Improved quantification of naphthalene using non-linear Partial Least Squares Regression
Abstract
A test dataset is generated using temperature cycled operation with a WO3 metal oxide semiconductor (MOS) gas sensor. Six concentrations of naphthalene from 0 to 40 ppb are measured and, subsequently, used to evaluate the performance of three variants of Partial Least Squares Regression (PLSR). Ordinary PLSR produces highly non-linear models due to the non-linear response of the sensor. Double-logarithmic data results in a model with much better linearity which has a resolution of 4 ppb in the range from 0 to 20 ppb. The more complex Locally Weighted PLSR (LW-PLSR) produces an even better model, especially for higher concentrations, without making any assumptions for relationships in the underlying data.
Keywords
Cite
@article{arxiv.1507.05834,
title = {Improved quantification of naphthalene using non-linear Partial Least Squares Regression},
author = {Manuel Bastuck and Martin Leidinger and Tilman Sauerwald and Andreas Schütze},
journal= {arXiv preprint arXiv:1507.05834},
year = {2015}
}
Comments
2 pages, 1 figure, ISOEN 2015, 16th International Symposium on Olfaction and Electronic Noses, Dijon, France