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

Abstract : 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.
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https://hal.inrae.fr/hal-02911762
Déposant : Dominique Fournier <>
Soumis le : mardi 4 août 2020 - 14:47:35
Dernière modification le : mercredi 16 septembre 2020 - 14:36:13

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Abdallah Zgouz, Daphné Heran, Bernard Barthès, Denis Bastianelli, Laurent Bonnal, et al.. Dataset of visible-near infrared handheld and micro-spectrometers – comparison of the prediction accuracy of sugarcane properties. Data in Brief, Elsevier, 2020, 31, pp.106013. ⟨10.1016/j.dib.2020.106013⟩. ⟨hal-02911762⟩

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