉曼散射光进行提取并进行光谱分析而得出拉曼光谱曲线。
图2、3a和3b给出了利用根据本发明的拉曼光谱测量方法检测毒品的示例。图2示出了第一种毒品的原始拉曼光谱曲线,从图2可见,第一种毒品的原始拉曼光谱曲线在850cm-1附近和1000cm-1附近有明显特征峰。对于该种毒品的检测,只需要获取待测样品的原始拉曼光谱曲线并与该种毒品的原始拉曼光谱曲线进行对比即可,而不需要利用增强拉曼光谱数据。
图3a和3b分别示出了第二种毒品的原始拉曼光谱曲线和增强拉曼光谱曲线,从图3a可见,第二种毒品的原始拉曼光谱曲线没有明显的特征峰,因而,如果直接利用原始的拉曼光谱数据进行检测,可能会影响检测的准确性。而通过将第二种毒品样本溶于水或有机溶剂并于增强剂混合之后所得到的毒品的增强拉曼光谱曲线则具有明显的特征峰,如图3b所示。第二种毒品的增强拉曼光谱曲线在530cm-1附近和630cm-1附近有明显特征峰。需要说明的是,并非增强拉曼光谱中的所有的峰都是特征峰,例如图3b中除去530cm-1附近和630cm-1附近的峰之外的峰都不是特征峰(比如可能由增强剂或溶剂产生),并不能表征毒品的特性。因此,对于第二种毒品的检测,应当对待测样品的增强拉曼光谱曲线进行测量和采集,并将其与该种毒品的增强拉曼光谱曲线进行对比来判定待测样品中是否存在第二种毒品。
虽然结合附图对本发明进行了说明,但是附图中公开的实施例旨在对本发明优选实施方式进行示例性说明,而不能理解为对本发明的一种限制。
虽然本发明总体构思的一些实施例已被显示和说明,本领域普通技术人员将理解,在不背离本总体发明构思的原则和精神的情况下,可对这些实施例做出改变,本发明的范围以权利要求和它们的等同物限定。
Claims (9)
- 一种用于毒品检测的拉曼光谱测量方法,包括以下步骤:(a)对待测样品的拉曼光谱进行测量以获得待测样品的原始拉曼光谱曲线;(b)判断所述待测样品的原始拉曼光谱曲线是否具有特征部分,如果待测样品的原始拉曼光谱曲线不具有特征部分,则对所述待测样品与增强剂的混合物进行测量以获得待测样本的增强拉曼光谱曲线;(c)如果待测样品的原始拉曼光谱曲线具有特征部分,则将所述待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品,而如果待测样品的原始拉曼光谱曲线不具有特征部分,则将所述待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库进行对比以判定待测样品中是否含有所述毒品。
- 根据权利要求1所述的用于毒品检测的拉曼光谱测量方法,其特征在于,在上述步骤(a)之前还包括以下步骤:(o1)对毒品样本的拉曼光谱进行测量以获得毒品样本的原始拉曼光谱曲线;和(o2)判断所述毒品样本的原始拉曼光谱曲线是否具有特征部分,如果原始拉曼光谱曲线具有特征部分,则基于所述毒品样本的原始拉曼光谱曲线建立毒品的原始拉曼光谱数据库,而如果所述原始拉曼光谱曲线不具有特征部分,则对所述毒品样本与增强剂的混合物进行测量以获得毒品样本的增强拉曼光谱曲线并基于所述毒品样本的增强拉曼光谱曲线建立毒品的增强拉曼光谱数据库。
- 根据权利要求1所述的用于毒品检测的拉曼光谱测量方法,其特征在于,在所述步骤(c)中,所述待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱数据库的对比通过计算待测样品的原始拉曼光谱曲线与毒品样本的原始拉曼光谱曲线的相似度来进行,所述 待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱数据库的对比通过计算所述待测样品的增强拉曼光谱曲线与毒品样本的增强拉曼光谱曲线的相似度来进行,如果待测样品的原始拉曼光谱曲线与毒品的原始拉曼光谱曲线的相似度超过第一阈值或待测样品的增强拉曼光谱曲线与毒品的增强拉曼光谱曲线的相似度超过第二阈值,则判定待测样品中含有所述毒品。
- 根据权利要求3所述的用于毒品检测的拉曼光谱测量方法,其特征在于,所述特征部分是一个或更多个特征峰,所述相似度基于所述特征峰的峰位、峰宽和/或峰高来进行加权计算。
- 根据权利要求1所述的用于毒品检测的拉曼光谱测量方法,其特征在于,所述待测样品与增强剂的混合物由待测样品与增强剂直接混合而成或由待测样品的水溶液或有机溶液与增强剂混合而成,所述毒品样本与增强剂的混合物由毒品样本与增强剂直接混合而成或由毒品样本的水溶液或有机溶液与增强剂混合而成。
- 根据权利要求1所述的用于毒品检测的拉曼光谱测量方法,其特征在于,所述增强剂包含尺度在1-1000nm范围内的金属纳米颗粒材料、金属纳米线、金属纳米团簇、碳纳米管和碳纳米颗粒中任一种或它们的组合。
- 根据权利要求1所述的用于毒品检测的拉曼光谱测量方法,其特征在于,所述增强剂包含金属纳米材料。
- 根据权利要求7所述的用于毒品检测的拉曼光谱测量方法,其特征在于,所述增强剂还包含氯离子、溴离子、钠离子、钾离子或硫酸根离子。
- 根据权利要求6-8中任一项所述的用于毒品检测的拉曼光谱测量方法,其特征在于,所述金属包括金、银、铜、镁、铝、铁、钴、镍、钯或铂中的任一种或它们的组合。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201310446679.2A CN104458693B (zh) | 2013-09-25 | 2013-09-25 | 用于毒品检测的拉曼光谱测量方法 |
CN201310446679.2 | 2013-09-25 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2015043417A1 true WO2015043417A1 (zh) | 2015-04-02 |
Family
ID=51610007
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2014/086851 WO2015043417A1 (zh) | 2013-09-25 | 2014-09-18 | 用于毒品检测的拉曼光谱测量方法 |
Country Status (5)
Country | Link |
---|---|
US (1) | US9250180B2 (zh) |
EP (1) | EP2853885B1 (zh) |
CN (1) | CN104458693B (zh) |
HK (1) | HK1204055A1 (zh) |
WO (1) | WO2015043417A1 (zh) |
Families Citing this family (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106198482B (zh) * | 2015-05-04 | 2019-07-05 | 清华大学 | 基于拉曼光谱的检测保健品中是否添加有西药的方法 |
CN105738037A (zh) * | 2016-02-01 | 2016-07-06 | 武汉新芯集成电路制造有限公司 | 一种等离子反应腔体的渗漏检测方法 |
CN107995237B (zh) * | 2016-10-27 | 2021-12-10 | 上海迪亚凯特生物医药科技有限公司 | 光谱数据兼容方法及系统 |
CN106501233A (zh) * | 2016-11-09 | 2017-03-15 | 无锡艾科瑞思产品设计与研究有限公司 | 一种异丙威残留量检测方法 |
CN106770166A (zh) * | 2016-12-23 | 2017-05-31 | 同方威视技术股份有限公司 | 安全检查装置和方法 |
CN108240978B (zh) * | 2016-12-26 | 2020-01-21 | 同方威视技术股份有限公司 | 基于拉曼光谱的自学习式定性分析方法 |
CN107330413B (zh) * | 2017-07-06 | 2018-11-13 | 中国科学院遥感与数字地球研究所 | 一种基于遥感技术的毒品原植物识别方法 |
CN107632007B (zh) * | 2017-08-02 | 2020-08-14 | 北京华泰诺安探测技术有限公司 | 一种黑火药的拉曼检测方法及其用途 |
TWI663400B (zh) | 2017-09-28 | 2019-06-21 | 國立臺灣大學 | 檢測試紙及檢測毒品的方法 |
WO2019183882A1 (zh) * | 2018-03-29 | 2019-10-03 | 深圳达闼科技控股有限公司 | 物质检测方法、装置、电子设备及计算机可读存储介质 |
CN109827943A (zh) * | 2019-02-27 | 2019-05-31 | 山东省食品药品检验研究院 | 一种佐匹克隆片的鉴别方法 |
CN109827944A (zh) * | 2019-02-27 | 2019-05-31 | 山东省食品药品检验研究院 | 一种阿德福韦酯片的鉴别方法 |
CN111239097A (zh) * | 2020-01-15 | 2020-06-05 | 公安部物证鉴定中心 | 一种集成表面增强拉曼与微流控的毒品快检系统 |
CN111504979B (zh) * | 2020-05-19 | 2021-03-16 | 江南大学 | 利用已知混合物拉曼光谱改善混合物成分识别精度的方法 |
US11614408B2 (en) | 2020-05-19 | 2023-03-28 | Jiangnan University | Method for improving identification accuracy of mixture components by using known mixture Raman spectrum |
CN114264641A (zh) * | 2020-09-16 | 2022-04-01 | 中国科学院过程工程研究所 | 一种微量离子液体浓度的检测方法 |
CN112763477B (zh) * | 2020-12-30 | 2022-11-08 | 山东省食品药品检验研究院 | 一种基于拉曼光谱的仿制药质量快速评价系统 |
CN114778515A (zh) * | 2022-04-26 | 2022-07-22 | 江南大学 | 一种同时检测减肥类保健品中西布曲明和芬氟拉明的方法 |
CN115508335A (zh) * | 2022-10-21 | 2022-12-23 | 哈尔滨工业大学(威海) | 基于傅里叶变换的拉曼光谱曲线数据增强方法 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050225758A1 (en) * | 2004-03-23 | 2005-10-13 | Knopp Kevin J | Raman optical identification tag |
CN101493415A (zh) * | 2009-03-03 | 2009-07-29 | 福建师范大学 | 吗啡定量和微量检测方法 |
CN101614667A (zh) * | 2008-06-27 | 2009-12-30 | 同方威视技术股份有限公司 | 拉曼光谱系统及拉曼光谱测量方法 |
CN101923649A (zh) * | 2010-06-22 | 2010-12-22 | 中国海洋大学 | 一种基于荧光光谱的溢油种类识别方法 |
CN102590178A (zh) * | 2012-03-19 | 2012-07-18 | 中国政法大学 | 一种利用表面增强拉曼光谱检测毒品吗啡的方法 |
CN102590177A (zh) * | 2012-03-19 | 2012-07-18 | 中国政法大学 | 一种利用表面增强拉曼光谱检测甲基苯丙胺的方法 |
Family Cites Families (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7242469B2 (en) * | 2003-05-27 | 2007-07-10 | Opto Trace Technologies, Inc. | Applications of Raman scattering probes |
US8326404B2 (en) * | 2003-11-28 | 2012-12-04 | British Columbia Cancer Agency Branch | Multimodal detection of tissue abnormalities based on raman and background fluorescence spectroscopy |
US7393692B2 (en) * | 2004-10-18 | 2008-07-01 | Real-Time Analyzers, Inc. | SERS method for rapid pharmacokinetic analysis of drugs in saliva |
US7524671B2 (en) * | 2005-01-27 | 2009-04-28 | Prescient Medical, Inc. | Handheld raman blood analyzer |
US7688440B2 (en) * | 2005-01-27 | 2010-03-30 | Prescient Medical, Inc. | Raman spectroscopic test strip systems |
US7505128B2 (en) * | 2006-04-10 | 2009-03-17 | General Electric Company | Compact, hand-held raman spectrometer microsystem on a chip |
CN100590423C (zh) * | 2007-12-21 | 2010-02-17 | 辽宁大学 | 一种拉曼光谱检测方法 |
NL2004275C2 (en) * | 2010-02-22 | 2011-08-23 | Univ Leiden | Raman spectrometry. |
BR112013033418A2 (pt) * | 2011-07-01 | 2017-01-24 | 3M Innovative Properties Co | método e aparelho para testar infratores no uso de drogas |
-
2013
- 2013-09-25 CN CN201310446679.2A patent/CN104458693B/zh active Active
-
2014
- 2014-09-18 WO PCT/CN2014/086851 patent/WO2015043417A1/zh active Application Filing
- 2014-09-24 EP EP14186248.2A patent/EP2853885B1/en active Active
- 2014-09-24 US US14/495,233 patent/US9250180B2/en active Active
-
2015
- 2015-05-13 HK HK15104526.8A patent/HK1204055A1/zh not_active IP Right Cessation
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20050225758A1 (en) * | 2004-03-23 | 2005-10-13 | Knopp Kevin J | Raman optical identification tag |
CN101614667A (zh) * | 2008-06-27 | 2009-12-30 | 同方威视技术股份有限公司 | 拉曼光谱系统及拉曼光谱测量方法 |
CN101493415A (zh) * | 2009-03-03 | 2009-07-29 | 福建师范大学 | 吗啡定量和微量检测方法 |
CN101923649A (zh) * | 2010-06-22 | 2010-12-22 | 中国海洋大学 | 一种基于荧光光谱的溢油种类识别方法 |
CN102590178A (zh) * | 2012-03-19 | 2012-07-18 | 中国政法大学 | 一种利用表面增强拉曼光谱检测毒品吗啡的方法 |
CN102590177A (zh) * | 2012-03-19 | 2012-07-18 | 中国政法大学 | 一种利用表面增强拉曼光谱检测甲基苯丙胺的方法 |
Non-Patent Citations (1)
Title |
---|
QIAO, XIYA.: "Raman Spectra Feature Extraction and Its Applications in Qualitative Analysis", CMFD, 31 December 2010 (2010-12-31) * |
Also Published As
Publication number | Publication date |
---|---|
CN104458693A (zh) | 2015-03-25 |
CN104458693B (zh) | 2018-06-01 |
HK1204055A1 (zh) | 2015-11-06 |
US20150085278A1 (en) | 2015-03-26 |
EP2853885B1 (en) | 2022-05-11 |
US9250180B2 (en) | 2016-02-02 |
EP2853885A1 (en) | 2015-04-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2015043417A1 (zh) | 用于毒品检测的拉曼光谱测量方法 | |
WO2016177002A1 (zh) | 基于拉曼光谱的检测保健品中是否添加有西药的方法 | |
US10001410B2 (en) | Quantitative elemental profiling in optical emission spectroscopy | |
Zhang et al. | Multiscale peak detection in wavelet space | |
CN108254351B (zh) | 用于物品查验的拉曼光谱检测方法 | |
Cai et al. | Gold nanoprobes-based resonance Rayleigh scattering assay platform: Sensitive cytosensing of breast cancer cells and facile monitoring of folate receptor expression | |
Fontana et al. | Raman spectroscopic sensors for inorganic salts | |
WO2018121082A1 (zh) | 基于拉曼光谱的自学习式定性分析方法 | |
WO2015096779A1 (zh) | 拉曼光谱检测方法 | |
CN108398416A (zh) | 一种基于激光拉曼光谱的混合物成分测定方法 | |
Ye et al. | Analyzing the anodic stripping square wave voltammetry of heavy metal ions via machine learning: Information beyond a single voltammetric peak | |
CN103293141A (zh) | 基于离子迁移谱/质谱/拉曼光谱融合技术的白酒年份识别方法 | |
Liu et al. | An improved method based on a new wavelet transform for overlapped peak detection on spectrum obtained by portable Raman system | |
Weng et al. | Dynamic surface-enhanced Raman spectroscopy and Chemometric methods for fast detection and intelligent identification of methamphetamine and 3, 4-Methylenedioxy methamphetamine in human urine | |
CN104297280A (zh) | 利用核磁共振技术定量评价岩心洗油效果的方法 | |
Acevska et al. | Fingerprinting of morphine using chromatographic purity profiling and multivariate data analysis | |
Zhang et al. | Mixture analysis using reverse searching and non-negative least squares | |
Pavillon et al. | Feature‐based recognition of Surface‐enhanced Raman spectra for biological targets | |
Cui et al. | Correlation analysis between cocaine samples seized in China by the rapid detection of organic impurities using direct analysis in real time coupled with high-resolution mass spectrometry | |
Monakhova et al. | Independent component analysis algorithms for spectral decomposition in UV/VIS analysis of metal-containing mixtures including multimineral food supplements and platinum concentrates | |
Gornushkin et al. | Insights into linear and rank correlation for material identification in laser-induced breakdown spectroscopy and other spectral techniques | |
Escarate et al. | X-ray fluorescence spectroscopy for accurate copper estimation | |
Xu et al. | Evaluation of cigarette flavoring quality via surface-enhanced Raman spectroscopy | |
CN110470645A (zh) | 检测白酒中西地那非的方法 | |
KR20110124868A (ko) | 비파괴적 6가 크롬 분석 방법 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 14847358 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 14847358 Country of ref document: EP Kind code of ref document: A1 |