Even though descriptive sensory analysis is the best approach to provide a comprehensive and objective description of sensory perception both in qualitative and quantitative terms, such approach is expensive, time consuming, and the number of samples per analysis is limited thus restricting its application when a large number of samples has to be described. This is the case of breeding programs, where the quality screening of hundreds or thousands of items has to be evaluated in a relatively short time frame. For this reason, the possibility to predict sensory attributes from instrumental analysis is desirable. Several predictive models, based on instrumental determination, have been successfully developed for sensory texture parameters. In the case of odour sensory attributes, the construction of reliable models is particularly challenging. Different reasons can explain this difficulty: 1/ to a single odour sensory attribute corresponds many chemical compounds underlying the complexity to train a panel for odour attributes,2/ they are elevated inter and intra individual physiological differences for odour threshold, chewing behaviour, saliva composition, and so on; 3/ a detailed model should take into account the multisensory interactions between senses. In order to predict the trained panellists’ responses, the development of regression models of odour sensory attributes in apple using the profiling of volatile organic compounds of apples, measured by SPME/GC-MS were investigated. In the present contribution, we report the preliminary results obtained from the comparison of sensory description performed by a trained panel and SPME/GC-MS profiling of several apple cultivars over two cropping seasons. Although the study is still on going, early results are encouraging. A clear relationship between several odour attributes and volatile compound composition was found and PLS diagnostic models allowed the identification of the compounds contributing more to the odour sensations
Aprea, E.; Corollaro, M.L.; Betta, E.; Endrizzi, I.; Charles, M.C.; Dematte', M.L.; Gasperi, F. (2013). Predicting odour attributes in apple from SPME/GC-MS data. In: F. Biasioli (editor), 3rd MS Food day, October 9-11, 2013, Trento. San Michele all'Adige (TN): Fondazione Edmund Mach: 212 (P.58). ISBN: 9788878430358. handle: http://hdl.handle.net/10449/22766
Predicting odour attributes in apple from SPME/GC-MS data
Aprea, Eugenio;Corollaro, Maria Laura;Betta, Emanuela;Endrizzi, Isabella;Charles, Mathilde Clemence;Dematte', Maria Luisa;Gasperi, Flavia
2013-01-01
Abstract
Even though descriptive sensory analysis is the best approach to provide a comprehensive and objective description of sensory perception both in qualitative and quantitative terms, such approach is expensive, time consuming, and the number of samples per analysis is limited thus restricting its application when a large number of samples has to be described. This is the case of breeding programs, where the quality screening of hundreds or thousands of items has to be evaluated in a relatively short time frame. For this reason, the possibility to predict sensory attributes from instrumental analysis is desirable. Several predictive models, based on instrumental determination, have been successfully developed for sensory texture parameters. In the case of odour sensory attributes, the construction of reliable models is particularly challenging. Different reasons can explain this difficulty: 1/ to a single odour sensory attribute corresponds many chemical compounds underlying the complexity to train a panel for odour attributes,2/ they are elevated inter and intra individual physiological differences for odour threshold, chewing behaviour, saliva composition, and so on; 3/ a detailed model should take into account the multisensory interactions between senses. In order to predict the trained panellists’ responses, the development of regression models of odour sensory attributes in apple using the profiling of volatile organic compounds of apples, measured by SPME/GC-MS were investigated. In the present contribution, we report the preliminary results obtained from the comparison of sensory description performed by a trained panel and SPME/GC-MS profiling of several apple cultivars over two cropping seasons. Although the study is still on going, early results are encouraging. A clear relationship between several odour attributes and volatile compound composition was found and PLS diagnostic models allowed the identification of the compounds contributing more to the odour sensationsFile | Dimensione | Formato | |
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