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Updated: Sep 7, 2025

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Chemometric and sensometric techniques in enological data analysis.

Mpho Mafata1,2, Jeanne Brand1, Andrei Medvedovici3

  • 1South African Grape and Wine Research Institute, Department of Viticulture and Oenology, Stellenbosch University, Stellenbosch, South Africa.

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Summary
This summary is machine-generated.

Integrating wine

Keywords:
Chemometricsdata analysisdata concatenationdata fusiondata integrationmulti-modalmultivariate analysissensometrics

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Area of Science:

  • Enology
  • Analytical Chemistry
  • Data Science

Background:

  • Wine evaluation involves diverse chemical and sensory techniques.
  • Integrating multi-modal data (e.g., sensory and chemistry) is crucial but challenging.
  • Low success rates in data fusion are linked to inadequate data handling strategies.

Approach:

  • This review examines data analysis stages in enological evaluations.
  • It identifies misconceptions and proposes rules for effective multi-modal data integration.
  • Focuses on the impact of data handling decisions on modeling outcomes.

Key Points:

  • Data handling strategies are critical for successful multi-modal data integration.
  • Both supervised and unsupervised modeling approaches have distinct pre-processing needs.
  • Neglected data collection and capturing aspects significantly hinder combined sensory and chemistry data analysis.

Conclusions:

  • Careful consideration of each data analysis stage is essential for robust wine evaluation models.
  • Addressing data collection and handling challenges can improve the integration of sensory and chemical data.
  • Purpose-driven strategies are needed to overcome limitations in multi-modal data fusion for wine science.