A simple, rapid and accurate method for the sample preparation and quantification of meso- and microplastics in food and food waste streams

Plastics are produced and used in large quantities worldwide (e.g. as food packaging). In line with this, plastic particles are found throughout the ecosphere and in various foods. As a result, plastics are also present in energy-rich waste biomass derived from the food industry, supermarkets, restaurants, etc. These waste streams are a valuable source for biogas production but can also be used to feed insects that in turn upcycle it into new high-value biomass. In both applications, the remaining residue can be used as fertilizer. Due to the present plastic particles, these applications could pose a continued threat to the environment, and both human and animal health. Therefore, the need of determining the (micro)plastic content to assess the potential danger is rising. In this research, a closed-vessel microwave-assisted acid digestion method was developed to accurately determine meso- and microplastic contents in food (waste) matrices by solubilising this food matrix. Polyvinyl chloride (PVC) food packaging foil was used to develop the method, using a full factorial design with three parameters (nitric acid concentration (c(HNO3)), temperature (T), and time (t)). According to this model, the best practical conditions were c(HNO3) = 0.50 mol/L, T = 170 °C, and t = 5.00 min. Subsequently, the method was tested on five other plastics, namely high- and low-density polyethylene (HDPE and LDPE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET), mixed with a food matrix, resulting in a mean plastic recovery of 102.2 ± 4.1%. Additionally, the polymers were not oxidised during the microwave digestion. For PVC and PS hardly any degradation was found, while HDPE, LDPE, and PP showed slight chain degradation, although without recovery loss. In conclusion, the method is an accurate approach to quantify the total meso- and microplastic content in food (waste) matrices with minimal change in their intrinsic characteristics.

Jaar van publicatie:2022

Toegankelijkheid:Open