ArticleScientific reports2015
Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Recent Advances in Spectroscopy and Imaging Techniques for Nondestructive Detection of Meat Quality and Safety.Food science & nutrition · 2025Review
- Near or mid-infra-red spectroscopy of the prefrontal cortex to identify previous stressful experience in an animal.Scientific reports · 2024Article
- Identifying antibiotic-resistant strains via cell sorting and elastic-light-scatter phenotyping.Applied microbiology and biotechnology · 2024Article
- Development of a Smartphone-Integrated Reflective Scatterometer for Bacterial Identification.Sensors (Basel, Switzerland) · 2022Article
- How to Resolve the Maximum Valuable Information in Complex NIR Signal: A Practicable Method Based on Wavelet Transform.Frontiers in chemistry · 2022Article
- Foodborne Disease Risk Prediction Using Multigraph Structural Long Short-term Memory Networks: Algorithm Design and Validation Study.JMIR medical informatics · 2021Article
- Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study.JMIR medical informatics · 2021Article
- Optical multi-channel interrogation instrument for bacterial colony characterization.PloS one · 2021Article
- Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens.Scientific reports · 2018Article
- Early detection of germinated wheat grains using terahertz image and chemometrics.Scientific reports · 2016Article
- Fast quantifying collision strength index of ethylene-vinyl acetate copolymer coverings on the fields based on near infrared hyperspectral imaging techniques.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An optical sensor system, namely NIR laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens. This developed system was used for images acquisition. The current study is focused on exploring the potential of this system combined with multivariate calibrations in classifying three categories of popular bacteria. Initially, normalization and Zernike moments extraction were performed, and the resultant translation, scale and rotation invariances were applied as the characteristic variables for subsequent discriminant analysis. Both linear (LDA, KNN and PLSDA) and nonlinear (BPANN, SVM and OSELM) pattern recognition methods were employed comparatively for modeling, and optimized by cross validation. Experimental results showed that the performances of nonlinear tools were superior to those of linear tools, especially for OSELM model with 95% discrimination rate in the prediction set. The overall results showed that it is extremely feasible for rapid and noninvasive classifying foodborne pathogens using this developed system combined with appropriate multivariate calibration.
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