Development of a model for intensity of flavour character in blackcurrant concentrates

• Published in: Journal of the science of food and agriculture 1999-08, Vol.79 (11), p.1495-1502
• Main Authors: Boccorh, R.K, Paterson, A, Piggott, J.R
• Format: Article
• Language: English
• Subjects: Aroma and flavouring agent industries; Biological and medical sciences; black currants; concentrates; concentration; cooling; flavor; flavor compounds; flavor intensity; Food industries; frozen storage; Fruit and vegetable industries; fruit drinks; fruit flavour; Fundamental and applied biological sciences. Psychology; gas chromatography; gas chromatography-olfactometry; geographical variation; least squares; multivariate analysis; multivariate statistical modeling; multivariate statistical modelling; odors; partial least square regression; sensory compositional relationships; statistical analysis; volatile compounds
• ISSN: 0022-5142; 1097-0010
• DOI: 10.1002/(SICI)1097-0010(199908)79:11<1495::AID-JSFA391>3.0.CO;2-R

The feasibility of routine quantification of flavour character intensity in fruit concentrates by chromatography was studied. A set of 37 volatile components, previously identified as flavour-active by gas chromatography-olfactometry, were quantified in 133 different blackcurrant concentrates from three seasons, by gas chromatography with flame ionisation detection. Sensory data on intensities of flavour character in these concentrates were obtained using ratio scaling. Partial least squares regression (PLS) was employed to develop models describing relationships between intensity of flavour character and concentrations of specific flavour components. Separate models were obtained for concentrates produced within three single seasons. Individual concentrates varied in: geographical origin; post-harvest fruit storage (chilled or frozen); and concentration strategy, either conventional thermal or freeze technology. Cross-validation (by PLS) determined the number of correlation factors required to reach minimum prediction error for each model. The final model had a regression coefficient of 0.80, dependent upon 10 flavour components, and was suited for predicting flavour character intensity in blackcurrant drinks from concentrates.