Journal
FOOD CHEMISTRY
Volume 256, Issue -, Pages 195-202Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2018.02.120
Keywords
Multi-block data analysis; Data orthogonalisation; Grape; Wine; Sensory
Funding
- Australia's grape growers and wine makers through Australian Federal Government [CSP1201]
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The current study determined the applicability of sequential and orthogonalised-partial least squares (SO-PLS) regression to relate Cabernet Sauvignon grape chemical composition to the sensory perception of the corresponding wines. Grape samples (n = 25) were harvested at a similar maturity and vinified identically in 2013. Twelve measures using various (bio)chemical methods were made on grapes. Wines were evaluated using descriptive analysis with a trained panel (n = 10) for sensory profiling. Data was analysed globally using SO-PLS for the entire sensory profiles (SO-PLS2), as well as for single sensory attributes (SO-PLS1). SO-PLS1 models were superior in validated explained variances than SO-PLS2. SO-PLS provided a structured approach in the selection of predictor chemical data sets that best contributed to the correlation of important sensory attributes. This new approach presents great potential for application in other explorative metabolomics studies of food and beverages to address factors such as quality and regional influences.
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