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

Applied Sciences

Matthew Eady¹  Bosoon Park² 

[1] Family Health International (FHI) 360, Product Quality and Compliance, Durham, NC 27713, USA;U.S. Department of Agriculture, Agricultural Research Service, U.S. National Poultry Research Center, Athens, GA 30605, USA;

关键词: Salmonella;    rapid detection;    hyperspectral microscopy;    SIMCA;   

DOI  :  10.3390/app11030895

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown