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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 of elytra image in gray scale and the corresponding frequency map.
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pone.0157940.g003: An example of elytra image in gray scale and the corresponding frequency map.

Mentions: With the spatial texture measures, it is difficult to discriminate whether elytra unit patterns are periodic or non-periodic. A better approach is to measure spectra properties in terms of pattern periodicity by transforming spatial domain to frequency domain; this takes into account the directionality of periodic patterns and the concentrations of low or high frequency/energy in the spectrum. Using two dimensional discrete Fast Fourier Transform [23], we calculate frequency responses. To describe the spectra features in two-dimensional frequency domain, we sum up absolute frequency responses (i.e. Euclidean norm of real and imaginary part coefficients) for different radii from the origin of the transformed frequency map. As shown in Fig 3, for each radius r, the corresponding spectra responses in feature map are computed by Eq 1.Spectra_Feature(radius=r)=∑θ=02π‖FM(r,θ)‖,(1)where FM stands for two dimensional frequenecy map and (r, θ) is the coordinate in polar with radius r and angle θ.


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 of elytra image in gray scale and the corresponding frequency map.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0157940.g003: An example of elytra image in gray scale and the corresponding frequency map.
Mentions: With the spatial texture measures, it is difficult to discriminate whether elytra unit patterns are periodic or non-periodic. A better approach is to measure spectra properties in terms of pattern periodicity by transforming spatial domain to frequency domain; this takes into account the directionality of periodic patterns and the concentrations of low or high frequency/energy in the spectrum. Using two dimensional discrete Fast Fourier Transform [23], we calculate frequency responses. To describe the spectra features in two-dimensional frequency domain, we sum up absolute frequency responses (i.e. Euclidean norm of real and imaginary part coefficients) for different radii from the origin of the transformed frequency map. As shown in Fig 3, for each radius r, the corresponding spectra responses in feature map are computed by Eq 1.Spectra_Feature(radius=r)=∑θ=02π‖FM(r,θ)‖,(1)where FM stands for two dimensional frequenecy map and (r, θ) is the coordinate in polar with radius r and angle θ.

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