An automated preprocessing framework for near infrared spectroscopic data

Preprocessing plays a vital role in the analysis of Near-infrared spectroscopy (NIRS) data as it aims to remove unintended artifacts. This process involves a series of steps, each with a specific focus on a particular artifact. However, due to the diverse range of NIRS applications, selecting the optimal combination of preprocessing methods remains a challenge. To address this issue, we propose an automated preprocessing framework that can quickly identify the optimal preprocessing strategy. The framework initially constructs a workflow consisting of multiple types of preprocessing methods. Then, a genetic algorithm (GA) technique is used to optimize the best pipeline, avoiding exhaustive searches. In addition, we impose a penalty for the loss function of the GA process to obtain a parsimonious solution. Results on three real-world datasets demonstrate that our approach outperforms several state-of-the-art ensemble preprocessing methods in terms of prediction error. Compared to the raw data, the optimal preprocessing method can improve model performance by at least 48%. Furthermore, our framework enables the identification of the most effective preprocessing methods included in the best pipeline. The source code for our approach is available on GitHub and can be easily integrated with other existing preprocessing techniques.