Hyperspectral imaging of foodborne pathogens at colony and cellular levels for rapid identification in dairy products.

Journal: Food science & nutrition
Published Date:

Abstract

This study evaluated the efficacy of hyperspectral imaging (HSI) for the rapid identification of pathogens in dairy products at the colony and cellular levels. The colony and cellular levels studies were designed as completely randomized with six replications. Three strains of , four strains of O157: H7, Big Six Shiga toxin-producing , three strains of , and ten serovars of were used in this study. Pure cultures were streaked for isolation on respective selective media, and hyperspectral data (400-1100 nm wavelength) at the colony and cellular levels were collected and stored as reference libraries. Whole milk and whole milk powder were artificially inoculated (<10 CFU/g or mL) with individual pathogenic strains/serovars. All milk and milk powder samples were enriched using brain heart infusion (BHI) broth at 37°C for 24 h, streaked for isolation on the respective selective media, and hyperspectral data for individual pathogenic strains/serovars at the colony and cellular levels were acquired and treated as test samples data. The acquired colony or cellular images were imported into ENVI software and three regions of interest were selected for each image to obtain hyperspectral data for reference libraries and test samples. Using the NN classifier and cross-validation technique, overall classification accuracies of 90.38% and 34% were obtained for the colony- and cellular-level identification, respectively. The individual classification accuracies of pathogens in dairy products at the colony level varied between 77.5% to 100%, whereas the accuracy varied between 2.78% and 49.17% for the cellular level.

Authors

  • Amninder Singh Sekhon
    School of Food Science Washington State University Pullman Washington USA.
  • Phoebe Unger
    School of Food Science Washington State University Pullman Washington USA.
  • Sonali Sharma
    School of Food Science Washington State University Pullman Washington USA.
  • Bhupinderjeet Singh
    Biological Systems Engineering Department Washington State University Pullman Washington USA.
  • Xiongzhi Chen
    Department of Mathematics and Statistics Washington State University Pullman Washington USA.
  • Girish M Ganjyal
    School of Food Science Washington State University Pullman Washington USA.
  • Minto Michael
    School of Food Science Washington State University Pullman Washington USA.

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