Result description
This KER introduces a UV-Vis spectroscopy-based method for classifying and quantifying sucrose origin. By analyzing absorbance differences in the 200-600 nm range and applying chemometric models (LDA, SIMCA, MLR), it allows accurate, low-cost, and fast distinction between sugar beet and sugarcane sources. The best model achieved 100% classification accuracy with only five wavelengths and demonstrated excellent regression metrics (R² = 0.99, RMSEP = 3.28%). The technique is promising for industrial application due to its simplicity and high performance.
Addressing target audiences and expressing needs
- Business partners – SMEs, Entrepreneurs, Large Corporations
- Use of research Infrastructure
- Collaboration
We are seeking research and industrial partners for further validation of the method and its integration into industry-grade quality control pipelines. Collaboration on hardware miniaturization and automation is welcome. Business partners from the food manufacturing or analytical instrument sectors are particularly encouraged to engage.
- Other Actors who can help us fulfil our market potential
- Research and Technology Organisations
- Academia/ Universities
R&D, Technology and Innovation aspects
Lab-scale proof-of-concept completed. Next steps include adapting the system for industrial-scale sugar analysis (e.g. handheld or inline sensors), as well as validating in real-time production conditions.
The method is replicable using conventional UV-Vis spectrophotometers (200-600 nm range) and common chemometric techniques such as LDA, MLR, and PLS. It relies on aqueous sucrose solutions and minimal sample preparation. The models demonstrated high accuracy and can be reproduced in routine lab settings with basic analytical infrastructure.
The method offers a sustainable value proposition by reducing the need for chemical reagents, lowering laboratory waste, and minimizing analysis time and costs. It supports traceability and compliance with agricultural policies, contributing to fair trade and transparency in the food industry. The approach is scalable, requires low resource input, and can be integrated into existing lab workflows, making it viable for long-term use in both industrial and research settings.
Result submitted to Horizon Results Platform by MIDDLE EAST TECHNICAL UNIVERSITY
