Macrowine 2021
IVES 9 IVES Conference Series 9 Beyond classical statistics – data fusion coupled with pattern recognition

Beyond classical statistics – data fusion coupled with pattern recognition

Abstract

AIM: Patterns in data obtained from wine chemical and sensory evaluations are difficult to infer using classical statistics. Pattern recognition can be resolved by coupling data fusion with machine learning techniques, possibly leading to new hypotheses being formed. This study demonstrates the applicability of two pattern recognition approaches using as case study involving Chenin Blanc wines (recently bottled and after two years storage) from young (35 years) vines.

METHODS: Sensory (sorting (Mafata et al. 2020)) and chemical (NMR: nuclear magnetic resonance, HRMS: high resolution mass spectrometry, and UV-Vis: ultraviolet spectrophotometry) data were collected for the young and aged (two years in the bottle) wines. Data sets were combined using multiple factor analysis (MFA). Exploratory unsupervised cluster analysis was performed by agglomerative hierarchical clustering (AHC) and Fuzzy-k means (Bezdek 1981). Optimal cluster conditions were found for both methods and the cophenetic coefficient was used to assess the most confident clustering method.

RESULTS: Since large data sets were fused, the models were very complex. There were no consistent clustering patterns when varying clustering conditions, signalling high similarity between samples. The samples could not confidently be distinguished from one another even at the highest optimized conditions. Although Fuzzy-k means gave more confident clustering, it was still not sufficient for solving classification issues in this sample set.

CONCLUSIONS:

Fuzzy-k means was better at resolving the natural grouping of samples. Coupled to data fusion, it could potentially lead to better pattern recognition, especially for oenological chemical and sensory data. The fuzzy approach should be explored, keeping in mind it is more sensitive to small differences in the data compared to classical statistics.

DOI:

Publication date: September 7, 2021

Issue: Macrowine 2021

Type: Article

Authors

Mpho Mafata, Jeanne

1South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University & 2School for Data Science and Computational Thinking, Stellenbosch University, South Africa, BRAND, South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University, South Africa  Astrid, BUICA, South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University

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data fusion, pattern recognition, machine learning, artificial intelligence, multiple factor analysis, fuzzy-k means, cluster analysis

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The plantation frame as a measure of adaptation to climate change

The mechanization of vineyard work originally led to a reduction in planting densities due to the lack of machinery adapted to the vineyard. The current availability of specific machinery makes it possible to establish higher planting densities. In this work, three planting densities (1.40×0.80 m, 1.80×1 m and 2.20×1.20 m, corresponding to 8928, 5555 and 3787 plants/ha respectively) were studied with four varieties autochthonous of Galicia (northwestern Spain): Albariño and Treixadura (white), Sousón and Mencía (red). The vines were trained in a vertical shoot positioning system using a single Royat cordon, and pruned to spurs with two buds each. Agronomic data (yield, pruning wood weight, Ravaz index) and oenological data in must were collected. The higher planting density (1.40×0.80 m) had no significant effect on grape yield per vine in white varieties, although production per hectare was much higher due to the greater number of plants. In red varieties, this planting density resulted in a significantly lower production per vine, compensated by the greater number of plants. In addition, it significantly reduced the Brix degree in the must of the Albariño, Treixadura and Sousón varieties, and increased the total acidity in the latter two and Mencía. It also caused an increase in extractable and total anthocyanins and IPT in red grapes. The effects of high planting density on grapes are of great interest for the adaptation of varieties in the context of climate change. In the future, it could be advisable to modify the limits imposed by the appellations of origin on the planting density of these varieties in order to obtain more balanced wines.

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Assessment of the impact of actions in the vineyard and its surrounding environment on biodiversity in Rioja Alavesa (Spain)

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Macrowine 2021
IVES 9 IVES Conference Series 9 Beyond classical statistics – data fusion coupled with pattern recognition

Beyond classical statistics – data fusion coupled with pattern recognition

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An analytical framework to site-specifically study climate influence on grapevine involving the functional and Bayesian exploration of farm data time series synchronized using an eGDD thermal index

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