Our Organisation Search
Quick Links
Toggle: Topics

In real time

TResearch Summer 2022

Teagasc is working with Spain’s University of Córdoba and Ireland’s leading dairy company Glanbia Ireland to evaluate a state-of-the-art process sensor for the real-time measurement of protein in milk protein concentrate.

Milk protein concentrate (MPC) powder is a high-protein dairy ingredient, often used in the production of
protein-fortified foods, weight management, baby formula and sports and fitness products.

In the production of MPC powder, monitoring and the prediction of key compositional components such as protein content is essential after ultrafiltration, to ensure an optimal process is achieved.

An increase in global demand for dairy products is driving Irish dairy processors to optimise processes and therefore increase product throughput to meet this demand. Inline and real-time measurements have been proven to be beneficial for ensuring the target protein set for the process is closely achieved.

A project titled “NIR4Dairy” is being led by Teagasc in collaboration with University of Córdoba (Spain) and leading dairy company Glanbia Ireland, to evaluate near-infrared (NIR) spectroscopy as a process analytical technology in combination with the development of a robust prediction model, for inline measurement of protein content in liquid MPC.

TResearch Summer 2022

Near-infrared (NIR) spectroscopy technology

Predicting protein content

NIR spectroscopy studies the interactions between incident light and the sample. ‘Spectral fingerprints’ are generated that contain information from the sample that can be linked to its chemical components, such as protein, total solids and fat. This information is then used as part of building a predictive model.

Since the 1970s, NIR has been utilised in the dairy industry as a laboratory analytical method (offline) for analysis of compositional components. Recent advances in NIR technology and instrumentation have widened its application to online and inline continuous process monitoring.

Optimal prediction models rely on high-quality spectra and reference data. As a preliminary study, an NIR sensor was investigated using laboratory conditions to optimise optical settings (i.e. spectral resolution, scans per sample, spectra repeatability). Using the correct optical settings can ensure high-quality spectra are obtained from the product.

A laboratory model was developed by Teagasc researchers to predict the protein content of liquid MPC, using 120 representative MPC samples provided by Glanbia Ireland.

The NIR sensor was also tested in pilot scale studies using a flow test skid at Teagasc’s Bio-functional Food Engineering (BFE) facility. This was to investigate the effect of process conditions such as temperature and flow rate on the spectra quality and model performance.

TResearch Summer 2022

Flow test skid

Giving dairy processors better control

The results of the studies showed that the use of NIR sensors for the inline measurement of protein was effective and would allow dairy processors to make timely decisions regarding adjustments for process optimisation. 

By integrating the prediction model developed using an NIR sensor with a process management system, dairy processors will have better control of their processes. The NIR sensing technology is a fast, non-destructive and chemical-free method and, based on the success of the laboratory model and pilot scale studies, it has been installed at the industry partner’s MPC process plant.

Ongoing work in this project is required, including to optimise optical settings for continuous collection of MPC spectra using real-life process conditions, and conduct experimental trials to update the laboratory protein model and validate the model performance at industry scale.

The project researchers have made significant advancements in the area of NIR application and dairy processing so far, and have shown the potential that this technology can help to minimise out-of-specification batches whilst also reducing extra protein give-away in the final powder.


This study received funding from the Research Leaders 2025 Fellowship programme (co-funded by Teagasc and the European Union’s Horizon 2020 Research and Innovation Programme), under the Marie Skłodowska-Curie grant agreement number 754380.


Yuanyuan Pu

Marie Sklodowska-Curie Fellow;
Research Leaders 2025 Fellow

Teagasc Food Research Center,
Moorepark, Co. Cork.


Ana Garrido Varo


Faculty of Agriculture
and Forestry Engineering

University of Cordoba, Spain.


Dolores Pérez-Marín


Faculty of Agriculture
and Forestry Engineering

University of Cordoba, Spain.


Norah O’Shea

Research Officer

Digital Dairy Specialist

Teagasc Food Research Center,
Moorepark, Co. Cork.