@article{ref1,
title="Intelligent framework for cannabis classification using visualization of gas chromatography/mass spectrometry data and transfer learning",
journal="Frontiers in analytical science",
year="2023",
author="Huang, Ting-Yu and Yu, Jorn Chi Chung",
volume="3",
number="",
pages="e1125049-e1125049",
abstract="INTRODUCTION: Gas chromatography combined with mass spectrometry (GC/MS) is popular analytical instrumentation for chemical separation and identification. A novel framework for chemical forensics based on the visualization of GC/MS data and transfer learning is proposed.<br><br>METHODS: To evaluate the framework, 228 GC/MS data collected from two standard cannabis varieties, i.e., hemp and marijuana, were utilized. By processing the raw GC/MS data, analytical features, including retention times, mass-to-charge ratios, intensities, and summed ion mass spectra, were successfully transformed into two types of image representations. The GC/MS data transformed images were fed into a pre-trained convolutional neural network (CNN) to develop intelligent classifiers for the sample classification tasks. The effectiveness of several hyper-parameters for improving classification performance was investigated during transfer learning.<br><br>RESULTS: The proposed analytical workflow could classify hemp and marijuana with 97% accuracy. Furthermore, the transfer-learning-based classifiers were established without requiring big data sets and peak alignment.<br><br>DISCUSSION: The potential application of the new artificial intelligence (AI)-powered framework for chemical forensics using GC/MS data has been demonstrated. This framework provides unique opportunities for classifying various types of physical evidence using chromatography and mass spectrometry signals.<p /> <p>Language: en</p>",
language="en",
issn="2673-9283",
doi="10.3389/frans.2023.1125049",
url="http://dx.doi.org/10.3389/frans.2023.1125049"
}