Genetic Programming for Symbolic Regression with Portable Near-Infrared Spectroscopy for the Prevention of Harm from Adulterated Illicit Drugs at Festivals in New Zealand

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@MastersThesis{Dockter:MSc,

• author = "Steven E. Dockter",
• title = "Genetic Programming for Symbolic Regression with Portable Near-Infrared Spectroscopy for the Prevention of Harm from Adulterated Illicit Drugs at Festivals in New Zealand",
• school = "Victoria University of Wellington",
• year = "2025",
• address = "New Zealand",
• keywords = "genetic algorithms, genetic programming, Artificial Intelligence",
• URL = "https://openaccess.wgtn.ac.nz/articles/thesis/Genetic_Programming_for_Symbolic_Regression_with_Portable_Near-Infrared_Spectroscopy_for_the_Prevention_of_Harm_from_Adulterated_Illicit_Drugs_at_Festivals_in_New_Zealand/28941851?file=54253688",
• URL = "https://openaccess.wgtn.ac.nz/ndownloader/files/54253688",
• size = "114 pages",
• abstract = "Illicit drugs are often mixed with cutting agents and adulterants, which can pose significant risks, either independently or when consumed with the drug itself. Drug checking services play a vital role in reducing these risks by providing information about substance composition and drug harm prevention. A widely adopted model for delivering drug checking services is at events and festivals. To effectively reduce drug-related harm in such settings, it is crucial to have rapid, accurate, non-destructive, safe, portable, and user-friendly substance identification methods. Near-infrared spectroscopy (NIRS) is a well-established technique for identifying and quantifying a wide range of substances, including illicit drug mixtures, particularly with high-end bench-top instruments. While using portable NIRS devices for mixture analysis is advantageous, challenges such as low sensitivity, limited wavelength range, and low resolution persist. To address these challenges, various artificial intelligence techniques have been applied to analyze portable NIRS data for mixtures. However, evolutionary computation methods, which are known for their robustness in solving complex optimization problems, have not been widely explored in NIRS-based mixture analysis. This thesis proposes a genetic programming for symbolic regression (GPSR) approach to develop explainable models for analyzing illicit drug mixtures using portable NIRS. Experimental results demonstrate that the proposed approach can accurately model the relationship between a mixture and its components based on NIRS data. Furthermore, the method shows promise in accurately identifying components within a drug sample, with performance comparable to traditional linear models. The potential for implementing this method into an integrated solution for drug checking services at point-of-care scenarios, such as festivals, is also explored.",

}

Genetic Programming entries for Steven E Dockter

Citations