Implementing Machine Learning for the Identification and Classification of Compound and Mixtures in Portable Raman Instruments

• NCJ Number: 305218
• Author(s): Travon Cooman; Tatiana Trejos; Aldo Romero; Luis E. Arroyo
• Date Published: January 2022
• Grant Number(s): 2019-DU-BX-0030
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Fast On-site Screening of Seized Drugs by Electrochemical and Spectroscopic Tools: Identification of Fentanyl and Novel Psychoactive Substances

Annotation

This study explored machine learning algorithms to classify single compounds, binary, ternary, and quaternary mixtures by the compound name, and the compound’s class, using seized drugs and common diluents as a model.

Abstract

The accuracies were ≥ 93% for most pure, binary mixtures, and quaternary mixtures algorithms. Therefore, incorporating machine learning algorithms in portable instruments, can improve the detection of unknown substances with high accuracies. (Publisher abstract provided)