Application of sequential and orthogonalised-partial least squares (SO-PLS) regression to predict sensory properties of Cabernet Sauvignon wines from grape chemical composition. (1st August 2018) - Niimi, Jun; Tomic, Oliver; Næs, Tormod; Jeffery, David W.; Bastian, Susan E.P.; Boss, Paul K.
Highlights: SO-PLS was applied to wine sensory profiles and grape chemistry of Cabernet Sauvignon. SO-PLS1 produced better models with higher validated explained variances than SO-PLS2. SO-PLS can be a method to handle multiple X data blocks for prediction. The proposed analysis procedure may assist in screening for important X data blocks. Abstract: 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.