N
Nicolaas (Klaas) M. Faber
Researcher at Radboud University Nijmegen
Publications - 59
Citations - 2613
Nicolaas (Klaas) M. Faber is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Calibration (statistics) & Partial least squares regression. The author has an hindex of 27, co-authored 59 publications receiving 2479 citations.
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Journal ArticleDOI
Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report)
Alejandro C. Olivieri,Nicolaas (Klaas) M. Faber,Joan Ferré,Ricard Boqué,John H. Kalivas,Howard Mark +5 more
TL;DR: In this paper, the authors give an introduction to multivariate calibration from a chemometrics perspective and review the various proposals to generalize the well-established univariate methodology to the multivariate domain.
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Recent developments in CANDECOMP/PARAFAC algorithms: a critical review
TL;DR: It is found that the ALS estimated models are generally of a better quality than any of the alternatives even when overfactoring the model, but it is also found that ALS is significantly slower.
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How to avoid over-fitting in multivariate calibration--the conventional validation approach and an alternative.
TL;DR: A randomization test that enables one to assess the statistical significance of each component that enters the model and is compared with cross-validation and independent test set validation for the calibration of a near-infrared spectral data set using partial least squares (PLS) regression.
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Prediction of pork quality attributes from near infrared reflectance spectra
G.H. Geesink,F.H. Schreutelkamp,R. Frankhuizen,H.W Vedder,Nicolaas (Klaas) M. Faber,R.W. Kranen,M.A Gerritzen +6 more
TL;DR: The current results indicate that NIRS enables the classification of pork longissimus muscles with a superior or inferior water-holding capacity as having a drip loss lower than 5% or higher than 7%.
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Standard error of prediction for multiway PLS 1 : background and a simulation study
TL;DR: In this article, the adequacy of two approximate expressions when using unfold-or tri-PLS for the calibration of second-order data is examined, under the assumption that the errors in the predictor variables are homoscedastic, i.e., of constant variance.