Peak finding and referencing in MCC/IMS-data

Bödeker, Bertram; Vautz, Wolfgang; Baumbach, Jan

doi:10.1007/s12127-008-0012-7

Cited by 61 publications

(57 citation statements)

References 26 publications

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“…from breath analysis) [3,4,13,14] and process control [15][16][17][18][19][20][21][22]. For such applications, IMS measurements faces challenges such as humid and rather complex samples, requirement of a specific sampling procedure adapted to the application, fast pre-separation techniques like multi-capillary columns and most importantly, suitable data processing techniques which include databases of relevant analytes for automatic characterisation of the signals detected in an IMS chromatogram [23][24][25][26][27][28], and different data pre-processing steps [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Empirical prediction of reduced ion mobilities of secondary alcohols

Hariharan

Baumbach

Vautz

2009

Int. J. Ion Mobil. Spec.

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Ion Mobility Spectrometry is a powerful method for the rapid identification of gas-phase analytes and finds its usage in various fields including the sensitive analysis of extremely complex and humid mixtures such as human breath when additional pre-separation techniques are applied. The output data from an ion mobility spectrometer (IMS), equipped with a Multi-Capillary Column (MCC) for pre-separation, is a chromatogram of the signal intensity versus a particular retention time and a specific reduced ion mobility which are the characteristics of the detected analyte. Hence, it is important to have a database of analytes with both the values for comparison and identification of peaks in any IMS chromatogram. Commonly, such databases are collected by measurements of reference analytes. It is obvious that a prognosis of the values, without the time consuming and costly reference measurements, would be a considerable facilitation for a preliminary identification of unknowns and development of databases. In this study, a correlation between the reduced ion mobilities and the number of carbon atoms was found for secondary alcohols. The correlation was then used to predict the reduced ion mobilities of other analytes in the same homologous series. To verify the accuracy of the prognosis, the analytes were measured individually using a 63 Ni-MCC-IMS and compared to the predicted values. The results of the prognosis show an accuracy higher than 99.5%.

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Section: Introductionmentioning

confidence: 99%

Empirical prediction of reduced ion mobilities of secondary alcohols

Hariharan

Baumbach

Vautz

2009

Int. J. Ion Mobil. Spec.

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“…The data matrix consists of 1.5 Million data points. The graphic is provided by BB_IMSAnalyse, a software tool for visualisation and evaluation of MCC-IMS data developed at ISAS [36][37][38] ~0.001 cm 2 V −1 s −1 . As a consequence many analytes having the same reduced ion mobility value when determined with the traditional method in an accuracy of 0.001 cm 2 V −1 s −1 can now be differentiated even during the measurement.…”

Section: Resultsmentioning

confidence: 99%

An implementable approach to obtain reproducible reduced ion mobility

Vautz

Bödeker

Baumbach

et al. 2009

Int. J. Ion Mobil. Spec.

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Ion mobility spectrometry is increasingly in demand for medical applications and its potential for implementation in food quality and safety or process control suggest rising use of instruments in this field as well. All those samples are commonly extremely complex and mostly humid mixtures. Therefore, pre-separation techniques have to be applied. As ion mobility spectrometers with gas-chromatographic pre-separation acquire a huge amount of data, effective data processing and automated evaluation by comparison of detected peak pattern with data bases have to be utilised. This requires accurate on-line calibration of the instruments to guarantee reproducible results, in particular with respect to identification of an analyte by determination of its ion mobility and retention time. To reduce environmental and instrumental influence, the reduced ion mobility is used. It is derived from the drift time normalised to electric field, length of the drift region and to temperature and pressure of the drift gas (traditional method). All data required for this normalisation are afflicted with a particular error and thus leading to a deviation of the calculated ion mobility value. Furthermore, this traditional method enables a calculation of the reduced ion mobility only after the measurement. To avoid those errors and to enable on-line calibration of ion mobility, an instrument specific factor is implemented generally representing all relevant variables. This factor can be determined from an initial measurement of few spectra and can thereafter be applied on the following measurement. The application of this approach obtained reproducible reduced ion mobility values for positive and negative ions over a broad drift time range and for common variation of ambient conditions as well for varying instrument conditions such as electric fields respectively drift times and in different drift gases. Moreover, the reduced ion mobility is available already during the measurements with a significantly higher reliability and accuracy which was increased to a factor of 5 compared to the traditional ion mobility determination and enables an on-line identification of analytes for the first time.

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“…The IMS had a voltage of 4380 V, a grid opening time of 300 μs and a throughput of 150 mL min −1 . We chose the following parameters [9]:…”

Section: Discussionmentioning

confidence: 99%

An Online Peak Extraction Algorithm for Ion Mobility Spectrometry Data

Kopczynski

Rahmann

2014

Lecture Notes in Computer Science

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Ion mobility (IM) spectrometry (IMS), coupled with multi-capillary columns (MCCs), has been gaining importance for biotechnological and medical applications because of its ability to detect and quantify volatile organic compounds (VOC) at low concentrations in the air or in exhaled breath at ambient pressure and temperature. Ongoing miniaturization of spectrometers creates the need for reliable data analysis on-the-fly in small embedded low-power devices. We present the first fully automated online peak extraction method for MCC/IMS measurements consisting of several thousand individual spectra. Each individual spectrum is processed as it arrives, removing the need to store the measurement before starting the analysis, as is currently the state of the art. Thus the analysis device can be an inexpensive low-power system such as the Raspberry Pi. The key idea is to extract one-dimensional peak models (with four parameters) from each spectrum and then merge these into peak chains and finally two-dimensional peak models. We describe the different algorithmic steps in detail and evaluate the online method against state-of-the-art peak extraction methods.

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Peak finding and referencing in MCC/IMS-data

Cited by 61 publications

References 26 publications

Empirical prediction of reduced ion mobilities of secondary alcohols

Empirical prediction of reduced ion mobilities of secondary alcohols

An implementable approach to obtain reproducible reduced ion mobility

An Online Peak Extraction Algorithm for Ion Mobility Spectrometry Data

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