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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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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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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