MS data

Journal of Analytical and Applied Pyrolysis 92 (2011) 202–208

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Journal of Analytical and Applied Pyrolysis journal homepage: www.elsevier.com/locate/jaap

Post-optimization of Py-GC/MS data: A case study using a new digital chemical noise reduction filter (NOISERA) to enhance the data quality utilizing OpenChrom mass spectrometric software Philip Wenig ∗ Department of Wood Science, University of Hamburg, 21031 Hamburg, Germany

a r t i c l e

i n f o

Article history: Received 7 January 2011 Accepted 23 May 2011 Available online 31 May 2011 Keywords: Data handling Post-optimization Software Database Chemical noise

a b s t r a c t Noise is a known drawback in the mass spectrometric analysis of chromatographic data. Different techniques exist to avoid or to remove noise before and after data acquisition. Noise occurs in two different types, electronic and chemical. The former can be lowered by applying smoothing filters. The latter needs more sophisticated methods since the distinction between chemical noise and real chromatographic data is not straightforward. Several approaches have been published with different scopes and algorithms. This work describes a new algorithm (NOISERA – Noise Reduction Algorithm) for a dynamic reduction of chemical noise in mass spectrometric, chromatographic data of nominal mass resolution. It offers an approach to detect and to reduce chemical noise with a minimum of required user interaction and adjustments. Due to the assumption, that chemical noise varies in its intensity and is dependent of the time, the algorithm tries to calculate and reduce noise locally along the retention time axis. Electronic noise will not be covered by the presented algorithm. It can be reduced by applying further filters, since OpenChrom provides methods for smoothing. The presented algorithm NOISERA is capable of reducing noise with minimal user interaction. It combines existing ideas and extends them to achieve a parameter-free chemical noise detection and reduction. NOISERA improves the signal-to-noise ratio of peaks, but as a drawback of all chemical noise filters, it tends to remove either too much, respectively, not enough data from chromatograms than necessary. That is why the algorithm should not be used without supervision of an analyst. The noise reduction filter NOISERA is available as a plug-in for OpenChrom and is released under the Eclipse Public License 1.0 (EPL). OpenChrom is available free of charge at http://www.openchrom.net. The noise reduction algorithm NOISERA is included in the latest OpenChrom release. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Hyphenated techniques of chromatography and mass spectrometry have been advanced in a tremendous way since the second half of the last century. High performance chromatography systems are available at relatively low cost. Automated samplers enable a high throughput and software allows an effective evaluation of the data. Moreover, it is possible to retrieve high amounts of sample information using analytical pyrolysis gas chromatography/mass spectrometry (Py-GC/MS) systems. Nonetheless, problems still exists regarding noise in chromatograms as the noise pollutes the real chromatographic information. It is difficult to detect and remove noise as it varies in type, intensity and in its time domain. Different types of noise are mentioned using several

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names like white and colored noise [1], low- and high-frequency noise [1] as well as chemical and detector noise [2] or chemical and random noise [3]. This article uses the terms chemical and electronic noise in the way Christensen et al. [4] have used them. One solution to reduce noise is a pre-optimization of the chromatographic separation and indeed, it should be always considered. But, a pre-optimization could be very time consuming and will lead to good results only if the samples are homogeneous and/or its content is well known by the analyst. If not, especially in hyphenated techniques like Py-GC/MS, the pre-optimized settings will be applicable only for a small group of samples. This is a drawback, especially in case of assembling a database using Py-GC/MS data [5]. Therefore, post-optimization is necessary and software algorithms offer a high potential to edit digital analysis data in a reliable way afterwards. Still, an appropriate software is needed to perform such a post-optimization and there is a lack of tools that are flexible and extendable enough. Furthermore, today’s most commonly used chromatography software is proprietary and does

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Table 1 The table shows a set of different techniques, published to detect and to reduce noise in chromatograms. Technique

Authors

Not categorized

Biller et al. [24], Dromey et al. [25], Colby [26] Stein [7], Samuelsson et al. [27] Zhu et al. [23] Ghosh et al. [12,13]

Regional noise calculation Differential background remove Backfolding Standard deviation Similarity index NIPALS (Non-Linear Iterative Partial Least Squares) PCA (Principle Component Analysis) Kalman tracking FFT (Fast Fourier Transformation)

Pool et al. [10,11,28] Christensen et al. [4] Windig et al. [8,9] Lee et al. [17] Statheropoulos et al. [18] Åberg et al. [19] Synovec et al. [16], Spangenberg [15]

not provide functions to include new algorithms. OpenChrom [6] challenges this shortcomings by supporting a cross-platform open source software that makes it possible to edit mass spectrometric and chromatographic data and to include customized functionality and algorithms. Several authors (Table 1) describe ways to remove background signals from chromatographic data. They differ in kind and complexity and partly use noise recognition methods only to enhance the peak detection algorithm, example given by AMDIS (Automated Mass Spectrometry Deconvolution and Identification System) [7] and CODA (Component Detection Algorithm) [8,9], rather than to write the improved data back to the chromatogram. Algorithms like “Backfolding” [10,11] and “Differential Gas-Chromatographic Mass-Spectrometry” [12–14] try to remove noise from the chromatogram by the subtraction of each subsequent scan. Other methods are also mentioned in literature, for instance the technique of FFT (Fast Fourier Transformation) used by Spangenberg [15] and Synovec et al. [16] as well as multivariate techniques like NIPALS (Non-linear Iterative Partial Least Squares) and PCA (Principle Component Analysis) utilized by Lee et al. [17] and Statheropoulos et al. [18] or the technique of Kalman tracking described by Åberg et al. [19]. A common way to enhance chromatographic data is the Savitzky-Golay [20] filter, named after its authors. The filter performs a smoothing operation on the data. It can be generally used to reduce electronic noise but not to reduce chemical noise. The situation of a missing flexible software tool leads to the demand of a modular system to reduce chemical as well as electronic noise in mass spectrometric and chromatographic data of nominal mass resolution. Three prerequisites are desirable for such a system. It should be available at low cost. It should be extensible and it should allow the combination of several techniques and filters if desired. 2. Experimental The chemical noise reduction filter (NOISERA) was tested on real chromatographic data. The presented approach provides a filter for the OpenChrom software that includes a new algorithm to reduce chemical noise in mass spectrometric chromatograms. Its focus is the application on nominal rather than high mass resolution data. The applicability was checked in combination with a Savitzky-Golay smoothing filter. 2.1. Py-GC/MS and software A set of 1009 pyrograms was used to evaluate the chemical noise reduction filter. The samples were pyrolyzed in an oven pyrolyzator (PY-2020iD) fitted with an automatic sampler (AS-1020E) from

Fig. 1. The noise reduction algorithm consists of five steps: removal of mass fragments (m/z), adjustment of threshold transitions, noise segment detection, noise mass spectrum calculation and noise reduction.

FrontierLabs, Japan. A GC/MS system was used with a gas chromatograph (6890 N) and a mass spectrometer (5973 N) from Agilent. Electron ionization (EI) at 70 eV and a source temperature of 170 ◦ C was used. OpenChrom Version 0.4.0 was used to evaluate the data files. 2.2. Algorithms The algorithm basically consists of five steps. The first two steps consist of an optional pre-optimization of the data followed by the local detection, calculation and reduction of chemical noise (Fig. 1). 2.2.1. Step 1: Removal of selected ions (m/z) A defined set of selected ions is removed preliminarily to optimize the mass spectrometric data. It basically consists of removing water (m/z 18), nitrogen (m/z 28), solvent tailing (m/z 84) and column bleed (m/z 207). It depends on the user settings, since it is also possible to remove further ions or even none at all. 2.2.2. Step 2: Adjustment of threshold transitions The second step adjusts zero ion values, described by Stein [7]. Zero ion values occur suddenly in the subsequent order of scans if the measured intensity falls below the stored threshold of the instrument. First of all, a pre-set threshold value (At) is calculated. All ions in the chromatogram are taken into account and the lowest m/z abundance is considered as the pre-set threshold value. The chromatogram selection is now divided into ten parts of equal length (Pw). Each part is taken to discover threshold transitions in its ion range. The amount of threshold transitions is counted (Tmz) for each ion. If a ion shows one or more threshold transitions in the selected range, its abundance values are adjusted. To calculate the

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abundance adjustment value for that ion, the fraction of threshold transitions (Fmz) is calculated preliminary. Fmz = Tmz/Pw

(1)

Note that only threshold transitions in the selected part and ion are adjusted, not zero values in the whole chromatogram range at all. Furthermore, the abundance adjustment value (Amz) is calculated by multiplying the pre-set threshold value with the square root of the fraction of threshold transitions for each distinct ion, as mentioned by Stein [7]. Amz = At ∗ SQRT(Fmz)

Table 2 The table shows the set of used filter combinations “F-I” to “F-V” to post-optimize and evaluate the Py-GC/MS test data set.

(2)

All zero abundance values for each distinct ion in the chromatogram part are adjusted to their calculated abundance value Amz. Furthermore, the adjustment of zero ions can be applied on demand or deactivated if not needed. 2.2.3. Step 3: Noise segment detection The algorithm tries to detect noise segments on basis of the total ion chromatogram (TIC) after pre-optimization of the mass spectral data. Therefore, the chromatogram selection is divided into segments of thirteen scans. Each segment is analyzed to determine whether it represents noise or not. The median of the scan total ion signal values of the segment is calculated. If the median is crossed more than six times by the total ion signal of each subsequent scan, than the segment is taken into consideration as noise (Fig. 2). In all other cases, it is assumed that the segment contains relevant chromatographic information. The segment detection has been implemented according to the method described by Stein [7] and the implementation in AMDIS [21] to calculate the noise factor (Nf) of a chromatogram. A default segment width of thirteen scans has been chosen corresponding to the method given by Stein [7]. As an improvement, it is possible to set another segment scan width for the noise detection. Allowed scan numbers are: seven, nine, eleven, thirteen, fifteen, seventeen or nineteen. The algorithm does not support the use of automatically varying segment widths for the noise detection in different regions of the chromatogram in its actual state. The chromatogram selection contains a series of accepted and rejected noise segments (Fig. 3). 2.2.4. Step 4: Noise mass spectrum calculation A noise mass spectrum is calculated in a further step for each accepted noise segment. The intensities of all ions of each scan in the noise segment are added (Fig. 4). After addition, the combined noise mass spectrum is normalized by its highest ion abundance to a value of 1000. Additionally, a set of predefined ions is removed from the calculated noise mass spectrum. This feature preserves certain ions that should not be considered as noise, for instance styrene (m/z 104). It is up to the users choice to define the set of ions that are preserved. 2.2.5. Step 5: Noise reduction The last step modifies the chromatographic data and tries to reduce the chemical noise in between the noise segments. A subtract mass spectrum for each part in between two noise segments is calculated using the noise mass spectra of both segments (Fig. 4). Both noise mass spectra are combined in the same way as mentioned in step 4 and are used to reduce the noise of each scan intermediate to both noise segments. Starting at the highest abundance value of the subtract mass spectrum, the algorithm iterates through all ions to detect the first ion value match between the subtract mass spectrum and each scan mass spectrum. The abundance of the first matched ion of the subtract mass spectrum (Ahn)

Use NOISERA filter NOISERA: adjust threshold transitions Use Savitzky-Golay filter

F-I

F-II

F-III

F-IV

F-V

No No No

Yes Yes Yes

Yes Yes No

Yes No Yes

Yes No No

and the scan mass spectrum (Ahs) is used to calculate a correlation factor (Ncorr). If no match is detected, the scan is left untouched. Ncorr = Ahs/Ahn

(3)

If a match is detected, the relative amount of each ion from the subtract mass spectrum is removed from the scan (Fig. 5). Each ion of the scan is reduced in its abundance by the obtained abundance reduction value (Ari). The abundance reduction value is calculated using the correlation factor (Ncorr) multiplied by the abundance (Ani) of the corresponding subtract m/z value. Ari = corr ∗ Ani

(4)

If the reduction value (Ari) is zero or negative, the reduction value for the next ion is computed. If it is positive, the reduction value is subtracted from the abundance of the corresponding m/z value in the scan (Asi) resulting in a new abundance (Aci) value for the ion in the scan. Aci = Asi − Ari

(5)

This procedure is repeated until each ion of the scan has been mapped against its dedicated ion in the subtract mass spectrum and is adjusted in its abundance. The first and the last part of the chromatogram selection are treated in a different way. The first part of the chromatogram has no left segment as well as the last part has no right segment. Therefore, the dedicated noise segment mass spectrum is used as the subtract mass spectrum without modifications. The algorithm applies also the noise reduction on each scan within each noise segment, but in a specialized way. Because the segment consists of an odd number of scans, as a default thirteen scans, it cannot be divided into two equal parts. Its first part, half of the segment width rounded to an even number, will be processed with the calculated subtract mass spectrum to its left side and the following part with the calculated subtract mass spectrum to its right side. After applying the noise reduction algorithm, the TIC of the chromatogram is lowered (Fig. 6). 2.3. Filter settings and evaluation Five different filter combinations “F-I” to “F-V” with different settings were used to post-optimize the set of 1009 pyrograms and to evaluate their applicability. OpenChrom was used to detect the baseline and to detect and integrate the peaks after applying the filters. The Savitzky-Golay smoothing filter was applied after running the chemical noise reduction filter NOISERA. It was used with a smoothing degree of five. Water (m/z 18), nitrogen (m/z 28), solvent tailing (m/z 84) and column bleed (m/z 207) were preliminarily removed in step 1, and a segment width of thirteen scans was used to determine noise segments in step 3 of the NOISERA filter. The combination “F-I” is the reference without using the chemical noise reduction and Savitzky-Golay filter. All combinations are listed in Table 2. 3. Results and discussion One approach to post-optimize chromatographic data is to reduce and/or remove unwanted signals. It can be achieved by applying chemical and electronic noise reduction filters. This part

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Fig. 2. Noise segments are assigned in the chromatogram selection if more than six crossings of the calculated median occur. Each segment has a width of thirteen scans and the total ion chromatogram (TIC) value of the each scan is taken into account.

Fig. 3. The chromatogram selection contains in almost every case regions of noise and therefore a set of accepted noise segments.

Fig. 4. A noise mass spectrum is calculated out of each accepted noise segment. Those noise mass spectra are used to calculate subtract mass spectra. Each subtract mass spectrum is applied on the scan range in between its adjacent noise segments to reduce chemical noise locally.

describes a digital filter to reduce chemical noise in mass spectrometric chromatograms and is capable to enhance data of nominal mass resolution. Further filters can be applied to remove electronic or detector noise, as OpenChrom supplies a filter that implements

the Savitzky-Golay [20] smoothing algorithm. The separation of the filters makes the system more flexible. Other filters than the known Savitzky-Golay filter can be applied on the chromatographic data, for instance the smoothing algorithm described by Barak [22].

Fig. 5. The calculated subtract mass spectrum is used to reduce noise in each scan of its assigned scan range. The best m/z value match, according to the highest intensity in the subtract mass spectrum, between the subtract and the scan mass spectrum is used to determine the percentage of m/z intensities, that will be removed from the scan mass spectrum. The arrow in the scan mass spectrum mark the m/z values that have been lowered in their intensities after noise reduction.

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Fig. 6. After the reduction of chemical noise, the chromatogram selection should contain only a minimum of chemical noise and the drift in the chromatogram selection is lowered.

Fig. 7. The chromatogram shows a Py-GC/MS data file with an increasing background starting at the retention time of approximately 35 min. The peak at 44.6 min is a real compound that arises from a hot-melt adhesive. The abundance range has been zoomed in to show more details.

But in all cases, noise reduction algorithms have shortcomings by removing too less or too much information from the chromatogram. This is a challenge, that the analyst has to take care of. The software can only support the detection and reduction of noise but it cannot replace the decision and/or knowledge, respectively, experience of an analyst. The filter tries to detect and reduce noise locally, inspired by Zhu et al. [23], and enables the postoptimization of chromatographic data. It picks up parts of methods and ideas described in other papers [7,10–14] and extends them to allow an almost parameter free reduction of chemical noise in chromatographic data. The concept of detecting noise segments [7] has the advantage of being non-interactive and automatic but it could lead to wrong assumptions. At least one noise segment must be detectable in the chromatogram selection, otherwise the algorithm fails. Surely, it can be expected that segments with more than six crossing of the median, in case of thirteen scans for noise segment detection, are chemical noise, but the calculated noise mass spectra could also contain important ions. A mechanism is needed to preserve information, that should not be removed from the chro-

matogram. That is why the software includes several settings, for instance to preserve certain ions. An example of the NOISERA filter on real chromatographic data is shown using a background rich chromatogram (Figs. 7 and 8). Furthermore, the algorithm allows some pre-optimization steps, for example to remove specific ions like water (m/z 18), nitrogen (m/z 28), solvent tailing (m/z 84) or column bleed (m/z 207), or to apply an adjustment of ion abundance values that are below the instrument’s threshold and therefore have dropped to zero. The pre-optimization steps can be seen as an advantage in comparison to the method described by Åberg et al. [19]. But, the detection of noise segments and mass spectra is one possible cause of failure. Another is the reduction or the removal of chromatographic data. It is important to keep in mind, that the process of subtracting ion abundance values, identified as noise, from a mass spectrum could significantly lead to changed isotopic distributions of relative abundances in comparison to the pristine scan. Contrary to the method of differential gas chromatography [10], known as backfolding, a local identification and reduction of chemical noise is favored instead of the subtraction of each sub-

Fig. 8. The chromatogram shows the same Py = GC/MS data file as in Fig. 7, but after the application of the NOISERA filter. The absolute abundance has been lowered, and the background has been removed in a significant way. The abundance range has been zoomed in to show more details.

P. Wenig / Journal of Analytical and Applied Pyrolysis 92 (2011) 202–208 Table 3 The table shows the calculated mean, median, standard deviation and variance of the detected percentage of background area after post-optimization of the Py-GC/MS test data set. Background area [%]

F-I

F-II

F-III

F-IV

F-V

Mean Median Standard deviation Variance

55.159 55.724 17.252 297.628

14.969 10.802 12.441 154.778

12.384 9.520 9.094 82.705

15.546 11.314 13.037 169.957

12.865 9.867 9.674 93.590

sequent scan. This approach tries to preserve the peak shape and retention times as best as possible but has a drawback if incorrect noise mass spectra have been calculated. In such case, the data reduction tends to destroy real chromatographic information. 3.1. Results background and peak area The total area of each pyrogram was used as a reference of 100% to calculate its percentage of background and peak area. The results of the calculated percentage background and peak area after running the filter combinations are listed in Tables 3 and 4. The mean value of the percentage background area decreases from approximately 50%, in case of no post-optimization, to roughly 15% after using the four different combinations and settings “F-II” to “F-V” of noise reduction filters. The filter combinations “F-II” to “FV” lead to a reduced background and an increased peak area as well to a smaller standard deviation in comparison to “F-I”, where the data was not post-optimized. The mean peak area increases from approximately 45%, if no post-optimization was performed, to roughly 60%, respectively 70% in case of the filter combinations “F-II” to “F-V”. Furthermore, the filter combinations “F-II” to “F-V” show the effect of an adjustment of threshold values and an additional data smoothing, using a Savitzky-Golay filter. The adjustment of threshold values seems to have no relevant effect in case of the selected set of pyrograms. But the smoothing filter has the effect of increasing the peak as well as the background area. The mean background area increases from approximately 13–15% and the peak area increases from approximately 60–70%. In contrary, the standard deviation of the background area increases but it decreases in case of the peak area. Therefore, a combination of the chemical (NOISERA) and electronic (Savitzky-Golay) noise reduction filter leads to better results than using the sole NOISERA filter. 3.2. Results amount of detected peaks The amount of detected peaks varies between the different filter combinations. It was divided into ranges of different peak signalto-noise (S/N) ratios (Table 5). Some peaks occur in the range higher than S/N 2000, but their amount is considered to be negligible. Analog to the results of the background and peak area, the amount of peaks increases if using both post-optimization filters in comparison to the non-optimized pyrograms. Altogether, roughly 35,500 peaks could be detected in the non-optimized 1009 pyrograms. In contrast, approximately 41,500, respectively 43,000 peaks could be counted, if the pyrograms were optimized by the filter combina-

207

Table 5 The table shows the amount of detected peaks, using the filter combinations “F-I” to “F-V”, differentiated by their S/N ratios. Range [S/N]

F-I

F-II

F-III

F-IV

F-V

2–10 10–50 50–100 100–150 150–200 200–250 250–300 300–350 350–400 400–450 450–500 500–550 550–600 600–650 650–700 700–750 750–800 800–850 850–900 900–950 950–1000 1000–1050 1050–1100 1100–1150 1150–1200 1200–1250 1250–1300 1300–1350 1350–1400 1400–1450 1450–1500 1500–1550 1550–1600 1600–1650 1650–1700 1700–1750 1750–1800 1800–1850 1850–1900 1900–1950 1950–2000

17,446 14,364 1922 670 332 213 168 145 85 55 41 22 20 24 22 14 15 11 9 4 4 5 1 0 0 2 1 2 1 0 1 0 0 1 0 1 0 0 2 1 0

21,749 15,666 2342 743 321 212 187 114 84 48 43 33 18 20 7 8 11 3 7 4 4 0 4 4 1 2 4 1 1 1 3 0 3 0 1 0 3 1 2 0 0

20,474 17,192 2925 999 437 257 175 137 79 69 42 26 36 30 15 20 16 11 7 11 7 8 2 3 4 2 1 1 2 3 1 1 0 0 2 2 1 3 1 0 1

21,795 15,701 2341 740 316 217 174 117 77 50 34 40 16 22 7 12 7 5 7 6 3 3 3 2 1 2 2 1 4 0 2 1 0 2 0 2 1 1 2 1 0

20,529 17,343 2972 984 450 237 179 127 84 61 47 36 34 24 15 23 13 17 9 8 5 8 5 4 3 2 0 2 0 1 0 1 2 1 1 0 1 4 1 2 0

Sum

35,604

41,655

43,003

41,717

43,235

tions “F-II” to “F-V”. It can be seen by comparing the summed values of the filter combinations “F-II”, “F-IV” and “F-III”, “F-V”, that the adjustment of threshold values seems to have no effect in this case too. The table shows, that the smoothing filter reduces the amount of detected peaks from approximately 43,000 to 41,500 peaks in summary. Furthermore, the smoothing filter has also the effect of decreasing the amount of peaks with a higher S/N ratio and increasing the amount of peaks with a smaller S/N ratio. It could explain, why the standard deviation of the percentage peak area decreases when using a Savitzky-Golay smoothing filter. In summary, the sole chemical noise reduction filter leads to a higher amount of peaks than using it in combination with the Savitzky-Golay smoothing filter. The post-optimization of the pyrograms increases the amount of peaks, regardless if the filters are used in combination or not. 4. Conclusions

Table 4 The table shows the calculated mean, median, standard deviation and variance of the detected percentage of peak area after post-optimization of the Py-GC/MS test data set. Peak area [%]

F-I

F-II

F-III

F-IV

F-V

Mean Median Standard deviation Variance

44.889 44.722 15.985 255.535

70.178 71.301 9.950 99.000

59.364 61.510 14.444 208.633

69.859 71.104 10.161 103.243

59.164 61.280 14.541 211.448

The paper presents a new algorithm, called NOISERA, that is capable of reducing chemical noise in mass spectrometric chromatograms, primarily designed for Py-GC/MS data. It is an automated approach and requires almost no user interaction. The algorithm is primarily designed to reduce noise in data with nominal mass resolution. Its focus is to reduce chemical rather than electronic noise. OpenChrom as a platform to edit and analyze mass spectrometric and chromatographic data allows the combi-

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nation of several filters and methods to post-optimize analytical data. NOISERA fits in this concept and offers a filter with a narrow and specialized scope. Further optimization steps of the data can be performed by combining subsequent methods, for instance a filter to remove electronic noise using the known Savitzky-Golay algorithm. The presented approach tries to detect and reduce chemical noise locally, as it can be assumed that chemical noise varies in its intensity and is dependent of the time. The filter is available free of charge and its algorithms can be examined because it is published under an open source license. Acknowledgements The author thanks all participants for their support and their helpful suggestions. References [1] M. Fredriksson, P. Petersson, M.K.B. Magnus, B.O. Axelsson, D. Bylund, An objective comparison of pre-processing methods for enhancement of liquid chromatography–mass spectrometry data, Journal of Chromatography A 1172 (2007) 135–150. [2] C.A. Hastings, S.M. Norton, S. Roy, New algorithms for processing and peak detection in liquid chromatography/mass spectrometry data, Rapid Communications In Mass Spectrometry (2002) 462–467. [3] M. Katajamaa, M. Oresic, Data processing for mass spectrometry-based metabolomics, Journal of Chromatography A 1158 (2007) 318–328. [4] J.H. Christensen, J. Mortensen, A.B. Hansen, O. Andersen, Chromatographic preprocessing of GC–MS data for analysis of complex chemical mixtures, Journal of Chromatography A 1062 (2005) 113–123. [5] P. Wenig, J. Odermatt, Efficient analysis of Py-GC/MS data by a large scale automatic database approach: an illustration of white pitch identification in pulp and paper industry, Journal of Analytical and Applied Pyrolysis (2010) 85–92. [6] P. Wenig, J. Odermatt, OpenChrom—a cross-plattform software for the analysis of mass-spectrometric chromatograms, BMC Bioinformatics (2010) 405–413. [7] S.E. Stein, An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry data, Journal of the American Society for Mass Spectrometry (1999) 770–781. [8] W. Windig, J.M. Phalp, A.W. Payne, A noise and background reduction method for component detection in liquid chromatography mass spectrometry, Analytical Chemistry (1996) 3602–3606. [9] W. Windig, W.F. Smith, Chemometric analysis of complex hyphenated data—improvements of the component detection algorithm, Journal of Chromatography A 1158 (2007) 251–257. [10] W.G. Pool, J.W. deLeeuw, B. vandeGraaf, Backfolding applied to differential gas chromatography mass spectrometry as a mathematical enhancement of chromatographic resolution, Journal of Mass Spectrometry (1996) 509–516.

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