User-independent nonlinear modeling using adjusted spline-interpolated knots (UNMASK) and indirect hard modeling for deriving compositions from spectra with background signals.

signals of different shapes and intensities pose a major challenge for the quantitative analysis of mixture spectra from Raman-, Mid-IR- or NMR-spectroscopy. Commonly used approaches for the treatment of such background signals are the subtraction of the background before the spectral analysis, derivative methods and the modeling of the background during the spectral analysis. Only the latter enables the unambiguous distinction between background and spectral features without any distortion of the spectral features. Indirect Hard Modeling (IHM) allows for such joint modeling of spectral features and background signals. However, so far, complex background signals containing distinctive peaks remain challenging for IHM. Furthermore, the user chooses and parametrizes the background treatment method for IHM on a case-by-case basis depending on the spectral properties (e.g. shapes and intensities of the backgrounds or signal-to-noise-ratio). We present a new method called User-independent Nonlinear Modeling using Adjusted Spline-interpolated Knots (UNMASK) that is parametrized by minimizing the prediction error of the calibration spectra, allowing to omit any user-decisions. Besides, unlike other background treatment methods, UNMASK is suited for many cases regardless of the spectral properties, and can even be applied for mixtures with complex background signals, e.g. caused by unknown/unspecified components. We validate UNMASK by applying it to three Raman mixture spectra, covering different application scenarios with varying spectral properties. By comparing the results from UNMASK with different commonly used methods, we show that UNMASK is the only method that consistently leads to low prediction errors for all application scenarios. • New baseline model for Indirect Hard Modeling using spline interpolated knots. • Modeling arbitrarily shaped background signals regardless of properties of spectra. • Parametrization of baseline model during calibration by minimizing prediction error. • User-independent and fully automated. • Low prediction errors for wide range of applications. [ABSTRACT FROM AUTHOR]