Intelligent framework for cannabis classification using visualization of gas chromatography/mass spectrometry data and transfer learning

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.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.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.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.

Assessment of artificial intelligence to detect gasoline in fire debris using HS‐SPME‐GC/MS and transfer learning

“…In those images, the x and y axes were m/z values and retention times, respectively, while the colors represent relative abundance in the mass spectra. More details about data structure construction and signal resampling for GC/MS data can be found in our previous work[31].The TICs were plotted in the retention time range of 3.5 and 12.5 min, with the x and y axes representing retention times and summed abundance of all mass spectral peaks recorded from the same scan. Examples of the three types of images showing neat gasoline, burned substrates, and spiked samples are shown in Figure1.…”