Dataset of visible-near infrared handheld and micro-spectrometers – comparison of the prediction accuracy of sugarcane properties
oleh: Abdallah Zgouz, Daphné Héran, Bernard Barthès, Denis Bastianelli, Laurent Bonnal, Vincent Baeten, Sebastien Lurol, Michael Bonin, Jean-Michel Roger, Ryad Bendoula, Gilles Chaix
| Format: | Article |
|---|---|
| Diterbitkan: | Elsevier 2020-08-01 |
Deskripsi
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 log10 (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 access dataset could also be used to test new chemometric methods, for training, etc.