ARC Training Centre for Innovative Wine Production publications

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Browsing ARC Training Centre for Innovative Wine Production publications by Author "Armstrong, C.E.J."

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    Effect of grape heterogeneity on wine chemical composition and sensory attributes for Vitis vinifera cv. Cabernet Sauvignon
    (Wiley, 2021) Armstrong, C.E.J.; Ristic, R.; Boss, P.K.; Pagay, V.; Jeffery, D.W.
    Background and Aims: The wine industry widely acknowledges that grape heterogeneity exists throughout any given vineyard at the time of harvest. There is a lack of understanding, however, of the effects that heterogeneity has on wine composition and sensory qualities. This study compared the chemical composition and sensory attributes of wines produced using parcels of grapes with known heterogeneity. Methods and Results: Cabernet Sauvignon berries were sorted into maturity classes using five density baths. Winemaking was undertaken with a varying proportion of sorted berries to obtain wines arising from low, moderate and high grape heterogeneity musts, containing the same level of TSS for comparison to a Control wine that represented the inherent heterogeneity of the harvested vines. Wines were distinguished by the concentration of 3-isobutyl-2-methoxypyrazine, total anthocyanins and total phenolics, with the low heterogeneity wine having the highest values of each measurement. The high heterogeneity wine was characterised by a higher concentration of acetic acid. Principal component analysis of rate-all-that-apply sensory attribute scores revealed that low and moderate heterogeneity wines were characterised by floral and pepper aroma attributes, and the high heterogeneity wine by sour taste. Conclusions: The differing levels of grape heterogeneity had a subtle effect on Cabernet Sauvignon wine colour, aroma and taste attributes, but it was demonstrated that the level of grape heterogeneity could be of relevance if it translated into differences in wine quality and style. Significance of the Study: The potential impact of grape heterogeneity on the chemical composition and sensory profile of Cabernet Sauvignon wine was highlighted.
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    Machine learning for classifying and predicting grape maturity indices using absorbance and fluorescence spectra
    (Elsevier, 2023) Armstrong, C.E.J.; Gilmore, A.M.; Boss, P.K.; Pagay, V.; Jeffery, D.W.
    Absorbance-transmission and fluorescence excitation-emission matrix (A-TEEM) spectroscopy was investigated as a rapid method for predicting maturity indices using Cabernet Sauvignon grapes produced under four viticulture treatments during two growing seasons. Machine learning models were developed with fused spectral data to predict 3-isobutyl-2-methoxypyrazine (IBMP), pH, total tannins (Tannin), total soluble solids (TSS), and malic and tartaric acids based on the results from traditional analysis methods. Extreme gradient boosting (XGB) regression yielded R² values of 0.92-0.96 for IBMP, malic acid, pH, and TSS for externally validated (Test) models, with partial least squares regression being superior for TSS prediction (R² = 0.97). R² values of 0.64-0.81 were achieved with either approach for tartaric acid and Tannin predictions. Classification of grape maturity, defined by quantile ranges for red colour, IBMP, malic acid, and TSS, was investigated using XGB discriminant analysis, providing an average of 78 % correctly classified samples for the Test model.