Chemometrics is the application of statistical, mathematical, and machine learning techniques to spectral data in order to identify materials, quantify chemical constituents, and predict sample properties. By analyzing subtle spectral patterns, chemometric models transform raw spectroscopy measurements into meaningful analytical results.
Chemometrics is most commonly used with near-infrared (NIR) spectroscopy for applications such as moisture analysis, ingredient verification, material identification, and process monitoring, making it a core technology behind StellarNet’s industrial analyzers and NIR measurement systems. However, chemometric modeling can be applied to spectroscopy data across the UV, VIS, NIR, Raman, fluorescence, and other wavelength regions whenever quantitative or qualitative analysis is required.
