Matrix-Enhanced Calibration Procedure for Multivariate Calibration Models with Near-Infrared Spectra

Matrix-Enhanced Calibration Procedure for MultivariateCalibration Models with Near-Infrared Spectra

A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra

Variable (or wavelength) selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra. A modified method of uninformative variable elimination (UVE) was proposed for variable selection in NIR spectral modeling based on the principle of Monte Carlo (MC) and UVE. The method builds a large number of models with randomly selected calibration samples at first, and th...

Multivariate Calibration Transfer between Near-Infrared Tablet Analyzers

calibration transfer method is evaluated by transferring multivariate models between two equivalent near-infrared (NIR) analyzers for the prediction of A an Active Pharmaceutical Ingredient (API) in tablets. The method includes selecting a few representative samples, building a transformation relation, and standardizing spectra. Backward and forward transfer approaches are compared. A practical...

Improving the robustness of multivariate calibration models for the determination of glucose by near-infrared spectroscopy

Near-infrared spectroscopy has proven to be one of the most promising techniques for the development of a noninvasive blood glucose monitoring system for diabetic patients. In this work, Fourier transform infrared (FT-IR) transmission measurements of the combination band region (4000 – 5000 cm) were analyzed for samples containing glucose (analyte) in a matrix of bovine serum albumin and triace...

Calibration of computer models with multivariate output

• 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...