Tackling calibration problems of spectroscopic analysis in high-throughput experimentation

High-throughput experimentation and screening methods are changing work flows and creating new possibilities in biochemistry, organometallic chemistry, and catalysis. However, many high-throughput systems rely on off-line chromatography methods that shift the bottleneck to the analysis stage. On-line or at-line spectroscopic analysis is an attractive alternative. It is fast, noninvasive, and nondestructive and requires no sample handling. The disadvantage is that spectroscopic calibration is time-consuming and complex. Ideally, the calibration model should give reliable predictions while keeping the number of calibration samples to a minimum. In this paper, we employ the net analyte signal approach to build a calibration model for Fourier transform near-infrared measurements, using a minimum number of calibration samples based on blank samples. This approach fits very well to high-throughput setups. With this approach, we can reduce the number of calibration samples to the number of chemical components in the system. Thus, the question is no longer how many but which type of calibration samples should one include in the model to obtain reliable predictions. Various calibration models are tested using Monte Carlo simulations, and the results are compared with experimental data for palladium-catalyzed Heck cross-couplin