Impact of standardization sample design on tikhonov regularization variants for spectroscopic calibration maintenance and transfer

Multivariate spectroscopic calibration models are only valid to predict samples within the span of the calibration sample space measured relative to the current instrument environment (the primary conditions). Predicting samples in secondary conditions with new variances, same sample variances as the calibration space but a new instrument environment, or both, requires some form of continual model maintenance and/or transfer. Previous work has shown that a Tikhonov regularization (TR) approach is capable of accomplishing both tasks by updating the primary model based on only a few samples (transfer or standardization set) measured under the secondary conditions. A distinction of the TR design for calibration maintenance and transfer is a defined weighting scheme for the small set of standardization samples augmented to the full set of primary calibration samples. Critical to successful calibration maintenance or transfer is the standardization sample set composition, i.e. standardization samples should properly represent that are less secondary conditions. This paper reports on using TR-based methods to investigate this issue and a consensus modeling approach is briefly evaluated.