Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy
https://orcid.org/0000-0001-7164-4071
This work aims to classify seven common household plastic types which include polyethylene terephthalate (PET), high density polyethylene (HDPE), polyvinyl chloride (PVC), low density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), and polycarbonate (PC) utilizing near-infrared (NIR) spectroscopy. Four methods including linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), spectral angle mapper (SAM), and support vector machine (SVM) were tested for their classification performances, and principal component analysis (PCA) was applied before LDA and SVM. All the classification models were built based on virgin plastics. The results showed that seven types of plastic could be classified excellently with all the methods when the test sets were composed of virgin samples. When the models were tested on waste plastics, most types could be well classified, and all the misclassifications occurred between HDPE and LDPE and PET and PC. Then for HDPE and LDPE and PET and PC that were prone to be misidentified, some specific spectral bands were reselected for further classification. To achieve the best result, an approach combining PCA, SVM, LDA, and PLS-DA was presented. The validation results showed significant improvement, with the F1 scores of LDPE and HDPE increasing from 65.2% to 86.7% and 24.2% to 84.7%, respectively, and 100% accuracy was achieved for the other five types.
Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy
https://orcid.org/0000-0001-7164-4071
This work aims to classify seven common household plastic types which include polyethylene terephthalate (PET), high density polyethylene (HDPE), polyvinyl chloride (PVC), low density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), and polycarbonate (PC) utilizing near-infrared (NIR) spectroscopy. Four methods including linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), spectral angle mapper (SAM), and support vector machine (SVM) were tested for their classification performances, and principal component analysis (PCA) was applied before LDA and SVM. All the classification models were built based on virgin plastics. The results showed that seven types of plastic could be classified excellently with all the methods when the test sets were composed of virgin samples. When the models were tested on waste plastics, most types could be well classified, and all the misclassifications occurred between HDPE and LDPE and PET and PC. Then for HDPE and LDPE and PET and PC that were prone to be misidentified, some specific spectral bands were reselected for further classification. To achieve the best result, an approach combining PCA, SVM, LDA, and PLS-DA was presented. The validation results showed significant improvement, with the F1 scores of LDPE and HDPE increasing from 65.2% to 86.7% and 24.2% to 84.7%, respectively, and 100% accuracy was achieved for the other five types.
Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy
https://orcid.org/0000-0001-7164-4071
This work aims to classify seven common household plastic types which include polyethylene terephthalate (PET), high density polyethylene (HDPE), polyvinyl chloride (PVC), low density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), and polycarbonate (PC) utilizing near-infrared (NIR) spectroscopy. Four methods including linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), spectral angle mapper (SAM), and support vector machine (SVM) were tested for their classification performances, and principal component analysis (PCA) was applied before LDA and SVM. All the classification models were built based on virgin plastics. The results showed that seven types of plastic could be classified excellently with all the methods when the test sets were composed of virgin samples. When the models were tested on waste plastics, most types could be well classified, and all the misclassifications occurred between HDPE and LDPE and PET and PC. Then for HDPE and LDPE and PET and PC that were prone to be misidentified, some specific spectral bands were reselected for further classification. To achieve the best result, an approach combining PCA, SVM, LDA, and PLS-DA was presented. The validation results showed significant improvement, with the F1 scores of LDPE and HDPE increasing from 65.2% to 86.7% and 24.2% to 84.7%, respectively, and 100% accuracy was achieved for the other five types.
Classification of Common Household Plastic Wastes Combining Multiple Methods Based on Near-Infrared Spectroscopy
Duan, Qinyuan (Autor:in) / Li, Jia (Autor:in)
ACS ES&T Engineering ; 1 ; 1065-1073
09.07.2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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