Automatic white balance (AWB) plays a crucial role in digital imaging, with modern learning-based methods achieving better performance. These methods, however, require extensive training data captured by a specific sensor, which cannot be directly deployed other sensors due to the different spectral sensitivity functions. This paper presents a novel cross-sensor adaptation method based on 3×3 color transformation matrices. By leveraging least-squares optimization and a Mahalanobis distance strategy, our approach constructs sensor-specific mapping matrices using 24-patch ColorChecker data. The results derived using the NUS dataset demonstrate that the proposed method has much smaller angular errors without requiring additional data collection or complicated network tuning.