From a circular economy perspective, feeding livestock with food leftovers or former foodstuff
products (FFPs) could be an effective option aimed at exploiting food leftover resources and reducing
food losses. FFPs are valuable energy sources, characterised by a beneficial starch/sugar content, and
also fats. However, besides these nutritional aspects, safety is a key concern given that FFPs are
generally derived from packaged food. Packaging materials, such as plastics and paper, are not
accepted as a feed ingredient which means that residues should be rigorously avoided. A sensitive
and objective detection method is thus essential for an accurate risk evaluation throughout the
former food production chain. To this end, former food samples were collected in processing plants
of two different European countries and subjected to multivariate analysis of red, green, and blue
(RGB) microscopic images, in order to evaluate the possible application of this non-destructive
technique for the rapid detection of residual particles from packaging materials. Multivariate Image
Analysis (MIA) was performed on single images at the pixel level, which essentially consisted in an
exploratory analysis of the image data by means of Principal Component Analysis, which highlighted
the differences between packaging and foodstuff particles, based on their colour. The whole dataset
of images was then analysed by means of a multivariate data dimensionality reduction method
known as the colourgrams approach, which identified clusters of images sharing similar features and
also highlighted outlier images due to the presence of packaging particles. The results obtained in
this feasibility study demonstrated that MIA is a promising tool for a rapid automated method for
detecting particles of packaging materials in FFPs.