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Prediction of wine sensory properties using mid-infrared spectra of Cabernet Sauvignon and Chardonnay grape berries and wines
University of Adelaide, Australia; CSIRO, Australia.ORCID-id: 0000-0002-2642-283x
Norwegian University of Life Sciences, Norway.
Norwegian University of Life Sciences, Norway.
University of Adelaide, Australia.
Vise andre og tillknytning
2021 (engelsk)Inngår i: Food Chemistry, ISSN 0308-8146, E-ISSN 1873-7072, Vol. 344, artikkel-id 128634Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The study determined optimal parameters to four preprocessing techniques for mid-infrared (MIR) spectra of wines and grape berry homogenates and tested MIR's ability to model sensory properties of research Cabernet Sauvignon and Chardonnay wines. Savitsky-Golay (SG) derivative, smoothing points, and polynomial order, and extended multiplicative signal correction (EMSC) polynomial were investigated as preprocessing techniques at 2, 2, 5, and 3 levels, respectively, all in combination. Preprocessed data were analysed with partial least squares regression (PLS) to model the wine sensory data and the regression coefficients of PLS calibration models (R2) were further analysed with multivariate analysis of variance (MANOVA). SG transformations were significant factors from the MANOVA that influenced R2, while EMSC did not. Overall, PLSR models that predicted wine sensory characteristics gave a poor to moderate R2. Consistently predicting wine sensory attributes within a variety and across vintages is challenging, regardless of using grape or wine spectra as predictors. 

sted, utgiver, år, opplag, sider
Elsevier Ltd , 2021. Vol. 344, artikkel-id 128634
Emneord [en]
Mid infrared, Modelling, Partial least squares, Prediction, Preprocessing, Wine sensory, Fruits, Infrared devices, Least squares approximations, Multivariant analysis, Sensory analysis, Extended multiplicative signal corrections, Mid-infrared spectra, Multivariate analysis of variances, Partial least squares regression, Preprocessing techniques, Regression coefficient, Sensory attributes, Sensory characteristics, Wine, article, berry, calibration, grape, homogenate, infrared radiation, multivariate analysis of variance, nonhuman, food analysis, fruit, human, infrared spectrophotometry, least square analysis, multivariate analysis, procedures, South Australia, taste, Vitis, Humans, Least-Squares Analysis, Spectrophotometry, Infrared
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Identifikatorer
URN: urn:nbn:se:ri:diva-56354DOI: 10.1016/j.foodchem.2020.128634Scopus ID: 2-s2.0-85099011211OAI: oai:DiVA.org:ri-56354DiVA, id: diva2:1592170
Merknad

Funding details: CSP1201; 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 winemakers 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 sensory data collection, and Sue Maffei and Emily Nicholson are acknowledged for their assistance in collecting the grape samples and chemical analyses. WIC Winemaking Services is thanked for producing the small scale wines.

Tilgjengelig fra: 2021-09-08 Laget: 2021-09-08 Sist oppdatert: 2023-05-23bibliografisk kontrollert

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