112 research outputs found
Functional Unfold Principal Component Regression Methodology for Analysis of Industrial Batch Process Data
Using warping information for batch process monitoring and fault classification
This paper discusses how to use the warping information obtained after batch synchronization for process monitoring and fault classification. The warping information can be used for i) building unsupervised control charts or ii) fault classification when a rich faulty batches database is available. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate the proposal.This research work was supported by the Spanish government (Ministry of Science and Innovation, MICINN) under project DPI2011-28112-C04-02. We gratefully acknowledge Associate Professor Jose Camacho for providing the simulation scheme of the fermentation process of Saccharomyces cerevisiae cultivation.Gonzalez-Martinez, J.; Westerhuis, J.; Ferrer Riquelme, AJ. (2013). Using warping information for batch process monitoring and fault classification. Chemometrics and Intelligent Laboratory Systems. 127:210-217. https://doi.org/10.1016/j.chemolab.2013.07.003S21021712
Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping
This paper addresses the real-time monitoring of batch processes with multiple different local time trajectories of variables measured during the process run. For Unfold Principal Component Analysis (U-PCA)—or Unfold Partial Least Squares (U-PLS)-based on-line monitoring of batch processes, batch runs need to be synchronized, not only to have the same time length, but also such that key events happen at the same time. An adaptation from Kassidas et al.'s approach [1] will be introduced to achieve the on-line synchronization of batch trajectories using the Dynamic Time Warping (DTW) algorithm. In the proposed adaptation, a new boundaries definition is presented for accurate on-line synchronization of an ongoing batch, together with a way to adapt mapping boundaries to batch length. A relaxed greedy strategy is introduced to avoid assessing the optimal path each time a new sample is available. The key advantages of the proposed strategy are its computational speed and accuracy for the batch process context. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisae cultivation are used to illustrate the performance of the proposed strategy.This research work was supported by the Spanish government under the project (DPI2008-06880-C03-03). We also gratefully acknowledge Jose Camacho PhD. for providing simulated data from a fermentation process of Saccharomyces cerevisae. The authors would also like to acknowledge the valuable suggestions made by Prof. Paul Taylor.González MartĂnez, JM.; Ferrer Riquelme, AJ.; Westerhuis, JA. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems. 105(2):195-206. https://doi.org/10.1016/j.chemolab.2011.01.003S195206105
Bilinear modeling of batch processes. Part III: Parameter Stability
A paramount aspect in the development of a model for a monitoring system is the so-called parameter stability. This is inversely related to the uncertainty, i.e., the variance in the parameters estimates. Noise affects the performance of the monitoring system, reducing its fault detection capability. Low parameters uncertainty is desired to ensure a reduced amount of noise in the model. Nonetheless, there is no sound study on the parameter stability in batch multivariate statistical process control (BMSPC). The aim of this paper is to investigate the parameter stability associated to the most used synchronization and principal component analysis-based BMSPC methods. The synchronization methods included in this study are the following: indicator variable, dynamic time warping, relaxed greedy time warping, and time linear expanding/compressing-based. In addition, different arrangements of the three-way batch data into two-way matrices are considered, namely single-model, K-models, and hierarchicalmodel approaches. Results are discussed in connection with previous conclusions in the first two papers of the series.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Authors also acknowledge the anonymous reviewers for their comments to improve the article.González MartĂnez, JM.; Camacho Páez, J.; Ferrer, A. (2014). Bilinear modeling of batch processes. Part III: Parameter Stability. Journal of Chemometrics. 28(1):10-27. https://doi.org/10.1002/cem.2562S1027281Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Camacho, J., PicĂł, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22(5), 299-308. doi:10.1002/cem.1113Camacho, J., PicĂł, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22(10), 533-547. doi:10.1002/cem.1179González-MartĂnez J Vitale R de Noord O Ferrer A Does synchronization matter in bilinear batch process monitoring?GarcĂa-Muñoz, S., Kourti, T., MacGregor, J. F., Mateos, A. G., & Murphy, G. (2003). Troubleshooting of an Industrial Batch Process Using Multivariate Methods. Industrial & Engineering Chemistry Research, 42(15), 3592-3601. doi:10.1021/ie0300023Zarzo, M., & Ferrer, A. (2004). Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(1), 15-27. doi:10.1016/j.chemolab.2003.11.009Wallace D Prosensus multivariate v12. 02 2010Louwerse, D. J., Tates, A. A., Smilde, A. K., Koot, G. L. M., & Berndt, H. (1999). PLS discriminant analysis with contribution plots to determine differences between parallel batch reactors in the process industry. Chemometrics and Intelligent Laboratory Systems, 46(2), 197-206. doi:10.1016/s0169-7439(98)00185-3Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Kaistha, N., & Moore, C. F. (2001). Extraction of Event Times in Batch Profiles for Time Synchronization and Quality Predictions. Industrial & Engineering Chemistry Research, 40(1), 252-260. doi:10.1021/ie990937cRamsay, J. O., & Silverman, B. W. (1997). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xKassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412González-MartĂnez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2860Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Gurden, S. P., Westerhuis, J. A., Bijlsma, S., & Smilde, A. K. (2000). Modelling of spectroscopic batch process data using grey models to incorporate external information. Journal of Chemometrics, 15(2), 101-121. doi:10.1002/1099-128x(200102)15:23.0.co;2-vKourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1999). Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13(3-4), 397-413. doi:10.1002/(sici)1099-128x(199905/08)13:3/43.0.co;2-iNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Wold, S., Kettaneh, N., FridĂ©n, H., & Holmberg, A. (1998). Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 331-340. doi:10.1016/s0169-7439(98)00162-2Chen, J., & Liu, K.-C. (2002). On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 57(1), 63-75. doi:10.1016/s0009-2509(01)00366-9Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2005). Fault detection properties of global, local and time evolving models for batch process monitoring. Journal of Process Control, 15(7), 799-805. doi:10.1016/j.jprocont.2005.02.001Lennox, B., Montague, G. A., Hiden, H. G., Kornfeld, G., & Goulding, P. R. (2001). Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering, 74(2), 125-135. doi:10.1002/bit.1102Ăśndey, C., Ertunç, S., & Çınar, A. (2003). Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis. Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Camacho, J., & PicĂł, J. (2006). Multi-phase principal component analysis for batch processes modelling. Chemometrics and Intelligent Laboratory Systems, 81(2), 127-136. doi:10.1016/j.chemolab.2005.11.003Rännar, S., MacGregor, J. F., & Wold, S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 41(1), 73-81. doi:10.1016/s0169-7439(98)00024-0Camacho, J., PicĂł, J., & Ferrer, A. (2009). The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter? Analytica Chimica Acta, 642(1-2), 59-68. doi:10.1016/j.aca.2009.02.001Van Sprang, E. N. ., Ramaker, H.-J., Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2002). Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18), 3979-3991. doi:10.1016/s0009-2509(02)00338-xLei, F., Rotbøll, M., & Jørgensen, S. B. (2001). A biochemically structured model for Saccharomyces cerevisiae. Journal of Biotechnology, 88(3), 205-221. doi:10.1016/s0168-1656(01)00269-3Camacho J González-MartĂnez J Ferrer A Multi-phase (MP) toolbox 2013 http://mseg.webs.upv.es/Software.htmlCamacho, J., PicĂł, J., & Ferrer, A. (2008). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22(11-12), 632-643. doi:10.1002/cem.115
Subspace decomposition and critical phase selection based cumulative quality analysis for multiphase batch processes
Quality analysis and prediction have been of great significance to ensure consistent and high product quality for chemical engineering processes. However, previous methods have rarely analyzed the cumulative quality effect which is of typical nature for batch processes. That is, with time development, the process variation will determine the final product quality in a cumulative manner. Besides, they can not get an early sense of the quality nature. In this paper, a quantitative index is defined which can check ahead of time whether the product quality result from accumulation or the addition of successive process variations and cumulative quality effect will be addressed for quality analysis and prediction of batch processes. Several crucial issues will be solved to explore the cumulative quality effect. First, a quality-relevant sequential phase partition method is proposed to separate multiple phases from batch processes by using fast search and find of density peaks clustering (FSFDP) algorithm. Second, after phase partition, a phase-wise cumulative quality analysis method is proposed based on subspace decomposition which can explore the non-repetitive quality-relevant information (NRQRI) from the process variation at each time within each phase. NRQRI refers to the quality-relevant process variations at each time that are orthogonal to those of previous time and thus represents complementary quality information which is the key index to cumulatively explain quality variations time-wise. Third, process-wise cumulative quality analysis is conducted where a critical phase selection strategy is developed to identify critical-to-cumulative-quality phases and quality predictions from critical phases are integrated to exclude influences of uncritical phases. By the two-level cumulative quality analysis (i.e., phase-wise and process-wise), it is feasible to judge whether the quality has the cumulative effect in advance and thus proper quality prediction model can be developed by identifying critical-to-cumulative-quality phases. The feasibility and performance of the proposed algorithm are illustrated by a typical chemical engineering process, injection molding
Multi-synchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms
Batch synchronization has been widely misunderstood as being only needed when variable trajectories have uneven
length. Batch data are actually considered not synchronized when the key process events do not occur at the
same point of process evolution, irrespective of whether the batch duration is the same for all batches or not.
Additionally, a single synchronization procedure is usually applied to all batches without taking into account the
nature of asynchronism of each batch, and the presence of abnormalities. This strategy may distort the original
trajectories and decrease the signal-to-noise ratio, affecting the subsequent multivariate analyses. The approach
proposed in this paper, named multisynchro, overcomes these pitfalls in scenarios of multiple asynchronisms.
The different types of asynchronisms are effectively detected by using the warping information derived from
synchronization. Each set of batch trajectories is synchronized by appropriate synchronization procedures, which
are automatically selected based on the nature of asynchronisms present in data. The novel approach also includes
a procedure that performs abnormality detection and batch synchronization in an iterative manner. Data from
realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate
the performance of the proposed approach in a context of multiple asynchronisms.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Part of this research work was carried out during an internship of the corresponding author at Shell Global Solutions International B.V. (Amsterdam, The Netherlands). The authors also thank the anonymous referees for their comments, which greatly helped to improve the text.González MartĂnez, JM.; De Noord, O.; Ferrer, A. (2014). Multi-synchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms. Journal of Chemometrics. 28(5):462-475. https://doi.org/10.1002/cem.2620S462475285Kourti, T. (2009). Multivariate Statistical Process Control and Process Control, Using Latent Variables. Comprehensive Chemometrics, 21-54. doi:10.1016/b978-044452701-1.00013-2Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Kourti, T. (2003). Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications. Annual Reviews in Control, 27(2), 131-139. doi:10.1016/j.arcontrol.2003.10.004Lakshminarayanan S Gudi R Shah S Monitoring batch processes using multivariate statistical tools: extensions and practical issues. 1996 241 246Zarzo, M., & Ferrer, A. (2004). Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(1), 15-27. doi:10.1016/j.chemolab.2003.11.009Louwerse, D. J., & Smilde, A. K. (2000). Multivariate statistical process control of batch processes based on three-way models. Chemical Engineering Science, 55(7), 1225-1235. doi:10.1016/s0009-2509(99)00408-xWesterhuis, J. A., Kourti, T., & MacGregor, J. F. (1999). Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13(3-4), 397-413. doi:10.1002/(sici)1099-128x(199905/08)13:3/43.0.co;2-iNomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Ăśndey, C., Ertunç, S., & Çınar, A. (2003). Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis. Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Neogi, D., & Schlags, C. E. (1998). Multivariate Statistical Analysis of an Emulsion Batch Process. Industrial & Engineering Chemistry Research, 37(10), 3971-3979. doi:10.1021/ie980243oKourti, T., Lee, J., & Macgregor, J. F. (1996). Experiences with industrial applications of projection methods for multivariate statistical process control. Computers & Chemical Engineering, 20, S745-S750. doi:10.1016/0098-1354(96)00132-9Duchesne, C., Kourti, T., & MacGregor, J. F. (2002). Multivariate SPC for startups and grade transitions. AIChE Journal, 48(12), 2890-2901. doi:10.1002/aic.690481216Zhang, Y., Dudzic, M., & Vaculik, V. (2003). Integrated monitoring solution to start-up and run-time operations for continuous casting. Annual Reviews in Control, 27(2), 141-149. doi:10.1016/j.arcontrol.2003.10.002Rothwell SG Martin EB Morris AJ Comparison of methods for handling unequal length batches 1998 66 71GarcĂa-Muñoz, S., Kourti, T., MacGregor, J. F., Mateos, A. G., & Murphy, G. (2003). Troubleshooting of an Industrial Batch Process Using Multivariate Methods. Industrial & Engineering Chemistry Research, 42(15), 3592-3601. doi:10.1021/ie0300023Wold, S., Kettaneh, N., FridĂ©n, H., & Holmberg, A. (1998). Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 331-340. doi:10.1016/s0169-7439(98)00162-2Kaistha, N., & Moore, C. F. (2001). Extraction of Event Times in Batch Profiles for Time Synchronization and Quality Predictions. Industrial & Engineering Chemistry Research, 40(1), 252-260. doi:10.1021/ie990937cRamsay, J. O., & Silverman, B. W. (1997). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xSrinivasan, R., & Qian, M. S. (2005). Off-line Temporal Signal Comparison Using Singular Points Augmented Time Warping. Industrial & Engineering Chemistry Research, 44(13), 4697-4716. doi:10.1021/ie049528tSrinivasan, R., & Sheng Qian, M. (2006). Online fault diagnosis and state identification during process transitions using dynamic locus analysis. Chemical Engineering Science, 61(18), 6109-6132. doi:10.1016/j.ces.2006.05.037Srinivasan, R., & Qian, M. (2007). Online Temporal Signal Comparison Using Singular Points Augmented Time Warping. Industrial & Engineering Chemistry Research, 46(13), 4531-4548. doi:10.1021/ie060111sChen, J., & Liu, J. (2000). Post analysis on different operating time processes using orthonormal function approximation and multiway principal component analysis. Journal of Process Control, 10(5), 411-418. doi:10.1016/s0959-1524(00)00016-0Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. doi:10.1109/tassp.1978.1163055Nielsen, N.-P. V., Carstensen, J. M., & Smedsgaard, J. (1998). Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography A, 805(1-2), 17-35. doi:10.1016/s0021-9673(98)00021-1Pravdova, V., Walczak, B., & Massart, D. L. (2002). A comparison of two algorithms for warping of analytical signals. Analytica Chimica Acta, 456(1), 77-92. doi:10.1016/s0003-2670(02)00008-9Tomasi, G., van den Berg, F., & Andersson, C. (2004). Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemometrics, 18(5), 231-241. doi:10.1002/cem.859Kassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412Gollmer, K., & Posten, C. (1996). Supervision of bioprocesses using a dynamic time warping algorithm. Control Engineering Practice, 4(9), 1287-1295. doi:10.1016/0967-0661(96)00136-0Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2003). Dynamic time warping of spectroscopic BATCH data. Analytica Chimica Acta, 498(1-2), 133-153. doi:10.1016/j.aca.2003.08.045Fransson, M., & Folestad, S. (2006). Real-time alignment of batch process data using COW for on-line process monitoring. Chemometrics and Intelligent Laboratory Systems, 84(1-2), 56-61. doi:10.1016/j.chemolab.2006.04.020González-MartĂnez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2869González-MartĂnez, J. M., Westerhuis, J. A., & Ferrer, A. (2013). Using warping information for batch process monitoring and fault classification. Chemometrics and Intelligent Laboratory Systems, 127, 210-217. doi:10.1016/j.chemolab.2013.07.003Kourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778González-MartĂnez, J. M., Camacho, J., & Ferrer, A. (2013). Bilinear modeling of batch processes. Part III: parameter stability. Journal of Chemometrics, 28(1), 10-27. doi:10.1002/cem.2562Camacho, J., & Ferrer, A. (2014). Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects. Chemometrics and Intelligent Laboratory Systems, 131, 37-50. doi:10.1016/j.chemolab.2013.12.003Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Lei, F., Rotbøll, M., & Jørgensen, S. B. (2001). A biochemically structured model for Saccharomyces cerevisiae. Journal of Biotechnology, 88(3), 205-221. doi:10.1016/s0168-1656(01)00269-3Camacho J González-MartĂnez JM Ferrer A Multi-phase (MP) toolbox 2013 http://mseg.webs.upv.es/Software.htmlUMETRICS SIMCA 13.0.3 Umea, Sweden 2013 [email protected] www.umetrics.comGonzález-MartĂnez, J. M., Vitale, R., de Noord, O. E., & Ferrer, A. (2014). Effect of Synchronization on Bilinear Batch Process Modeling. Industrial & Engineering Chemistry Research, 53(11), 4339-4351. doi:10.1021/ie402052
Evaluation of gas chromatography mass spectrometry and pattern recognition for the identification of bladder cancer from urine headspace
Previous studies have indicated that volatile organic compounds specific to bladder cancer may exist in urine headspace, raising the possibility that they may be of diagnostic value for this particular cancer. To further examine this hypothesis, urine samples were collected from patients diagnosed with either bladder cancer or a non-cancerous urological disease/infection, and from healthy volunteers, from which the volatile metabolomes were analysed using gas chromatography mass spectrometry. The acquired data were subjected to a specifically designed pattern recognition algorithm, involving cross-model validation. The best diagnostic performance, achieved with independent test data provided by healthy volunteers and bladder cancer patients, was 89% overall accuracy (90% sensitivity and 88% specificity). Permutation tests showed that these were statistically significant, providing further evidence of the potential for volatile biomarkers to form the basis of a non-invasive diagnostic technique
Multivariate Statistical Process Monitoring Based on Synchronization of Batch Trajectories Using DTW
- …