Dual-Mode Multispectral Imaging System for Food and Agricultural Product Quality Estimation

Multispectral imaging (MSI) coupled with artificial intelligence (AI), machine-learning (ML), and signal-processing techniques work as a feasible alternative for laboratory testing, especially in food quality control. Most of the recent related research has been focused on reflectance MSI but a system with both reflectance and transmittance capabilities would be ideal for a wide array of specimen types including solid and liquid samples. In this article, a device that includes a dedicated reflectance mode and a dedicated transmittance mode is proposed. Dual-mode operation where fast switching between two modes is facilitated. An innovative merged mode is introduced in which both reflectance and transmittance information of a specimen are combined to form a higher-dimensional dataset with more features. Spatial and temporal variations of measurements are analyzed to ensure the quality of measurements. The concept is validated using a standard color palette, and specific case studies are done for standard food samples such as turmeric powder and coconut oil proving the validity of proposed contributions. The classification accuracy of standard color palette testing was over 90% and the accuracy of coconut oil adulteration was over 95%. while the merged mode was able to provide the best accuracy of 99% for the turmeric adulteration. A linear functional mapping was done for coconut oil adulteration with an R2 value of 0.9558.