The efficacy of smoothing first-order data as a preprocessing method for multivariate calibration is discussed. In particular, the use of symmetric smoothing filters (such as Savitzky–Golay filters) is examined from the perspective of calibration performance, in contrast with past studies based on univariate signal-to-noise improvement. It is shown mathematically that in the limit of a perfect ...

This thesis considers issues related to the multivariate analysis and calibration of chemical data collected in a vector format (one–way data). The work is designed to facilitate the task of the analytical chemist, focusing on methods of maximizing the amount of information that can be obtained by multivariate modeling from full sets of data generated by instruments or experiments. It is shown ...

A new method for the elimination of uninformative variables in multivariate data sets is proposed. To achieve this, artificial (noise) variables are added and a closed form of the PLS or PCR model is obtained for the data set containing the experimental and the artificial variables. The experimental variables that do not have more importance than the artificial variables, as judged from a crite...

A new method for multivariate calibration [1 – 3] eliminates many of the problems associated with traditional methods, making the process easier and more transparent to the user. It uses physical methods to estimate the spectral signal and statistical methods to estimate noise. It is also less expensive to apply than partial least squares (PLS) or principal component regression (PCR) calibratio...

Introduction: ChemCam is an active remote sensing instrument suite being built for MSL [1,2]. It uses laser pulses to remove dust and to profile through weathering coatings of rocks up to 9 m away. Laser-induced breakdown spectroscopy (LIBS) obtains emission spectra of materials ablated from the samples in electronically excited states over an analysis spot < 1 mm in diameter at the focal point...

FTIR spectroscopy in combination with multivariate calibrations, i.e. partial least square (PLS) and principle component regression (PCR) was developed for quantitative analysis of cod liver oil (CLO) in binary mixture with corn oil (CO). The spectra of CLO, CO and their blends with certain concentrations were scanned using horizontal attenuated total reflectance (HATR) accessory at mid infrare...

the potential of near infrared reflectance spectroscopy (nirs) as a fast method to predict the crude protein (cp) and moisture (m) content in fishmeal by scanning spectra between 1000 and 2500 nm using multivariate regression technique based on partial least squares (pls) was evaluated. the coefficient of determination in calibration (r2c) and standard error of calibration (sec) were 0.95 and 1...

This chapter is concered with how to calibrate a computer model to observational data when the model produces multivariate output and is temporally expensive to run. The significance of considering models with long run times is that they can only be run at a limited number of different inputs, ruling out a brute-force Monte Carlo approach. Consequently, all inference must be done with a limited...