Abstract Preprocessing of raw near-infrared (NIR) spectra is typically required prior to multivariate calibration since the measured spectra of complex samples are often subject to overwhelming background, light scattering, varying noises and other unexpected factors. Various preprocessing methods have been developed aimed at removing or reducing the interference of these effects. However, it is usually difficult to determine the best preprocessing method for a given data. Instead of selecting the best one, a selective ensemble preprocessing strategy is proposed for NIR spectral quantitative analysis. Firstly, numerous preprocessing methods and their combinations are obtained by full factorial design in order of baseline correction, scattering correction, smoothing and scaling. Then partial least squares (PLS) model is built for each preprocessing method. The models which have better predictions than PLS are selected and their predictions are averaged as the final prediction. The performance of the proposed method was tested with corn, blood and edible blend oil samples. Results demonstrate that the selective ensemble preprocessing method can give comparative or even better results than the traditional selected best preprocessing method. Therefore, in the framework of selective ensemble preprocessing, more accurate calibration can be obtained without searching the best preprocessing method.

中文翻译：

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一种用于复杂样品近红外光谱定量分析的选择性集合预处理策略

摘要 在多元校准之前，通常需要对原始近红外 (NIR) 光谱进行预处理，因为复杂样品的测量光谱通常会受到压倒性背景、光散射、变化的噪声和其他意外因素的影响。已经开发了各种预处理方法，旨在消除或减少这些影响的干扰。然而，对于给定的数据，通常很难确定最佳的预处理方法。不是选择最好的，而是提出了一种用于 NIR 光谱定量分析的选择性集合预处理策略。首先，按照基线校正、散射校正、平滑和缩放的顺序，通过全因子设计获得多种预处理方法及其组合。然后为每种预处理方法建立偏最小二乘（PLS）模型。选择比 PLS 具有更好预测的模型，并将它们的预测平均作为最终预测。使用玉米、血液和食用调和油样品测试了所提出方法的性能。结果表明，与传统的选择最佳预处理方法相比，选择性集成预处理方法可以给出比较甚至更好的结果。因此，在选择性集成预处理的框架下，无需寻找最佳预处理方法即可获得更准确的校准。结果表明，与传统的选择最佳预处理方法相比，选择性集成预处理方法可以给出比较甚至更好的结果。因此，在选择性集成预处理的框架下，无需寻找最佳预处理方法即可获得更准确的校准。结果表明，与传统的选择最佳预处理方法相比，选择性集成预处理方法可以给出比较甚至更好的结果。因此，在选择性集成预处理的框架下，无需寻找最佳预处理方法即可获得更准确的校准。