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WO2011157781A1 - Procédé de spectrométrie de mobilité ionique - Google Patents

Procédé de spectrométrie de mobilité ionique Download PDF

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Publication number
WO2011157781A1
WO2011157781A1 PCT/EP2011/060000 EP2011060000W WO2011157781A1 WO 2011157781 A1 WO2011157781 A1 WO 2011157781A1 EP 2011060000 W EP2011060000 W EP 2011060000W WO 2011157781 A1 WO2011157781 A1 WO 2011157781A1
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WO
WIPO (PCT)
Prior art keywords
peak
analyte
ims
ion mobility
spectra
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/EP2011/060000
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German (de)
English (en)
Inventor
Günter RÖSEL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
STEP SENSORTECHNIK und ELEKTRONIK POCKAU GmbH
Zentrum fur Angewandte Forschung und Technologie Ev
Original Assignee
STEP SENSORTECHNIK und ELEKTRONIK POCKAU GmbH
Zentrum fur Angewandte Forschung und Technologie Ev
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by STEP SENSORTECHNIK und ELEKTRONIK POCKAU GmbH, Zentrum fur Angewandte Forschung und Technologie Ev filed Critical STEP SENSORTECHNIK und ELEKTRONIK POCKAU GmbH
Priority to DE112011102013.9T priority Critical patent/DE112011102013B4/de
Publication of WO2011157781A1 publication Critical patent/WO2011157781A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/62—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating the ionisation of gases, e.g. aerosols; by investigating electric discharges, e.g. emission of cathode
    • G01N27/622—Ion mobility spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7206—Mass spectrometers interfaced to gas chromatograph

Definitions

  • the invention relates to a method of
  • IMS Ion Mobility Spectrometry
  • the ion mobility spectrometry due to the high detection sensitivity even in the presence of minor outgassing the detection and the
  • Ion mobility spectrometers are currently used primarily for warfare detection and in the industry
  • Ion mobility spectrometer preferably at airports, in use.
  • the physical principle of ion mobility spectrometry is based on the different drift velocities of ions in the electric field in air at atmospheric pressure. It is also shown pictorially in FIG. 1. About one
  • radioactive beta emitters such as tritium, whose activity is approximately 50 MBq. If the analyte or the analyte mixture is not introduced together with the ambient air, but separately, this is done i.d.R.
  • Amphetamine pollutants such as organophosphorus compounds or halogenated hydrocarbons, aromatics, mercaptans, etc.
  • pollutants such as organophosphorus compounds or halogenated hydrocarbons, aromatics, mercaptans, etc.
  • the thus ionized air-analyte mixture from the ion source enters an electric field in which the ions are arranged according to their drift velocities. They walk in the drift tube the distance from the In elementsort to
  • Detection location ie a detector, such as a Faraday plate with shielding grid.
  • a detector such as a Faraday plate with shielding grid.
  • the ions are separated in a weak electric field, which according to their mobility against the
  • the drift rates are the molecular size, the molecular charge and the molecular shape
  • Ion mobility spectrum called waveform allows an analyte separation, since ions of the same charge but different mass and / or structure in the signal-time course maxima generate at different times.
  • Ionization reactions is caused.
  • three environmental chemicals benzene, toluene and xylene
  • benzene, toluene and xylene are dosed simultaneously into the IMS, only one peak is visible, the ion formation of the other compounds is suppressed due to their physico-chemical properties.
  • the combination of the ion mobility spectrometry with the chromatographic separation technique allows the representation of the peaks as a function of the drift and the
  • Retention time in the form of three-dimensional diagrams Instead of a representation of the peaks as a function of the pure drift time by the ion mobility spectrometer, the total retention time is used here.
  • Measuring conditions such as pressure and temperature during the acquisition of the IMS spectra, or even to improve the analysis capability, e.g. by temperature-induced separation of gas components and preventing condensation effects at higher temperatures
  • Threshold method is used. On this basis, peaks in chromatograms can be determined by appropriate
  • Chromatogram evaluation must be normalized.
  • Measurement data acquisition is possible. This results in a minimum duration until the measurement result of approx. 8 min, in which changed environmental parameters are to be expected.
  • IMS ion mobility spectrometry
  • Ion mobility spectrometer is ionized and thus forms the reaction ions, an analyte or a mixture of several analytes, ie a substance to be analyzed or a
  • Ion mobility spectrometer is initiated and either also directly ionized or a
  • Peak position, the peak height and the half width of the peak determined. These characteristic parameters peak position, peak height and peak half width are normalized taking into account the current measurement conditions and thus a
  • Noise reduction techniques use differences in frequency or time domain as well
  • Noise reduction dependent, adaptive digital filtering for smoothing and group delay equalization is performed. Such preprocessing minimizes errors in the calculation of the characteristic parameters peak position and peak height.
  • Daubechies-4 wavelet performed at the extensive simulation calculations to the best results. Daubechies wavelets are exactly time-limited. Although the spectrum is not band limited, it falls off quickly to low and high frequencies, so that in addition to the good time selectivity also a good effective
  • Daubechies-4-wavelet it is relatively edgy.
  • u (t) contains in the time intervals 0 ⁇ t ⁇ ti and t 2 ⁇ t ⁇ t M practically no measurement information.
  • the times ti and t 2 are usually unknown and must be estimated.
  • Time interval ti ⁇ t ⁇ t 2 are usually in all
  • Definitions for the course and magnitude of g (t) can be empirically derived from the estimates for the statistical
  • Input measurement signal is therefore an analogue
  • Digital conversion is available as a discrete-time signal.
  • Alpasses ie electrical filters, which in the ideal case have a constant magnitude frequency response for all frequencies, while the phase shift depends on the frequency, are characterized by a frequency-independent
  • Peak positions of the analytes can be given in the form of a difference distance to the reaction peak and this
  • Temperature correction is subjected.
  • the position of the peaks of the analytes can be specified independently of the measurement conditions: The same amounts are always obtained for the peak concentrations of the analytes for the same system to be analyzed, even if the temperature and pressure conditions differ greatly for different measurements. This makes measurements comparable to different temperature and pressure conditions comparable.
  • temperature and pressure-independent reference values can also be generated and used again for subsequent measurements.
  • the position or time position of the reaction ion peak (RIP) depends on a number of device parameters as well as on the measurement and ambient conditions. These influencing factors influence the position of the wanted peaks in the IMS spectrum to the same or at least similar extent. In order to increase the selectivity of the measurement method, the time intervals of the peaks in the IMS spectrum to the reaction ion peak (RIP) are virtually invariably determined in order to exclude a considerable proportion of errors of unknown size.
  • Reaction peak peaks of the maximum peak charge in the IMS spectrum. Depending on the sample concentration, however, the number of reaction ions decreases
  • the RIP position can not be found in a number of the determined IMS spectra, it must be determined by an adaptive estimation. For this it is useful to distinguish the following peak types in the IMS spectrum: Peak type 0: maximum value in the IMS spectrum, U
  • Peak type 1 relative maxima in the IMS spectrum with U p ⁇ U peak type 2: time-concealed peaks on a falling or rising pulse edge
  • Peaks of types 0 and 1 are characterized by the zero crossing of the first order difference quotient u '(n) as well as negative values of the second order difference quotient u "(n).
  • min 0 (3)
  • the RIP positions of the reference spectra in the positive (p) and negative (n) measurement modes as well as the corresponding pressure and temperature data are needed to calculate the RIP positions during the measurements.
  • the respective last spectrum of the positive measurement mode or the negative measurement mode sequence is declared as a positive reference spectrum or negative reference spectrum.
  • the algorithms are independent of the measuring mode (positive or negative). The following process steps are required:
  • the method according to the invention therefore limits the total analyzable range only to the part which is essential for determining the analyte. This makes it possible to evaluate the spectra on the essential peaks and hereby as described on their
  • Multivariate methods such as clustering and classification, methods of dimensionality reduction and pattern recognition allow in conjunction with an effective
  • Cluster analysis takes place, with the temperature and pressure conditions measured at a suitable point
  • the total retention time of an analyte is understood to be the time which the corresponding analyte has for its
  • the injection site located either within the ion mobility spectrometer or in the ion mobility spectrometer
  • upstream device is needed to the detection site, and the retention time in the
  • Cluster analysis requires effort-based constraints on a small number of relevant features. This requirement is met by the proposed approach by dividing the drift time interval into a correspondingly selectable number of regions, possibly of different widths. This process concept makes it possible in principle, under real-time conditions, reference data from current
  • Measurement conditions are automatically taken into account and, if necessary, allow subsequent normalization for Entries in a substance database too.
  • the principle is also applicable for IMS measuring tasks, where a
  • Gas chromatographic pre-separation can be omitted.
  • the cluster analysis is applied to a sequence of IMS spectra at equidistant time intervals. Such an analysis is simplified on a regular basis
  • Method is able to provide complete analysis data and thus analysis-important data not due to a
  • Retention time are compared and at appropriate
  • Peak height and peak half width approximately estimated peak area the respective analyte concentration are estimated.
  • Analytical capability for subsequent analyzes of unknown analytes or analyte mixtures series of measurements with known analytes or analyte mixtures are carried out such that in the typical for the known analytes Search ranges of the obtained spectra, the characteristic parameters are determined and selected and for multiply successively recorded IMS spectra with the same cluster features in one or more search areas and taking into account the real measurement conditions, these sets of characteristic for the respective analyte or analyte mixtures parameters as reference parameter sets for two- or three-dimensional spectra declared and registered in a substance database.
  • Measurement series with unknown analytes or analyte mixtures can then be carried out with the method according to the invention
  • Search ranges are defined as reference search ranges for the analyte or the analyte mixture, the corresponding characteristic parameters as
  • Reference parameters for two- or three-dimensional spectra are declared and used for recognition and recognition of the same analyte or analyte mixtures and registered in a substance database.
  • drift time intervals for determining the analytes or the analyte mixture will make the decisive contribution.
  • analyte or the analyte mixture can be evaporated without decomposition. This makes it possible, especially for analytes or mixtures of analytes, whose peaks in direct use of a
  • Ion mobility spectrometer composed. The temperature and pressure conditions in the gas chromatographic
  • Fig. 1 shows the structure of a typical
  • Fig. 2 shows the problem of assignability of IMS spectra in analyte mixtures, as is usually in
  • Fig. 5 shows a sequence of impulse responses of
  • FIGS. 6a to 6c show possible typical IMS spectra, wherein FIG. 6a shows an IMS spectrum with the peak of the reaction ion peak, FIG. 6b shows an IMS spectrum with a formed and labeled IMS spectrum
  • Fig. 7 shows an IMS spectrum with a pronounced
  • FIG. 8 shows a typical table with the estimates of the peak half-value widths as a function of the drift time of the IMS spectrum shown in FIG. 2, FIG.
  • Fig. 10 shows a diagram with the drift times of known, registered in the substance database analytes based on the position of the reaction ion peak, wherein for two selected drift time intervals the characteristic parameters
  • Peaklage and peak height are shown as reference values with tolerances for the Peaklage
  • Fig. 11 is a diagram showing the response time depending on the drift time of the reaction ion peak
  • FIG. 12 shows the values calculated separately for both drift time intervals for the quadratic Euclidean distance as a result of the cluster analysis for two different ones
  • FIG. 13 shows five diagrams with the characteristic parameters Peaklage and., Which are determined metrologically as a function of the drift time related to the reaction ion peak
  • FIG. 13c time T_n + 2 for two selected ones
  • FIG. 14 shows the values calculated separately for both drift time intervals for the quadratic Euclidean distance as a result of the cluster analysis for five different ones
  • FIG. 15 shows the assignment in a matrix
  • the table of FIG. 16 illustrates in the form of a table the assignment of the column number j in FIG. 16
  • FIG. 17 shows a relative example
  • the ion mobility spectrometers 1 described above are coupled by coupling with a gas chromatographic device, shown only schematically here, e.g. one
  • Multicapillary column known, produced from a series of IMS - spectra existing three-dimensional data structures, which are often given in the form of IMS chromatograms, also not shown here.
  • FIG. 3 shows a typical IMS spectrum.
  • Ambient air or the air-sample mixture enters an ion source and is by means of a weak radioactive Beta emitter, such.
  • a weak radioactive Beta emitter such as tritium having an activity of 50 MBq is ionized.
  • FIG. 4 shows an IMS spectrum with a
  • First and second order difference quotients which are based on a wavelet - based noise reduction method and an effort - optimized adaptive smoothing with
  • the noise reduction by means of wavelet thresholding leads to an increase in the "angularity" of the noise-reduced IMS measurement signal, which, for reasons of cost, expediently results from a low-pass filtering by means of a time-discrete
  • derivable signal properties adaptively selects a suitable impulse response.
  • impulse responses are used in addition to
  • FIG. 5 shows by way of example
  • Peak position of the maximum value to determine Within a tolerance range to be determined in each case, which is corrected for temperature and pressure, the RIP position has to be defined, whereby this is done weighted according to the peak height and the degree of peak shaping. In addition, a moving averaging of the calculated peak positions, the temperature and vanishing amplitude occurs
  • FIGS. 6a to 6c show by way of example the method for different qualities of the reaction ion peak: While in the IMS spectrum of FIG. 6a the reaction ion peak
  • the reaction ion peak in the IMS spectrum of Fig. 6b is still formed, but has only a very low peak height, and in the IMS spectrum of Fig. 6c, the reaction ion peak is no longer detectable, so that calculated position of the reaction ion peak, which is marked accordingly in the diagram.
  • the RIP position forms a suitable reference basis for the standardized pressure, temperature and device parameter related representation of the metrologically recorded characteristic parameters of the analyte peak position and peak amplitude in the IMS spectrum in FIG.
  • Analyte concentration are those from the respective peak height and the value of the difference quotient second order calculated estimates for the peak half-value widths, which are entered here in the table in Fig. 8.
  • Device parameters sufficiently describe the respective current IMS spectrum as well as all previously acquired IMS spectra belonging to the data set.
  • the IMS-typical drift time is divided into a selectable number of drift time intervals of the same or different interval width and the
  • FIG. 10 shows, by way of example, the corresponding reference parameters independent of T_retent_l and labeled with
  • Fig.11 shows the corresponding ratios to a
  • Time T_retent_2 compared to time T_retent_l. In this case, there is no pattern conformity in
  • Drift time interval Dl is one
  • Drift time interval Dl a good match between the measurement and the reference parameters of FIG. 10 for the
  • Drift time interval D2 has no or only a small similarity.
  • FIG. 12 Also shown in FIG. 12 are the values for the relative quadratic Euclidean distance, calculated for the time T_retent_2, of the cluster analysis performed separately for the two drift time intervals D1 and D2.
  • Reference parameters diagnosed in the drift time interval Dl is no or only a small similarity.
  • Drift time domains Dl and D2 to be assigned analyte is determined by the relationship
  • Delta_T_retent_2, 1 T_retent_2 - T_retent_l (10).
  • Retention times or retention time differences can be corrected or normalized by taking into account the operating temperature in the gas chromatographic separator.
  • the results of the cluster analysis which are based on the characteristic parameters used, can be compared with corresponding entries for the drift and retention time and, if they match, the corresponding analytes can be identified.
  • the peak height or from the peak area the approximately by
  • characteristic parameters of. can be determined from current series of known analytes or analyte mixtures
  • Measuring conditions can be assigned and therefore
  • FIGS. 13a to 13e show diagrams with the characteristic parameters measured as a function of the drift time
  • Pattern matching at equal drift time intervals in Figure 14 implies that these IMS spectra are time-interval reference spectra with respect to drift represent and therefore assign to the corresponding measured information parameters the property as a reference parameter. From current measurement series of an unknown analyte or analyte mixture further characteristic parameters of a selectable number can advantageously
  • Drift time intervals selected in accordance with the measurement conditions and in case of multiple immediate
  • Search ranges are defined as reference search ranges for the analyte or the analyte mixture. These corresponding characteristic parameters can then be declared as reference parameters for three-dimensional or in special cases for two-dimensional spectra and for entries in a substance database for
  • FIG. 15 shows in the form of a matrix
  • the respective IMS spectrum corresponds to the line number i, the column number j characterizes the drift time interval within the IMS spectrum.
  • FIG. 16 illustrates in the form of a table the assignment of the column number j in FIG. 15 to the drift time interval t_j_min to t_j_max.
  • the matrix elements m_ij in Fig. 15 indicate the number
  • the value of 18 is assigned here by way of example to the matrix elements m_3 7 to m_20.
  • the determination of the frequency that can be realized by forming the column sums determines the selection of relevant ones
  • FIG. 17 shows by way of example a weighted relative
  • the characteristic parameters which are detected in the relevant drift time intervals are then assigned to the analyte mixture as reference parameters and thus enable the recognition of similar analyte mixtures.

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Abstract

L'invention concerne un procédé de spectrométrie de mobilité ionique (IMS), en particulier un procédé correspondant de détermination des analytes. Elle a pour objet de développer un procédé permettant d'exploiter automatiquement en temps réel des spectres IMS enregistrés dans des conditions de mesure variables afin de pouvoir déterminer les analytes avec une grande précision tant du point de vue qualitatif que quantitatif. Cet objet est réalisé par un procédé IMS, dans lequel, lors d'une étape de prétraitement, on soumet chaque signal du spectre IMS de la série de mesures à un lissage adaptatif et à une égalisation du temps de propagation de groupe, on forme des quotients différentiels du premier et du second ordre et, pour chaque pic, on détermine les paramètres caractéristiques que sont la position du pic, la hauteur du pic et la largeur de valeur moyenne du pic et on les normalise en tenant compte des conditions de mesures réelles, et pour finir on identifie les analytes à partir des paramètres caractéristiques normalisés décrivant les pics au moyen d'un procédé de reconnaissance de formes. Ceci peut se faire au choix avec et sans dispositif de séparation par chromatographie en phase gazeuse placé en amont du spectromètre de mobilité ionique.
PCT/EP2011/060000 2010-06-17 2011-06-16 Procédé de spectrométrie de mobilité ionique Ceased WO2011157781A1 (fr)

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DE112011102013.9T DE112011102013B4 (de) 2010-06-17 2011-06-16 Verfahren für die lonenmobilitätsspektrometrie

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DE102010030254.6 2010-06-17
DE102010030254 2010-06-17
DE102010063990 2010-12-22
DE102010063990.7 2010-12-22

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DE102013100693A1 (de) 2012-01-24 2013-07-25 Step Sensortechnik Und Elektronik Pockau Gmbh Verfahren zur Identifizierung und Klassifizierung von Geruchsmustern und anderen Multikomponentengemischen aus Ionenmobilitätsspektren
CN103868981A (zh) * 2012-12-12 2014-06-18 中国科学院大连化学物理研究所 定性识别样品中一种或二种以上特定物质的方法
CN103884767A (zh) * 2012-12-21 2014-06-25 中国科学院大连化学物理研究所 一种提高离子迁移谱仪识别检测性能的方法
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CN103868981A (zh) * 2012-12-12 2014-06-18 中国科学院大连化学物理研究所 定性识别样品中一种或二种以上特定物质的方法
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CN104111281A (zh) * 2013-04-18 2014-10-22 中国科学院大连化学物理研究所 一种快速分析酒中塑化剂的方法
CN106198702A (zh) * 2015-05-06 2016-12-07 中国科学院大连化学物理研究所 一种快速检测唾液中毒品的方法
CN106198702B (zh) * 2015-05-06 2019-01-25 中国科学院大连化学物理研究所 一种快速检测唾液中毒品的方法
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CN106527169A (zh) * 2017-01-20 2017-03-22 深圳大图科创技术开发有限公司 基于蓝牙的智能家居控制系统
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CN111141860A (zh) * 2019-12-19 2020-05-12 中国农业科学院茶叶研究所 一种基于离子迁移谱的绿茶香气动态指纹分析及香型判别方法
CN111141860B (zh) * 2019-12-19 2023-01-10 中国农业科学院茶叶研究所 基于离子迁移谱的绿茶香气动态指纹分析及香型判别方法
CN111443371B (zh) * 2020-04-20 2022-06-17 山东省科学院海洋仪器仪表研究所 一种海水放射性核素峰漂移的判断方法
CN111585663A (zh) * 2020-04-20 2020-08-25 杭州华立电力系统工程有限公司 低压电力线载波通信特征干扰噪声的复现方法
CN111443371A (zh) * 2020-04-20 2020-07-24 山东省科学院海洋仪器仪表研究所 一种海水放射性核素峰漂移的判断方法
US12352726B2 (en) 2020-05-18 2025-07-08 Nuctech Company Limited Trace detection device
WO2021233210A1 (fr) * 2020-05-18 2021-11-25 同方威视技术股份有限公司 Dispositif de détection de trace
CN111693625A (zh) * 2020-06-23 2020-09-22 中山市食品药品检验所 一种基于gc-ims鉴定燕窝中掺假物的方法及应用
CN114062585A (zh) * 2020-08-04 2022-02-18 戴安公司 用于在色谱图中鉴定分析物的峰分布
CN112098502B (zh) * 2020-09-15 2023-04-18 中国科学院空天信息创新研究院 利用多离子峰标定离子迁移谱仪的检测方法
CN112098502A (zh) * 2020-09-15 2020-12-18 中国科学院空天信息创新研究院 利用多离子峰标定离子迁移谱仪的检测方法
CN114694771A (zh) * 2020-12-31 2022-07-01 清华大学 样品分类方法、分类器的训练方法、设备和介质
CN112782320B (zh) * 2021-02-24 2022-07-22 广东省药品检验所（广东省药品质量研究所、广东省口岸药品检验所） 一种用于鉴别北柴胡与藏柴胡的方法及其应用
CN112782320A (zh) * 2021-02-24 2021-05-11 广东省药品检验所（广东省药品质量研究所、广东省口岸药品检验所） 一种用于鉴别北柴胡与藏柴胡的方法及其应用
CN113610817A (zh) * 2021-08-11 2021-11-05 贵州中烟工业有限责任公司 一种特征峰识别方法及计算设备、存储介质
CN113610817B (zh) * 2021-08-11 2024-03-26 贵州中烟工业有限责任公司 一种特征峰识别方法及计算设备、存储介质
CN114113391A (zh) * 2021-11-24 2022-03-01 贵州中烟工业有限责任公司 一种膨胀介质含量检测方法
CN114113391B (zh) * 2021-11-24 2024-03-26 贵州中烟工业有限责任公司 一种膨胀介质含量检测方法
CN115307715A (zh) * 2022-08-09 2022-11-08 南昌航空大学 一种基于sagnac光纤声传感系统的改进小波去噪方法
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