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- W2318577547 abstract "During flight operations, air quality of an aircraft cabin is critical to crew and passenger safety as well as comfort. Yet, there are currently no environmental monitoring sensors present in the aircraft cabin. Without sensors, the amounts of gases such CO, CO2, ozone, etc., are assumed to be below harmful levels simply by dilution with high quantities of outside air. This paper highlights the use of principal component analysis (PCA) applied to Fourier Transform infrared (FTIR) spectroscopy data to determine gas concentrations in CO, CO2, H2O, O2 and N2 gas mixtures that could potentially be present in an aircraft cabin environment. The analysis determines regression coefficients for prediction data sets based on principal component regression (PCR) of FTIR spectra calibration data sets that spans the range of expected concentrations in normal aircraft operation, as well as elevated CO concentrations that could be present during an aircraft bleed air contamination event. The data sets are produced from known gas mixing in a controlled and isolated system. From the PCR, a root mean square error of calibration (RMSEC) is calculated. To verify and compute the root mean squared error of prediction (RMSEP) of the prediction data set, individual component FTIR calibrations of gas from certified gas cylinders for set flow rates within the system for CO and CO2 were used." @default.
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- W2318577547 date "2011-07-17" @default.
- W2318577547 modified "2023-09-24" @default.
- W2318577547 title "Principal Component Analysis (PCA) Application to FTIR Spectroscopy Data of CO/CO2 Contaminants of Air" @default.
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- W2318577547 doi "https://doi.org/10.2514/6.2011-5091" @default.
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