How Spectral Preprocessing Improves NIR Models
Near-infrared spectra contain information about molecular composition, but they can also be influenced by physical and measurement-related factors. Particle size, surface texture, temperature, instrument noise, and variations in sample presentation may introduce spectral changes unrelated to the property being measured.
Spectral preprocessing techniques help reduce these effects. Common approaches include baseline correction, smoothing, derivatives, standard normal variate (SNV), and multiplicative scatter correction (MSC). Each method addresses different types of spectral variation.
The choice of preprocessing depends on the material, instrument, and calibration objective. Excessive or inappropriate preprocessing can remove useful chemical information or introduce unwanted noise. For reliable results, preprocessing methods should be selected and validated alongside the chemometric calibration model.
Evaluating NIR Calibration and Data Quality
For more information about the technique, review PAS’s Near Infrared Spectroscopy technology overview. To compare suitable instruments, explore the range of NIR analysers from Portable Analytical Solutions, or contact our team to discuss spectral data quality and calibration requirements.