Spectral reflectance characterization and fiber type discrimination for common natural textile materials using a portable spectroradiometer

Zhao, Hongyu; Wang, Yunli; Liu, Shuai; Li, Kunheng; Gao, Wei

doi:10.1016/j.jas.2019.105026

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…Imaging, on the other hand, is a completely non-invasive technique that can aid archivists and conservation scientists in carrying out monitoring and analytical tasks. High-resolution imaging can be easily accessed in those spectral ranges with low radiating energy (longer wavelengths) in which textiles often exhibit particular features regarding their material composition: visible light (from 400 nm to 700 nm) can help in discerning different dyes [8], while it has been found that different fibers are prone to present characteristic features when exposed to infrared radiation [9].…”

Section: Introductionmentioning

confidence: 99%

Full VNIR-SWIR Hyperspectral Imaging Workflow for the Monitoring of Archaeological Textiles

Grillini,

Thomas,

George

2023

archiving

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A practical workflow to capture and process hyperspectral images in combined VNIR-SWIR ranges is presented and discussed. The pipeline demonstration is intended to increase the visibility of the possibilities that advanced hyperspectral imaging techniques can bring to the study of archaeological textiles. Emphasis is placed on the fusion of data from two hyperspectral devices. Every aspect of the pipeline is analyzed, from the practical and optimal implementation of the imaging setup to the choices and decisions that can be made during the data processing steps. The workflow is demonstrated on an archaeological textile belonging to the Paracas Culture (Peru, 200 BC -100 AD ca.) and displays an example in which an inappropriate selection of the processing steps can lead to a misinterpretation of the hyperspectral data.

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Section: Introductionmentioning

confidence: 99%

Full VNIR-SWIR Hyperspectral Imaging Workflow for the Monitoring of Archaeological Textiles

Grillini,

Thomas,

George

2023

archiving

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“…On the other hand, non-invasive methods such as external reflection FTIR and handheld portable NIR spectrometry-based on miniaturized technology that may impact on the instrumental performance (e.g. noisier detectors and lower spectral resolution) [17]-proved suitable for the identification of fibres without sampling [18][19][20][21] as well as fibre optics reflectance spectroscopy (FORS) in the NIR region [22,23]. The latter relies on the use of optic fibres to carry the reflected light (UV, Vis, and NIR), and generally employ better performance components (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The NIR spectroscopy combined with classification techniques is an established approach in industrial domain, whereas in the heritage science field a lower number of publications is available [4,8,20,38]. For this reason, our scope was to compare the performance of two classification techniques, principal component analysis-linear discrimination analysis (PCA-LDA) and soft independent modelling of class analogy (SIMCA), to identify cotton, wool, and silk and their blends in historical textiles in a transportable, fast, and non-invasive way using FORS, in the 1000-1700 nm range, a technique easily accessible to the heritage professionals.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive identification of textile fibres using near-infrared fibre optics reflectance spectroscopy and multivariate classification techniques

et al. 2022

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The identification of textile fibres from cultural property provides information about the object's technology. Today, microscopic examination remains the preferred method, and molecular spectroscopies (e.g. Fourier transform infrared (FTIR) and Raman spectroscopies) can complement it but may present some limitations. To avoid sampling, non-invasive fibre optics reflectance spectroscopy (FORS) in the near-infrared (NIR) range showed promising results for identifying textile fibres; but examining and interpreting numerous spectra with features that are not well defined is highly time-consuming. Multivariate classification techniques may overcome this problem and have already shown promising results for classifying textile fibres for the textile industry but have been seldom used in the heritage science field. In this work, we compare the performance of two classification techniques, principal component analysis–linear discrimination analysis (PCA-LDA) and soft independent modelling of class analogy (SIMCA), to identify cotton, wool, and silk fibres, and their mixtures in historical textiles using FORS in the NIR range (1000–1700 nm). We built our models analysing reference samples of single fibres and their mixtures, and after the model calculation and evaluation, we studied four historical textiles: three Persian carpets from the nineteenth and twentieth centuries and an Italian seventeenth-century tapestry. We cross-checked the results with Raman spectroscopy. The results highlight the advantages and disadvantages of both techniques for the non-invasive identification of the three fibre types in historical textiles and the influence their vicinity can have in the classification.

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“…ese methods are tedious, time consuming, and destructive and cannot be easily automated [12]. Newer methods use spectroscopy [13,14], machine vision [15,16], FTIR reflectance [17,18], texture feature extraction [19], and computer-based algorithms for fiber identification. Especially in large-scale fabric separation and sorting, it is necessary to have a rapid, dependable, and automated method that classifies fiber content accurately [20].…”

Section: Introductionmentioning

confidence: 99%

Classification of Material Type from Optical Coherence Tomography Images Using Deep Learning

Sabuncu

Özdemi̇r

2021

International Journal of Optics

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Classification of material type is crucial in the recycling industry since good quality recycling depends on the successful sorting of various materials. In textiles, the most commonly used fiber material types are wool, cotton, and polyester. When recycling fabrics, it is critical to identify and sort various fiber types quickly and correctly. The standard method of determining fabric fiber material type is the burn test followed by a microscopic examination. This traditional method is destructive, tedious, and slow since it involves cutting, burning, and examining the yarn of the fabric. We demonstrate that the identification procedure can be done nondestructively using optical coherence tomography (OCT) and deep learning. The OCT image scans of fabrics that are composed of different fiber material types such as wool, cotton, and polyester are used to train a deep neural network. We present the results of the created deep learning models’ capability to classify fabric fiber material types. We conclude that fiber material types can be identified nondestructively with high precision and recall by OCT imaging and deep learning. Because classification of material type can be performed by OCT and deep learning, this novel technique can be employed in recycling plants in sorting wool, cotton, and polyester fabrics automatically.

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Spectral reflectance characterization and fiber type discrimination for common natural textile materials using a portable spectroradiometer

Cited by 12 publications

References 27 publications

Full VNIR-SWIR Hyperspectral Imaging Workflow for the Monitoring of Archaeological Textiles

Full VNIR-SWIR Hyperspectral Imaging Workflow for the Monitoring of Archaeological Textiles

Non-invasive identification of textile fibres using near-infrared fibre optics reflectance spectroscopy and multivariate classification techniques

Classification of Material Type from Optical Coherence Tomography Images Using Deep Learning

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