This study uses a novel analysis methodology based on the Hierarchical Clustering Analysis (HCA) to determine the effectiveness of different preprocessing methods in minimizing undesired spectral variability in near infrared spectroscopy due to both the consecutive and repetitive acquisition of the spectrum and the sample temperature. Nine preprocessing methods and different combinations of them were evaluated in four case studies: reproducibility, repeatability, sample temperature, and combination of the before mentioned cases. Eighty-four spectra acquired on seven different hydrocarbon samples from catalytic conversion processes have been selected as the real case study to illustrate the potential of the mentioned methodology. The approach proposed allows a more detailed discriminatory analysis compared to the classical methods for comparing the between-class and the within-class variances, such as the Wilks’ lambda criterion, and hence constitutes a powerful tool to determine adequate spectral preprocessing strategies. This study also proves the potential of the discrimination analysis methodology as a general scheme to identify atypical behaviors either in the spectrum acquisition or in the measured samples.

本研究使用一种基于层次聚类分析 (HCA) 的新型分析方法来确定不同预处理方法在最大限度地减少由于光谱的连续和重复采集以及样品温度导致的近红外光谱中不希望的光谱变异性方面的有效性。在四个案例研究中评估了九种预处理方法及其不同组合：再现性、重复性、样品温度和上述案例的组合。从催化转化过程中获得的七种不同碳氢化合物样品上获得的八十四张光谱已被选为真实案例研究，以说明上述方法的潜力。与用于比较类间和类内方差的经典方法（例如 Wilks 的 lambda 标准）相比，所提出的方法允许进行更详细的判别分析，因此构成了确定适当光谱预处理策略的强大工具。该研究还证明了鉴别分析方法作为一种通用方案来识别频谱采集或测量样本中的非典型行为的潜力。