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A Novel Approach for the Characterization of the Textural Properties of Table Olives: Acoustic Compression Related to Sensory Analysis
This research was performed on marketed table olives. We investigated possible correlations among textural parameters obtained using both sensory assessment and instrumental textural analysis. The purpose of this research study was to find out any possible correlation between the two different analy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857422/ https://www.ncbi.nlm.nih.gov/pubmed/36673333 http://dx.doi.org/10.3390/foods12020241 |
Sumario: | This research was performed on marketed table olives. We investigated possible correlations among textural parameters obtained using both sensory assessment and instrumental textural analysis. The purpose of this research study was to find out any possible correlation between the two different analysis methods, especially in relation to acoustic compression. Up to now, there are no available studies on this topic. Samples from different olive cultivars and different processing methods were analysed, and a data matrix resulting from four textural/acoustic and six sensorial kinaesthetic parameters was processed. The two parameters “S_crunch” and “T_noise” (the “S” letter is for “sensorial”, and the “T” letter is for “textural”) showed complementarity, but they did not discriminate properly. The textural values of “T_flesh_h” and the sensory values of “S_flesh_h” were directly correlated to “S_crunch”, and as an unexpected result, the textural values of “T_skin_bs” and the sensory values of “S_skin_h” were closely linked to each other. Regarding the analysed parameters, the results showed that the two techniques are clearly complementary and could constitute a valid tool for varietal characterization and for determining the instrumental and organoleptic qualities of the product; it was not possible to proceed with the characterization by type of processing method, as the dataset was not large enough. |
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