Multivariate calibration models often fail to extrapolate beyond the calibration samples due to changes associated with the instrumental response, environmental condition or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devices while generic methods for calibration model adaptation are largely missing. To fill this gap, we here introduce domain-invariant partial least squares (di-PLS) regression, which extends ordinary PLS by a domain regularizer in order to align source
and target distributions in the latent variable space. We show that a domain-invariant weight vector can be derived in closed-form which allows integration of (partially) labeled data from the source and target domain as well as entirely unlabeled data from the latter. We test our approach on a simulated data set where the aim is to desensitize a
source calibration model to an unknown interferrent in the target domain (i.e. unsupervised model adaptation). In addition, we demonstrate unsupervised, semi-supervised and supervised model adaptation by di-PLS on two real-world near infrared (NIR) spectroscopic data sets.