Near-Infrared Spectroscopy Preprocessing Methods

In this document, we explore several common preprocessing techniques for near-infrared spectroscopy data, including Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative (1-Der), Second Derivative (2-Der), Smoothing, and Centering. These methods help improve the understanding and analysis of near-infrared spectral data by enhancing our ability to identify and quantify compound content and structures in samples. From a code implementation perspective, MSC typically involves linear regression against a reference spectrum, while SNV applies z-score normalization to each spectrum individually. Derivative calculations often utilize Savitzky-Golay filtering for noise-resistant differentiation, and smoothing algorithms may employ moving averages or Gaussian filters. We will also examine the advantages and limitations of each method, along with practical guidance for selecting the most appropriate preprocessing technique in real-world applications, considering factors such as spectral noise levels, baseline variations, and specific analytical objectives.