Principal component analysis of hyperspectral data for early detection of mould in cheeselets

The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples’ surface. It is also shown that the PCA loadings obtained from a set of training samples can be applied to hyperspectral data from new test samples to detect the presence of mould on these. For both the agar and cheeselets, the first three principal components contained more than 99 % of the total variance. The spatial projection of the second principal component highlights the presence of mould on cheeselets. The proposed analysis methods can be adopted in industry to detect mould on cheeselets at an early stage and with further testing this application may also be extended to other food products. [Display omitted] •Hyperspectral imaging can be used for non-destructive analysis of contamination in food products.•Principal component analysis (PCA) was applied to milk agar and cheeselet samples to localise and visualise mould growing on the samples.•The results from PCA applied to hyperspectral data shows that the technique can be used to identify and localise growth of mould on food samples.