Dataset of visible-near infrared handheld and micro-spectrometers – comparison of the prediction accuracy of sugarcane properties

• Published in: Data in brief Vol. 31; p. 106013
• Main Authors: Zgouz, Abdallah, Héran, Daphné, Barthès, Bernard, Bastianelli, Denis, Bonnal, Laurent, Baeten, Vincent, Lurol, Sebastien, Bonin, Michael, Roger, Jean-Michel, Bendoula, Ryad, Chaix, Gilles
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2020; Elsevier
• Subjects: Agricultural and Biological Science; chemometrics, sugarcane; Computer Science; Food engineering; Handheld devices; Life Sciences; Micro-spectrometers; Modeling and Simulation; Visible-near infrared spectroscopy; chemometrics, sugarcane; Visible-near infrared spectroscopy; Micro-spectrometers; Handheld devices; chemometrics; sugarcane
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2020.106013

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.