scholarly journals Comparing local vs. global visible and near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) calibrations for the prediction of soil clay, organic C and inorganic C

Geoderma ◽  
2008 ◽  
Vol 148 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Joel B. Sankey ◽  
David J. Brown ◽  
Melisa L. Bernard ◽  
Rick L. Lawrence
Keyword(s):  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Organic C ◽  
Soil Clay

Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa

Geoderma ◽  
2019 ◽  
Vol 354 ◽  
pp. 113840 ◽  
Author(s):  
Jean-Martial Johnson ◽  
Elke Vandamme ◽  
Kalimuthu Senthilkumar ◽  
Andrew Sila ◽  
Keith D. Shepherd ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Rice Fields ◽  
Sub Saharan Africa ◽  
Mid Infrared ◽  
Sub Saharan

scholarly journals Identification of Rice Varieties and Transgenic Characteristics Based on Near-Infrared Diffuse Reflectance Spectroscopy and Chemometrics

Molecules ◽  
2019 ◽  
Vol 24 (24) ◽  
pp. 4568 ◽  
Author(s):  
Yong Hao ◽  
Pei Geng ◽  
Wenhui Wu ◽  
Qinhua Wen ◽  
Min Rao
Keyword(s):  
Transgenic Rice ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Genetically Modified ◽  
Reflectance Spectroscopy ◽  
Genetically Modified Foods ◽  
Optimal Model ◽  
Rice Varieties ◽  
Svm Model

Background: In recent years, genetically modified technology has developed rapidly, and the potential impact of genetically modified foods on human health and the ecological environment has received increasing attention. The currently used methods for testing genetically modified foods are cumbersome, time-consuming, and expensive. This paper proposed a more efficient and convenient detection method. Methods: Near-infrared diffuse reflectance spectroscopy (NIRDRS) combined with multivariate calibration methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), were used for identification of different rice varieties and transgenic (Bt63)/non-transgenic rice. Spectral pretreatment methods, including Norris–Williams smooth (NWS), standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay 1st derivative (SG 1st-Der), were used for spectral noise reduction and effective information enhancement. Accuracy was used to evaluate the qualitative discriminant models. Results: The results showed that the SG 1st-Der pretreatment method, combined with the SVM, provided the optimal model to distinguish different rice varieties. The accuracy of the optimal model was 98.33%. For the discrimination model of transgenic/non-transgenic rice, the SNV-SVM model, MSC-SVM model, and SG 1st-Der-PLS-DA model all achieved good analysis results with the accuracy of 100%. Conclusion: The results showed that portable NIR spectroscopy combined with chemometrics methods could be used to identify rice varieties and transgenic characteristics (Bt63) due to its fast, non-destructive, and accurate advantages.

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