Rheological and chemical predictors of texture and taste in dessert banana (Musa spp.)

To be able to account for sensory qualities earlier in the assessment of a new banana hybrid in a selectionscheme, predicting the sensory perception of banana texture and taste by instrumental parameters wasinvestigated. Thirteen cultivated banana and four new triploid hybrids were characterized by sensoryprofiling, and rheological and chemical analyses. Multilinear regressions were used to calibrate predic-tions using 13 cultivated bananas, and the quality of predictions was validated using four hybrids. Thesensory characteristics sourness and sweetness were predicted by titratable acidity (R2= 0.68) and pH(R2= 0.66). Malate and citrate were the main contributors to sweetness and sourness. Astringency waspredicted by total tannins (R2= 0.55). Rheological parameters from texture profile analyses (stress at frac-ture, fracturability) were more suitable than pulp puncture force to predict the sensory texture propertiesfirmness (R2= 0.47) and melting (R2= 0.60). These textural properties were predicted by titratable acidityand dry matter content (R2= 0.62). Predictions of mealiness, adhesiveness, and heterogeneity were notefficient. Differences of 3.6-3.7 meq 100 g?1FW in titratable acidity or of 0.30 g 100 g?1FW in malate orcitrate were required to ensure a detectable difference in sourness or sweetness (p = 0.9). Pulp punc-ture force needed to differ by a minimum of 0.9 N before a difference in firmness could be perceived bythe panelists. In conclusion, while models to predict sourness and sweetness can now be used for highthroughput phenotyping, we recommend additional tests for other sensory attributes.

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Bibliographic Details
Main Authors: Bugaud, Christophe, Cazevieille, Patrick, Daribo, Marie Odette, Telle, Nelly, Julianus, Philippe, Fils-Lycaon, Bernard, Mbéguié-A-Mbéguié, Didier
Format: article biblioteca
Language:eng
Subjects:Q04 - Composition des produits alimentaires, U30 - Méthodes de recherche, banane, analyse organoleptique, propriété rhéologique, propriété physicochimique, propriété organoleptique, texture, citrate, malate, technique analytique, http://aims.fao.org/aos/agrovoc/c_806, http://aims.fao.org/aos/agrovoc/c_16006, http://aims.fao.org/aos/agrovoc/c_6553, http://aims.fao.org/aos/agrovoc/c_1521, http://aims.fao.org/aos/agrovoc/c_5399, http://aims.fao.org/aos/agrovoc/c_15578, http://aims.fao.org/aos/agrovoc/c_1630, http://aims.fao.org/aos/agrovoc/c_4531, http://aims.fao.org/aos/agrovoc/c_1513, http://aims.fao.org/aos/agrovoc/c_4635, http://aims.fao.org/aos/agrovoc/c_3081,
Online Access:http://agritrop.cirad.fr/568977/
http://agritrop.cirad.fr/568977/1/document_568977.pdf
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Summary:To be able to account for sensory qualities earlier in the assessment of a new banana hybrid in a selectionscheme, predicting the sensory perception of banana texture and taste by instrumental parameters wasinvestigated. Thirteen cultivated banana and four new triploid hybrids were characterized by sensoryprofiling, and rheological and chemical analyses. Multilinear regressions were used to calibrate predic-tions using 13 cultivated bananas, and the quality of predictions was validated using four hybrids. Thesensory characteristics sourness and sweetness were predicted by titratable acidity (R2= 0.68) and pH(R2= 0.66). Malate and citrate were the main contributors to sweetness and sourness. Astringency waspredicted by total tannins (R2= 0.55). Rheological parameters from texture profile analyses (stress at frac-ture, fracturability) were more suitable than pulp puncture force to predict the sensory texture propertiesfirmness (R2= 0.47) and melting (R2= 0.60). These textural properties were predicted by titratable acidityand dry matter content (R2= 0.62). Predictions of mealiness, adhesiveness, and heterogeneity were notefficient. Differences of 3.6-3.7 meq 100 g?1FW in titratable acidity or of 0.30 g 100 g?1FW in malate orcitrate were required to ensure a detectable difference in sourness or sweetness (p = 0.9). Pulp punc-ture force needed to differ by a minimum of 0.9 N before a difference in firmness could be perceived bythe panelists. In conclusion, while models to predict sourness and sweetness can now be used for highthroughput phenotyping, we recommend additional tests for other sensory attributes.