This paper presents a solution to the challenge of cross-device model generalization in blood-based infrared spectroscopy. As infrared spectroscopy becomes increasingly popular for analyzing human blood, ensuring that machine learning models trained on one device can be effectively transferred to others is essential. However, variations in device characteristics often reduce model performance when applied across different devices. To address this issue, we propose a straightforward domain adaptation method based on data augmentation incorporating device-specific differences. By expanding the training data to include a broader range of nuances, our approach enhances the model's ability to adapt to the unique characteristics of various devices. We validate the effectiveness of our method through experimental testing on two Fourier-Transform Infrared (FTIR) spectroscopy devices from different research laboratories, demonstrating improved prediction accuracy and reliability.