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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
University of Adelaide, Australia; CSIRO, Australia.ORCID iD: 0000-0002-2642-283x
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2018 (English)In: Food Chemistry, ISSN 0308-8146, E-ISSN 1873-7072, Vol. 256, p. 195-202Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier Ltd , 2018. Vol. 256, p. 195-202
Keywords [en]
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
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Food Science
Identifiers
URN: urn:nbn:se:ri:diva-56365DOI: 10.1016/j.foodchem.2018.02.120Scopus ID: 2-s2.0-85042639739OAI: oai:DiVA.org:ri-56365DiVA, id: diva2:1592317
Note

Funding text 1: The authors would like to thank the industry partners CCW Co-operative Ltd, Yalumba Wine Company and Treasury Wine Estates for generously allowing access to vineyards and grape samples. The study was funded by Australia’s grape growers and wine makers through their investment body Wine Australia with matching funding from the Australian Federal Government (CSP1201). Sandra Olarte-Mantilla and Trent Johnson are acknowledged for assistance in data collection, and Sue Maffei and Emily Nicholson are acknowledged for their assistance in collecting the grape samples and chemical analyses and the WIC Winemaking service is thanked for producing the small scale wines.

Available from: 2021-09-08 Created: 2021-09-08 Last updated: 2023-05-23Bibliographically approved

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Niimi, Jun

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