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

Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Xuyang Pan ◽  
Laijun Sun ◽  
Guobing Sun ◽  
Panxiang Rong ◽  
Yuncai Lu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Prediction Model ◽  
Spectral Data ◽  
Corn Stover ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Neutral Detergent Fiber

AbstractNeutral detergent fiber (NDF) content was the critical indicator of fiber in corn stover. This study aimed to develop a prediction model to precisely measure NDF content in corn stover using near-infrared spectroscopy (NIRS) technique. Here, spectral data ranging from 400 to 2500 nm were obtained by scanning 530 samples, and Monte Carlo Cross Validation and the pretreatment were used to preprocess the original spectra. Moreover, the interval partial least square (iPLS) was employed to extract feature wavebands to reduce data computation. The PLSR model was built using two spectral regions, and it was evaluated with the coefficient of determination (R2) and root mean square error of cross validation (RMSECV) obtaining 0.97 and 0.65%, respectively. The overall results proved that the developed prediction model coupled with spectral data analysis provides a set of theoretical foundations for NIRS techniques application on measuring fiber content in corn stover.

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

scholarly journals Development of Moisture Content Prediction Model for Larix kaempferi Sawdust Using Near Infrared Spectroscopy

2015 ◽  
Vol 43 (3) ◽  
pp. 304-310 ◽  
Author(s):  
Yoon-Seong Chang ◽  
Sang-Yun Yang ◽  
Hyunwoo Chung ◽  
Kyu-Young Kang ◽  
Joon-Weon Choi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Moisture Content ◽  
Prediction Model ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Larix Kaempferi

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.

scholarly journals Soil nutrient information extraction model based on transfer learning and near infrared spectroscopy

2021 ◽  
Vol 60 (3) ◽  
pp. 2741-2746
Author(s):  
Hao-Tian Cai ◽  
Jun Liu ◽  
Jie-Ying Chen ◽  
Ke-Hong Zhou ◽  
Jie Pi ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Information Extraction ◽  
Transfer Learning ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Soil Nutrient ◽  
Model Based ◽  
Extraction Model
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