Optimization criteria in sample selection step of local regression for quantitative analysis of large soil NIRS database

By Fabien Gogé, R. Joffre, Claudy Jolivet, I. Ross, L. Ranjard. 2012

88 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biophysics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/339df3ec5db67ac945f9a0a8c55a0d56877f45a6

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In Analytical Chemistry

1. Abdul Mounem Mouazen
2. Boyan Kuang

In Environmental Engineering

1. Abdul Mounem Mouazen
2. Boyan Kuang

Citations in the corpus

• Assessment of soil organic carbon at local scale with spiked NIR calibrations: effects of selection and extra‐weighting on the spiking subset (2014, result): “This could also explain why better results are obtained with local (spectrumspecific) models (Genot et al., 2011; Gogé et al., 2012), where a subset of library samples that are similar to the unknown sample is used to construct the calibration (Pérez-Marín et al.”
• Prediction of soil organic matter using a spatially constrained local partial least squares regression and the Chinese vis–NIR spectral library (2015, methodology): “Because of the spectral complexity of soils, we expect non-linear effects to strengthen as database size increases (Gogé et al., 2012; Nocita et al., 2014).”