Development and performance test of a vehicle-mounted total nitrogen content prediction system based on the fusion of near-infrared spectroscopy and image information

2022 ◽  
Vol 192 ◽  
pp. 106613
Author(s):  
Weichao Wang ◽  
Wei Yang ◽  
Peng Zhou ◽  
Yulu Cui ◽  
Dong Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Nitrogen Content ◽  
Total Nitrogen ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Performance Test ◽  
Total Nitrogen Content ◽  
Prediction System ◽  
Image Information ◽  
And Performance

Determination of API Gravity and Total and Basic Nitrogen Content by Mid- and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection Tools

2019 ◽  
Vol 52 (18) ◽  
pp. 2914-2930 ◽  
Author(s):  
Karla Pereira Rainha ◽  
Júlia Tristão do Carmo Rocha ◽  
Rayza Rosa Tavares Rodrigues ◽  
Betina Pires de Oliveira Lovatti ◽  
Eustáquio Vinicius Ribeiro de Castro ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Crude Oil ◽  
Nitrogen Content ◽  
Near Infrared Spectroscopy ◽  
Multivariate Regression ◽  
Near Infrared ◽  
Basic Nitrogen ◽  
Selection Tools

scholarly journals FT-NIR Spectroscopic Analysis of Nitrogen in Cotton Leaves

2002 ◽  
Vol 56 (11) ◽  
pp. 1484-1489 ◽  
Author(s):  
Mark R. Riley ◽  
Loreto C. Cánaves
Keyword(s):  
Nitrogen Content ◽  
Total Nitrogen ◽  
Standard Error ◽  
Near Infrared ◽  
Total Nitrogen Content ◽  
Least Squares Regression ◽  
Nitrogen Levels ◽  
Plant Chemical ◽  
Grid Search Method ◽  
Kjeldahl Method

Near-infrared spectroscopy was evaluated as a means to quantify the nitrogen content in fresh cotton leaves ( Gossypium hirsutum L. var. Delta Pine 90) subjected to a factorial design experiment of varying nitrogen and water applications. Absorbance spectra were collected in the 10 000–4000 cm−1 (1000–2500 nm) region from fresh cotton leaves over a two month portion of the growing season. Total nitrogen content was quantified by a wet chemistry Kjeldahl method for validation purposes. Partial least-squares regression analysis, using an automated grid search method, selected the spectral region 6041 to 5651 cm−1 (1650–1770 nm) for analysis based on having the lowest standard error of prediction of total nitrogen content. This region includes protein spectral features. Nitrogen predictions resulted in a correlation coefficient of 0.83, and a standard error of prediction of 0.29% for nitrogen levels ranging from 3.1 to 5.2% total nitrogen. This approach has promise for providing rapid plant chemical analyses for cotton crop fertilization management purposes.

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