RUGGED SPECTROSCOPIC CALIBRATION FOR PROCESS-CONTROL

Multivariate spectroscopic calibration is now finding increased indust rial use in the determination of mixture composition and product quali ty. Typically, these applications involve measurement of batch process es or process streams by UV, visible, near-infrared or infrared spectr oscopy, followed by prediction of product composition or quality with multiple linear regression or partial least squares calibration models . Real time predictions of composition or quality measures may then be used to control the process to increase efficiency, purity, etc. One obstacle that limits widespread use of this strategy is the lack of ca libration model ruggedness. Lack of ruggedness in calibration models m ay manifest itself in the form of large prediction errors following sm all perturbations in instrument response or slight changes in the samp le system, fiber-optic probe, or process stream composition. In this p aper, we describe a strategy for developing rugged calibration models using artificial neural networks and demonstrate the method on several NIR process data sets. Fourier transform or principal component prepr ocessing was used to reduce noise and the number of input measurements per sample. A large number of neural networks with different network architectures and random initializations were trained to predict compo sition using the pre-processed data. A sensitivity analysis was perfor med with monitoring data sets to screen the resulting networks for one s that were insensitive to simulated wavelength calibration errors, ba seline offsets, path length changes or high levels of stray light. Ext ernal validation data sets were used to demonstrate the ruggedness of selected neural network calibration models. (C) 1997 Elsevier Science B.V.