Stacking and ridge regression-based spectral ensemble preprocessing method and its application in near-infrared spectral analysis.

Spectral preprocessing techniques can, to a certain extent, eliminate irrelevant information, such as current noise and stray light from spectral data, thereby enhancing the performance of prediction models. However, current preprocessing techniques mostly attempt to find the best single preprocessing method or their combination, overlooking the complementary information among different preprocessing methods. These preprocessing techniques fail to maximize the utilization of useful information in spectral data and restrict the performance of prediction models.

Performance Improvement of NIR Spectral Pattern Recognition from Three Compensation Models' Voting and Multi-Modal Fusion.

Inspired by aquaphotomics, the optical path length of measurement was regarded as a perturbation factor. Near-infrared (NIR) spectroscopy with multi-measurement modals was applied to the discriminant analysis of three categories of drinking water. Moving window- nearest neighbor (MW-kNN) and Norris derivative filter were used for modeling and optimization.