Packaging contaminants in former food products: Using Fourier Transform Infrared Spectroscopy to identify the remnants and the associated risks.

Food waste and feed-food competition can be reduced by replacing traditional feed ingredients such as cereals, with former food products (FFPs) in livestock diets. These foodstuffs, initially intended for human consumption, are recovered, mechanically unpacked, and then ground. Despite this simple and inexpensive treatment, packaging contaminants (remnants) are often unavoidable in the final product. To maximize the exploitation of FFPs and to minimize the associated risks, packaging remnants need to be quantified and characterized. This study tested the efficacy of the Fourier Transform Infrared Spectroscopy coupled with an optical microscope (µFT-IR) in identifying packaging remnants in 17 FFP samples collected in different geographical areas. After a visual sorting procedure, presumed packaging remnants were analyzed by µFT-IR. The results showed significant differences (p < 0.05) between the FFPs in terms of the total number of foreign particles found (plastics, cellulose and aluminum remnants, ranging from 4 to 19 particles per 20 g fresh matter), and also regarding the number of cellulose and aluminum particles. These data clearly demonstrate the need for sensitive instruments that can characterize the potential contaminants in the FFPs. This would then help to reduce the overestimation of undesirable contaminants typical of simple visual sorting, which is currently the most common method.

Multivariate image analysis for the rapid detection of residues from packaging remnants in former foodstuff products (FFPs) - a feasibility study.

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.