Partial least squares modelling of spectroscopic data from microplasma emissions for determination of CO₂ concentration

Abstract

The spectral emissions from a microplasma have been used to predict the CO2 concentration in gas samples covering a concentration range of 0-100%. Different models based on partial least squares have been evaluated, comparing two different spectral pre-processing filtersmultiplicative scatter correction (MSC) and standard normal variate correction (SNV) -and three different wavelength ranges. The models were compared with respect to accuracy, precision, stability and linearity. CO2 samples were mixed with either air or nitrogen. The choice of mixing gas influenced the predicted concentration and basing the models on data from only one mixing gas resulted in higher prediction power. Using air as mixing gas and SNV filtering resulted in a root mean square error of prediction (RMSEP) of 0.03 for an independent test dataset. This RMSEP was of the same range as the experimental error. On the other hand, the models with the best long term stability, reaching the lowest Allan variance, were based on observations with both mixing gases. Models based on MSC filtering generally had slightly higher RMSEP than those based on SNV filtering. Generally, the CO2 concentration could be accurately predicted in the concentration range of 5-90 %. For higher and lower concentrations, the models underestimated the CO2 concentration and were less accurate and precise. Basing the models on fewer wavelengths resulted in reduced linearity. The models were also evaluated by applying them for transcutaneous blood gas monitoring, where they helped to reveal new physiological information.

Microplasma emission spectroscopy of stable isotope ratios in carbon dioxide

“…As can be seen, the previously observed linear correlation remained on this scale, which is advantageous since emission spectroscopy often becomes non-linear at low concentrations. For CO 2 mixed in N 2 or air, this nonlinear region has previously been found to begin at concentrations of a few percent or less [19,20], but the present results suggest that non-linearity is not an issue for δ 13 C measurements.…”

“…The relative uncertainty of the isotope abundances in these samples depended primarily on the number of mixing steps, n, necessary to prepare them. This uncertainty has previously [20] been estimated to ±0.022 √ n, based on reference measurements with a residual gas analyzer. The details of these samples are presented in table 1.…”

Modeling and Evaluation of a Rate-Based Transcutaneous Blood Gas Monitor

“…The 1 Hz output of the microplasma emission spectrometer were spectra between 498 and 1109 nm with an average wavelength step of 0.17 nm. Previous studies have investigated several advanced ways of interpreting the spectra to CO2 concentrations, including partial least square regression modelling [17], and machine learning [23], but as such methods sometimes are considered blackboxing, and the intentions of this study was to allow for maximum reviewability, a less complex and more transparent method was employed. Firstly, the spectra were subjected to two bandpass filters, the first centered around the CO emission at 561 nm [13], and the other around an N2 peak at 624 nm.…”