Research Outreach Interdisciplinary Activity to classify olive oil blends integrating multicolor imaging, image processing, and machine learning

Abstract

This outreach undergraduate research project presents a low-cost method to distinguish the quality of different olive oils. The proposed method is based on an indirect measurement of the chlorophyll molecules present when a green laser diode illuminates the oil sample. Oil blends can be classified into five classes (no olive oil, light olive oil, medium olive oil, olive oil, and extra virgin olive oil) by quantifying the ratio of the red channel versus the green channel along the laser illumination path from a color image. After labeling each oil blend, a convolutional neural network has been implemented and trained to automatically classify oil blends from a color image. The trained convolutional neural network has an accuracy of 90% in identifying and categorizing oil blends. This undergraduate research project introduces students to an interdisciplinary application requiring the combination of optical spectroscopy (i.e., multicolor imaging), image processing, and machine learning. In addition, due to the simplicity of the optical apparatus and computational analysis, high school students could implement and validate their own costeffective oil-quality classification device.