Mikulasek, BFonseca Diaz, VGabauer, DavidHerwig, CNikzad-Langerodi, R2025-03-142023-03-262023-050886-9383G7QP5 (isidoc)https://hdl.handle.net/10182/18285This paper introduces the multiple domain-invariant partial least squares (mdi-PLS) method, which generalizes the recently introduced domain-invariant partial least squares method (di-PLS). In contrast to di-PLS which solely allows transferring of knowledge from a single source to a single target domain, the proposed approach enables the incorporation of data from an arbitrary number of domains. Additionally, mdi-PLS offers a high level of flexibility by accepting labeled (supervised) and unlabeled (unsupervised) data to cope with dataset shifts. We demonstrate the application of the mdi-PLS method on a simulated and one real-world dataset. Our results show a clear outperformance of both PLS and di-PLS when data from multiple related domains are available for training multivariate calibration models underpinning the benefit of mdi-PLS.17 pagesen© 2023 John Wiley & Sonscalibration model maintenancecalibration transferdomain adaptationpartial leastsquares regressiontransfer learningJournal Article10.1002/cem.34771099-128XANZSRC::3401 Analytical chemistry