1. Multivariate image analysis for the rapid detection of residues from packaging remnants in former foodstuff products (FFPs) – a feasibility study
- Author
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Alessandro Ulrici, Luciano Pinotti, Matteo Ottoboni, Marco Tretola, Rosalba Calvini, and A. Luciano
- Subjects
Paper ,Multivariate statistics ,Natural resource economics ,Health, Toxicology and Mutagenesis ,Food Contamination ,Toxicology ,01 natural sciences ,Rapid detection ,colourgrams ,feed ,food safety ,Former foodstuffs ,multivariate image analysis (MIA) ,packaging remnants ,business.industry ,Circular economy ,010401 analytical chemistry ,Food Packaging ,0402 animal and dairy science ,Public Health, Environmental and Occupational Health ,04 agricultural and veterinary sciences ,General Chemistry ,General Medicine ,Food safety ,040201 dairy & animal science ,0104 chemical sciences ,Multivariate Analysis ,Feasibility Studies ,Livestock ,business ,Nutritive Value ,Plastics ,Food Analysis ,Food Science - Abstract
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.
- Published
- 2020