An Unsupervised Prediction Model for Salmonella Detection with Hyperspectral Microscopy: A Multi-Year Validation

Matthew Eady,Bosoon Park

doi:10.3390/app11030895

Abstract

Hyperspectral microscope images (HMIs) have been previously explored as a tool for the early and rapid detection of common foodborne pathogenic bacteria. A robust unsupervised classification approach to differentiate bacterial species with the potential for single cell sensitivity is needed for real-world application, in order to confirm the identity of pathogenic bacteria isolated from a food product. Here, a one-class soft independent modelling of class analogy (SIMCA) was used to determine if individual cells are Salmonella positive or negative. The model was constructed and validated with a spectral library built over five years, containing 13 Salmonella serotypes and 14 non-Salmonella foodborne pathogens. An image processing method designed to take less than one minute paired with the one-class Salmonella prediction algorithm resulted in an overall classification accuracy of 95.4%, with a Salmonella sensitivity of 0.97, and specificity of 0.92. SIMCA’s prediction accuracy was only achieved after a robust model incorporating multiple serotypes was established. These results demonstrate the potential for HMI as a sensitive and unsupervised presumptive screening method, moving towards the early (<8 h) and rapid (<1 h) identification of Salmonella from food matrices.

Highlights

Salmonella is a leading cause of gastroenteritis, with severe cases occasionally resulting in death
The number of outliers detected by the Mahalanobis distance (MD) method was less than 1% for the calibration dataset and less than 2% for the validation dataset, which was due to the image processing method setting thresholding limits that removed large clumps of cells
In order to build an unsupervised Hyperspectral microscope images (HMIs) classification model for bacterial species with the sensitivity potential of single cell detection, it was essential to include HMI collected from a range of timeframes and repetitions for adequate model boundary definition

Summary

Introduction

Salmonella is a leading cause of gastroenteritis, with severe cases occasionally resulting in death. Nontyphoidal Salmonella represents one of the four primary pathogenic bacteria responsible [1]. Traditional detection methods such as the use of a nutrient enriched growth medium or polymerase chain reaction (PCR) have been used as the standard for the detection of Salmonella for years. While these methods are effective, the incubation time required for nutrient enriched growth media, or the reoccurring costs along with the advanced training requirement of PCR are disadvantages that influence the time required to correctly identify the causative agent and source of a foodborne disease outbreak. Anderson et al [8]

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