WO2011157781A1

WO2011157781A1 - Method for ion mobility spectrometry

Info

Publication number: WO2011157781A1
Application number: PCT/EP2011/060000
Authority: WO
Inventors: Günter RÖSEL
Original assignee: Step Sensortechnik Und Elektronik Pockau Gmbh; Zentrum für angewandte Forschung und Technologie e.V.
Priority date: 2010-06-17
Filing date: 2011-06-16
Publication date: 2011-12-22
Also published as: DE112011102013A5; DE112011102013B4

Abstract

The invention relates to a method of ion mobility spectrometry (IMS), in particular a corresponding method for determining the analytes. It is the object of the invention to develop a method with which IMS spectra, recorded under varying measurement conditions, are automatically evaluatable in real time such that a qualitative and also a quantitative determination of the analytes with high accuracy is possible. This is achieved by way of an IMS method, in which from each IMS spectrum signal of the measurement row adaptive smoothing and group delay equalization are carried out in a pre-processing step, first-order and second-order difference quotients are formed and for each peak the characteristic parameters peak position, peak height and full width at half maximum of the peak are ascertained and normalized taking into account the current measurement conditions, and finally the analytes are identified on the basis of the normalized characteristic parameters that describe the peaks using a method for pattern recognition. This is possible optionally with and without connecting a gas chromatographic separation device upstream of the ion mobility spectrometer.

Description

Method for ion mobility spectrometry

The invention relates to a method of

Ion Mobility Spectrometry (IMS), in particular a corresponding method for the determination of the analytes.

The ion mobility spectrometry, due to the high detection sensitivity even in the presence of minor outgassing the detection and the

Identification of environmental and industrial pollutants, chemical warfare agents, explosives and drugs.

Ion mobility spectrometers are currently used primarily for warfare detection and in the industry

Workplace monitoring used. Due to the

specific features of this technology, in which air can be used as a carrier gas, the devices are cheaper to produce than, for example

Analytical instruments such as mass spectrometers or

chromatographic analyzers. In addition, allow

Ion mobility spectrometer to a high degree

Miniaturization. The detection of traces of explosives or drugs currently has more than 80,000 worldwide

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

Gas inlet into the ion source, the ambient air or the air-analyte mixture in an ion source at Introduced into the ion mobility spectrometer in the ionization site and ionized. This can be done, for example, by means of a weak

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.

by means of a transport gas. This arrives at a membrane through which the still neutral analyte molecules migrate and thus likewise enter the ion source. During ionization, positive ions are produced from constituents of the air of the types NH ⁺ , NO ⁺ , (H ₂ O) nH ⁺ , which form the positive reaction ion peak (RIP ⁺ ). Negative ions of the type 0 ₂ ^~ and (H ₂ 0) ^~ _m form the negative reaction ion peak (RIP ^~ ). These ions of air (RIP ⁺ ) and (RIP ^~ ) are in the

Ion mobility spectrometer constantly available.

If other molecules, such as the analytes (e.g.

Amphetamine, pollutants such as organophosphorus compounds or halogenated hydrocarbons, aromatics, mercaptans, etc.) in the air or in the air-analyte mixture, there is a charge transfer of the reaction ions to the

Pollutant molecules instead. This charge transfer can be simplified as follows: RP ⁺ + M> RP and M ⁺ (positive mode)

RP ^~ + M> RP and M ^~ (negative mode)

By means of electrical impulses on a control grid, 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 ektionsort to

Detection location, ie a detector, such as a Faraday plate with shielding grid. During this "migration" the ions are separated in a weak electric field, which according to their mobility against the

Flow direction of an inert gas happens. The time taken by each type of ion for this "hike" is called its drift time, which is specific to the particular type of ion, as long as the measurement conditions (such as pressure and temperature) are kept constant the interactions of the ionic species with its environment on the way from

Injection to the detection site or its mobility determined. Thus, the drift rates are the molecular size, the molecular charge and the molecular shape

dependent. The different drift rates cause different types of ions to

different times the detection site, so the

Collector electrode, reach.

The recorded at the detection site, as

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.

A disadvantage, however, when using the now widely used methods for the determination and evaluation of IMS spectra, that in the typical for many applications

Analysis of multicomponent mixtures

Overlapping effects occur that are too significant

Misinterpretations can lead. This problem is troublesome, for example, in the study of mixtures, as often in environmental analysis

occur and in the sole application of the usual methods of ion mobility spectrometry caused by the ongoing ionization reactions. The following example, which is shown in FIG. 2, illustrates this problem, which arises in the study of mixtures, such as are common in environmental analysis, and in the sole application of the

Ion mobility spectrometry by the expiring

Ionization reactions is caused. In the example, three environmental chemicals (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.

Analogous to the combination of mass and gas chromatography, ion mobility spectrometers also have the

Possibility of gas chromatographic pre-separation of the analyte mixture with a gas chromatographic

Separator, e.g. by means of multi-capillary columns, whereby the effect mentioned can be avoided.

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. These

Total Retention Time is composed of the

Retention time within the gas chromatographic

Separator and the drift time through the

Ion-mobility spectrometer .

The representations in the form of so-called chromatograms are obtained when the signal size as a function of drift and retention time or the composite thereof

Total retention time is mapped. In principle, there is the possibility of being dependent on the position in the chromatogram

However, these are the "patterns" to be evaluated with image processing methods and the analytes sought Spectrometric methods characterized by a high volume of generated data that can hardly be handled without the means of computer-aided statistics and in particular under real time conditions requires effective data preprocessing.

In particular, however, if there are changes in the

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

Temperatures that are desired are in their analysis

additional corrective action is required to complete the

To normalize measured spectra and with sufficient certainty to make qualitative and quantitative statements on the analyte or the analyte mixture and to reduce the risk of misinterpretation. These

Normalization of the data requires an additional considerable amount of computation. For this there is still no satisfactory solution.

The problem is exacerbated once the measurement methodology on the three - dimensional case by comparing the

measured IMS chromatograms is extended with appropriate reference chromatograms.

An approach is described in DE 10 2007 059 461. In this method, the information is obtained by the evaluation of the complete chromatogram, wherein to reduce the numerical effort

Threshold method is used. On this basis, peaks in chromatograms can be determined by appropriate

Parameters are described and identified. As a rule, the individual spectra belonging to the chromatogram are also subjected to preprocessing in these methods, with the Meßdatenverfälschungen by noise, environment and device parameters to be minimized. Even after preprocessing the single spectra, the chromatogram contains millions of data points, most of them in the

Noise are or are redundant and therefore only a comparatively small amount of information is needed to characterize the analytes. Another problem is that the complete analysis data has waveforms that are strongly affected by the

Environmental conditions depend and therefore before the

Chromatogram evaluation must be normalized.

This is for example the result of a series of 500

Single spectra with 2000 data points existing chromatogram decomposed using a threshold value method so that related peak sequences aufein ^¬ subsequent individual spectra can be described by elliptical parameters and identified. The reaction ion peak serves as the reference value of the measured value sequences.

For the application-relevant case of a completely vanishing reaction ion peak, no solution is explicitly stated here.

Another disadvantage is that in the above method, an analysis of the results only after the complete

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.

For the known application areas, such as

Explosive detection and process monitoring in the general industry, the accuracy of the quantitative statements was sufficient. With increasing sensitivity and the consequent new fields of application, e.g. in medicine for respiratory diagnostics and the

Biofilm detection, or in the semiconductor industry must now also additionally the quality of the quantitative statements be decisively improved.

The moisture contained in each sample has a significant impact on the quantitative

Reading statement. The possibility of using higher

Working temperatures would therefore also be desirable for this purpose.

Last but not least, the use of the

Ion mobility spectrometry for many of the purposes listed above, a mobile IMS device and in particular a

corresponding procedure, which offers high-quality signal analysis qualitatively and quantitatively despite mobility.

It is an object of the present invention to develop a method of ion mobility spectrometry, with which IMS spectra recorded under varying measuring conditions can be automatically evaluated in real time in such a way that qualitative and quantitative determination of the analytes is possible with high accuracy.

This object is achieved by a

Method of ion mobility spectrometry (IMS) with the features of claim 1. Claims 2 to 12 represent embodiments of this solution according to the invention.

In a method of ion mobility spectrometry (IMS), in which a known gas or gas mixture in

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

corresponding mixture of substances, optionally by means of a carrier gas, in the area of action of a

Ion mobility spectrometer is initiated and either also directly ionized or a

Charge transfer from the reaction ions to the atoms or Molecules of the analyte to be analyzed takes place, and this Reaktionsionen- and positively or negatively charged analyte mixture traversed the drift region between the injection and the detection range of the ion mobility spectrometer, a time-dependent series of measurements of IMS spectra of this Reaktionsionen- and positively or negatively charged analyte mixture in the detection range of

Recorded ion mobility spectrometer and analyzed the IMS spectra of the series so that qualitative and / or quantitative statements regarding the analyte or analyte mixture are given.

According to the invention, for each IMS spectral signal of the series of measurements in a preprocessing step, a

adaptive smoothing and group delay equalization

performed and difference quotients first and second order formed. From this, for each peak, the characteristic parameter describing the peak becomes

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

Identification of the analytes from the normalized, describing the peak characteristic parameters for the currently known spectra sequences performed by a pattern recognition method.

On the one hand, the exclusive description of the peaks in the IMS individual spectrum by the characteristic parameters mentioned does not lead to information losses in the subsequent pattern recognition or the immediate one

Determination of the analytes or components of the

Analytengemischs. On the other hand, this procedure also allows the description of partially hidden peaks. In addition, the required numerical effort for normalization and detection of the drift time under real time conditions is minimized by this approach. Measurement signals with low signal power are usually superimposed by interference signals that are too significant

Falsifications of the characteristic to be determined

Parameters can lead. Noise reduction techniques use differences in frequency or time domain as well

different statistical properties of both

Signal components. Therefore, in an advantageous embodiment, the

Method characterized in that for each IMS single spectrum first a wavelet-based smoothing and then a thresholding result for

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.

The limitation of peak detection and peak description to absolutely relevant measurement information, as well as the possible

Calculation of the peak half width from the peak height and the value of the difference quotient of the second order for each particular peak of the IMS spectrum assuming a Gaussian or cosine squared curve of the IMS peak cause the desired and for a

instantaneous evaluation of the data and immediate determination of the analytes required redundancy reduction.

Due to the process, this also gives estimates for partially hidden peaks, so that a complex baseline correction can usually be dispensed with.

Examinations show that to solve the problem exactly time-limited wavelet systems with low

Coefficient number needed.

The 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

Band limitation is realized. The shortest is that

Daubechies-4-wavelet, it is relatively edgy.

As the number of coefficients increases, the wavelets are rounded off, while the number of coefficients increases

Frequency selectivity.

Daubechies wavelets are among the most commonly used wavelet transforms for the digital

Signal analysis and data compression.

Of particular note is the ease of implementation as fast wavelet transformation (Fast wavelet

Transformation, FWT).

Based on the measured signals thus determined, a noise reduction in the IMS spectra is achieved by means of wavelet thresholding. In principle, the following are

Assumptions for the signal made - which can then be realized by an appropriate parameter selection: 1.) 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. The approximate determination of ti and t ₂

allows in principle the determination of statistical

Parameters for the waveforms in the different resolution levels j (also called level) of the wavelet transformation.

2.) Significant signal components in the measuring signal in

Time interval ti <t <t ₂ are usually in all

Resolution levels (level) of the wavelet transform clearly from the sizes in the time intervals 0 <t <ti and t ₂ <t <t _M to distinguish. This property makes it possible to control the proportion of the high-pass filtered signal component to be taken into account during signal reconstruction by the inverse discrete wavelet transformation (IDWT) by means of a multiplicative evaluation with a factor g (t) in the range of zero to one , The

Definitions for the course and magnitude of g (t) can be empirically derived from the estimates for the statistical

Obtain signal parameters of the high-pass filtered signal components and optimize them on the basis of simulation calculations.

In order to eliminate the increase in the "edginess" of the noise-reduced IMS measurement signal associated with the wavelet thresholding, a wavelet-based controlled low-pass filtering was also developed and discrete-time

Folding realized.

The group delay equalization is corrected

realized as follows:

The required digitization of analog source signals sets frequency-band-limited functional characteristics before the

Analog-to-digital conversion ahead. Scanned functions have a frequency spectrum which is periodically continued at the sampling frequency f _a . The unambiguous description of an analog signal by its samples assumes that the

Overlapping (aliasing) of the periodically continued spectra is safely avoided.

It is expedient to limit the signal in frequency so that at minimum bandwidth as possible

Measurement information is lost. There is the

Possibility to eliminate with the analog filter all spectral components, which are above that for the measuring signal

significant frequency limit.

This additional noise reduction is achieved, also will not be explained here other disorders eliminated.

The frequency band limited by means of an analog filter

Input measurement signal is therefore an analogue

Digital conversion is available as a discrete-time signal.

That with a Butterworth analog filter, so a filter for signal processing, which is designed so that the achieved frequency response in the transmission range is as flat as possible, evaluated magnitude spectrum largely corresponds to the requirements for a distortion-free

Transmission of a measurement signal of high bandwidth, while the Phasenfrequenzgang or the group delay behavior leads to significant linear signal distortion when the passband of the analog filter is adapted to the significant part of the signal spectrum. A realizable within the following digital signal processing phase or group delay correction allows the

Minimization of linear signal distortions. There at

Using a Butterworth filter that

Signal spectrum can be considered optimal, an all-pass is required for the digital phase correction. 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

Amount history and one dependent on the filter degree

Variation range for the phase off.

It is now particularly favorable in the process according to the invention, the position of the reaction ion peak pressure and

temperature dependent to capture and replace this reaction ion peak in the event of its complete disappearance over a limited period of time by a current temperature and pressure corrected moving average. In contrast to previously existing method allows this, even with a temporary lack of the reaction ion peak to produce a reference value, the respective

Temperature and pressure situation in the system corresponds, and on with the measured at that time peaks of the

Analytes can be referenced and thus their Peaklage can be determined exactly.

In particular, it is advantageous that the determined

Peak positions of the analytes can be given in the form of a difference distance to the reaction peak and this

Difference distance also a pressure and

Temperature correction is subjected. Thus, 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. In addition, 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.

Under constant measuring conditions (such as pressure and temperature) and with stable device parameters (such as the field strength E) the timing of the RIP position is practical

constant and variations are only on the order of magnitude of the temporal quantization error of ± 10ps.

Pressure and temperature changes and the polarity change, however, lead to a not negligible

Change of position of RIP positions.

The correction of the RIP position after the derivable

Relationship t _D (RIP) too (RIP) (1)

is usually faulty, since p and T can not be determined directly in the drift space on the one hand and only with a corresponding dead time error on the other hand.

Therefore, it is necessary to take the current RIP position for each measurement directly from the IMS spectrum.

In the simplest case, the peak position of the

Reaction peak peaks of the maximum peak charge in the IMS spectrum. Depending on the sample concentration, however, the number of reaction ions decreases

Charge transfer, so that the RIP position no longer correlates with the detector current maximum.

The evaluation of measurement series shows that several consecutive IMS spectra can arise, which have no relative maximum at the expected RIP position. This property has physical causes and must therefore be taken into account in automatic RIP position detection.

If 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). U" (n) <0 (2) u' (n) | _min = 0 (3)

For the peak type 2, u "(n) <0 (4)

| u '(n) | _min > 0 (5)

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.

To determine the parameter of the reference spectrum, a sequence of approximately eight to ten spectra per measuring mode

needed to cope with the temperature and temperature errors

Reduce pressure measurement by averaging.

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. With the exception of the specific quantitative information, the algorithms are independent of the measuring mode (positive or negative). The following process steps are required:

(1) Input of the starting value RIP ₀ ( _n , _P ) and the limits RIP _h ( _n , _P ) and RIPi ₍ n _{fP) of} the tolerance range

(2) Calculation of moving averages of pressure p and temperature T

( ⁶ )

^) =) Z ^) «= ^ / _(B ,,) = 7 ⁷ (7)

* i = 0

(3) Pressure- and temperature-dependent correction of the values mentioned under (1): RIP _{corr (0} , h, i) = RIP ₍ o, n, i) d + (p (i) - p) lp) (\ - (T (i) -T) lf) (8)

(4) Limitation of the tolerance or search range for the expected value RIP _ref . (5) calculation of the estimate RIP _ref .

The calculation of the RIP position during the measurements of the analyte spectra is carried out according to a similar algorithm, but modifications must be carried out in such a way that in each case the RIP position of the reference spectrum must be referred to and thus a twofold

Correction must be carried out.

A decisive step for the reduction of the data volume in the analysis procedure and thus for the particularly fast and safe determination of the analytes consists in the

Limitation of pattern recognition in the IMS single spectra to the characteristic parameters peak position, peak height and Peak half width, as well as a selectable number of search areas within the drift time, which may be the same or different interval width. These

Search areas are also called drift time intervals below.

Depending on the analyte to be determined, there are always areas in the IMS spectra that are less relevant for the corresponding evaluation. In an advantageous embodiment of the

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

characteristic parameters peak position, peak height and

To limit peak half width. By eliminating the non-relevant analyte peaks, the analysis of which would also be costly, but which do not provide a decisive contribution to the desired analyte determination, so the computational effort, which is necessary for unambiguous determination of the analytes, further reduced and the efficiency of

Pattern recognition increased.

Multivariate methods, such as clustering and classification, methods of dimensionality reduction and pattern recognition allow in conjunction with an effective

Preprocessing of data to extract specific

Properties of the individual spectra statistically verified statements regarding the comparison of several series of measurements with similar multicomponent mixtures. In a particular variant of the invention

Procedure is the respective provision of

Total retention time of an analyte under

Real time conditions, i. during the current measurements, realized by a temporally successive evaluation of all search areas, ie the selected

Drift time intervals, each IMS single spectrum by means of a related to the respective search area

Cluster analysis takes place, with the temperature and pressure conditions measured at a suitable point

Normalization of the retention time data can be used, and this method is repeatedly applied to a recorded at defined intervals sequence of IMS spectra. In this case, the total retention time of an analyte is understood to be the time which the corresponding analyte has for its

Migration from a defined starting location, 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

Ion mobility spectrometer upstream device and the drift time in the ion mobility spectrometer

includes. The sufficiently accurate pattern recognition by means of

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

Select substance measurement series. Environment or

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. In a particular variant of the invention

Method, the cluster analysis is applied to a sequence of IMS spectra at equidistant time intervals. Such an analysis is simplified on a regular basis

in turn, the corresponding analysis in many cases, since it is guaranteed that the data will ideally be analyzed at such regular intervals in which the

Method is able to provide complete analysis data and thus analysis-important data not due to a

lost randomly chosen larger distance.

It is cheap and easy to implement with the inventive method that the results of

Cluster analysis with known entries in a substance database for drift or

Retention time are compared and at appropriate

Match the corresponding analytes are identified. In addition, from the peak height or from the

Peak height and peak half width approximately estimated peak area the respective analyte concentration are estimated. Thus, both a qualitative analysis of the analyte (s) in question and a corresponding quantitative analysis of the corresponding analyte are possible by the method according to the invention. In particular, with the inventive method, for the purpose of completing and improving the

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

be carried out that their characteristic parameters in a selected number of search areas of the same or different interval width, the drift time intervals, are determined and selected taking into account the real measurement conditions, for several times immediately

consecutive IMS spectra with domain-specific similar cluster features in one or more

Search ranges, these 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.

In the case of completely unknown analytes or mixtures of analytes, it may be advantageous to have the search areas consecutively consecutive, since this may not be the case here

It can be seen which drift time intervals for determining the analytes or the analyte mixture will make the decisive contribution.

It is in a special variant of the according to the invention, that the analyte or the analyte mixture before entering the

Ion mobility spectrometer a the

Ion mobility spectrometer upstream

Gas chromatographic separator happens.

The prerequisite for this is that the 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 without upstream

Gas chromatographic separator very close to each other or even partially overlapped, the time to better differentiate Peaklagen. In the subsequent determination of the peaks, the fact that a

Gas chromatographic separator was preceded, of course, taken into account at this point the

Total retention time is used, resulting from the retention time within the gas chromatographic

Separator and the drift time through the

Ion mobility spectrometer composed. The temperature and pressure conditions in the gas chromatographic

Separator can thereby to normalize the

Retention times are used.

If a gas chromatographic separator the the

Ion mobility spectrometer upstream, it is usually during the total time of analysis of the IMS spectra, the connection between the gas chromatographic

Separator and the ion mobility spectrometer maintained. In special cases, however, it is also possible, according to a given timetable

Connection between the gas chromatographic

Separate separation device and the ion mobility spectrometer or restore. The solution according to the invention will now be based on Application examples and corresponding figures will be explained.

Fig. 1 shows the structure of a typical

Ion Mobility Spectrometer according to the prior art, however, as it can be used for the inventive method,

Fig. 2 shows the problem of assignability of IMS spectra in analyte mixtures, as is usually in

Method according to the prior art. These two figures, which also serve to illustrate the state of the art, have already been explained above.

3 shows first a typical IMS spectrum of an ion mobility spectrometer,

4 shows an IMS spectrum with a pronounced reaction ion peak and the first and second order difference ratios after the wavelet-based noise reduction and the group delay correction as well as the thereof

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

Reaction ion peak, which does not represent the maximum of the IMS spectrum, and Fig. 6c does not show an IMS spectrum with a labeled reaction ion peak

represents detectable peak height.

Fig. 7 shows an IMS spectrum with a pronounced

Reaction ion peak and the first and second order difference quotients after the wavelet - based noise reduction and the group delay correction as well as the thereof

calculated and marked relative extreme values, related to the RIP position,

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.

9 shows a diagram with the drift times of

unknown analytes related to the location of the

Reaction ion peaks, where for two selected

Drift time intervals were measured by the characteristic parameters peak position and peak height were determined

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

characteristic parameters Peaklage and peak height and the corresponding reference values of an IMS single spectrum at the times T_retent_l and T_retent_2 for two selected drift time intervals,

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

Measuring times T_retent_l and T_retent_2,

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

Peak height for two selected drift time intervals too

different times:

Fig. 13a: time T_n for two selected

Drift time intervals and the characteristic parameters determined at time T_n-1 as reference values,

Fig. 13b: time T_n + 1 for two selected

Drift time intervals and those determined at time T_n characteristic parameters as reference values, FIG. 13c: time T_n + 2 for two selected ones

Drift time intervals and the characteristic parameters determined at time T_n + 1 as reference values,

Fig. 13d: time T_n + 3 for two selected

Drift time intervals and the characteristic parameters determined at time T_n + 2 as reference values,

Fig. 13e: time T_n + 4 for two selected

Drift time intervals and the characteristic parameters determined at time T_n + 3 as reference values,

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

Measuring times T_n to T_n + 4,

FIG. 15 shows the assignment in a matrix

detailed consecutive similar pattern per drift time interval in a continuously running measuring process of an unnamed analyte mixture,

The table of FIG. 16 illustrates in the form of a table the assignment of the column number j in FIG

Drift time interval t_j_min to t_j_max,

and FIG. 17 shows a relative example

Frequency distribution. In a known, shown in Fig. 1, above

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. The

Ambient air or the air-sample mixture enters an ion source and is by means of a weak radioactive Beta emitter, such. For example, tritium having an activity of 50 MBq is ionized. There are positive ions

Components of air of the types NH ⁺ , NO ⁺ , (H ₂ 0) nH ⁺ , which form the positive reaction ion peak (RIP ⁺ ). Negative ions of the type 0 ^{2 ~} and (H ₂ 0) ^~ _m form the negative

Reaction ion peak (RIP ^~ ). These ions of air (RIP ⁺ ) and (RIP ^~ ) are constantly available in ion mobility spectrometer 1. Each IMS spectrum of the measurement series is smoothed so that the difference ratios of the first and second order can be formed with sufficient accuracy from the signal. FIG. 4 shows an IMS spectrum with a

pronounced reaction ion peak and the course of

First and second order difference quotients, which are based on a wavelet - based noise reduction method and an effort - optimized adaptive smoothing with

Group delay correction and zero-line correction and, as well as the calculated and labeled relative

Extreme values.

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

Folding the noise-reduced IMS signal can be realized with a derivable from the low-pass impulse response. Depending on the wavelet thresholding

derivable signal properties adaptively selects a suitable impulse response. Expediently, impulse responses are used in addition to

Phase frequency responses were included, the one

Perform group delay correction to compensate for the linear distortion of antialiasing filters and other precursors. Surprisingly, the extra leads Group delay correction only to a slightly higher numerical effort. Fig. 5 shows by way of example

Impulse responses of digital low-pass filters with and without discrete-time all-pass component.

The RIP position can be advantageous after a

machine adaptive methods for arbitrary amplitudes. It is expedient to distinguish between shaped and partially hidden peaks and the

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

corrected for pressure is defined as RIP position. 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

Although the maximum of the spectrum represents the maximum value of the spectrum, 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. The estimate for the peak half-width is obtained analytically for Gaussian pulses and cosine square pulses from the relationship t_HW = b * sqrt (u (n) / u "(n)) (9) with 2.22 <b <2.355, where the two limit values of b in each case from the

Estimates based on cosine square pulses or

Gaussian pulses result. The individual preprocessing steps lead to

To produce records that according to the invention with little information almost no loss of measurement information and taking into account the measurement conditions and current

Device parameters sufficiently describe the respective current IMS spectrum as well as all previously acquired IMS spectra belonging to the data set.

According to the invention, the IMS-typical drift time is divided into a selectable number of drift time intervals of the same or different interval width and the

Analyte identification by means of pattern recognition immediately consecutively performed separately for each drift time interval. For this purpose, a comparison of the for the

Drift time interval relevant characteristic parameters of the current IMS measurement with appropriate

Reference parameters taking into account allowable

Tolerance ranges. In Fig. 9, assuming two

Drift time intervals 1 and 2 the characteristic

Parameters of the current measurement at time T_retent_l shown. FIG. 10 shows, by way of example, the corresponding reference parameters independent of T_retent_l and labeled with

permissible tolerance ranges for the peak position.

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, in contrast, is one

Pattern match given in drift time interval D2.

In Fig. 12, then, the calculated values for the relative square Euclidean distance are those for the two

Drift time intervals Dl and D2 performed separately

Cluster analysis specified. Exemplary becomes for the

Drift time interval Dl a good match between the measurement and the reference parameters of FIG. 10 for the

Retention time T_retent_l diagnosed, im

Drift time interval D2 has no or only a small similarity.

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.

By way of example, for the drift time interval D2 a good match between the measuring and the

Reference parameters diagnosed in the drift time interval Dl is no or only a small similarity.

The retention time difference for the patterns in the

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. Furthermore, 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. In addition, from the peak height or from the peak area, the approximately by

Product formation from the peak height and the peak half width is determined, the analyte concentration are estimated.

In this case, characteristic parameters of. Can be determined from current series of known analytes or analyte mixtures

Reference spectra are selected, the multiple

successive IMS spectra with the same

Cluster features taking into account the current

Measuring conditions can be assigned and therefore

corresponding reference parameters for two- or

represent three-dimensional spectra. As such, they can also be used for entries in a substance database. FIGS. 13a to 13e show diagrams with the characteristic parameters measured as a function of the drift time

Peak position and peak height for different times T_retent_n = T_n to T-retent_n + 4 = T_n + 4 for two selected ones

Drift time intervals Dl and D2.

Multiple successive plots of the relative quadratic Euclidean distance to

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

gapless successive search areas of the same or different interval width, ie the

Drift time intervals, selected in accordance with the measurement conditions and in case of multiple immediate

successive IMS spectra with domain-specific similar cluster features in one or more

Search ranges, these 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

Recognition of the same analyte or analyte mixtures can be used.

Thus, FIG. 15 shows in the form of a matrix the

Assignment of detailed successive similar patterns per drift time interval in a continuous measuring process of an unnamed analyte mixture.

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

immediately following IMS spectra, which within a drift time interval, j can be assigned the same patterns as a result of the cluster analysis.

By way of example, the spectra i = 3 to i = 20 with respect to the drift time interval j = 7 can be assigned identical patterns, which are determined from the time interval for this drift interval

characteristic parameters are derived, therefore, 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

Drift time intervals for the unnamed analyte mixture. Fig. 17 shows by way of example a weighted relative

Frequency distribution, which assigns a high relevance to the drift time interval j = 7.

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.

Method for ion mobility spectrometry

LIST OF REFERENCE NUMBERS

I ion mobility spectrometer

2 gas inlet

3 gas outlet

4 drift gas inlet

5 analyte molecules

6 reaction ions

7 ionized analyte molecules

8 transport gas

9 Flow direction of the inert gas

10 ion source

II drift tube

12 detector

13 membrane inlet

14 control grid

15 external auxiliary equipment (valves, pump, flow meter)

16 gas chromatographic separator

Claims

Method for ion mobility spectrometry

1. Method of ion mobility spectrometry (IMS), in which

a known gas or gas mixture in the

Ion mobility spectrometer (1) is ionized and thus forms the reaction ions (6),

an analyte or a mixture of several analytes, ie a substance to be analyzed or a corresponding mixture of substances, if appropriate by means of a carrier gas, in

In the ionization range of an ion mobility spectrometer (1) is initiated and either also directly ionized or a charge transfer of the reaction ions (6) to the atoms or molecules of the analyzed

Analyte (5) takes place,

the reaction ion (5) and positively or negatively charged analyte mixture passes through the drift region between the injection and the detection region of the ion mobility spectrometer (1),

a time-dependent series of measurements of IMS spectra of this reaction ion (5) and positively or negatively charged

Analyte mixture in the detection range of

Ion mobility spectrometer is recorded,

the IMS spectra of the measurement series are analyzed in such a way that qualitative and / or quantitative statements regarding the analyte or analyte mixture are given,

characterized in that

from each IMS spectral signal of the measurement series, in one preprocessing step, an adaptive smoothing and Group delay equalization is performed and first and second order difference quotients are formed,

from this for each peak its position, its height and its half width as the peak descriptive,

characteristic parameters are determined

- the characteristic parameters peak position, peak height and peak half width, taking into account the current one

Normalized measuring conditions,

- An identification of the analytes from the normalized, describing the peak characteristic parameters for the currently known spectra sequences is performed by a pattern recognition method.

2. The method according to claim 1, characterized in that for each IMS single spectrum first a wavelet-based smoothing and then a dependent of Thresholding result for noise reduction, adaptive digital filtering for smoothing and group delay equalization is performed.

3. The method according to claim 1 or 2, characterized in that the position of the reaction ion peak pressure and

temperature dependent is detected and in the case of

complete disappearance of the reaction ion peak over a limited period of time this is replaced by a current temperature and pressure corrected moving average.

4. The method according to any one of claims 1 to 3, characterized in that the determined peak positions are given in the form of a difference distance to the reaction peak and this difference distance is also subjected to a pressure and temperature correction.

5. The method according to any one of claims 1 to 4, characterized characterized in that the data volume in the analysis method is reduced by the fact that the pattern recognition in the individual IMS spectra on the parameters Peaklage, peak height and peak half width and a selectable number of

Search areas of the same or different

Interval width within the drift time, the so-called drift time intervals is limited.

6. The method according to any one of claims 1 to 5, characterized in that the respective determination of

Total retention time of an analyte, ie the time that the corresponding analyte for its migration of one

defined starting location, the In ektionsort located either within the ion mobility spectrometer or in an ion mobility spectrometer upstream

Device is required to the detection site under real time conditions is realized that

- a timely evaluation of all

Search ranges of each IMS single spectrum by means of a cluster based on the respective search area cluster analysis, where measured at a suitable location

Temperature and pressure ratios are used to normalize the retention times,

- This method is repeatedly applied to a detected at defined intervals sequence of IMS spectra.

7. The method according to any one of claim 6, characterized

characterized in that the time intervals of the detection of the sequence of IMS spectra are equidistant.

8. The method according to claim 6 or 7, characterized in that the results of the cluster analysis with known

Entries in a substance database for drift or

Retention time can be compared and

- if they match, the corresponding analytes be identified,

from the peak height or from the peak height and

Peak half-value width approximately calculated peak area the respective analyte concentration is estimated.

9. The method according to any one of claims 6 to 8, characterized in that series of measurements are carried out with known analytes or analyte mixtures,

the characteristic parameters are determined and selected in the search areas of the obtained spectra typical for the analytical field,

- For multiple consecutive IMS spectra with the same cluster characteristics 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.

10. The method according to any one of claims 6 to 8, characterized in that series of measurements are carried out with unknown analytes or analyte mixtures,

their characteristic parameters in a selected number of search areas of the same or different interval width, the drift time intervals, below

Considering and selecting the real measuring conditions,

for multiple directly consecutive IMS spectra with area-specific similar cluster features in one or more search areas, these search areas as reference search areas for the analyte or the

Be determined analyte mixture,

the corresponding characteristic parameters are declared as reference parameters for two- or three-dimensional spectra, - be used for the detection and recognition of the same analytes or mixtures of analytes and registered in a substance database.

11. The method according to claim 10, characterized in that the selected search areas follow each other without gaps.

12. The method according to any one of claims 1 to 11, characterized in that the analyte or the analyte mixture prior to entry into the ion mobility sspektrometer (1) an ion mobility sspektrometer (1) upstream

Gas chromatographic separator (16) happens.