Ripe nectarines segregated through sensory quality evaluation and electronic nose assessment

Abstract

Introduction. The aim of this study was to determine the ability of an electronic nose (e-nose) to predict the quality of nectarines and peaches, and, in particular, the aroma. Materials and methods. Four nectarine cultivars ('María Dolce', 'Maillarlate', 'Nectaross' and 'Venus') and one peach cultivar ('Royal Glory') were evaluated. The fruit was harvested ripe and the quality evaluations were carried out just one day after harvest. The intensity of the main descriptors of fruit quality was described, and fruits were subjected to an e-nose assessment. The sensory analysis and the e-nose results were presented through a Principal Component Analysis (PCA). A multiple linear regression (MLR) was also used to create a predictive model for the attribute 'aroma' compared with the other sensory parameters and the most informative e-nose sensor data. Results and discussion. 'Royal Glory' and 'María Dolce' were placed in a separate cluster far from 'Venus', 'Nectaross' and 'Maillarlate'. The result of the MLR included the attributes 'acidity', 'sweetness' and 'acceptability' in the model, and the data registered by sensor 6 of the e-nose (SnO2-sensor, RGTO Mo, 45 thick layer), which were those factors that best related to the aroma, reached a R 2 of 0.48 and a mean square error (MSE) of 3.85. It was concluded that the e-nose is an instrument able to discriminate peach varieties through their aromatic features, which are among the descriptors that mainly determine acceptability by the peach consumer. © 2011 Cirad/EDP Sciences.