Coffee, cereals and nuts – analysis of spectrum differences
Problem: Grains of coffee, cereals and nuts can be very similar in shape and color.
Goal: to find out whether grains of different varieties can be separated by spectrum.
Approach: Using our hyperspectral system, we captured images of various types of grains: raw and roasted coffee, soy, peanuts, barley, oats, corn, proso millet, spelt, buckwheat, quinoa, amaranth, kamut, rye, black and brown rice.
Findings: Using the combination of principal component analysis and unsupervised clustering algorithm, imaged grains were appropriately classified into 16 different classes. In combination with grain shape and size detection, reliable automatic classification and foreign matter detection are possible.