Nondestructive detection of lipid oxidation in frozen pork using hyperspectral imaging technology

Lipid oxidation is an important cause of pork quality degradation during freezing. Traditional chemical methods are time-consuming and destructive. In this paper, two hyperspectral imaging (HSI) techniques, including visible near-infrared (vis-NIR HSI) (400–1002 nm) and fluorescence (F-HSI) (400–1002 nm), were tested for non-destructive detection of lipid oxidation in pork. Two types of hyperspectral image data were collected from pork samples with 0–9 freeze-thaw cycles. The model performance based on the two spectra data was then tested by three multivariate analysis methods of partial least squares regression, support vector regression and Gaussian process regression (GPR). It was found that the GPR models using vis-NIR HSI and F-HSI acquired optimal prediction results of R2p = 0.9697, RMSEP = 0.0184 mg/kg and R2p = 0.9726, RMSEP = 0.0182 mg/kg, respectively. The results showed that the two techniques have shown reliable performance in predicting TBARS, and the performance of F-HSI was slightly superior to vis-NIR HSI. A pseudo-color map of TBARS was drawn using the F-HSI model to provide a visual screening method for lipid oxidation in pork. Moreover, another batch of pork with different freeze-thaw cycles (0–5 cycles) was successfully quantified and visualized for TBARS content using the F-HSI model. It demonstrated the feasibility of using F-HSI for quantitative monitoring of lipid oxidation in pork.