Sample‐wise spectral multivariate calibration desensitized to new artifacts relative to the calibration data using a residual penalty

Calibration maintenance is an important aspect of multivariate calibration. With spectral measurements, the goal of calibration maintenance involves sustaining the predictability of a primary calibration model in new secondary conditions. Among the many methodologies, penalty‐based Tikhonov regularization variants have been successful by sample augmenting primary calibration data with a matrix of just a few secondary samples as well as operating with an additional sparse penalty to include wavelength selection. Studied in this paper is a new sample‐wise (local) Tikhonov regularization–based penalty calibration approach. Penalized is a diagonal matrix with the residual vector (relative to the primary calibration space) of the new secondary sample. Thus, the same full calibration set is used for each new sample. Changing for each secondary sample is the corresponding sample‐wise residual vector on the penalized diagonal matrix. The intent of the presented approach is to form sample‐wise regression vectors desensitized to characteristics of the new sample not present in the primary calibration set. The more distinct the secondary conditions are relative to the primary conditions, the more unsuccessful this local model updating becomes. Proposed is a sample‐wise outlier mechanism to discern when the residual penalty can or cannot be used to form a useful updated model. The residual penalty modeling and outlier detection processes require tuning parameter optimizations. A fusion approach is used to automatically select tuning parameter values. Simulated and near‐infrared data are evaluated, demonstrating the applicability of the method.