Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles.

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety.

Correction: Species Identification of Food Contaminating Beetles by Recognizing Patterns in Microscopic Images of Elytra Fragments.

[This corrects the article DOI: 10.1371/journal.pone.

Species Identification of Food Contaminating Beetles by Recognizing Patterns in Microscopic Images of Elytra Fragments.

A crucial step of food contamination inspection is identifying the species of beetle fragments found in the sample, since the presence of some storage beetles is a good indicator of insanitation or potential food safety hazards. The current pratice, visual examination by human analysts, is time consuming and requires several years of experience. Here we developed a species identification algorithm which utilizes images of microscopic elytra fragments.