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- Niimi, Jun, et al.
(författare)
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Application of sequential and orthogonalised-partial least squares (SO-PLS) regression to predict sensory properties of Cabernet Sauvignon wines from grape chemical composition
- 2018
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Ingår i: Food Chemistry. - : Elsevier Ltd. - 0308-8146 .- 1873-7072. ; 256, s. 195-202
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Tidskriftsartikel (refereegranskat)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
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- Niimi, Jun, et al.
(författare)
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Objective measures of grape quality : From Cabernet Sauvignon grape composition to wine sensory characteristics
- 2020
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Ingår i: Lebensmittel-Wissenschaft + Technologie. - : Academic Press. - 0023-6438 .- 1096-1127. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- In an investigation of objective measures that link grape composition to wine quality, this study sought to identify Cabernet Sauvignon grape parameters that predict the sensory properties of the corresponding wines. Eleven chemical measures comprising volatile and non-volatile compounds, enzyme activity plus standard industry harvest measurements were applied to grape samples obtained from different regions throughout South Eastern Australia over three vintages. Grapes underwent controlled vinification and the resulting wines evaluated with sensory descriptive analysis. The entire multi-vintage data sets were combined and modelled using a combination of partial least squares (PLS) and sequential and orthogonalised (SO) -PLS regression techniques. Optimal models were obtained with single sensory attributes rather than global modelling with the entire sensory profile. Five grape chemical measures, which in the main were harvest parameters, were used along with colour, total phenolics and tannin, targeted volatiles, and flavonols, and orthogonalised to model 14 sensory attributes of the Cabernet Sauvignon wines. The seven remaining measures were not used due to their poor ability to model wine sensory attributes, with enzyme activity and tannin by HPLC explaining the least. The study revealed new insights into the relationship between grape chemistry and wine sensory characters, which has implications for developing an objective measurement system for determining grape quality.
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