Western Red nectarines, harvested at commercial maturity, were stored for up to 20 days at 1, 4, or 8 °C and then transferred to 25 °C for 0 or 4 days. The main physicochemical attributes, phytochemicals, and volatile compounds were then determined. During storage and ripening, firmness, titratable acidity, organic acids, and C6 volatile compounds decreased, whereas ethylene production, lactones, and C13 norisoprenoids greatly increased. Soluble solids content, sugars, and polyphenols remained quite constant during both stages. During storage, vitamin C decreased and carotenoids did not significantly change, whereas both greatly increased during ripening. Increased time of low-temperature storage has been found to decrease lactones and C13 norisoprenoids in nectarine and, consequently, to limit its aroma during maturation. Finally, Western Red nectarine was found hardly chilling injury sensitive, and trends for sugars, polyphenols and lactones observed in this study were contrary to those generally reported in the literature for chilling-injured fruit.

The sugar content of Golden Delicious apples is predicted using near infrared (NIR) spectrometry. The study focuses on the metrological characteristics of the sugar content measurement and external parameters involved in the lack of robustness of the NIR-based model. The external parameters were fruit temperature, spectrometer temperature and ambient light. The first two factors influenced the prediction accuracy: (i) a fruit temperature variation altered the prediction, the relationship seems to be described by a non-linear model within the considered temperature range, (ii) a variation of the spectrometer temperature also altered the prediction, the relationship is described by a linear function for a temperature between 4 and 30°C. Ambient light did not show to have any influence on the NIR-based model. The analysis of the metrological parameters showed a satisfactory repeatibility in sugar prediction with a low error, 0.073°Brix. The model reproducibility was good regarding bias-corrected standard error of prediction ( SEPc) without significant differences between experiments, on the other hand a bias remained even if the previous parameters were maintained constant. These results will be taken into account in future measurements, in order to improve the robustness of the NIR-based model developed for apples.

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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 access dataset could also be used to test new chemometric methods, for training, etc.

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