21–26 Jun 2026
U. Ottawa - Learning Crossroads (CRX) Building
America/Toronto timezone
Welcome to the 2026 CAP Congress Program website! / Bienvenue au siteweb du programme du Congrès de l'ACP 2026!

Establishing the limit of detection of laser-induced breakdown spectroscopy for Escherichia coli in artificial cerebrospinal fluid for the diagnosis of bacterial meningitis

23 Jun 2026, 11:00
15m
U. Ottawa - Learning Crossroads (CRX) Building

U. Ottawa - Learning Crossroads (CRX) Building

100 Louis-Pasteur Private, Ottawa, ON K1N 9N3
Oral Competition (Undergraduate Student) / Compétition orale (Étudiant(e) du 1er cycle) Physics in Medicine and Biology / Physique en médecine et en biologie (DPMB-DPMB) (DPMB) T1-2 | (DPMB)

Speaker

Mr Abdullah Mustafa (University of Windsor)

Description

Bacterial meningitis is a severe and potentially lethal infection of the meninges, afflicting more than a million people per year globally. Delays in treatment have been shown to increase both mortality rates and the strain on the healthcare system. In an effort to develop a near-instantaneous and clinically simple diagnostic test for bacterial meningitis, our group has shown that laser-induced breakdown spectroscopy (LIBS) has the capability to detect bacterial pathogens in various media to a high-degree of accuracy, including Escherichia coli and Mycobacterium smegmatis in artificial cerebrospinal fluid (aCSF) to simulate a meningitis infection. Ongoing work seeks to establish the current detectable limit of bacterial presence within aCSF and subsequently reduce the lower bound, as needed, to create a viable diagnostic tool.

In this work, the lower limit of detection of LIBS was determined by assaying several concentrations of E. coli in aCSF on a nitrocellulose medium. Optical densitometry measurements were performed to measure and fix concentrations of eight test suspensions. Dilutions ranging from 26 500 down to 180 colony forming units per laser shot were created. A 1064 nm Nd:YAG laser with 8 mJ pulse energy was focused to 100 µm spot size. Each laser shot created a microplasma and the resulting atomic emission was dispersed by a high-resolution Échelle spectrometer to produce a spectrum spanning 200 nm to 800 nm. An artificial neural network with principal component analysis preprocessing was constructed to identify the presence of bacterial cells in the spectra. In addition, a partial least squares discriminant analysis was performed on a model comprised of 15 line intensities of observed elements from the full spectrum. Both models were used to ascertain the lower limit of detection.

The sensitivities and specificities of each test will be presented. The lower limit of detection from these data will be analyzed and discussed. Future studies contributing to the reduction of this lower bound will be discussed, including dismembrating samples, introducing a double-centrifugation system, and investigating the dependence of this lower limit on the ablation laser wavelength.

Keyword-1 Laser spectroscopy
Keyword-2 Bacteria
Keyword-3 Machine learning

Authors

Mr Abdullah Mustafa (University of Windsor) Lauren Dmytrow (University of Windsor)

Co-authors

Rachel Chevalier Ms Jasmine Fric (University of Windsor) Ms Simona Brezeanu (University of Windsor) Ms Emma Pesce (University of Windsor) Ms Katherine Keller (University of Windsor) Mr Jack Holland (University of Windsor) Mr Will Conlon (University of Windsor) Ms Danielle Renard (University of Windsor) Ms Faiza Fric (University of Windsor) Mr Ayman Younes (University of Windsor) Prof. Steven Rehse (University of Windsor)

Presentation materials

There are no materials yet.