Evidence map›Paper›PMID 29695741›Full record

ArticleScientific reports2018

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

Halil Bisgin, Tanmay Bera, Hongjian Ding, Howard G Semey, Leihong Wu, Zhichao Liu, Amy E Barnes, Darryl A Langley, Monica Pava-Ripoll, Himansu J Vyas and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

  1. Artificial intelligence in food safety.NPJ science of food · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Halil BisginDepartment of Computer Science, Engineering and Physics, University of Michigan-Flint, Flint, MI, USA.
Tanmay BeraDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
Hongjian DingFood Chemistry Laboratory-1, Arkansas Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, Jefferson, AR, USA.
Howard G SemeyFood Chemistry Laboratory-1, Arkansas Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, Jefferson, AR, USA.
Leihong WuDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
Zhichao LiuDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
Amy E BarnesFood Chemistry Laboratory-1, Arkansas Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, Jefferson, AR, USA.
Darryl A LangleyFood Chemistry Laboratory-1, Arkansas Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, Jefferson, AR, USA.
Monica Pava-RipollOffice for Food Safety, Center for Food Safety and Applied Nutrition, US Food and Drug Administration, College Park, MD, USA.
Himansu J VyasFood Chemistry Laboratory-1, Arkansas Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, Jefferson, AR, USA.
Weida TongDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA.
Joshua XuDivision of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, USA. Zhihua.Xu@fda.hhs.gov.ORCID 0000-0001-5313-5847

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Indexed as

AlgorithmsAnimalsArtificial IntelligenceColeopteraFood ContaminationFood SafetyMachine LearningNeural Networks, ComputerSupport Vector Machine

Identifiers

PMID29695741
PMCPMC5917025

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.