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30 April 2019 Multiplexed detection of lanthanides using laser-induced breakdown spectroscopy: a survey of data analysis techniques
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Laser-induced breakdown spectroscopy (LIBS) is a technique developed in the last few decades for simultaneous multi-element characterization of various materials. Multiplexed detection of analytes is particularly useful in the realm of food contaminant detection, where the contaminant can be one or a combination of adulterants. Paper-based assays are an emerging platform for food-contaminant detection. However, most paper-based assays do not perform multiplexed detection. For food contaminant outbreak prevention and remediation, rapid multiplexed detection could make a difference in response speed. This study applies LIBS to the concept of multi-analyte detection on paper-based bioassays. In the envisioned bioassay, a variety of analytes are labeled with unique lanthanides, a technique common to the well-established field of mass cytometry. The presence of single or multiple lanthanide labels indicates the presence of single or multiple types of contaminants. We aim to implement LIBS for multiplexed detection of lanthanide labels. To investigate data analysis approaches for multi-lanthanide detection, we evaluate univariate data analysis and spectral unmixing approaches on samples containing combinations of europium, dysprosium, gadolinium, praseodymium, and neodymium. We find that the intense signal generated by Eu, matrix effects, selfabsorption, and spectral overlap affect the outcome of the results. Future studies will continue the investigation to identify the most appropriate approach.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Carmen Gondhalekar, Bartek Rajwa, Euiwon Bae, Valery Patsekin, Jennifer Sturgis, Huisung Kim, Iyll-Joon Doh, Prasoon Diwakar, and J. Paul Robinson "Multiplexed detection of lanthanides using laser-induced breakdown spectroscopy: a survey of data analysis techniques", Proc. SPIE 11016, Sensing for Agriculture and Food Quality and Safety XI, 1101609 (30 April 2019);

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