ABSTRACT The increasing availability of multivariate analytical data requires modelling strategies that can integrate complementary sources of information while maintaining interpretability and robustness. In this work, we propose a structured chemometric framework, termed Correlation‐Weighted Dynamic Feature Fusion for Multiblock Regression (CW‐DFF‐MBR), to support multiblock regression by transparently integrating complementary data blocks. Rather than introducing a new learning algorithm, the proposed approach organizes established chemometric operations into a coherent workflow consisting of four steps: (i) correlation‐guided variable screening, (ii) modelling of interblock interaction features, (iii) dimensionality control using principal component analysis (PCA), and (iv) correlation‐weighted block fusion followed by partial least squares (PLS) regression. The methodology is first evaluated on a near‐infrared (NIR) dataset of cassava roots for predicting total β ‐carotene content, which is characterized by strong collinearity and distributed spectral information. An ablation analysis is performed to examine the contribution of the different processing steps to model stability and prediction performance. The framework is then applied to a second dataset comprising wheat–flour spectra acquired under different experimental conditions to assess robustness across datasets. Results show that the proposed workflow provides stable predictive performance and consistent model interpretation across both datasets. The approach does not necessarily outperform simpler models when predictive information is concentrated in a single spectral region. However, it offers a structured, interpretable strategy for handling information distributed across correlated spectral domains. These results suggest that CW‐DFF‐MBR can serve as a practical framework for multiblock chemometric modelling of complex analytical datasets.
NINI et al. (Sun,) studied this question.