Abstract
As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.
Original language | English (US) |
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Article number | 1614 |
Pages (from-to) | 1614 |
Journal | Scientific Reports |
Volume | 12 |
Issue number | 1 |
DOIs | |
State | Published - Jan 31 2022 |
Keywords
- Barium/analysis
- COVID-19/blood
- Data Accuracy
- Discriminant Analysis
- False Negative Reactions
- False Positive Reactions
- Humans
- Immunity
- Lasers
- Machine Learning
- Pandemics
- Reverse Transcriptase Polymerase Chain Reaction/methods
- SARS-CoV-2/genetics
- Sensitivity and Specificity
- Spectrophotometry, Atomic/methods
- Zinc/analysis
ASJC Scopus subject areas
- General