Learning algorithms for identification of whisky using portable Raman
  spectroscopy
Kwang Jun Lee Alexander C. Trowbridge Graham D. Bruce George O. Dwapanyin Kylie R. Dunning Kishan Dholakia Erik P. Schartner
Reliable identification of high-value products such as whisky is an
increasingly important area, as issues such as brand substitution (i.e.
fraudulent products) and quality control are critical to the industry. We have
examined a range of machine learning algorithms and interfaced them directly
with a portable Raman spectroscopy device to both identify and characterize the
ethanol/methanol concentrations of commercial whisky samples. We demonstrate
that machine learning models can achieve over 99% accuracy in brand
identification across twenty-eight commercial samples. To demonstrate the
flexibility of this approach we utilised the same samples and algorithms to
quantify ethanol concentrations, as well as measuring methanol levels in spiked
whisky samples. Our machine learning techniques are then combined with a
through-the-bottle method to perform spectral analysis and identification
without requiring the sample to be decanted from the original container,
showing the practical potential of this approach to the detection of
counterfeit or adulterated spirits and other high value liquid samples.