2023/09/22 by Kwang Jun Lee, Lee, Kwang Jun, Alexander C. Trowbridge +11
Biochemistry, Genetics and Molecular Biology · Chemistry · Engineering · #Advanced Chemical Sensor Technologies #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Spectroscopy Techniques in Biomedical and Chemical Research #Spectroscopy and Chemometric Analyses #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2309.13087
openalex publication_date 2023/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
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.