Meat Species Authentication using Portable Hyperspectral Imaging

Yuewen Yu ^1,2 Wei Chen ^3,4,5* Dongjie Zhao ^1,2 Hanwen Zhang ^1,2 Wenliang Chen ^1,2 Rong Liu ^1,2 Chenxi Li ^2,6*

• ¹ School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
• ² State Key Laboratory of Precision Measurement Technology and Instruments, Tianjin University, Tianjin, China
• ³ Department of Ophthalmology, Tianjin Eye Hospital, Nankai University Affiliated Eye Hospital, Nankai University, Tianjin, China
• ⁴ Clinical College of Ophthalmology, Tianjin Medical University, Tianjin, China
• ⁵ Tianjin Key Laboratory of Ophthalmology and Visual Science, Tianjin Eye Hospital, Tianjin, China
• ⁶ National Key Laboratory of Precision Testing Techniques and Instrument, Tianjin University, Tianjin, China

Meat species fraud seriously harms the interests of consumers and causes food safety problems. Hyperspectral imaging is capable of integrating spectral and imaging technology to simultaneously obtain spectral and spatial information, and has been widely applied to detect adulteration and authenticity of meat. This study aims to develop a portable hyperspectral imager (HSI) and a discrimination model for meat adulteration detection. The portable push broom HSI was designed with the spectral resolution of 5 nm and spatial resolution of 0.1 mm, and controlled with the Raspberry Pi to meet the requirement of on situ rapid detection. To improve generalization, the model transfer method was also developed to achieve model sharing across instruments, providing a reliable solution for rapid assessment of meat species. The results demonstrate that the model transfer method can effectively correct the spectral differences due to instrument variation and improve the robustness of the model. The support vector machine (SVM) classifier combined with spectral space transformation (SST) achieved a best accuracy of 94.91%. Additionally, a visualization map was proposed to provide the distribution of meat adulteration, offering valuable insights for fraud detection. Furthermore, this study demonstrates the potential of implementing the HSI in the food industry.