Online near-infrared spectroscopy combined with alternating trilinear decomposition for process analysis of industrial production and quality assurance

Industrial and Engineering Chemistry Research

Volumn 50, Issue 12, 2011, Pages 7677-7681

  Liu, Jingjing ^a   Ma, Xiang ^b   Wen, Yadong ^b   Wang, Yi ^b   Cai, Wensheng ^a   Shao, Xueguang ^a  

^a NANKAI UNIVERSITY   (China)

^b RandD Center   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALTERNATING TRILINEAR DECOMPOSITION; COMMON INFORMATION; COMMON PROPERTY; DISCRIMINATION ANALYSIS; IN-PROCESS; INDUSTRIAL PROCESSS; INDUSTRIAL PRODUCTION; PROCESS ANALYSIS; PRODUCTION PROCESS; TOBACCO PRODUCTION;

INDUSTRY; INFRARED DEVICES; INFRARED SPECTROSCOPY; QUALITY ASSURANCE; TEXTILES;

QUALITY CONTROL;

EID: 79958812743     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie200543v     Document Type: Article

References (48)

1. Vanarase, A. U.; Alcala, M.; JerezRozo, J. I.; Muzzio, F. J.; Romanach, R. J. Real-time monitoring of drug concentration in a continuous powder mixing process using NIR spectroscopy Chem. Eng. Sci. 2010, 65, 5728-5733
2. Alcala, M.; Leon, J.; Ropero, J.; Blanco, M.; Romanach, R. J. Analysis of low content drug tablets by transmission near infrared spectroscopy: Selection of calibration ranges according to multivariate detection and quantitation limits of PLS models J. Pharm. Sci. 2008, 97, 5318-5327
3. Li Vigni, M.; Durante, C.; Foca, G.; Marchetti, A.; Ulrici, A.; Cocchi, M. Near infrared spectroscopy and multivariate analysis methods for monitoring flour performance in an industrial bread-making process Anal. Chim. Acta 2009, 642, 69-76
4. Marengo, E.; Longo, V.; Robotti, E.; Bobba, M.; Gosetti, F.; Zerbinati, O.; Martino, S. D. Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy, multivariate statistical tools, and artificial neural networks J. Appl. Polym. Sci. 2008, 109, 3975-3982
5. Lee, M. W.; Hong, S. H.; Choi, H.; Kim, J. H.; Lee, D. S.; Park, J. M. Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors Process Biochem. 2008, 43, 1107-1113
6. Wold, S.; Esbensen, K.; Geladi, P. Principal component analysis Chemom. Intell. Lab. Syst. 1987, 2, 37-52
7. Dunia, R.; Qin, S.; Edgar, T.; McAvoy, T. Identification of faulty sensors using principal component analysis AIChE J. 1996, 42, 2797-2811 (Pubitemid 126452402)
8. Lu, N.; Gao, F.; Yang, Y.; Wang, F. PCA-based modeling and on-line monitoring strategy for uneven-length batch processes Ind. Eng. Chem. Res. 2004, 43, 3343-3352 (Pubitemid 38781349)
9. Kruger, U.; Chen, Q.; Sandoz, D. J.; McFarlane, R. C. Extended PLS approach for enhanced condition monitoring of industrial process AIChE J. 2001, 47, 2076-2091
10. Zheng, L. L.; Mc Avoy, T. J.; Huang, Y. B.; Chen, G. Application of multivariate statistical analysis in batch processes Ind. Eng. Chem. Res. 2001, 40, 1641-1649 (Pubitemid 33001067)
11. Reiss, R.; Wojsznis, W.; Wojewodka, R. Partial least squares confidence interval calculation for industrial end-of-batch quality prediction Chemom. Intell. Lab. Syst. 2010, 100, 75-82
12. Jia, F.; Martin, E. B.; Morris, A. J. Non-linear principal components analysis for process fault detection Comput. Chem. Eng. 1998, 22, S851-S854
13. Choi, S. W.; Lee, C.; Lee, J. M.; Park, J. H.; Lee, I. B. Fault detection and identification of nonlinear processes based on kernel PCA Chemom. Intell. Lab. Syst. 2005, 75, 55-67 (Pubitemid 40038191)
14. Cui, P.; Li, J.; Wang, G. Improved kernel principal component analysis for fault detection Expert Syst. Appl. 2008, 34, 1210-1219 (Pubitemid 350117491)
15. Lee, C.; Choi, S. W.; Lee, I. B. Sensor fault identification based on time-lagged PCA in dynamic processes Chemom. Intell. Lab. Syst. 2004, 70, 165-178 (Pubitemid 38210410)
16. Yao, Y.; Gao, F. R. Batch process monitoring in score space of two-dimensional dynamic principal component analysis (PCA) Ind. Eng. Chem. Res. 2007, 46, 8033-8043 (Pubitemid 350208012)
17. Pollanen, K.; Hakkinen, A.; Reinikainen, S. P.; Rantanen, J.; Minkkinen, P. Dynamic PCA-based MSPC charts for nucleation prediction in batch cooling crystallization processes Chemom. Intell. Lab. Syst. 2006, 84, 126-133 (Pubitemid 44708403)
18. Rannar, S.; MacGregor, J. F.; Wold, S. Adaptive batch monitoring using hierarchical PCA Chemom. Intell. Lab. Syst. 1998, 41, 73-81 (Pubitemid 28337890)
19. Choi, S. W.; Martin, E. B.; Julian Morris, A.; Lee, I. B. Adaptive multivariate statistical process control for monitoring time-varying processes Ind. Eng. Chem. Res. 2006, 45, 3108-3118
20. Zhao, C. H.; Wang, F. L.; Gao, F. R.; Lu, N. Y.; Jia, M. X. Adaptive monitoring method for batch processes based on phase dissimilarity updating with limited modeling data Ind. Eng. Chem. Res. 2007, 46, 4943-4953
21. Yoo, C. K.; Lee, J. M.; Vanrolleghem, P. A.; Lee, I. B. On-line monitoring of batch processes using multiway independent component analysis Chemom. Intell. Lab. Syst. 2004, 71, 151-163 (Pubitemid 38591486)
22. Zhao, C. H.; Gao, F. G.; Wang, F. L. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA) Ind. Eng. Chem. Res. 2009, 48, 9163-9174
23. Lee, J. M.; Yoo, C.; Lee, I. B. Statistical process monitoring with independent component analysis J. Process Control 2004, 14, 467-485
24. Nomikos, P.; MacGregor, J. F. Monitoring batch processes using multiway principal component analysis AIChE J. 1994, 40, 1361-1375
25. Nomikos, P.; MacGregor, J. F. Multivariate SPC charts for monitoring batch processes Technometrics 1995, 37, 41-59
26. Nomikos, P.; MacGregor, J. F. Multi-way partial least square in monitoring batch processes Chemom. Intell. Lab. Syst. 1995, 30, 97-108
27. Kourti, T. Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions J. Chemom. 2003, 17, 93-109
28. Bro, R. PARAFAC. Tutorials and applications Chemom. Intell. Lab. Syst. 1997, 38, 149-170
29. Andersen, C. M.; Bro, R. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data J. Chemom. 2003, 17, 200-215
30. Matero, S.; Poutiainen, S.; Leskinen, J.; Reinikainen, S. P.; Ketolainen, J.; Jarvinen, K.; Poso, A. Monitoring the wetting phase of fluidized bed granulation process using multi-way methods: The separation of successful from unsuccessful batches Chemom. Intell. Lab. Syst. 2009, 96, 88-93
31. Amigo, J. M.; Surribas, A.; Coello, J.; Montesinos, J. L.; Maspoch, S.; Valero, F. On-line parallel factor analysis. A step forward in the monitoring of bioprocesses in real time Chemom. Intell. Lab. Syst. 2008, 92, 44-52
32. Kroonenburg, P. M. Three-Mold Principal Component Analysis: Theory and applications; DSWO Press: Leiden, 1983.
33. Wu, H. L.; Shibukawa, M.; Oguma., K. An alternating trilinear decomposition algorithm with application to calibration of HPLC-DAD for simultaneous determination of overlapped chlorinated aromatic hydrocarbons J. Chemom. 1998, 12, 1-26 (Pubitemid 128478963)
34. Zhang, Y.; Wu, H. L.; Ding, Y. J.; Xia, A. L.; Cui, H.; Yu, R. Q. Simultaneous determination of cortisol and prednisolone in body fluids by using HPLC-DAD coupled with second-order calibration based on alternating trilinear decomposition J. Chromatogr. B 2006, 840, 116-123 (Pubitemid 44163472)
35. Li, S. F.; Wu, H. L.; Yu, Y. J.; Li, Y. N.; Nie, J. F.; Fu, H. Y.; Yu, R. Q. Quantitative analysis of levodopa, carbidopa and methyldopa in human plasma samples using HPLC-DAD combined with second-order calibration based on alternating trilinear decomposition algorithm Talanta 2010, 81, 805-812
36. Escandar, G. M.; Olivieri, A. C.; Faber, N. K. M.; Goicoechea, H. C.; de la Pena, A. M.; Poppi, R. J. Second- and third-order multivariate calibration: data, algorithms and applications TrAC-Trend Anal. Chem. 2007, 26, 752-765 (Pubitemid 47208586)
37. Booksh, K. S.; Kowalski, B. R. Theory of analytical chemistry Anal. Chem. 1994, 66, 782A-791A (Pubitemid 124005696)
38. Li Vigni, M.; Durante, C.; Foca, G.; Marchetti, A.; Ulrici, A.; Cocchia, M. Near infrared spectroscopy and multivariate analysis methods for monitoring flour performance in an industrial bread-making process Anal. Chim. Acta 2009, 642, 69-76
39. Stordrange, L.; Rajalahti, T.; Libnau, F. O. Multiway methods to explore and model NIR data from a batch process Chemom. Intell. Lab. Syst. 2004, 70, 137-145 (Pubitemid 38213158)
40. Larrechi, M. S.; Callao, M. P. Strategy for introducing NIR spectroscopy and multivariate calibration techniques in industry TrAC-Trend Anal. Chem. 2003, 22, 634-640 (Pubitemid 41685810)
41. Nomikos, P.; MacGregor, J. F. Multivariate SPC charts for monitoring batch process Technometrics 1995, 37, 41-59
42. Shao, X. G.; Leung, A. K. M.; Chau, F. T. Wavelet: A new trend in chemistry Acc. Chem. Res. 2003, 36, 276-283
43. Ma, C. X.; Shao, X. G. Continuous wavelet transform applied to removing the fluctuating background in near-infrared spectra Chem. Inf. Comput. Sci. 2004, 44, 907-911
44. Shao, X. G.; Shao, L. M.; Zhao, G. W. Extraction of extended X-ray absorption fine structure information from the experimental data using the wavelet transform Anal. Commun. 1998, 35, 135-137
45. Shao, X. G.; Ma, C. X. A general approach to derivative calculation using wavelet transform Chemom. Intell. Lab. Syst. 2003, 69, 157-165
46. Cai, W. S.; Li, Y. K.; Shao, X. G. A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra Chemom. Intell. Lab. Syst. 2008, 90, 188-194
47. Shao, X. G.; Bian, X. H.; Liu, Z. C.; Cai, W. S. An improved boosting partial least squares for near-infrared spectroscopic quantitative analysis Anal. Chim. Acta 2010, 666, 32-37
48. Hotelling, H. Multivariate Quality Control-Illustrated by the Air Testing of Sample Bombsights. In Techniques of Statistical Analysis; McGraw-Hill: New York, 1947; 11-184.