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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2018: Advances in Knowledge Discovery and Data Mining pp 298–309Cite as

Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10937))

Abstract

Alcoholic spirits are a common target for counterfeiting and adulteration, with potential costs to public health, the taxpayer and brand integrity. Current methods to authenticate spirits include examinations of superficial appearance and consistency, or require the tester to open the bottle and remove a sample. The former is inexact, while the latter is not suitable for widespread screening or for high-value spirits, which lose value once opened. We study whether non-invasive near infrared spectroscopy, in combination with traditional and time series classification methods, can correctly classify the alcohol content (a key factor in determining authenticity) of synthesised spirits sealed in real bottles. Such an experimental setup could allow for a portable, cheap to operate, and fast authentication device. We find that ethanol content can be classified with high accuracy, however methanol content proved difficult with the algorithms evaluated.

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Notes

  1. 1.

    http://www.bbc.co.uk/news/uk-12456360.

  2. 2.

    https://www.theguardian.com/sustainable-business/2015/sep/16/china-fake-alcohol-industry-counterfeit-bathtub-booze-whisky.

  3. 3.

    http://research.cmp.uea.ac.uk/DetectingForgedAlcohol/Data/.

  4. 4.

    http://research.cmp.uea.ac.uk/DetectingForgedAlcohol/FiguresAndTables/.

  5. 5.

    http://research.cmp.uea.ac.uk/DetectingForgedAlcohol/Code/.

  6. 6.

    http://research.cmp.uea.ac.uk/DetectingForgedAlcohol/Results/.

References

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Acknowledgements

This work is supported by the Biotechnology and Biological Sciences Research Council [grant number BB/M011216/1], and the UK Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/M015087/1]. Work was performed in collaboration with the Quadram Institute, Norwich and the Scotch Whisky Research Institute, Edinburgh. The classification experiments were carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.

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Authors and Affiliations

  1. School of Computing Sciences, University of East Anglia, Norwich, UK

    James Large & Anthony Bagnall

  2. Quadram Institute, Norwich Research Park, Norwich, UK

    E. Kate Kemsley & Nikolaus Wellner

  3. Scotch Whisky Research Institute, Research Avenue North, Edinburgh, UK

    Ian Goodall

Authors
  1. James Large
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  2. E. Kate Kemsley
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  3. Nikolaus Wellner
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  4. Ian Goodall
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  5. Anthony Bagnall
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Corresponding author

Correspondence to Anthony Bagnall .

Editor information

Editors and Affiliations

  1. Deakin University, Geelong, Victoria, Australia

    Dinh Phung

  2. National Chiao Tung University, Hsinchu City, Taiwan

    Vincent S. Tseng

  3. Monash University, Clayton, Victoria, Australia

    Geoffrey I. Webb

  4. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Bao Ho

  5. University of Melbourne, Melbourne, Victoria, Australia

    Mohadeseh Ganji

  6. University of Melbourne, Melbourne, Victoria, Australia

    Lida Rashidi

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Cite this paper

Large, J., Kemsley, E.K., Wellner, N., Goodall, I., Bagnall, A. (2018). Detecting Forged Alcohol Non-invasively Through Vibrational Spectroscopy and Machine Learning. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_24

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  • DOI: https://doi.org/10.1007/978-3-319-93034-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93033-6

  • Online ISBN: 978-3-319-93034-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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