Predicting Soil Nitrogen and Organic Carbon Using Near Infrared SpectroscopyCoupled with Variable Selection

2014 ◽  
pp. 641-647
Keyword(s):  
Organic Carbon ◽  
Variable Selection ◽  
Soil Nitrogen ◽  
Near Infrared

scholarly journals Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection

2020 ◽  
Vol 12 (20) ◽  
pp. 3394
Author(s):  
Lu Xu ◽  
Yongsheng Hong ◽  
Yu Wei ◽  
Long Guo ◽  
Tiezhu Shi ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Variable Selection ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Least Squares Regression ◽  
Jianghan Plain ◽  
Model Accuracy ◽  
Near Infrared Reflectance ◽  
Soc Estimation ◽  
Anthropogenic Soil

Visible and near-infrared reflectance (VIS-NIR) spectroscopy is widely applied to estimate soil organic carbon (SOC). Intense and diverse human activities increase the heterogeneity in the relationships between SOC and VIS-NIR spectra in anthropogenic soil. This fact results in poor performance of SOC estimation models. To improve model accuracy and parsimony, we investigated the performance of two variable selection algorithms, namely competitive adaptive reweighted sampling (CARS) and random frog (RF), coupled with five spectral pretreatments. A total of 108 samples were collected from Jianghan Plain, China, with the SOC content and VIS-NIR spectra measured in the laboratory. Results showed that both CARS and RF coupled with partial least squares regression (PLSR) outperformed PLSR alone in terms of higher model accuracy and less spectral variables. It revealed that spectral variable selection could identify important spectral variables that account for the relationships between SOC and VIS-NIR spectra, thereby improving the accuracy and parsimony of PLSR models in anthropogenic soil. Our findings are of significant practical value to the SOC estimation in anthropogenic soil by VIS-NIR spectroscopy.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Geoderma ◽  
2012 ◽  
Vol 183-184 ◽  
pp. 41-48 ◽  
Author(s):  
A.H. Cambule ◽  
D.G. Rossiter ◽  
J.J. Stoorvogel ◽  
E.M.A. Smaling
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
National Park ◽  
Near Infrared ◽  
Spectral Library ◽  
Infrared Spectral

Variable Selection for Partial Least-Squares Calibration of Near-Infrared Data from Orthogonally Designed Experiments

2002 ◽  
Vol 56 (3) ◽  
pp. 337-345 ◽  
Author(s):  
S. Kamaledin Setarehdan ◽  
John J. Soraghan ◽  
David Littlejohn ◽  
Daran A. Sadler
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Designed Experiments ◽  
Selection For ◽  
Infrared Data
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