Online Detection and Identification Interferents in Multivariate Predictions of Organic Gases Using FT-IR Spectroscopy

One of the most serious problems that can occur when a multivariate model is used for the compositional analysis of an unknown mixture is the presence of an unexpected constituent, not modeled in the calibration phase, The interferent will almost certainly influence the predicted concentrations of the modeled constituents, which leads to erroneous and, more seriously, misleading results. Usually, a recalibration-building a new calibration model in which the interferent is included-will be necessary. However, in many applications of multivariate calibration, recalibration will be possible only if an unambiguous identification of the interferent can be made, In this paper, we describe how spectral residuals resulting from a multivariate prediction can be used to detect and identify unknown interferents. The identification of the interferent is performed by matching the residual spectrum with a library of residual spectra, This library was built by processing the members of a regular spectral library by the calibration model and storing the resulting residual spectra. After successful identification, a straightforward procedure can be used to correct the concentrations of the modeled constituents, without the need for a recalibration, The methods are demonstrated using a relatively simple principal component calibration model to predict the concentrations of organic vapors and gases in ambient air with FT-W spectroscopy. In addition, the influence of different interferents on the predicted concentrations of the modeled constituents is described