Tormod Næs

Bio: Tormod Næs is an academic researcher from University of Copenhagen. The author has contributed to research in topics: Calibration (statistics) & Partial least squares regression. The author has an hindex of 56, co-authored 262 publications receiving 11398 citations. Previous affiliations of Tormod Næs include Norwegian Food Research Institute & University of Oslo.

A user-friendly guide to multivariate calibration and classification

Abstract: Chichester: NIR Publication, 2002. User Friendly Guide to Multivariate Calibration and Classification. Martens H, Martens M: Multivariate analysis of quality. Publication as an International Standard requires approval by at least 75 % of the Note 2 to entry: It is possible to develop and validate NIR methods for other T., Davies, T. A user-friendly guide to multivariate calibration and classification.

The Effect of Multiplicative Scatter Correction (MSC) and Linearity Improvement in NIR Spectroscopy

Abstract: Near-infrared (NIR) reflectance spectra of five different food products were measured. The spectra were transformed by multiplicative scatter correction (MSC). Principal component regression (PCR) was performed, on both scatter-corrected and uncorrected spectra. Calibration and prediction were performed for four food constituents: protein, fat, water, and carbohydrates. All regressions gave lower prediction errors (7-68% improvement) by the use of MSC spectra than by the use of uncorrected absorbance spectra. One of these data sets was studied in more detail to clarify the effects of the MSC, by using PCR score, residual, and leverage plots. The improvement by using nonlinear regression methods is indicated.

Multivariate calibration. I. Concepts and distinctions

Abstract: This is the first of two introductory papers on multivariate calibration. We show how precise quantitative chemical analysis is made possible even in 'dirty' sample types like biological tissue, by compensating for systematic interferences in the measured data. This drastically reduces the required sample preparation work, making high-speed, nonspecific instrument measurements possible. Multivariate calibration also allows various types of automatic error detection, improving reliability in chemical analysis. The present paper treats on a conceptual basis the following topics: Univariate vs. multivariate calibration, direct vs. indirect calibration and controlled vs. natural calibration. Some aspects of multivariate quantitative modelling is illustrated, and multiwavelength near infrared spectrometry is given as a practical example. The compromise between necessary complexity vs. danger of statistical overfitting is discussed.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

A Statistical View of Some Chemometrics Regression Tools

Abstract: Chemometrics is a field of chemistry that studies the application of statistical methods to chemical data analysis. In addition to borrowing many techniques from the statistics and engineering literatures, chemometrics itself has given rise to several new data-analytical methods. This article examines two methods commonly used in chemometrics for predictive modeling—partial least squares and principal components regression—from a statistical perspective. The goal is to try to understand their apparent successes and in what situations they can be expected to work well and to compare them with other statistical methods intended for those situations. These methods include ordinary least squares, variable subset selection, and ridge regression.

Review of the most common pre-processing techniques for near-infrared spectra

Abstract: Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing.

Locally Weighted Learning

Abstract: This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.