Double Regularization for Temperature Correction with Tikhonov Regularization. Application to Calibration Transfer

GOICOECHEA, HÉCTOR C; GABRIEL SIANO; E. ANDRIES; KALIVAS, J.H.

Montpellier, Francia

Congreso; 11th Internacional Conference on Chemometrics for Analytical Chemistry (CAC 2008).; 2008

Qualities of an analyte, such as species concentration, can be quantitatively determined using predictive multivariate calibration models. Among a number of different methods it can be found Tikhonov regularization (TR) [2] which is an approach to form a multivariate calibration model for y = Xb. It includes a regulation operator matrix L which can be used to remove unwanted spectral artifacts. In this way a regression vector is sought that can correct for irrelevant spectral variation in predicting y. In this study near-infrared (NIR) spectra measured at different temperatures, which have been studied by several other investigators [5,7], were considered. Because IR spectroscopic absorption depends on vibrational modes of molecules and these modes are affected by forces such as hydrogen bonding, which in turn are affected by temperature, these spectra act as temperature-sensitive data, which was useful for studying calibration transfer between two temperatures.