This paper tries ®rst to introduce and motivate the methodology of multivariate calibration. Next a review is given, mostly avoiding technicalities, of the somewhat messy theory of the subject. Two approaches are distinguished: the estimation approach (controlled calibration) and the prediction approach (natural calibration). Among problems discussed are the choice of estimator, the choice of c...

Model calibration entails the inference about unobservable modeling parameters based on experimental observations of system response. When the model being calibrated is an expensive computer simulation, special techniques such as surrogate modeling and Bayesian inference are often fruitful. In this work we show how the flexibility of the Bayesian calibration approach can be exploited in order t...

The estimation problem in multivariate linear calibration with elliptical errors is considered under a loss function which can be derived from the Kullback-Leibler distance. First, we discuss the problem under normal errors and we give unbiased estimate of risk of an alternative estimator by means of the Stein and Stein-Haff identities for multivariate normal distribution. From the unbiased est...

Computer model calibration involves combining information from simulations of a complex computer model with physical observations of the process being simulated by the model. Increasingly, computer model output is in the form of multiple spatial fields, particularly in climate science. We study a simple and effective approach for computer model calibration with multivariate spatial data. We dem...

Extent-based kinetic identification is a kinetic modeling technique that uses concentration measurements to compute extents and identify reaction kinetics by the integral method of parameter estimation. This article considers the case where spectroscopic data are used together with a calibration model to predict concentrations. The calibration set is assumed to be constructed from reacting data...

The Delaunay tessellation and topological regression is a local simplex method for multivariate calibration. The method, developed within computational geometry, has potential for applications in online analytical chemistry and process monitoring. This study proposes a novel approach to perform prediction and extrapolation using Delaunay calibration method. The main property of the proposed ext...

With a view to ensuring the validity of multivariate calibration models in the presence of temperature variations, a new methodology, individual contribution standardization (ICS), is proposed to correct for temperature-induced spectral variations. The methodology was applied to shortwave NIR spectral data sets recorded at different temperatures. The results showed that ICS can almost eliminate...

A multivariate data analysis procedure that uses singular value decomposition and the Ho-Kashyap algorithm is proposed to obtain calibration constants for x-ray fluorescence spectrometry. These calibration constants can be used to obtain results from experimental data by means of a simple dot product calculation. The method was tested on experimental data from the literature. Comparison of resu...

Introduction Background and literature sources Specialized journals Internet addresses Previous reviews Fundamentals of the technique Principles of NIR spectroscopy NIR diffuse reflectance spectroscopy Operational procedures in the NIR Mathematical processing of signals Qualitative analysis Identification and qualification of raw materials and pharmaceutical preparations Determination of homoge...

The recent improvements of Graphics Processing Units (GPU) have provided to the bio-inspired algorithms a powerful processing platform. Indeed, a lot of highly parallelizable problems can be significantly accelerated using GPU architecture. Among these algorithms, the Firefly Algorithm (FA) is a newly proposed method with potential application in several real world problems such as variable sel...