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Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data
Abstract
Two different algorithms for time‐alignment as a preprocessing step in linear factor models are studied. Correlation optimized warping and dynamic time warping are both presented in the literature as methods that can eliminate shift‐related artifacts from measurements by correcting a sample vector towards a reference. In this study both the theoretical properties and the practical implications of using signal warping as preprocessing for chromatographic data are investigated. The connection between the two algorithms is also discussed. The findings are illustrated by means of a case study of principal component analysis on a real data set, including manifest retention time artifacts, of extracts from coffee samples stored under different packaging conditions for varying storage times. We concluded that for the data presented here dynamic time warping with rigid slope constraints and correlation optimized warping are superior to unconstrained dynamic time warping; both considerably simplify interpretation of the factor model results. Unconstrained dynamic time warping was found to be too flexible for this chromatographic data set, resulting in an overcompensation of the observed shifts and suggesting the unsuitability of this preprocessing method for this type of signals. Copyright © 2004 John Wiley & Sons, Ltd.
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Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
A User's Guide to Principal Components
Leigh, S., & Jackson, J. E. (1993) · Technometrics · doi:10.2307/1269292 · Cited by 3,332
Dunteman, G. (1989) · Sage eBooks · doi:10.4135/9781412985475 · Cited by 2,658
A Use's Guide to Principal Components
Jackson, J. E. (1991) · Wiley Series in Probability and Statistics · doi:10.1002/0471725331 · Cited by 2,516
Time Series Analysis Forecasting and Control
Bartholomew, D. J. (1971) · Journal of the Operational Research Society · doi:10.1057/jors.1971.52 · Cited by 1,792
Minimum prediction residual principle applied to speech recognition
Itakura, F. (1975) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1975.1162641 · Cited by 1,627
Nielsen, N.-P. V., Carstensen, J. M., & Smedsgaard, J. (1998) · Journal of Chromatography A · doi:10.1016/s0021-9673(98)00021-1 · Cited by 760
Performance tradeoffs in dynamic time warping algorithms for isolated word recognition
Myers, C., Rabiner, L., & Rosenberg, A. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163491 · Cited by 568
Aligning gene expression time series with time warping algorithms
Aach, J., & Church, G. M. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.6.495 · Cited by 496
Synchronization of batch trajectories using dynamic time warping
Kassidas, A., MacGregor, J. F., & Taylor, P. A. (1998) · AIChE Journal · doi:10.1002/aic.690440412 · Cited by 456
Sakoe, H. (1979) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1979.1163310 · Cited by 261
A level building dynamic time warping algorithm for connected word recognition
Myers, C., & Rabiner, L. (1981) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1981.1163527 · Cited by 261
Introduction to operations research. 7th Edition
Hiller, F. S., & Lieberman, G. J. (2001) · Andalas University Repository (Andalas University) · Cited by 236
Wülfert, F., Kok, W. T., & Smilde, A. K. (1998) · Analytical Chemistry · doi:10.1021/ac9709920 · Cited by 227
A comparison of two algorithms for warping of analytical signals
Pravdova, V., Walczak, B., & Massart, D. L. (2002) · Analytica Chimica Acta · doi:10.1016/s0003-2670(02)00008-9 · Cited by 215
Munck, L., Nørgaard, L., Engelsen, S. B., Bro, R., & Andersson, C. A. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00074-4 · Cited by 166
Self-citationSupervision of bioprocesses using a dynamic time warping algorithm
Gollmer, K.-U., & Posten, C. H. (1996) · Control Engineering Practice · doi:10.1016/0967-0661(96)00136-0 · Cited by 61
Performance trade-offs in dynamic time warping algorithms for isolated word recognition
Myers, C. S., Rabiner, L. R., & Rosenberg, A. E. (1979) · The Journal of the Acoustical Society of America · doi:10.1121/1.2017729 · Cited by 60
Kassidas, A., Taylor, P. A., & MacGregor, J. F. (1998) · Journal of Process Control · doi:10.1016/s0959-1524(98)00025-0 · Cited by 58
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Katajamaa, M., & Orešič, M. (2007) · Journal of Chromatography A · doi:10.1016/j.chroma.2007.04.021 · Cited by 616
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Ratanamahatana, C. A., & Keogh, E. (2005) · Proceedings of the 2005 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.50 · Cited by 393
Guo, S., Popp, J., & Bocklitz, T. (2021) · Nature Protocols · doi:10.1038/s41596-021-00620-3 · Cited by 361
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Metadata
- Authors
- Tomasi, G., van den Berg, F., & Andersson, C.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2004-05
- Volume
- 18
- Issue
- 5
- Pages
- 231-241
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W2120858595
- Cited by
- 709
- References
- 20
- FWCI
- 18.75
- Type
- journal article
- Language
- en
- Open access
- closed
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