###### Abstract  
Foreign material (FM) found on a poultry product lowers the quality and safety of the product. We developed a fusion method combining two hyperspectral imaging (HSI) modalities in the visible-near infrared (VNIR) range of 400-1000 nm and the short-wave infrared (SWIR) range of 1000-2500 nm for the detection of FMs on the surface of fresh raw broiler breast fillets. Thirty different types of FMs that could be commonly found in poultry processing plants were used as samples and prepared in two different sizes (5 \(\times\) 5 mm\({}^{2}\) and 2 \(\times\) 2 mm\({}^{2}\)). The accuracies of the developed Fusion model for detecting 2 \(\times\) 2 mm\({}^{2}\) pieces of polymer, wood, and metal were 95%, 95%, and 81%, respectively, while the detection accuracies of the Fusion model for detecting 5 \(\times\) 5 mm\({}^{2}\) pieces of polymer, wood, and metal were all 100%. The performance of the Fusion model was higher than the VNIR- and SWIR-based detection models by 18% and 5%, respectively, when F1 scores were compared, and by 38% and 5%, when average detection rates were compared. The study results suggested that the fusion of two HSI modalities could detect FMs more effectively than a single HSI modality. foreign material; data fusion; hyperspectral imaging; visible-near infrared; short-wave infrared; chicken fillet; food safety and quality +
Footnote †: journal: Applied  
10 December 2021  
Footnote †: journal: Applied  
10 December 2021  
thermal imaging can be affected by the temperature of the surrounding environment [9]. Terahertz spectroscopy using non-ionizing radiation and low photon energies can detect various materials because the terahertz radiation penetrates the materials but cannot penetrate water whose concentration is high in poultry meat [10]. Hyperspectral imaging (HSI) is a non-destructive spectral imaging technique that combines spectroscopy with imaging. HSI has shown outstanding performance in the quality and safety evaluation of agricultural and food products [11]. HSI produces a vast amount of spatial and spectral information which is carried over across image pixels and wavelengths. HSI is known to be effective for differentiating different types of materials and analyzing spatial distributions of physical and bio-chemical components in materials qualitatively and qualitatively. In the meat industry, many studies used HSI for evaluating quality attributes in various meat types, including beef [12], pork [13], lamb [14], and poultry [15; 16; 17]. Recently, HSI techniques have been used to detect FMs in various agricultural products. [PERSON] et al. [18] used near-infrared (NIR) spectral imaging and discriminant analysis to detect leaves and stems from frozen blueberries. [PERSON] et al. [19] used short-wave infrared (SWIR) hyperspectral transmittance imaging to detect stem, bark, leaf, paper, and plastic in cotton. [PERSON] et al. [20] used fluorescence HSI to detect worms on fresh lettuce. [PERSON] et al. [21] detected microplastics in the intestine tract of fish. [PERSON] et al. [22] used deep learning techniques based on region-proposal networks and 3D convolutional neural networks for FM detection in red meat with HSI. [PERSON] et al. [23] detected fishbone in fillets by Raman HSI. [PERSON] et al. [24] used visible-near infrared (VNIR) hyperspectral images to detect bark, stone, plastic, clods, and bamboo stalks in dried pickles. [PERSON] et al. [25] used a multispectral approach to detect foreign objects in dried seaweed. Data fusion is a process of merging informative data from different sources to provide more accurate and complete explanations about target materials of interest [26]. This is because various data sources typically provide more detailed and potentially complementary information than using individual analytical sources [27]. Low-level (data-level) and mid-level (feature-level) data fusions combine data from different sources. These types of data fusion will provide more complete data and increase the number of variables.