Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method

Abstract

Microbial contamination during fish flesh spoilage process can easily induce food-borne outbreaks and consumer health problems. Hyperspectral imaging in the spectral range of 400–1000 nm was developed to measure the Escherichia coli (E. coli) loads in grass carp fish for evaluation and visualization of microbial spoilage. Partial least square regression (PLSR) model was conducted to build prediction models between the spectral data and the reference E. coli loads estimated by classical microbiological plating method. The PLSR model based on full wavelengths showed good performance on predicting E. coli loads with the residual predictive deviation (RPD) of 5.47 and determination coefficient of R2P = 0.880. Six characteristic wavelengths were selected by the weighted regression coefficients from PLSR analysis and used to simplify the models. The simplified PLSR and multiple linear regression (MLR) models also presented good prediction capability. The better simplified MLR model (RPD = 5.22 and R2P = 0.870) was used to transfer each pixel in the image for visualizing the spatial distribution of E. coli loads. The results demonstrated that hyperspectral imaging technique with multivariate analysis has the potential to rapidly and non-invasively quantify and visualize the E. coli loads in grass carp fish flesh during the spoilage process.