Abstract Number:
3463
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Marieke Sorge (1), Marie Tuft (2), Stephanie White (3), Cody Corbin (3)
Institutions:
(1) Arizona State University, N/A, (2) N/A, N/A, (3) Sandia National Laboratories, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Colorimetric sensor arrays typically consist of a matrix of agents meant to provide unique color responses to target stimuli. Polydiacetylenes (PDAs) are a suitable candidate for colorimetric sensor arrays in tamper identification settings as they will change color from visibly blue to red. PDAs also may elicit an electrochemical signature visualized via electrochemical impedance spectroscopy (EIS), which can be utilized to identify molecular species such as volatile organic compounds (VOCs). Thus, a suitably calibrated matrix of known PDAs can be utilized to uniquely identify stimulants in several settings using the three-dimensional scalar color values from the colorimetric array, and functional impedance measurements. Reliably and accurately identifying a range of stimuli using these metrics poses a challenging classification problem. A suite of classification algorithms supplemented by dimension reduction techniques to combine the scalar and functional responses and uncertainty quantification incorporated through conformal prediction were leveraged to classify stimuli.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. SAND2024-01105A.
Keywords:
classification|conformal prediction|colorimetric|chemometrics| |
Sponsors:
Section on Statistics in Defense and National Security
Tracks:
Miscellaneous
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