Zgouz A., Heran D., Barthès Bernard, Bastianelli D., Bonnal L., Baeten V., Lurol S., Bonin M., Roger J. M., Bendoula R., Chaix G. (2020). Dataset of visible-near infrared handheld and micro-spectrometers-comparison of the prediction accuracy of sugarcane properties [Data paper]. Data in Brief, 31, 106013 [6 p.]. ISSN 2352-3409.
Titre du document
Dataset of visible-near infrared handheld and micro-spectrometers-comparison of the prediction accuracy of sugarcane properties [Data paper]
Zgouz A., Heran D., Barthès Bernard, Bastianelli D., Bonnal L., Baeten V., Lurol S., Bonin M., Roger J. M., Bendoula R., Chaix G.
Source
Data in Brief, 2020,
31, 106013 [6 p.] ISSN 2352-3409
In the dataset presented in this article, sixty sugarcane samples were analyzed by eight visible / near infrared spectrometers including seven micro-spectrometers. There is one file per spectrometer with sample name, wavelength, absorbance data [calculated as log(10) (1/Reflectance)], and another file for reference data, in order to assess the potential of the micro spectrometers to predict chemical properties of sugarcane samples and to compare their performance with a LabSpec spectrometer. The Partial Least Square Regression (PLS-R) algorithm was used to build calibration models. This open ac cess dataset could also be used to test new chemometric methods, for training, etc.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Sciences du monde végétal [076]
