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

By Harald Martens, M. Martens. 2000

738 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Animal Science and Zoology, Food Science

View Paper: https://www.semanticscholar.org/paper/4bd4c8288b4e8f9b817e87eec77f839f7b47bb62

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In Analytical Chemistry

In Animal Science and Zoology

In Food Science

Citations in the corpus

Evaluation of Shelf Life of Superchilled Cod (Gadus morhua) Fillets and the Influence of Temperature Fluctuations During Storage on Microbial and Chemical Quality Indicators (2006, methodology): “The Jack-knife method (Martens and Martens 1999) was used to determine significant variables in X with a significance level of 5% (P 0.05).”
Near infrared spectral fingerprinting for confirmation of claimed PDO provenance of honey (2009, methodology): “This test may be used in order to avoid misinterpreting spurious effects, to identify the dominating sources of instability in the modelling and to allow more or less automatic optimisation of the models (Martens & Martens, 2000).”
A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands (2011, neutral): “…is necessary before both PLS and SVM regression models can be used to establish relationships between remotely sensed data and vegetation properties (Kubinyi 1996; Martens and Martens 2000; Davies 2001; Gauchi and Chagnon 2001; Lazraq et al. 2003; Schmidtlein and Sassin 2004; Durbha et al. 2007).”
Assessing the performance of a sensory panel–panellist monitoring and tracking (2005, methodology): “The method is based on Jack-kni ng and is described in details in [18].”
Variable selection in regression—a tutorial (2010, background): “Jack-knifing is often applied for significance testing in multivariate calibration [18,19].”
Umami taste, free amino acid composition, and volatile compounds of brown seaweeds (2018, methodology): “Jack-knifing with replicates served as the validation tool for all A-PLSR, comparing the perturbed model parameter estimates from cross-validation with the estimates for the full model (Martens and Martens, 2000).”