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.
Highlights
An optical sensor system, namely near infrared (NIR) laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens
Specific procedures were outlined as follows: (1) a NIR laser scatter imaging system was developed to collect scattering images; (2) image normalization was performed by regular moment, and the characteristic variables of Zernike moment invariances were extracted from the normalized images; (3) multivariate analysis tools such as linear discriminant analysis (LDA), K-nearest neighbor (KNN), partial least squares discriminant analysis (PLSDA), back-propagation artificial neural networks (BPANN), support vector machine (SVM), and online sequential extreme learning machine (OSELM) were applied comparatively for modeling; (4) the independent samples were used for model test
A bacterial colony was a characteristic dome-shaped structure formed by an exponential multiplication of a single cell on a nutrient agar surface
Summary
Namely NIR laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens. Conventional methods for the classification of microbial pathogenic agents mainly rely on specific microbiological and biochemical identification, among which, the culture and colony counting methods involve counting of bacteria, immunology based methods involve antigen-antibody interactions and the third polymerase chain reaction (PCR) method involves DNA analysis These methods can be sensitive, inexpensive and give both qualitative and quantitative information of the tested microorganisms, they are greatly restricted by assay time. An www.nature.com/scientificreports emerging technology of laser optical sensor, which was based on the concept that variations in refractive indices and size, relative to the arrangement of cells in bacterial colonies growing on a semi-solid agar surface will generate different light scatter patterns, has been studied combined with chemometrics, and achieved good performances[12,13,14]. Specific procedures were outlined as follows: (1) a NIR laser scatter imaging system was developed to collect scattering images; (2) image normalization was performed by regular moment, and the characteristic variables of Zernike moment invariances were extracted from the normalized images; (3) multivariate analysis tools such as linear discriminant analysis (LDA), K-nearest neighbor (KNN), partial least squares discriminant analysis (PLSDA), back-propagation artificial neural networks (BPANN), support vector machine (SVM), and online sequential extreme learning machine (OSELM) were applied comparatively for modeling; (4) the independent samples were used for model test
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