World's Best Scientists 2026 revealed!
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Mathematics
Norway
2026

D-Index & Metrics

Mathematics

D-Index
48
Citations
13921
World Ranking
1186
National Ranking
8

Research.com Recognitions

  • 2026 - Research.com Mathematics in Norway Leader Award
  • 2025 - Research.com Mathematics in Norway Leader Award

Overview

Harald Martens is affiliated with the Norwegian University of Science and Technology in Norway. Their research spans multiple fields within engineering and chemistry, with a focus on analytical chemistry and control and systems engineering as core subfields. The scientist's work also touches on media technology, biophysics, and molecular biology.

The main topics covered in Martens's research portfolio include:

  • Spectroscopy and Chemometric Analyses
  • Fault Detection and Control Systems
  • Advanced Data Processing Techniques
  • Metallurgical Processes and Thermodynamics
  • Remote-Sensing Image Classification
  • Remote Sensing in Agriculture
  • Spectroscopy Techniques in Biomedical and Chemical Research

Their recent publications demonstrate contributions to both theoretical and applied research in chemistry and materials science. Notable papers include:

  • "Metamodeling of the Electrical Conditions in Submerged Arc Furnaces," 2021, published in Metallurgical and Materials Transactions B
  • "Multivariate image fusion: A pipeline for hyperspectral data enhancement," 2020, Chemometrics and Intelligent Laboratory Systems
  • "Causality, machine learning and human insight," 2023, Analytica Chimica Acta
  • "Exploring the use of extended multiplicative scattering correction for near infrared spectra of wood with fungal decay," 2024, Chemometrics and Intelligent Laboratory Systems
  • "Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding," 2022, Frontiers in Chemistry

Frequent coauthors in Martens's publications include:

  • I. Burud
  • Raffaele Vitale
  • Cyril Ruckebusch
  • Arnoud Jochemsen
  • Gry Alfredsen

Their research findings have appeared repeatedly in specialized venues such as:

  • Chemometrics and Intelligent Laboratory Systems
  • Frontiers in Chemistry
  • NIR News
  • SSRN Electronic Journal
  • Analytica Chimica Acta

Harald Martens's work integrates methodologies in spectroscopy with advanced data processing and control systems, reflecting a multidisciplinary approach that spans engineering and chemistry. The breadth of their research includes applications in metallurgical engineering, hyperspectral imaging, and the enhancement of near-infrared spectral analysis, among others.

Best Publications

  • Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat

    P. Geladi;D. MacDougall;H. Martens

  • The multivariate calibration problem in chemistry solved by the PLS method

    S. Wold;H. Martens;H. Wold

  • Multivariate Analysis of Quality : An Introduction

    Harald A. Martens;M. Martens

  • Modified Jack-knife estimation of parameter uncertainty in bilinear modelling by partial least squares regression (PLSR)

    Harald Martens;Harald Martens;Magni Martens

  • Light scattering and light absorbance separated by extended multiplicative signal correction. application to near-infrared transmission analysis of powder mixtures.

    Harald Martens;Jesper Pram Nielsen;Søren Balling Engelsen

  • Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy.

    Harald Martens;Edward Stark

  • Validation and verification of regression in small data sets

    Harald A. Martens;Harald A. Martens;Pierre Dardenne

  • Resonant Mie Scattering (RMieS) correction of infrared spectra from highly scattering biological samples

    Paul Bassan;Achim Kohler;Harald Martens;Joe Lee

  • A multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables

    Michael Sjöström;Svante Wold;Walter Lindberg;Jan-Åke Persson

  • Variable Selection in near Infrared Spectroscopy Based on Significance Testing in Partial Least Squares Regression

    F. Westad;Harald Martens

  • Multiplicative signal correction method and apparatus

    Edward W. Stark;Harald Martens

  • Principal component regression in NIR analysis: Viewpoints, background details and selection of components

    Tormod Næs;Harald Martens

  • Thermal denaturation of proteins in post rigor muscle tissue as studied by differential scanning calorimetry

    Eva Stabursvik;Harald Martens

  • Recursive algorithm for partial least squares regression

    Kristian Helland;Hans E. Berntsen;Odd S. Borgen;Harald Martens

  • Reducing over-optimism in variable selection by cross-model validation

    Endre Anderssen;Knut Dyrstad;Frank Westad;Harald Martens

  • Comparison of prediction methods for multicollinear data

    Tormod Naes;Harald Martens

  • Reliable and relevant modelling of real world data: a personal account of the development of PLS Regression

    Harald Martens;Harald Martens;Harald Martens

  • 150 years of the mass action law.

    Eberhard Voit;Harald Martens;Stig William Omholt

  • Multivariate Calibration by Data Compression

    H. Martens

  • Estimating and correcting mie scattering in synchrotron-based microscopic fourier transform infrared spectra by extended multiplicative signal correction.

    A. Kohler;J. Sulé-Suso;G. D. Sockalingum;M. Tobin

  • Multivariate calibration. I. Concepts and distinctions

    Harald Martens;Tormod Næs

Frequent Co-Authors

Svante Wold
Svante Wold Umeå University
Sigbjørn Lien
Sigbjørn Lien Norwegian University of Life Sciences
Noel W. Clarke
Noel W. Clarke University of Salford
Rasmus Bro
Rasmus Bro University of Copenhagen
Anders Karlsson
Anders Karlsson Swedish University of Agricultural Sciences
Peter Hunter
Peter Hunter University of Auckland
Ganesh D. Sockalingum
Ganesh D. Sockalingum University of Reims Champagne-Ardenne
Paul Dumas
Paul Dumas Soleil Synchrotron
Olaf Wolkenhauer
Olaf Wolkenhauer University of Rostock
Margareth Øverland
Margareth Øverland Norwegian University of Life Sciences

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