1. Prediction of wine sensory properties using mid-infrared spectra of Cabernet Sauvignon and Chardonnay grape berries and wines.
- Author
-
Niimi J, Liland KH, Tomic O, Jeffery DW, Bastian SEP, and Boss PK
- Subjects
- Food Analysis statistics & numerical data, Fruit, Humans, Least-Squares Analysis, Multivariate Analysis, South Australia, Taste, Food Analysis methods, Spectrophotometry, Infrared methods, Vitis, Wine analysis
- Abstract
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 (R
2 ) 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., (Copyright © 2020 Elsevier Ltd. All rights reserved.)- Published
- 2021
- Full Text
- View/download PDF