{"title":"利用高光谱成像技术自动识别和分类可堆肥和可生物降解塑料","authors":"Nutcha Taneepanichskul, H. Hailes, M. Miodownik","doi":"10.3389/frsus.2023.1125954","DOIUrl":null,"url":null,"abstract":"In the UK waste management systems biodegradable and compostable packaging are not automatically detected and separated. As a result, their fate is generally landfill or incineration, neither of which is an environmentally good outcome. Thus, effective sorting technologies for compostable plastics are needed to help improve composting rates of these materials and reduce the contamination of recycling waste streams. Hyperspectral imaging (HSI) was applied in this study to develop classification models for automatically identifying and classifying compostable plastics with the analysis focused on the spectral region 950–1,730 nm. The experimental design includes a hyperspectral imaging camera, allowing different chemometric techniques to be applied including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) to develop a classification model for the compostable materials plastics. Materials used in this experimental analysis included compostable materials (sugarcane-derived and palm leaf derived), compostable plastics (PLA, PBAT) and conventional plastics (PP, PET, and LDPE). Our strategy was to develop a classification model to identify and categorize various fragments over the size range of 50 x 50 mm to 5 x 5 mm. Results indicated that both PCA and PLS-DA achieved classification scores of 100% when the size of material was larger than 10 mm x 10 mm. However, the misclassification rate increased to 20% for sugarcane-derived and 40% for palm leaf-based materials at sizes of 10 x 10 mm or below. In addition, for sizes of 5 x 5 mm, the misclassification rate for LDPE and PBAT increased to 20%, and for sugarcane and palm-leaf based materials to 60 and 80% respectively while the misclassification rate for PLA, PP, and PET was still 0%. The system is capable of accurately sorting compostable plastics (compostable spoons, forks, coffee lids) and differentiating them from identical looking conventional plastic items with high accuracy.","PeriodicalId":253319,"journal":{"name":"Frontiers in Sustainability","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Automatic identification and classification of compostable and biodegradable plastics using hyperspectral imaging\",\"authors\":\"Nutcha Taneepanichskul, H. Hailes, M. 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The experimental design includes a hyperspectral imaging camera, allowing different chemometric techniques to be applied including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) to develop a classification model for the compostable materials plastics. Materials used in this experimental analysis included compostable materials (sugarcane-derived and palm leaf derived), compostable plastics (PLA, PBAT) and conventional plastics (PP, PET, and LDPE). Our strategy was to develop a classification model to identify and categorize various fragments over the size range of 50 x 50 mm to 5 x 5 mm. Results indicated that both PCA and PLS-DA achieved classification scores of 100% when the size of material was larger than 10 mm x 10 mm. However, the misclassification rate increased to 20% for sugarcane-derived and 40% for palm leaf-based materials at sizes of 10 x 10 mm or below. In addition, for sizes of 5 x 5 mm, the misclassification rate for LDPE and PBAT increased to 20%, and for sugarcane and palm-leaf based materials to 60 and 80% respectively while the misclassification rate for PLA, PP, and PET was still 0%. 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引用次数: 1
摘要
在英国的废物管理系统中,可生物降解和可堆肥的包装不会被自动检测和分离。因此,它们的命运通常是填埋或焚烧,这两种方式对环境都不好。因此,需要有效的可堆肥塑料分类技术来帮助提高这些材料的堆肥率并减少回收废物流的污染。本研究利用高光谱成像技术(HSI)建立了可堆肥塑料自动识别和分类的分类模型,分析范围集中在950 ~ 1730 nm光谱区域。实验设计包括一个高光谱成像相机,允许应用不同的化学计量技术,包括主成分分析(PCA)和偏最小二乘判别分析(PLS-DA),以开发可堆肥材料塑料的分类模型。本实验分析中使用的材料包括可堆肥材料(甘蔗衍生和棕榈叶衍生),可堆肥塑料(PLA, PBAT)和常规塑料(PP, PET和LDPE)。我们的策略是开发一个分类模型,以识别和分类尺寸范围从50 x 50毫米到5 x 5毫米的各种碎片。结果表明,当材料尺寸大于10 mm × 10 mm时,PCA和PLS-DA的分类分数均达到100%。然而,在尺寸为10 × 10毫米或以下的甘蔗基材料中,错误分类率增加到20%,棕榈叶基材料增加到40%。此外,对于尺寸为5 × 5 mm的材料,LDPE和PBAT的误分类率增加到20%,甘蔗和棕榈叶基材料的误分类率分别增加到60%和80%,而PLA、PP和PET的误分类率仍然为0%。该系统能够准确地分类可堆肥塑料(可堆肥的勺子、叉子、咖啡盖),并以高精度将它们与外观相同的传统塑料物品区分开来。
Automatic identification and classification of compostable and biodegradable plastics using hyperspectral imaging
In the UK waste management systems biodegradable and compostable packaging are not automatically detected and separated. As a result, their fate is generally landfill or incineration, neither of which is an environmentally good outcome. Thus, effective sorting technologies for compostable plastics are needed to help improve composting rates of these materials and reduce the contamination of recycling waste streams. Hyperspectral imaging (HSI) was applied in this study to develop classification models for automatically identifying and classifying compostable plastics with the analysis focused on the spectral region 950–1,730 nm. The experimental design includes a hyperspectral imaging camera, allowing different chemometric techniques to be applied including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) to develop a classification model for the compostable materials plastics. Materials used in this experimental analysis included compostable materials (sugarcane-derived and palm leaf derived), compostable plastics (PLA, PBAT) and conventional plastics (PP, PET, and LDPE). Our strategy was to develop a classification model to identify and categorize various fragments over the size range of 50 x 50 mm to 5 x 5 mm. Results indicated that both PCA and PLS-DA achieved classification scores of 100% when the size of material was larger than 10 mm x 10 mm. However, the misclassification rate increased to 20% for sugarcane-derived and 40% for palm leaf-based materials at sizes of 10 x 10 mm or below. In addition, for sizes of 5 x 5 mm, the misclassification rate for LDPE and PBAT increased to 20%, and for sugarcane and palm-leaf based materials to 60 and 80% respectively while the misclassification rate for PLA, PP, and PET was still 0%. The system is capable of accurately sorting compostable plastics (compostable spoons, forks, coffee lids) and differentiating them from identical looking conventional plastic items with high accuracy.