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.

Automated summary

The use of hyperspectral imaging to detect mold on cheese and milk agar was explored in this study, showing that PCA loadings can be applied to new test samples to detect the presence of mold. The research highlights the potential of adopting these analysis methods in industry to detect mold on food products at an early stage.