Abstract
Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments.
Highlights
This work investigated the use of NIR spectroscopy and machine learning models to identify different powdered food materials which naturally contain allergens, such as gluten
This work focussed on some crucial measurement parameters which ought to be considered when using NIR in real-world environments
These were the height between the sensor and the sample, the light intensity of the sensor, and the pre-processing techniques applied to the recorded spectra
Summary
Role of Powdered Foods in the Food Industry. Powdered foods represent a significant share of the modern human diet, mostly as ingredients in food products or cooked meals. Powdered food materials can be baked (e.g., cakes), used as seasoning (e.g., spices), or added to liquids to produce drinks (e.g., powdered milk). The properties of food powders drastically affect the final product quality and the consumer appreciation. The primary properties of powders, such as shape and density, have significant effects on product performance, such as rehydration rates of powdered drinks [4]. It is important to monitor and measure the primary properties of powdered foods to ensure the final food products have the required performance
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