Optimization of Near-Infrared Spectroscopic Process Monitoring at Low Signal-to-Noise Ratio

摘要

An approach for the optimization of near-infrared (NIR) spectroscopic process monitoring at low signal-to-noise ratio is presented. It compromises the combined adjustment of different measurement variables and data pretreatments considering the prediction error, economic aspects of the application, and process constraints. The integration time, light intensity, and number of averaged spectra were varied; their mutual influence on the prediction error of partial least squares (PLS) models (i.e., rootmean-square error of cross-validation (RMSECV)) was evaluated in the laboratory. At low signal levels, the spectral uncertainty had a strong impact on the prediction error. It leveled off with increasing values of all three parameters and was finally dominated by other sources of uncertainty. The experimental findings could be characterized and explained by a mathematical equation, which was deduced from theoretical principles. The knowledge about the interaction of the measurement variables allowed their combined adjustment resulting in a reduced impact of spectral uncertainty on the prediction error (i.e., root-mean-square error of prediction (RMSEP)) without additional costs or process modifications. Moreover, a convenient procedure to compensate the stray light caused by strongly absorbing windows was developed. The whole approach was successfully applied to a challenging process, namely, the NIR inline monitoring of the liquid content of two model substances in a rotating suspension dryer.