  Optical emission spectroscopy from a small-volume, 5 uL, atmospheric pressure
RF-driven helium plasma was used in conjunction with Partial Least Squares
Discriminant Analysis (PLS-DA) for the detection of trace concentrations of
methane gas. A limit of detection of 1 ppm was obtained and sample
concentrations up to 100 ppm CH4 were classified using a nine-category model. A
range of algorithm enhancements were investigated including regularization,
simple data segmentation and subset selection, VIP feature selection and
wavelength variable compression in order to address the high dimensionality and
collinearity of spectral emission data. These approaches showed the potential
for significant reduction in the number of wavelength variables and the
spectral resolution/bandwidth. Wavelength variable compression exhibited
reliable predictive performance, with accuracy values > 97%, under more
challenging multi-session train - test scenarios. Simple modelling of plasma
electron energy distribution functions highlights the complex
cross-sensitivities between the target methane, its dissociation products and
atmospheric impurities and their impact on excitation and emission.
