Background: Multivariate calibration by Partial Least Squares (PLS) on near-infrared data has been applied successfully in several industrial sectors, including pulp and paper. The creation of multivariate calibration models relies on a set of well-characterised samples that cover the range of the intended application. However, sample sets that originate from an industrial process often show an uneven distribution of reference values. This can be addressed by curation of the reference data and the methodology for multivariate calibration. It needs to be better understood, how these approaches affect the quality and scope of the final model. Results: We describe the effect of log₁₀ transformation of the reference values, regular PLS, robust PLS, the newly introduced bin PLS, and their combinations to select more evenly distributed reference values for the quantification of five pulp characteristics (kappa number, R18, R10, cuen viscosity, and brightness; 200 samples) by near-infrared spectroscopy. The quality of the models was assessed by root mean squared error of prediction, calibration range, and coverage of sample types. The best models yielded uncertainty levels equivalent to that of the reference measurement. The optimal approach depended on the investigated reference value. Significance: Robust PLS commonly gives the model with the lowest error, but this usually comes at the cost of a notably reduced calibration range. The other approaches rarely impacted the calibration range. None of them stood out as superior; their performance depended on the calibrated parameter. It is therefore worthwhile to investigate various calibration options to obtain a model that matches the requirements of the application without compromising calibration range and sample coverage.