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In this study, fruits of two nectarine cultivars, harvested at commercial ripeness, were sorted into three density groups. Physicochemical parameters (mean weight, firmness, dry matter, soluble solids content, titratable acidity) and the levels of sugars, organic acids, vitamin C, polyphenols and volatiles were then determined at harvest and after ripening. A sensory ranking test was also performed on ripened nectarines to compare quality attributes (firmness, sweetness, acidity, and aroma). In both cultivars, ripened nectarines of higher density had significantly higher levels of dry matter, soluble solids content, sugars, ascorbic acid, polyphenols and volatiles. Among polyphenols, the levels of hydroxycinnamic acids were 30–40% significantly higher in nectarines of higher density. Among volatiles, levels of lactones, well-known key aroma compounds in nectarines, were also 2–3 times significantly higher in ripened fruits of higher density. Consistent with these results, nectarines of higher density were significantly ranked sweeter and more aromatic in both cultivars.

Sugar content in Golden delicious apples is predicted using NIR spectrometry. The study is focused 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 ºC. 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 ºBrix. 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., Le taux de sucre de pommes Golden Delicious est prédit par spectrométrie proche infrarouge (NIR). L'étude se concentre sur les caractéristiques métrologiques de la mesure du taux de sucre et sur les grandeurs d'influence principalement impliquées dans les problèmes de robustesse des modèles basés sur le NIR. Les grandeurs étudiées sont : la température du fruit, la température du spectromètre et la lumière ambiante. Les deux premières influencent la prédiction : (i) Une variation de la température du fruit altère la mesure d'une façon apparemment non linéaire dans l'étendue de variation étudiée ; (ii) La variation de la température du spectromètre altère la mesure d'une manière linéaire entre 4 et 30 °C. La lumière ambiante ne semble pas avoir d'influence sur le modèle NIR. L'analyse des paramètres métrologiques montre une répétabilité satisfaisante (erreur de 0,073 Brix). D'un côté, la reproductibilité est bonne vis à vis de l'erreur de prédiction corrigée du biais (SEPc) sans différence significative entre les expériences. D'un autre côté, un bias de prédiction subsiste, même si les paramètres d'influence restent constants. Ces résultats devront être pris en compte dans les prochaines mesures, pour améliorer la robustesse des modèles spectrométriques appliqués aux pommes.