| 光谱学与光谱分析 |
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| Near Infrared Spectroscopy and Multivariate Statistical Process Analysis for Real-Time Monitoring of Production Process |
| WANG Yi1, MA Xiang1, WEN Ya-dong1, ZOU Quan1, WANG Jun1, TU Jia-run2, CAI Wen-sheng2, SHAO Xue-guang2* |
1. R & D Center, Hongta Tobacco Group, Yuxi 653100, China
2. College of Chemistry, Nankai University, Tianjin 300071, China |
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Abstract Near infrared diffusive reflectance spectroscopy has been applied in on-site or on-line analysis due to its characteristics of fastness, non-destruction and the feasibility for real complex sample analysis. The present work reported a real-time monitoring method for industrial production by using near infrared spectroscopic technique and multivariate statistical process analysis. In the method, the real-time near infrared spectra of the materials are collected on the production line, and then the evaluation of the production process can be achieved by a statistic Hotelling T2 calculated with the established model. In this work, principal component analysis (PCA) is adopted for building the model, and the statistic is calculated by projecting the real-time spectra onto the PCA model. With an application of the method in a practical production, it was demonstrated that a real-time evaluation of the variations in the production can be realized by investigating the changes in the statistic, and the comparison of the products in different batches can be achieved by further statistics of the statistic. Therefore, the proposed method may provide a practical way for quality insurance of production processes.
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Received: 2012-09-25
Accepted: 2012-11-30
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Corresponding Authors:
SHAO Xue-guang
E-mail: xshao@nankai.edu.cn
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[1] Bosco G L. Trends in Analytical Chemistry, 2010, 29(3): 197.
[2] Ma C X, Shao X G, Journal of Chemical Information and Computer Sciences, 2004, 44: 907.
[3] Chen D, Shao X G, Hu B, et al. Analytica Chimica Acta, 2004, 511(1): 37.
[4] Xu H, Liu Z C, Cai W S, et al. Chemometrics and Intelligent Laboratory Systems, 2009, 97: 189
[5] Shao X G, Du G R, Jing M, et al. Chemometrics and Intelligent Laboratory Systems, 2012, 114: 44
[6] Shao X G, Bian X H, Liu J J, et al. Analytical Methods, 2010, 2: 1662
[7] Liu J J, Ma X, Wen Y D, et al. Industrial & Engineering Chemistry Research, 2011, 50(12): 7677.
[8] Liu J J, Xu H, Cai W S, et al. Chinese Chemical Letters, 2011, 22(10): 1241
[9] Westerhuis J A, Gurden S P, Smilde A K. Journal of Chemometrics, 2000, 14(4): 335.
[10] Pomerantsev A L, Rodionova O Y, Journal of Chemometrics, 2011, 26: 299.
[11] Thissen U, Swierenga H, de Weijer A P, et al. Journal of Chemometrics, 2005, 19: 23
[12] Choi S W, Martin E B, Morris A J, et al. Industrial & Engineering Chemistry Research, 2006, 45: 3108. |
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