Assessment of sensory firmness and crunchiness of tablegrapes by acoustic and mechanical properties

2015 
Background and Aims The instrumental measurement of crunchiness in tablegrapes has been the subject of little research in spite of the great relevance of this sensory texture trait to consumer preference. Therefore, our aim was to evaluate the potential of several mechanical and acoustic properties to assess the perceived firmness and crunchiness of tablegrape cultivars. Methods and Results The ripening effect was minimised by densimetric sorting of the berries before testing. The textural quality of seven tablegrape cultivars was evaluated by sensory analysis. Furthermore, three mechanical tests (texture profile analysis, cutting and denture) were performed on the berry flesh or on whole berries, and the acoustic emission produced was recorded simultaneously. Correlation studies showed strong and significant relationships between sensory texture attributes and instrumental parameters, particularly for the denture test. Nevertheless, satisfactory predictive accuracy for the perceived crunchiness required multivariate linear regression involving both mechanical and acoustic properties resulting from the denture test performed on whole berries. In this case, residual predictive interquartile amplitude was higher than 2. Most of the reliable models developed for perceived firmness are fairly recommended not for quantitative purposes but for fast screening (1.6 < residual predictive interquartile amplitude < 2). Conclusions The standardised protocol proposed permits more objective and quantitative sensory data to be obtained for firmness and crunchiness of tablegrapes. Significance of the Study A combined mechanical–acoustic strategy has not previously been used in tablegrapes and represents a powerful tool for a more complete and exhaustive texture characterisation, particularly firmness and crunchiness, by means of a more objective and standardised protocol.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    49
    References
    8
    Citations
    NaN
    KQI
    []