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Analytical chemistry relies on the qualitative and quantitative analysis of multivariate data obtained from various measurement techniques. However, artifacts such as missing data, noise, multiplicative and additive effects, and peak shifts can adversely affect the accuracy of chemical measurements. To ensure the validity and accuracy of results, it is crucial to preprocess the data and correct for these artifacts. This paper proposes a fusion recalibration algorithm, called Spectral Offset Recalibration (SOR), that combines the Extended Multiplicative Signal Correction (EMSC) and Correlation-Optimized Warping (COW) algorithms to address both multiplicative and additive effects and peak shifts. The algorithm incorporates prior spectroscopic knowledge to down-weight or disregard spectral regions with strong absorption or significant distortion caused by peak alignment algorithms. Experimental validation on wood NIR datasets and simulated datasets demonstrates the effectiveness of the proposed method. The fusion recalibration approach offers a comprehensive solution for accurate analyses and predictions in analytical chemistry by mitigating the impact of artifacts.