ChemMLometrics. Perform. Repeat.
Doing a one-off research project can be very satisfying, but in industry the money is made when the results of such a project can be placed into routine quality assessment. There is much discussion today revolving around machine learning and some of this discussion has invaded the space that has been occupied by chemometrics over the last few decades. There is a large overlap as these techniques are applied in instruments aimed at process quality control. To a large extent, any differentiation will simply be a question of the jargon chosen. An on-line Stanford University class on machine learning covers Principal Components Analysis, Principal Components Regression, Partial Least Squares, K-Nearest Neighbor, and other mainstays of the chemometrics toolbox. However, there are techniques that are new in the machine learning realm that can be employed for targeted tasks, improving the traditional chemometrics regime of supervised learning. The key is to narrow the focus for each component in a system and to understand the extent to which you can control the inputs. This talk highlights examples in optical spectroscopy calibration particularly for the identification of outliers, and in chromatography for both signal processing and the management of chromatographic libraries. The emphasis is to highlight both the similarities and the uniqueness of machine learning and chemometrics and show how they combine to form robust solutions for industry.