INSTRUMENTAL AND SENSORY CHARACTERIZATION OF MANGO FRUIT TEXTURE

ABSTRACT The texture of 64 mangoes of different commercial varieties was analyzed by a trained sensory panel. The intensities of the characters “firmness,”“crunchiness,”“melting” and “juiciness” were noted from 0 to 10. Soluble solids content, visible/NIR spectrum and texture of the same fruits were determined using instrumental methods. Texture was determined by means of mechanical and acoustic tests. Four sensory classes were identified by agglomerative hierarchical clustering from among the fruits analyzed and were characterized in terms of the measured physicochemical parameters. The relationships between the sensory and instrumental texture variables were visualized using a multiple factorial analysis. The prediction of sensory attributes from instrumental data was better for firmness, crunchiness and melting than for juiciness. Power‐law or logarithmic models were proposed to predict firmness (r = 0.89), crunchiness (r = 0.89) and melting (r = 0.88). Multivariate models (multiple linear regression, partial least squares regression) were also proposed. PRACTICAL APPLICATIONS Development of mango consumption in the European Union requires a good understanding of consumer preferences in terms of tastes, flavors and texture of fruit. Texture is a good indicator of the eating quality and freshness of fruit and is also considered as a major factor of product acceptability. Texture is a multidimensional sensory property linked to mechanical and geometrical properties, and to product composition. The objective of the study was to construct a mango texture typology from four relevant sensory textural descriptors (firmness, crunchiness, melting and juiciness) in order to obtain qualitative fruit classes to be associated with consumer preferences. Instrumental texture measurements were also achieved in order to find explanatory variables of the sensory attributes and to predict the sensory score of a textural attribute by means of one or more instrumental measures.