Development of an inter-confirmatory plastic characterization system using spectroscopic techniques for waste management

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Advanced chemometric methodologies on single shot hyphenated LIBS data for rapid and reliable characterization of plastic classes

BACKGROUND

Plastic Solid Waste (PSW) sorting is a procedure of paramount importance in the mechanical recycling process of plastics waste. The major limitation of the techniques relying on physical properties of plastics is the time taken for analysis and poor accuracy. Spectroscopy has been shown to be a suitable method in plastic sorting due to its atomic and molecular characterization capabilities, and ability to give results in very short time scales. However, for practical applications it is essential to translate this technique into an automatic technology, by combining it with advanced chemometric tools which can give observer independent judgement.

RESULTS

The indigenously developed bi-model Laser Induced Breakdown Spectroscopy (LIBS)-Raman system with single source and single detector can record the LIBS-Raman spectral signals in single-shot mode in a total time frame of 20 ms. Out of the combinations of Principal Component Analysis (PCA) and Partial Least Squares (PLS) with Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Partial Least Squares-Discriminant Analysis (PLS-DA) based classifiers, the PLS-DA based model showed the maximum classification accuracy with 95 % based on LIBS data and 100 % based on Raman data. The reliability of the model was assessed using 4-fold cross-validation which showed a sensitivity of 90.28 % and specificity of 98.29 % for predictions based on LIBS data, and 99 % sensitivity and 99.82 % specificity for predictions relying on Raman data.

SIGNIFICANCE

The results show how the combination of multimodal spectroscopy with chemometric analysis enhances the applicability of spectroscopic techniques for plastic sorting. The classification model successfully classified seven types of post-consumer plastic samples based on combined LIBS and Raman data. With the home-built software for automated prediction, the system takes less than a second to predict the plastic type illustrating the potential of the method for translation to regular routine industrial applications.

Integrated LIBS-Raman spectroscopy: A comprehensive approach to monitor microplastics and heavy metal contamination in water resources

The increased use of plastic products and global industrial conditions have contaminated natural resources, especially water, with pollutants such as microplastics and trace elements, including heavy metals. Hence, continuous monitoring of water samples is an urgent requirement. However, the existing microplastic-heavy metal monitoring methodologies require discrete and sophisticated sampling approaches. The article proposes a multi-modal LIBS-Raman spectroscopy system for detecting microplastics and heavy metals from water resources with unified sampling and pre-processing approaches. The accomplishment of the detection process is using a single instrument by exploiting the trace element affinity of microplastics, which operates in an integrated methodology to monitor water samples for microplastic-heavy metal contamination. The polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET) plastic types dominate the identified microplastics from different sampling spots: in an estuary formed by the Swarna River near Kalmadi (Malpe) in Udupi district, and from River Netravathi in Mangalore, Dakshina Kannada District, Karnataka, India. The detected trace elements from microplastic surfaces include heavy metals such as Al, Zn, Cu, Ni, Mn, and Cr and other elements counting Na, Mg, Ca, and Li. The system could record concentrations of trace elements down to 10 ppm, and comparing results with the conventional technique of Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) confirms the ability of the system to detect trace elements from microplastic surfaces. In addition, comparing results with direct LIBS analysis of water from the sampling site shows better results in microplastic-based trace element detection.