Application of sequential and orthogonalised-partial least squares (SO-PLS) regression to predict sensory properties of Cabernet Sauvignon wines from grape chemical composition

• 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

Ämnesord

LANTBRUKSVETENSKAPER  -- Lantbruksvetenskap, skogsbruk och fiske -- Livsmedelsvetenskap (hsv//swe)

AGRICULTURAL SCIENCES  -- Agriculture, Forestry and Fisheries -- Food Science (hsv//eng)

Nyckelord

Data orthogonalisation

Grape

Multi-block data analysis

Sensory

Wine

Least squares approximations

Sensory analysis

Sensory perception

Chemical compositions

Descriptive analysis

Multi blocks

Partial least square (PLS)

Structured approach

article

clinical article

data analysis

human

maturity

metabolomics

nonhuman

partial least squares regression

sensation

analysis

chemistry

least square analysis

taste

Vitis

Least-Squares Analysis

Taste Perception

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)