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

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.

Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation

“…Over the recent years, MEMS have been inaugurated to the spectroscopy field through the release of numerous miniaturized spectral radiometers, covering various domains of the electromagnetic spectrum. These sensors have been utilized in the food industry to determine different allergens in powdered foods [35], for estimating the moisture content of natural fibers [36], for estimating milk ingredients [37], for the determination of rosmarinic acid content of dried and powdered Rosmarini folium [38], and for the detection of microplastics in soil [39]. To that end, the technological evolution of miniaturized NIR sensors that are already being used in different sectors could be evaluated for soil analysis purposes.…”

Advanced Optical Technologies in Food Quality and Waste Management

“…Savitzky-Golay filtering is a popular pre-processing technique that has been used extensively for food spectral analysis. Examples since 2020 include the use of Savitzky-Golay filtering in pre-processing NIR spectra to improve classification performance in the identification of allergens in powdered food materials [54], filter noise from FTIR spectra of instant freeze-dried coffee and MIR spectra of fruit puree samples [53], and NIR reflectance spectra of Indonesia rice flour-based food to enable accurate classification and level estimation of added sweeteners [55].…”

Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

“…The methods included linear discriminant analysis (LDA), Knearest neighbor (Knn), PLS-DA, and artificial neural networks (ANN). In the case of LDA, the Euclidean distance was used to assign each sample to a certain class, and principle component analysis (PCA) was conducted on the fused spectral data to avoid the colinearity problem (Rady et al 2020). The components responsible for > 99% of the total variation between tubers were considered in the subsequent classification tasks (Duda et al 2012).…”

“…The components responsible for > 99% of the total variation between tubers were considered in the subsequent classification tasks (Duda et al 2012). In the case of Knn, the Euclidean distance with k values of 4 was selected (Rady et al 2020). For the PLS-DA, 20 latent variables were used to build classification models (Rady and Guyer 2015b).…”

“…Near-infrared spectroscopy (NIR) is a rapid, relatively lowcost technique that has been used for quantitative and qualitative quality evaluation of agricultural products (Lohumi et al 2015;Qu et al 2015;Rady et al 2020;Sahni et al 2004). NIR diffuse reflectance spectroscopy can be used for detecting various chemical compounds depending on different light absorption mechanisms inside a material (Griffiths and Dahm 2007).…”