In:
Food Science and Technology International, SAGE Publications, Vol. 13, No. 6 ( 2007-12), p. 423-435
Kurzfassung:
To characterise the flavour of the indigenous Chinese potherb mustard (Brassica juncea, Coss.) pickle, 28 samples representing two typical types with different brining storage time were sourced from 14 cities in the Yangtze River Delta Megalopolis area in China, either short time storage or long time storage pickles sampled from cities open markets. Pickle samples were assessed by descriptive sensory evaluation conducted using a trained panel and an established pickle flavour lexicon. Pickle samples were also analysed for flavour components, as expressed by physico-chemical parameters. The relationships between the flavour compounds and sensory properties of pickles were studied by the partial least squares (PLS) regression method. Results demonstrated that some latent variables both in the independent variables (physico-chemical parameters) and dependent variables (taste sensory attributes) can be extracted to build the PLS models for prediction. PLS1 regression of sensory data revealed that preference was predominantly related to umami taste, whereas umami taste was mainly related to free amino acid of glutamic acid and aspartic acid contents in the pickle juice. The biplot of PLS2 with all the samples and variables explained 100% of the X-variables (instrumental measurements) and 85% of the Y-variables (taste sensory attributes). The prediction abilities of the PLS models obtained yield good results for the prediction of taste sensory characteristics of potherb mustard pickle according to physico-chemical parameters analysis. However, the total instrumental analysing parameters collected for judgement of potherb mustard pickle quality was a rather large set of data, sensory values evaluated by trained panelists are relatively convenient to obtain. These results illustrated the application of multivariate analysis as an effective strategy for improving the quality of potherb mustard pickles.
Materialart:
Online-Ressource
ISSN:
1082-0132
,
1532-1738
DOI:
10.1177/1082013207087813
Sprache:
Englisch
Verlag:
SAGE Publications
Publikationsdatum:
2007
ZDB Id:
2081257-7
