Infrared spectroscopy has been widely used in fast and non-destructive quantitative analysis fields. However, some physical phenomena in the infrared spectra will limit the prediction accuracy of the quantitative analysis. Due to the high correlations of the spectral signatures, the infrared spectral dataset has the low-rank property, which can be used as a constraint to remove the undesired variations of the infrared spectra. In this paper, a low-rank strategy for improving the prediction accuracy of Partial Least Squares (PLS) chemometric model is proposed. The low-rank PLS (LR-PLS) method is used for the quantitative analysis based on the infrared spectra of different samples. Compared with the traditional methods, the proposed method has better performance in improving the prediction accuracy.

红外光谱已广泛应用于快速无损定量分析领域。然而，红外光谱中的一些物理现象会限制定量分析的预测精度。由于光谱特征的高度相关性，红外光谱数据集具有低秩属性，可以用作约束以去除红外光谱的不希望的变化。在本文中，提出了一种用于提高偏最小二乘（PLS）化学计量模型预测精度的低秩策略。低秩PLS（LR-PLS）方法用于基于不同样品的红外光谱的定量分析。与传统方法相比，所提出的方法在提高预测精度方面具有更好的性能。