The modelling near-infrared (NIR) spectroscopy data requires removal of scattering effects from the data before applying advanced chemometrics methods. Often different scatter-correction techniques are explored, and the scatter-correction technique with the best performance is selected. However, the information highlighted by different scatter-correction techniques may be complementary and their fusion may result in better models for predicting characteristics, such as meat quality. To test this, sequential and parallel preprocessing fusion approaches will be used in this work to fuse information from different scatter-correction techniques to try to improve the predictive performance of NIR models. Three different chemical properties, i.e., moisture, fat and protein content, were predicted. For comparison, partial least-squares regression (PLSR) was performed on standard normal variate (SNV) corrected data, as this is a widely used scatter-correction technique. Compared to this commonly used procedure, the scattering fusion approaches reduced the error and bias by up to 52% and 84%, respectively. The results suggest that fusion of scatter-correction techniques is essential to achieve optimal NIR prediction models for predicting meat characteristics such as moisture, fat and protein content.