Researchers at Hefei University of Technology have demonstrated the use of laser-induced breakdown spectroscopy (LIBS) to automatically identify, classify, and subclassify recyclable waste in real time. The resource re-use application enabled the researchers to identify and sort samples, based on material composition, into six consumer-level categories: paper, plastic, glass, metal, textile, and wood. The detection method holds promise as a solution for the environmental protection and waste management fields, where improved solutions to identifying and automatically classifying recyclable waste are essential to environmental sustainability. To maximize the re-use of material, waste products must be disposed of based on their material properties. Existing methods to identify and categorize waste based on physical characteristics, or by using image-based techniques, can be inaccurate and unreliable. Current spectroscopy methods for identifying and classifying waste must process the detected samples in advance, which rules out automatic, real-time detection. The researchers combined LIBS technology with drop-dimension algorithms and machine-learning algorithms. In addition to detecting the elemental composition of a sample based on emission spectra, LIBS is not affected by the ambient environment and light, or the shape and color of the sample. Further, the technique does not require preprocessing of the sample. The researchers collected the spectra of 80 recyclable waste samples, using LIBS to make the initial set of identifications and classifications. They next used LIBS to subclassify metals and plastics for reuse at the level of a recycling factory. Metals were subclassified into the subcategories of iron, stainless steel, copper, and aluminum. Plastics were subclassified into six subcategories. Due to the high dimension and large amount of redundant information in the LIBS spectral data, the researchers applied principal component analysis and linear discriminant analysis to drop the dimensions of the collected full-spectra data. They input the drop-dimensional spectra into random forest and back propagation neural network (BPNN) machine-learning models for training models and predicting results. Among those that the researchers explored, the optimal model for classifying recyclable waste and the most effective model for subclassifying metals and plastics were determined. They achieved 100% accuracy in classifying recyclable waste. They achieved 98.77% accuracy for metal subclassification and 99.52% accuracy for plastic subclassification. The identification and classification system for recyclable waste. Courtesy of Lei Yang. The researchers plan to increase the number of waste samples in their study and incorporate other forms of waste, such as kitchen waste, into their investigations. They also hope to use LIBS to expand scientific understanding of transparent glass detection, which could open new avenues for recycling and waste management. The research was published in AIP Advances (www.doi.org/10.1063/5.0149329).
