Abstract

Multispectral imaging of autofluorescence was carried out to investigate the feasibility of mapping the degree of lipid oxidation in ground chicken meat. Meat samples from both breast and thighs were collected from 32 chickens, ground and freeze-stored for different time intervals. Sixteen samples were imaged at the time, making up two sets of multispectral images, A and B. Lipid oxidation was measured by a method using 2-thiobarbituric acid reactive substances (TBARS), and samples were in the range 0.15-3.23. Principal component analysis was performed on image set A, and variation in score images of the two first components corresponded well with lipid oxidation reference values. The multivariate image regression model based on image set A was tested on set B. Pixel-wise prediction gave large individual errors, but averaging predicted values within samples improved accuracy and resulted in a correlation of 0.98. Increasing the amount of spatial variation (number of pixel vectors) in the regression models led to more robust models with lower prediction errors. The technique has potential for nondestructive investigation of distribution and kinetics of lipid oxidation in food.

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