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

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. (C) 2020 The Author(s). Published by Elsevier Inc.