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

Park SI, Bisgin H, Ding H, Semey HG, Langley DA, Tong W, Xu J - PLoS ONE (2016)

Bottom Line: 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.Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification.Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Texas A&M University, College Station, Texas, United States of America.

ABSTRACT
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. The elytra, or hardened forewings, occupy a large portion of the body, and contain distinctive patterns. In addition, elytra fragments are more commonly recovered from processed food products than other body parts due to their hardness. As a preliminary effort, we chose 15 storage product beetle species frequently detected in food inspection. The elytra were then separated from the specimens and imaged under a microscope. Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification. With leave-one-out cross validation, we achieved overall accuracy of 80% through the proposed global and local features, which indicates that our proposed features could differentiate these species. Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification. Future work will include robust testing with more beetle species and algorithm refinement for a higher accuracy.

No MeSH data available.


Related in: MedlinePlus

Examples of elytra colors.
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pone.0157940.g004: Examples of elytra colors.

Mentions: Along with the texture analysis in spatial and spectrum domain, color distribution of elytra is invariant or relatively insensitive to surface orientation and illumination. (Note that the elytra images are collected in a lab environment.) Color is one of the key elements used to distinguish between species. Fig 4 shows examples of different elytra colors. As shown in Fig 4, elytra of Tribolium madens (Species 15) have a different color from those of species Lasioderma serricorne (Species 2) or Tribolium castaneum (Species 12).


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

Park SI, Bisgin H, Ding H, Semey HG, Langley DA, Tong W, Xu J - PLoS ONE (2016)

Examples of elytra colors.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920424&req=5

pone.0157940.g004: Examples of elytra colors.
Mentions: Along with the texture analysis in spatial and spectrum domain, color distribution of elytra is invariant or relatively insensitive to surface orientation and illumination. (Note that the elytra images are collected in a lab environment.) Color is one of the key elements used to distinguish between species. Fig 4 shows examples of different elytra colors. As shown in Fig 4, elytra of Tribolium madens (Species 15) have a different color from those of species Lasioderma serricorne (Species 2) or Tribolium castaneum (Species 12).

Bottom Line: 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.Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification.Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Texas A&M University, College Station, Texas, United States of America.

ABSTRACT
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. The elytra, or hardened forewings, occupy a large portion of the body, and contain distinctive patterns. In addition, elytra fragments are more commonly recovered from processed food products than other body parts due to their hardness. As a preliminary effort, we chose 15 storage product beetle species frequently detected in food inspection. The elytra were then separated from the specimens and imaged under a microscope. Both global and local characteristics were quantified and used as feature inputs to artificial neural networks for species classification. With leave-one-out cross validation, we achieved overall accuracy of 80% through the proposed global and local features, which indicates that our proposed features could differentiate these species. Through examining the overall and per species accuracies, we further demonstrated that the local features are better suited than the global features for species identification. Future work will include robust testing with more beetle species and algorithm refinement for a higher accuracy.

No MeSH data available.


Related in: MedlinePlus