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Multispectral imaging coupled with Artificial Intelligence, Machine Learningand Signal Processing techniques work as a feasible alternative for laboratorytesting, especially in food quality control. Most of the recent relatedresearch has been focused on reflectance multispectral imaging but a systemwith both reflectance, transmittance capabilities would be ideal for a widearray of specimen types including solid and liquid samples. In this paper, adevice which includes a dedicated reflectance mode and a dedicatedtransmittance mode is proposed. Dual mode operation where fast switchingbetween two modes is facilitated. An innovative merged mode is introduced inwhich both reflectance and transmittance information of a specimen are combinedto form a higher dimensional dataset with more features. Spatial and temporalvariations of measurements are analyzed to ensure the quality of measurements.The concept is validated using a standard color palette and specific casestudies are done for standard food samples such as turmeric powder and coconutoil proving the validity of proposed contributions. The classification accuracyof standard color palette testing was over 90% and the accuracy of coconut oiladulteration was over 95%. while the merged mode was able to provide the bestaccuracy of 99% for the turmeric adulteration. A linear functional mapping wasdone for coconut oil adulteration with an R2 value of 0.9558.

Dual mode multispectral imaging system for food and agricultural product quality estimation