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. We describe the effect of log transformation of the reference values, regular PLS, robust PLS, the newly introduced , 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. 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.

中文翻译：

---

通过近红外光谱对纸浆和纸张进行多元表征和分类的策略


通过偏最小二乘法 (PLS) 对近红外数据进行多变量校准已成功应用于纸浆和造纸等多个工业领域。多变量校准模型的创建依赖于一组涵盖预期应用范围的充分表征的样本。然而，源自工业过程的样本集通常显示出参考值的不均匀分布。这可以通过参考数据的管理和多变量校准的方法来解决。需要更好地理解这些方法如何影响最终模型的质量和范围。我们描述了参考值、常规 PLS、稳健 PLS、新引入的对数变换及其组合的效果，以选择更均匀分布的参考值来量化五种纸浆特性（卡伯值、R18、R10、cuen 粘度、和亮度；200 个样品）通过近红外光谱。模型的质量通过预测均方根误差、校准范围和样本类型的覆盖范围来评估。最好的模型产生的不确定性水平相当于参考测量的不确定性水平。最佳方法取决于所研究的参考值。稳健的 PLS 通常会给出误差最低的模型，但这通常是以显着减小校准范围为代价的。其他方法很少影响校准范围。他们中没有一个人是出类拔萃的。它们的性能取决于校准参数。因此，值得研究各种校准选项，以获得符合应用要求且不影响校准范围和样品覆盖范围的模型。