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

James Large, E Kate Kemsley, Nikolaus Wellner, Ian Goodall, Anthony Bagnall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
16 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationPAKDD 2018: Advances in Knowledge Discovery and Data Mining
EditorsDinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
PublisherSpringer
Pages298-309
Number of pages12
ISBN (Electronic)978-3-319-93034-3
ISBN (Print)978-3-319-93033-6
DOIs
Publication statusPublished - 19 Jun 2018
Event22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining: Advances in Knowledge Discovery and Data Mining - Melbourne, Australia
Duration: 3 Jun 20186 Jun 2018

Conference

Conference22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining
Abbreviated titlePAKDD 2018
Country/TerritoryAustralia
CityMelbourne
Period3/06/186/06/18

Keywords

  • Classification
  • Spectroscopy
  • Non-invasive
  • Authentication

Cite this