Development of a PLS approach for the determination of the conversion in UV-cured white-pigmented coatings by NIR chemical imaging and its transfer to other substrates

摘要

Near-infrared (NIR) chemical imaging was used for in-line monitoring of the conversion in thick UV-cured white-pigmented acrylate coatings applied to various substrates such as glass, stainless steel, PVC and glass fiber reinforced plastic (GRP) boards. Quantitative results were obtained by means of chemometric calibration models based on the partial least square (PLS) algorithm. Two spectroscopic techniques were tested for their potential for the characterization of the conversion after UV irradiation with a broad range of UV doses in order to provide reliably precise reference data for the calibration. NIR reflection spectroscopy using band integration of the acrylate band at 1620 nm was selected as method of choice. Using glass as substrate for the coatings at the beginning, a PLS model was established and evaluated for its performance to predict the conversion in independent samples. The acrylate conversion was predicted with an error of 2%. Generally, any calibration model is specific to a well-defined sample system (e.g. material and thickness of substrate and coating). In order to reduce the effort required for the development of specific calibration models for each substrate used in this study, methods for calibration transfer were developed. It was found that the requirements for such transfer depend on the optical properties of the substrate. In case of stainless steel and PVC boards, simple preprocessing of the spectra by baseline correction and normalization (similar to samples on glass) led to satisfactory results, which is confirmed by prediction of the conversion with similar error margins as for coatings on glass. In case of glass fiber boards, the spectrum of the pristine GRP board had to be subtracted from the spectrum of the coated sample before the transfer of the PIS model. The resulting prediction error (RMSEP) was found to be 3.6%. The comparison between this transferred and a specific PLSGRP model that was built up for evaluation only proved