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Spectral Data Processing Software Connects the Laboratory with the End User 

Analitica d.o.o., together with the Biotechnical Faculty and the Faculty of Mathematics and Physics of the University of Ljubljana, took part in the project Application of Modern Spectroscopic Approaches to Food Analysis. The collaboration focused on knowledge transfer in software development, data quality and the use of models in real-world operating environments. 

An Industry Perspective on Spectral Data 

Spectral measurements generate large volumes of data, yet actionable information emerges only through a structured processing workflow. Each spectrum must be linked to a sample identifier, the recipe or reference composition, instrument settings and the model version used. This level of traceability enables reproducibility, error detection and the responsible use of predictions.

Within the project, Analitica introduced students to the full software development lifecycle, from defining user requirements to structuring data, visualising spectra, testing and documentation. Particular attention was given to how software recognises invalid inputs and how it clearly presents results to the user, including the range within which they are valid.

Joint Development on a Real-World Case 

The project partners combined complementary expertise. The Biotechnical Faculty carried out sample preparation and reference composition analyses, the Faculty of Mathematics and Physics contributed the optical measurement system and expertise in hyperspectral imaging, and Analitica led the design of the data management approach and accompanying software solution. 

Students worked with real food products as well as model samples of known composition. They tested several pre-processing and modelling methods and examined how water, protein, fat and starch affect the spectral response. The results demonstrated the advantage of hyperspectral data over fluorescence alone, while also confirming that prediction quality depends heavily on data preparation, calibration set design and the validation approach. 

Knowledge for Further Development 

The collaboration demonstrated how software development and advanced analytics can support research measurements and bring them closer to industrial application. The experience gained provides a foundation for further improvements in spectral data processing, user interfaces and adaptable analytical solutions. 

The project ran from April to August 2026 as part of the public call Problem-Based Student Learning in the Work Environment 2024–2027. 

Modern spectroscopic approaches bring together food analysis, optical measurements and data processing.