Abstract The spectral wavelength selection method is important in near-infrared spectroscopy. Eliminating redundant information and extracting useful information can improve the prediction accuracy and modeling efficiency of the quantitative analysis model for spectral analysis to obtain a near-infrared calibration model with strong predictability and good robustness. This paper proposes a wavelength selection method for near-infrared spectroscopy by combining the partial least squares and false nearest neighbor methods. In this method, the correlation between the characteristic wavelength variables and the measured index is assessed by means of a similarity-based distance measure of the characteristic wavelength variable, and the characteristic wavelength is selected according to the order of the correlation. The method was used to select characteristic wavelengths from the near-infrared spectrum of waste liquid to establish a prediction model for the chemical oxygen demand. Compared with the full-spectrum partial least squares and interval partial least squares based models, the number of characteristic wavelength variables is reduced from 1557 to 176, and the prediction accuracy of the model is improved. This method both simplifies the model and achieves higher prediction accuracy. Therefore, this study provides a novel solution for wavelength selection for multivariate calibration in near-infrared spectroscopy.

中文翻译：

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一种基于近红外光谱和偏最小二乘伪近邻的波长选择方法的表征及其在废液化学需氧量检测中的应用

摘要 光谱波长选择方法在近红外光谱中具有重要意义。消除冗余信息，提取有用信息，可以提高光谱分析定量分析模型的预测精度和建模效率，从而获得可预测性强、鲁棒性好的近红外定标模型。本文提出了一种结合偏最小二乘法和假最近邻法的近红外光谱波长选择方法。该方法通过特征波长变量的基于相似性的距离度量来评估特征波长变量与测量指标之间的相关性，并根据相关性的顺序选择特征波长。该方法用于从废液近红外光谱中选取特征波长，建立化学需氧量预测模型。与基于全谱偏最小二乘和区间偏最小二乘的模型相比，特征波长变量数从1557个减少到176个，提高了模型的预测精度。这种方法既简化了模型，又实现了更高的预测精度。因此，本研究为近红外光谱多变量校准的波长选择提供了一种新的解决方案。特征波长变量个数从1557个减少到176个，提高了模型的预测精度。这种方法既简化了模型，又实现了更高的预测精度。因此，本研究为近红外光谱多变量校准的波长选择提供了一种新的解决方案。特征波长变量个数从1557个减少到176个，提高了模型的预测精度。这种方法既简化了模型，又实现了更高的预测精度。因此，本研究为近红外光谱多变量校准的波长选择提供了一种新的解决方案。