Application of data fusion techniques to direct geographical traceability indicators.

Area of Science:

• Geochemistry
• Soil Science
• Chemometrics

Background:

• Understanding soil geographical variability is crucial for agricultural traceability.
• Lambrusco wine production relies on specific terroir characteristics.
• Existing methods for soil characterization may not fully capture complex geographical variations.

Purpose of the Study:

• To develop and apply a hierarchical data fusion approach for soil characterization.
• To assess the geographical variability of soil samples in the Modena District.
• To establish a foundation for geographical traceability models for Lambrusco Wines.

Main Methods:

• Multivariate Curve Resolution (MCR) was employed as a variable reduction tool.
• A multi-step data fusion strategy integrated X-ray powder diffraction (XRDP) spectra, elemental concentrations, and strontium isotopic ratios ((87)Sr/(86)Sr).
• Hierarchical data fusion combined resolved components from individual datasets to capture complex relationships.

Main Results:

• MCR successfully resolved XRDP spectra into calcite, quartz, and clay contributions.
• Data fusion revealed common variation patterns between metals concentrations and strontium isotopic ratios.
• The study identified links between mineralogical components, metal patterns, and strontium isotope variations, capturing soil variability.

Conclusions:

• The hierarchical data fusion approach effectively integrates diverse soil datasets for comprehensive characterization.
• The method provides insights into geographical soil variability relevant for wine traceability.
• This pilot study demonstrates the potential for developing robust geographical traceability models for Lambrusco Wines.