We present a hybrid multivariate calibration method, constrained regularization (CR), and demonstrate its utility via numerical simulations and experimental Raman spectra. In this new method, multivariate calibration is treated as an inverse problem in which an optimal balance between model complexity and noise rejection is achieved with the inclusion of prior information in the form of a spect...

• y(u) aims at reproducing some real phenomenon which we denote by y 1.2 Validation of computer models •Question of interest: Does the computer model adequately represent reality? •Obtain field data yi that results from observing reality in a physical experiment yFi = y +εi , εi ∼ Np(0,Σ F ), ,Σ = Λ R Λ, Λ = diag(σ i ) •Combine model and field data with a statistical model y = y(u) + b (Kennedy...

In multivariate calibrations usually a minimal residual error in the model’s predictions is aimed at, while generally less attention is paid to the robustness of the model with respect to changes in instrumentation, laboratory conditions, or sample composition. In this paper, we propose a strategy for selecting a multivariate calibration model which possesses a small prediction error and, simul...

Article history: In general, linearity is assum Received 16 November 2008 Received in revised form 20 January 2009 Accepted 3 February 2009 Available online 12 February 2009

A newly developed multivariate method involving net analyte preprocessing (NAP) was tested using central composite calibration designs of progressively decreasing size regarding the multivariate simultaneous spectrophotometric determination of three active components (phenylephrine, diphenhydramine and naphazoline) and one excipient (methylparaben) in nasal solutions. Its performance was evalua...