Learning algorithms for identification of whisky using portable Raman spectroscopy

TL;DR

This study develops machine learning models interfaced with portable Raman spectroscopy to accurately identify whisky brands and measure ethanol/methanol levels, enabling non-invasive, real-time quality control and counterfeit detection.

Contribution

It introduces a flexible machine learning approach integrated with portable Raman spectroscopy for non-destructive whisky identification and chemical analysis, including through-the-bottle spectral analysis.

Findings

Achieved over 99% accuracy in brand identification

Successfully quantified ethanol and methanol concentrations

Enabled non-invasive, through-the-bottle analysis

Abstract

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.