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

NCJ Number
Travon Cooman; Tatiana Trejos; Aldo Romero; Luis E. Arroyo
Date Published
January 2022

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.


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)