Skip to main content
SpringerLink
Log in

Advantages of Multiscale Detection of Defective Pills during Manufacturing

  • Conference paper
High Performance Computing and Applications

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5938))

Abstract

We explore methods to automatically detect the quality in individual or batches of pharmaceutical products as they are manufactured. The goal is to detect 100% of the defects, not just statistically sample a small percentage of the products and draw conclusions that may not be 100% accurate. Removing all of the defective products, or halting production in extreme cases, will reduce costs and eliminate embarrassing and expensive recalls. We use the knowledge that experts have accumulated over many years, dynamic data derived from networks of smart sensors using both audio and chemical spectral signatures, multiple scales to look at individual products and larger quantities of products, and finally adaptive models and algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. http://www.fda.gov/diabetes/qna.htm

  2. http://www.nlm.nih.gov/medlineplus/diabetes.html

  3. http://www.diabetes.org/diabetes-statistics/cost-of-diabetes-in-us.jsp

  4. http://www.fda.gov/ohrms/dockets/ac/02/briefing/3869B1_08_woodcock/sld001.htm

  5. http://www.dddas.org

  6. Baldridge, K., Biros, G., Chaturvedi, A., Douglas, C.C., Parashar, M., How, J., Saltz, J., Seidel, E., Sussman, A.: DDDAS Workshop Report, National Science Foundation (January 2006)

    Google Scholar 

  7. Douglas, C.C., Deshmukh, A., Ball, M., Ewing, R.E., Johnson, C.R., Kesselman, C., Lee, C., Powell, W., Sharpley, R.: Dynamical data driven application systems: Creating a dynamic and symbiotic coupling of application/simulations with measurements/ experiments, National Science Foundation, Arlington, VA (2000)

    Google Scholar 

  8. Darema, F. (ed.): 2007 Dynamic Data-Driven Application Workshop, Computational Science – ICCS 2007: 7th International Conference, Beijing, P.R. China, May 28-31 (2007); Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.): ICCS 2007. LNCS, vol. 4487, pp. 955–1245. Springer, Heidelberg (2007)

    Book  Google Scholar 

  9. Douglas, C.C. (ed.): 2009 Dynamic data-driven application systems - DDDAS 2008, in Computational Science – ICCS 2008: 8th International Conference, May 25-27 (2009); Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J.J., Sloot, P.M.A. (eds.): ICCS 2009, Part II. LNCS, vol. 5545, pp. 445–509. Springer, Heidelberg (2009)

    Book  Google Scholar 

  10. Dai, B., Urbas, A., Douglas, C.C., Lodder, R.A.: Molecular factor computing for predictive spectroscopy. Pharm Res. 8, 1441–1444 (2007)

    Article  Google Scholar 

  11. Beadle, J.R., Saunders, J.P., Wajda Jr., T.J.: Process for Manufacturing Tagatose. Biospherics Incorporated (US patent 5,002,612) (1991), http://www.uspto.gov/patft/index.html

  12. Beadle, J.R., Saunders, J.P., Wajda Jr., T.J.: Process for Manufacturing Tagatose. Biospherics Incorporated (US patent 5,078,796) (1992), http://www.uspto.gov/patft/index.html

  13. Lu, Y., Levin, G.V., Donner, T.W.: Tagatose, a new antidiabetic and obesity control drug. Diabetes Obes Metab. 10, 109–134 (2008)

    Article  Google Scholar 

  14. The MathWorks, Simulink and the Communications Toolbox, http://www.mathworks.com

  15. Woodcock, J.: US Food and Drug Administration, http://www.fda.gov/ohrms/dockets/ac/02/briefing/3869B1_08_woodcock/sld001.htm

  16. Parashar, M., Matossian, V., Klie, H., Thomas, S.G., Wheeler, M., Kurc, T., Saltz, J., Versteeg, R.: Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006, Part III. LNCS, vol. 3993, pp. 384–392. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Medendorp, J.P., Fackler, J.A., Douglas, C.C., Lodder, R.A.: Integrated sensing and processing acoustic resonance spectrometry (ISP-ARS) for sample Classification. J. Pharm. Innov. 2, 125–134 (2007)

    Article  Google Scholar 

  18. Hannel, T., Link, D., Lodder, R.A.: ISP-ARS in discriminating Tagatose and other toll manufactured drugs. ISPE Journal of Pharmaceutical Innovation 3, 152–160 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Wyoming, 1000 University Avenue, Dept. 3036, Laramie, WY, 82071-3036, USA

    Craig C. Douglas

  2. JSOL Corporation, Chuo-ku, Tokyo, 104-0053, Japan

    Li Deng

  3. Mathematics Department, Texas A&M University, College Station, TX, USA

    Yalchin Efendiev

  4. Mathematics and Computational Sciences Department, Karl-Franzens University of Graz, A-8010, Graz, Austria

    Gundolf Haase & Andreas Kucher

  5. Chemistry Department, University of Kentucky, Lexington, KY, 40506, USA

    Robert Lodder

  6. Department of Chemical and Petroleum Engineering, University of Wyoming, 1000 University Avenue, Dept. 3295, Laramie, WY, 82071-3295, USA

    Guan Qin

Authors
  1. Craig C. Douglas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yalchin Efendiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gundolf Haase
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andreas Kucher
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert Lodder
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Guan Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Engineering and Science, Shanghai University, 200072, Shanghai, P.R. China

    Wu Zhang  & Weiqin Tong  & 

  2. Schulich School of Engineering, University of Calgary, T2N 1N4, Calgary, AB, Canada

    Zhangxin Chen

  3. Department of Mathematics, University of Wyoming, 1000 University Avenue, 82071-3036, Laramie, WY, USA

    Craig C. Douglas

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Douglas, C.C. et al. (2010). Advantages of Multiscale Detection of Defective Pills during Manufacturing. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds) High Performance Computing and Applications. Lecture Notes in Computer Science, vol 5938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11842-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11842-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11841-8

  • Online ISBN: 978-3-642-11842-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation