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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

An example heatmap of final computed local feature vectors.
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pone.0157940.g010: An example heatmap of final computed local feature vectors.

Mentions: After division, an array of filters and processing (i.e., filter bank) is applied to each cell. The operations within each partitioned area (20x20-pixel grid for each partition) include (1) horizontal and vertical histogram projections with interpolation (7 data points for each x,y axis), (2) mean and variance calculation, and within the individual local windows, color distribution in RGB domain (number of bins is 5 in each domain, total 15 bins). The resulted values are all concatenated in a vector. Finally, the computed vectors from individual local areas (i.e., from each interest point) are averaged for a final local feature set. The individual local feature vectors are illustrated in Fig 10; the y-axis and x-axis present the individual local windows centered at feature points and the corresponding local feature vectors, respectively. As shown in Fig 10, a discernible pattern in the local feature vectors is observed along the y-axis.


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)

An example heatmap of final computed local feature vectors.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0157940.g010: An example heatmap of final computed local feature vectors.
Mentions: After division, an array of filters and processing (i.e., filter bank) is applied to each cell. The operations within each partitioned area (20x20-pixel grid for each partition) include (1) horizontal and vertical histogram projections with interpolation (7 data points for each x,y axis), (2) mean and variance calculation, and within the individual local windows, color distribution in RGB domain (number of bins is 5 in each domain, total 15 bins). The resulted values are all concatenated in a vector. Finally, the computed vectors from individual local areas (i.e., from each interest point) are averaged for a final local feature set. The individual local feature vectors are illustrated in Fig 10; the y-axis and x-axis present the individual local windows centered at feature points and the corresponding local feature vectors, respectively. As shown in Fig 10, a discernible pattern in the local feature vectors is observed along the y-axis.

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