ArticleJournal of analytical atomic spectrometry2018
Calibration Approaches for Measurement of Aerosol Multielemental Concentration using Spark Emission Spectroscopy.
Article in Journal of analytical atomic spectrometry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Improving quantitative analysis of spark-induced breakdown spectroscopy: Multivariate calibration of metal particles using machine learning.Journal of aerosol science · 2022Article
- The performance of an inexpensive spark-induced breakdown spectroscopy instrument for near real-time analysis of toxic metal particles.Atmospheric environment (Oxford, England : 1994) · 2021Article
- Quantification of toxic metals using machine learning techniques and spark emission spectroscopy.Atmospheric measurement techniques · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A multivariate calibration approach, using partial least squares regression, has been developed for measurement of aerosol elemental concentration. A training set consisting of 25 orthogonal aerosol samples with 9 factors (elements: Cr, Mn, Fe, Ni, Cu, Zn, Cd, Pb, Ti) and 5 levels (elemental concentrations) was designed. Spectral information was obtained for each aerosol sample using aerosol spark emission spectroscopy (ASES) at a time resolution of 1 minute. Simultaneous filter samples were collected for determination of elemental concentration using an inductively coupled plasma mass spectrometry (ICP-MS) analysis. Two regression models, PLS1 and PLS2, were developed to predict mass concentration from spectral measurements. Prediction ability of the models improved substantially when only signature wavelengths were included instead of the entire spectrum. The PLS1 model with 45 selected spectral variables (PLS1-45 model) presented the lowest relative root mean square error of cross validation (RMSECV; 16 - 35%). The detection limits using the PLS1-45 model, for the nine elements were in the range of 0.16 - 0.50 μg/m
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Registered trials
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