Abstract
Comprehensive two-dimensional gas chromatography (GC × GC) can separate thousands of different compounds, and is used for many important applications such as petrochemical processing and environmental monitoring, etc. GC × GC generates rich and complex information, which requires automated processing for rapid chemical identification and classification. A challenge is to remove unwanted streaks that may affect the quantification and identification of analytes. It is difficult to detect streaks because of complex backgrounds, low-contrast data, and variable shapes, scales, and orientations of streaks in GC × GC data. This paper proposes a new approach to detect streaks effectively based on image analysis techniques. By adopting a pseudo-log function and preprocessing methods to compress the original data and enhance the low-contrast data, we employ steerable Gaussian filtering to delineate streak regions based on the specific orientations of streaks. A marker-controlled watershed algorithm is then used to segment the streaks, and highly discriminating characteristics are used to identify candidate regions and reject false streaks. In the end, with a diverse data set generated from gas chromatograph, experiments are carried out and the results demonstrate that our streak detection approach is effective and robust with respect to changes in streak patterns, even in variable chromatographic conditions. The proposed object detection method effective in complex backgrounds and low-contrast conditions is also helpful for object detection in other scenes.
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References
Liu Z, Phillips JB (1991) Comprehensive two-dimensional gas chromatography using an on-column thermal modulator interface. J Chromatogr Sci 29:227–231. https://doi.org/10.1093/chromsci/29.6.227
Reichenbach S, Tian X, Boateng A, Mullen C, Cordero C, Tao Q (2013) reliable peak selection for multisample analysis with comprehensive two-dimensional chromatography. Anal Chem 85(10):4974–4981. https://doi.org/10.1021/ac303773v
Rafal Gieleciak, Darcy Hager, Heshka Nicole E (2016) Application of a quantitative structure retention relationship approach for the prediction of the two-dimensional gas chromatography retention times of polycyclic aromatic sulfur hetero cycle compounds. J Chromatogr A 1437:191–202. https://doi.org/10.1016/j.chroma.2016.02.006
Latha I, Reichenbach S, Tao Q (2011) Comparative analysis of peak-detection techniques for comprehensive two-dimensional chromatography. J Chromatogr A 1218(38):6792–6798. https://doi.org/10.1016/j.chroma.2011.07.052
Beens J, Boelens H, Tijssen R, Blomberg J (1998) Quantitative aspects of comprehensive twodimensional gas chromatography (GC × GC). J High Resolut Chromatogr 21:47–54
Peters S, Vivótruyols G, Marriott PJ, Schoenmakers PJ (2007) Development of an algorithm for peak detection in comprehensive two-dimensional chromatography. J Chromatogr A 1156(1):14–24. https://doi.org/10.1016/j.chroma.2006.10.066
Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13(6):583–598. https://doi.org/10.1109/34.87344
Reichenbach S, Ni M, Kottapalli V, Visvanathan A (2004) Information technologies for comprehensive two-dimensional gas chromatography. Chemometr Intell Lab Syst 71(2):107–120. https://doi.org/10.1016/j.chemolab.2003.12.009
Reichenbach S, Carr P, Stoll D, Tao Q (2009) Smart templates for peak pattern matching with comprehensive two-dimensional liquid chromatography. J Chromatogr A 1216(16):3458–3466. https://doi.org/10.1016/j.chroma.2008.09.058
Ni M, Reichenbach S (2003) A statistics-guided progressive RAST algorithm for peak template matching in GC × GC. In: 2003 IEEE workshop on statistical signal processing, pp 383–386
Van Stee LL, Brinkman UA (2011) Peak clustering in two-dimensional gas chromatography with mass spectrometric detection based on theoretical calculation of two-dimensional peak shapes: the 2DAid approach. J Chromatogr A 1218(43):7878–7885. https://doi.org/10.1016/j.chroma.2011.08.081
Reichenbach S, Tian X, Tao Q, Stoll D, Carr P (2010) Comprehensive feature analysis for sample classification with comprehensive two-dimensional liquid chromatography (LC × LC). J Sep Sci 33(10):1365–1374
Aparicioruiz R, Garcíagonzález DL, Morales MT et al (2018) Comparison of two analytical methods validated for the determination of volatile compounds in virgin olive oil: GC-FID vs GC-MS. Talanta 187:133
Mondello L, Tranchida PQ, Dugo P, Dugo G (2008) Comprehensive two-dimensional gas chromatography-mass spectrometry: a review. Mass Spectrom Rev 27:101–124. https://doi.org/10.1002/mas.20158
Achieving Low Levels of GC Column Bleed. http://kinesis-usa.com/knowledgebase/achieving-low-levels-of-gc-column-bleed. Accessed 20 Jan 2018
Rosario HS, Saber E, Wu W, Chandu K (2007) Streak detection in mottled and noisy images. J Electron Imaging 16(4):043005
Sadeghi M, Lee TK, Mclean D, Lui H, Atkins MS (2013) Detection and analysis of irregular streaks in dermoscopic images of skin lesions. IEEE Trans Med Imaging 32(5):849–861. https://doi.org/10.1109/TMI.2013.2239307
Virtanen J, Poikonen J, Säntti T, Komulainen T, Torppa J, Granvik M et al (2016) Streak detection and analysis pipeline for space-debris optical images. Adv Space Res 57(8):1607–1623. https://doi.org/10.1016/j.asr.2015.09.024
Shin S, Kim W Y (2018) Fast satellite streak detection for high-resolution image. In: IEEE international workshop on advanced image technology (IWAIT) 2018, pp 1–4
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906. https://doi.org/10.1109/34.93808
Pang J. Steerable filter. https://www.mathworks.com/matlabcentral/fileexchange/44956-steerable-filter. Accessed 16 May 2015
Gonzalez RC, Woods RE (2002) Digital Image Processing. Prentice-Hall, Upper Saddle River
Vincent L (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2(2):176–201. https://doi.org/10.1109/83.217222
Jr CRM, Qi R, Raghavan V (2003) A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans Pattern Anal Mach Intell 25(2):265–270. https://doi.org/10.1109/TPAMI.2003.1177156
Lee Y, Hwang D (2018) Periodicity-based nonlocal-means denoising method for electrocardiography in low SNR non-white noisy conditions. Biomed Signal Process Control 39:284–293. https://doi.org/10.1016/j.bspc.2017.08.006
Definitions of properties (shape measurements) of function regionprops in the help documentation of image processing toolbox of Matlab, Version R2010A
Acknowledgements
This work was supported by the Project of State Ethnic Affairs Commission of China (No. 14ZNZ019), National Natural Science Foundation of China (61772562), Hubei Provincial Natural Science Foundation of China for Distinguished Young Scholars (2017CFA043), and Youth Elite Project of State Ethnic Affairs Commission of China. The authors would like to thank Zoex Corporation for the original data, and Dr. Jingang Yu, Dr. Zongxiao Zhu for the constructive discussions and suggestions.
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Li, B., Reichenbach, S.E., Tao, Q. et al. A streak detection approach for comprehensive two-dimensional gas chromatography based on image analysis. Neural Comput & Applic 32, 649–663 (2020). https://doi.org/10.1007/s00521-018-3917-z
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DOI: https://doi.org/10.1007/s00521-018-3917-z