The regression model based on near-infrared spectroscopy cannot be used in its present form to predict the size of SOC pools in “new” topsoil samples

2018 ◽  
Author(s):  
Lauric Cécillon
Keyword(s):  
Infrared Spectroscopy ◽  
Regression Model ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Present Form ◽  
Model Based

Soil Near-infrared Spectroscopy Prediction Model Based on Deep Sparse Learning

2017 ◽  
Vol 38 (1) ◽  
pp. 109-116 ◽  
Author(s):  
王儒敬 WANG Ru-jing ◽  
陈天娇 CHEN Tian-jiao ◽  
汪玉冰 WANG Yu-bing ◽  
汪六三 WANG Liu-san ◽  
谢成军 XIE Cheng-jun ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Sparse Learning ◽  
Model Based

scholarly journals Quantitative assessment of near-infrared spectroscopy time course under hypercapnia using an a priori model-based fitting

2020 ◽  
Vol 118 ◽  
pp. 103638
Author(s):  
Victor Vagné ◽  
Emmanuelle Le Bars ◽  
Jérémy Deverdun ◽  
Olivier Rossel ◽  
Stéphane Perrey ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Quantitative Assessment ◽  
Time Course ◽  
Near Infrared ◽  
A Priori ◽  
Model Based

Stiffness Prediction of Radiata Pine Clearwood Test Pieces Using near Infrared Spectroscopy

2001 ◽  
Vol 9 (2) ◽  
pp. 117-122 ◽  
Author(s):  
Armin Thumm ◽  
Roger Meder
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Pinus Radiata ◽  
Modulus Of Elasticity ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Radiata Pine ◽  
Model Based ◽  
Test Pieces ◽  
The Mean

Near infrared (NIR) spectroscopy has been used to predict the modulus of elasticity (stiffness) of samples taken from knot-free sapwood specimens of radiata pine ( Pinus radiata D. Don). The method shows the potential of using NIR spectroscopy for assessment of lumber stiffness. A model based on NIR spectra taken on the radial face of 404 samples of radiata pine clearwood was established to predict stiffness. Samples were moved past the detector at a rate of 900 mm min−1. This model then was used to predict the stiffness of a further 80 samples and the results show an error in prediction of 14% of the mean measured value.

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