Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning

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Abstract
Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.
Funding Information
  • Korea Planning &amp; Evaluation Institute of Industrial Technology funded by the Ministry of the Interior and Safety (MOIS, Republic of Korea) (RS-2024-00409733, RS-2023-00210498, 2020R1A6C101B197)
  • National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, Republic of Korea) (RS-2024-00409733, RS-2023-00210498, 2020R1A6C101B197)
  • Chonnam National University (Smart Plant Reliability Center) grant funded by the Ministry of Education (MOE, Republic of Korea) (RS-2024-00409733, RS-2023-00210498, 2020R1A6C101B197)

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