  Coconut oil known for its wide range of uses is often adulterated with other
edible oils. Repeated use of coconut oil in food preparation could lead to many
health issues. Existing methods available for evaluating quality of oil are
laborious and time consuming. Therefore, we propose an imaging system hardware
and image processing-based algorithm to estimate the adulteration of coconut
oil with palm oil as the adulterant. A clear functional relationship between
adulteration level and Bhattacharyya distance was observed as R2 = 0.9876 on
the training samples. Thereafter, another algorithm is proposed to develop a
spectral-clustering based classifier to determine the effect of reheat and
reuse of coconut oil. Distinct clusters were obtained for different levels of
reheated oil classes and the classification was performed with an accuracy of
0.983 on training samples. Further, the input images for the proposed
algorithms were generated using an in-house developed transmittance based
multispectral imaging system.
