  Multispectral imaging coupled with Artificial Intelligence, Machine Learning
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 multispectral imaging but a system
with both reflectance, transmittance capabilities would be ideal for a wide
array of specimen types including solid and liquid samples. In this paper, a
device which 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.

Signal Processing