Hyperspectral imaging using RGB color for foodborne pathogen detection

Seung-Chul Yoon,Gary R Gamble,Kurt C Lawrence,Bosoon Park,Tae-Sung Shin,Gerald W Heitschmidt

doi:10.1117/1.jei.24.4.043008

Abstract

This paper reports the development of a spectral reconstruction technique for predicting hyperspectral images from RGB color images and classifying food-borne pathogens in agar plates using reconstructed hyperspectral images. The six representative non-O157 Shiga-toxin producing Escherichia coli (STEC) serogroups (O26, O45, O103, O111, O121, and O145) grown on Rainbow agar plates were used for the study. A line-scan pushbroom hyperspectral imaging spectrometer was used to scan full reflectance spectra of pure non-O157 STEC cultures in the visible and near-infrared spectral range from 400 to 1000 nm. RGB color images were generated by simulation from hyperspectral images. Polynomial multivariate least-squares regression analysis was used to reconstruct hyperspectral images from RGB color images. The mean R-squared value for hyperspectral image reconstruction was ∼0.98 in the spectral range between 400 and 700 nm for linear, quadratic, and cubic polynomial regression models. The accuracy of the hyperspectral image classification algorithm based on k-nearest neighbors algorithm of principal component scores was validated to be 92% with the test set (99% with the original hyperspectral images). The results of the study suggested that color-based hyperspectral imaging would be feasible without much loss of prediction accuracy compared to true hyperspectral imaging.

Highlights

Detection and identification of foodborne pathogens are increasingly important for development of intervention and verification strategies for the food industry and regulatory agencies
Hyperspectral image classification algorithms in the visible and near-infrared (VNIR) spectral range from 400 to 1000 nm have been previously developed for automated screening of pathogen colonies on agar plates, which included Campylobacter, Salmonella, and Shiga toxinproducing Escherichia coli (STEC).[1,2,3,4,5]
This study showed a potential of the use of RGB color to reconstruct hyperspectral data and its application to classification of non-O157 STEC colonies on agar media using a hyperspectral image classification algorithm

Summary

Introduction

Detection and identification of foodborne pathogens are increasingly important for development of intervention and verification strategies for the food industry and regulatory agencies. The culture methods are labor intensive and prone to human subjective errors. Another challenge with direct plating is that competitive microflora often grow together with target microorganisms on agar media and can appear morphologically similar. Hyperspectral image classification algorithms in the visible and near-infrared (VNIR) spectral range from 400 to 1000 nm have been previously developed for automated screening of pathogen colonies on agar plates, which included Campylobacter, Salmonella, and Shiga toxinproducing Escherichia coli (STEC).[1,2,3,4,5] The key idea of using hyperspectral imaging was to find spectral and spatial features unique to the bacterial colonies on agar and utilize the spectral and/or spatial features for detection and

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Conclusion