Quantification of ternary microplastic mixtures through an ultra-compact near-infrared spectrometer coupled with chemometric tools.

The ubiquitous distribution of plastics and microplastics (MPs) and their resistance to biological and chemical decay is adversely affecting the environment. MPs are considered as emerging contaminants of concern in all the compartments, including terrestrial, aquatic, and atmospheric environments. Efficient monitoring, detection, and removal technologies require reliable methods for a qualitative and quantitative analysis of MPs, considering point-of-need testing a new evolution and a great trend at the market level. In the last years, portable spectrometers have gained popularity thanks to the excellent capability for fast and on-site measurements. Ultra-compact spectrometers coupled with chemometric tools have shown great potential in the polymer analysis, showing promising applications in the environmental field. Nevertheless, systematic studies are still required, in particular for the identification and quantification of fragments at the microscale. This study demonstrates the proof-of-concept of a Miniaturized Near-Infrared (MicroNIR) spectrometer coupled with chemometrics for the quantitative analysis of ternary mixtures of MPs. Polymers were chosen representing the three most common polymers found in the environment (polypropylene, polyethene, and polystyrene). Daily used plastic items were mechanically fragmented at laboratory scale mimicking the environmental breakdown process and creating "true-to-life" MPs for the assessment of analytical methods for MPs identification and quantification. The chemical nature of samples before and after fragmentation was checked by Raman spectroscopy. Sixty three different mixtures were prepared: 42 for the training set and 21 for the test set. Blends were investigated by the MicroNIR spectrometer, and the dataset was analysed using Principal Component Analysis (PCA) and Partial Least Square (PLS) Regression. PCA score plot showed a samples distribution consistent with their composition. Quantitative analysis by PLS showed the great capability prediction of the polymer's percentage in the mixtures, with R2 greater than 0.9 for the three analytes and a low and comparable Root-Mean Square Error. In addition, the developed model was challenged with environmental weathered materials to validate the system with real plastic pollution. The findings show the feasibility of employing a portable tool in conjunction with chemometrics to quantify the most abundant forms of MPs found in the environment.

Miniaturized Near-Infrared (MicroNIR) Spectrometer in Plastic Waste Sorting.

Valorisation of the urban plastic waste in high-quality recyclates is an imperative challenge in the new paradigm of the circular economy. In this scenario, a key role in the improvement of the recycling process is exerted by the optimization of waste sorting. In spite of the enormous developments achieved in the field of automated sorting systems, the quest for the reduction of cross-contamination of incompatible polymers as well as a rapid and punctual sorting of the unmatched polymers has not been sufficiently developed. In this paper, we demonstrate that a miniaturized handheld near-infrared (NIR) spectrometer can be used to successfully fingerprint and classify different plastic polymers. The investigated urban plastic waste comprised polyethylene (PE), polypropylene (PP), poly(vinyl chloride) (PVC), poly(ethylene terephthalate) (PET), and poly(styrene) (PS), collected directly in a recycling plastic waste plant, without any kind of sample washing or treatment. The application of unsupervised and supervised chemometric tools such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) on the NIR dataset resulted in a complete classification of the polymer classes. In addition, several kinds of PET (clear, blue, coloured, opaque, and boxes) were correctly classified as PET class, and PE samples with different branching degrees were properly separated.