Learning algorithms for identification of whisky using portable Raman spectroscopy
This addresses brand substitution and quality control issues in the whisky industry, offering a practical tool for detecting counterfeit or adulterated spirits, though it is incremental as it applies existing machine learning methods to a new domain.
The researchers tackled the problem of identifying and characterizing whisky brands and ethanol/methanol concentrations using portable Raman spectroscopy, achieving over 99% accuracy in brand identification across 28 commercial samples and enabling through-the-bottle analysis without decanting.
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.