Development of on-line process analytical technologies based on spectral imaging and chemometrics for process and product quality control

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This research program aims at developing new on-line process analytical sensors based on spectral imaging and chemometrics methods for measuring variability in chemical processes and product quality that is difficult to quantify using traditional sensors. This information will be useful for dynamic process modelling, monitoring, fault detection and diagnosis, and ultimately for designing automatic process and quality control schemes. The sensors will be developed based on the spatial-spectral information contained in hyperspectral images of processes and products. The UV-VIS-NIR spectral imagers and a high temperature camera recently obtained from two Canada Foundation for Innovation (CFI) grants will be used for collecting spectral images. Chemometrics methods such as Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) will be used for efficient extraction and analysis of process and/or product quality relevant information contained in the massive amounts of stochastic data making each spectral image data cube. The spectral sensors should allow on-line detection, characterization, and monitoring of composition (organic and several inorganic compounds), morphology, and appearance across the surface of heterogeneous materials and media (i.e. spatial distributions).