Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

Scientific Reports, Apr 2015

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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Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

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. Introduction Food safety has emerged as an important global issue with international trade and public health implications, and foodborne outbreaks from microbial contamination, chemicals and toxins, take a major crisis on health. The World Health Organization (WHO) defines foodborne illnesses as diseases, usually either infectious or toxic in nature, caused by agents that enter the body through the ingestion of food1. Though the global incidence of foodborne disease is difficult to ascertain, it has been reported that in 2005 alone 1.8 million people died from diarrhoeal diseases and a great proportion of these cases can be attributed to biogenic contaminated food and drinking water2. Thus foodborne pathogens are the primary reason causing foodborne diseases, leading to an intense inspiration into the area of food pathogens detection which is the solution to the prevention and recognition of problems related to health and safety. 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. However, although these methods can be sensitive, inexpensive and give both qualitative and quantitative information of the tested microorganisms, they are greatly restricted by assay time. While time and speed are of vital importance, they are not sufficient and should be accompanied by rapid prescreening methods, which would provide preliminary results immediately3,4. Besides, initial enrichment is needed in this process in order to classify pathogens which typically occur in low numbers in food. Over the past few decades, there has been increasing attention on noninvasive light scattering technologies for studying on detecting bacterial cells5,6. Light scattering instruments such as surface plasmon resonance (SPR), flow cytometry and so on, have enabled the research of various biological molecules and ultimately have been a commercial success7,8. Besides, spectroscopic technologies for rapid identification of bacterial cells in suspension has become very popular9,10. However, there are challenges associated with bacteria in suspension, such as purity and homogeneity of cultures and arrangement of cells (usually in chains or clusters and few in individual) as the orientations of cells and distances between cells change with time. Whereas, the colony on a solid agar surface is more stable and its optical response could be modeled with scalar diffraction theory11. In recent years, an 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 performances12,13,14. However, visible laser was adopted in these researches mentioned above, which is susceptible to the external environment, so strict test requirements were demanded for the satisfied results12,13,14,15. The method like this has limitations in practical usage. NIR laser exhibits the properties of remarkably less biological damaging and deeper tissue penetration16; besides, it would not be absorbed by biological samples and induces no autofluorescence, and thus the signal-to-background ratio can be greatly improved17. The main objective of this study was to develop a NIR laser scatter imaging system for classifying common foodborne pathogens. This system, as an efficient, noninvasive, reagent-less and user-friendly bio-sensing instrument, can be used for rapidl (...truncated)


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Wenxiu Pan, Jiewen Zhao, Quansheng Chen. Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration, Scientific Reports, 2015, Issue: 5, DOI: 10.1038/srep09524