End-of-Life Textile Recognition in a Circular Economy Perspective: A Methodological Approach Based on Near Infrared Spectroscopy

被引：17

作者：

Bonifazi, Giuseppe ^{[1
,2
]}

Gasbarrone, Riccardo ^{[1
]}

Palmieri, Roberta ^{[1
]}

Serranti, Silvia ^{[1
,2
]}

机构：

[1] Sapienza Univ Rome, Dept Chem Engn Mat & Environm, Via Eudossiana 18, I-00184 Rome, Italy

[2] Sapienza Univ Rome, Res Ctr Biophoton, Corso Repubbl 79, I-04100 Latina, Italy

来源：

SUSTAINABILITY | 2022年 / 14卷 / 16期

关键词：

end-of-life textiles; fabric; waste characterization; hyperspectral imaging; recycling; circular economy; near infrared spectroscopy;

D O I：

10.3390/su141610249

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The life cycle of textiles (i.e., fabrics and apparel products) generates many environmental impacts, such as resource consumption, water, soil, and air pollution through the dispersion of chemical substances and greenhouse gases. For these reasons, in 2019, textiles were identified as a "priority product category for the circular economy" by the European Commission that proposed a new circular economy action plan focusing on recycling. An in-depth characterization of textile fabrics could lead to an ad hoc recycling procedure, reducing resource consumption and chemicals utilization. In this work, NIR (1000-1650 nm) spectroscopy was applied to extract information regarding fabric composition, with reference to cotton, silk, viscose, and some of their blends, using two different devices: a hyperspectral imaging (HSI) platform and a portable spectroradiometer. The different fabrics were correctly classified based on their spectral features by both detection instruments. The proposed methodological approach can be applied for quality control in the textile recycling sector at industrial and/or laboratory scale thanks to the easiness of use and the speed of detection.

引用

页数：15

共 41 条

[1]

Agency E.P, TEXTILES MAT SPECIFI

[2]

EUROPEAN COMMISSION

[3]

[Anonymous], 2016, FieldSpec 4 User Manual, ASD Document 600979

[4]

ASD Inc, 2008, RS3 User Manual, ASD Document 600545

[5] Non-invasive identification of textile fibres using near-infrared fibre optics reflectance spectroscopy and multivariate classification techniques [J].

Balbas, Diego Quintero ;

Lanterna, Giancarlo ;

Cirrincione, Claudia ;

Fontana, Raffaella ;

Striova, Jana .

EUROPEAN PHYSICAL JOURNAL PLUS, 2022, 137 (01)

[6] Multivariate comparison of classification performance measures [J].

Ballabio, Davide ;

Grisoni, Francesca ;

Todeschini, Roberto .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2018, 174 :33-44

[7] Classification tools in chemistry. Part 1: linear models. PLS-DA [J].

Ballabio, Davide ;

Consonni, Viviana .

ANALYTICAL METHODS, 2013, 5 (16) :3790-3798

[8]

Beasley J., 2014, Advancing Resource Efficiency in Europe: Indicators and waste policy scenarios to deliver a resource efficient and sustainable Europe

[9]

Blanch-Perez-del-Notario C., 2019, J SPECTR IMAGING, V8, pa17, DOI [10.1255/jsi.2019.a17, DOI 10.1255/JSI.2019.A17]

[10] Hazelnuts classification by hyperspectral imaging coupled with variable selection methods [J].

Bonifazi, G. ;

Capobianco, G. ;

Gasbarrone, R. ;

Serranti, S. .

SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY XIII, 2021, 11754

← 1 2 3 4 5 →