NIRONE sensors show promising results on detection on food allergen identification

A recent study by Rady et al. at Nottingham University conducted a study in which Spectral Engines NIRONE S sensor was used with machine learning models to identify different powdered food samples from the food and drink sector, including flours, spices and other materials which naturally contain allergens such as gluten. 


Food allergens present a significant health risk to the human population. Therefore, their presence during food production must be monitored and controlled. This is especially important for powdered food potentially containing nearly all known food allergens. The fourth industrial revolution (Industry 4.0) with digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes are implemented in growth rate. 


The NIRONE S2.0 sensor utilized in the study is small, low cost and has a low power requirement, making it suitable for use within production environments. The work showed that measurements from the NIR sensor under the optimal combination of sensor height and light intensity and using the features extracted from the recorded spectra with classification machine learning methods was a suitable approach to identify materials containing allergens, with classification prediction accuracy as high as 100%. 


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Read also our blog Industry 4.0 and how smart sensors make the difference.


For further information on the Spectral Engines’ MEMS-based NIR spectral sensors please read more at: 



Application notes