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The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

1
Food, Water, Waste Research Group, Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
2
Mixed Reality Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
3
Agents Laboratory, School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 230; https://doi.org/10.3390/s20010230
Received: 30 October 2019 / Revised: 18 December 2019 / Accepted: 27 December 2019 / Published: 31 December 2019
(This article belongs to the Section Optical Sensors)
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. View Full-Text
Keywords: NIR spectroscopy; machine learning; allergen detection; powdered food; industry 4.0; digital manufacturing NIR spectroscopy; machine learning; allergen detection; powdered food; industry 4.0; digital manufacturing
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MDPI and ACS Style

Rady, A.; Fischer, J.; Reeves, S.; Logan, B.; James Watson, N. The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods. Sensors 2020, 20, 230. https://doi.org/10.3390/s20010230

AMA Style

Rady A, Fischer J, Reeves S, Logan B, James Watson N. The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods. Sensors. 2020; 20(1):230. https://doi.org/10.3390/s20010230

Chicago/Turabian Style

Rady, Ahmed; Fischer, Joel; Reeves, Stuart; Logan, Brian; James Watson, Nicholas. 2020. "The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" Sensors 20, no. 1: 230. https://doi.org/10.3390/s20010230

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