A comparative investigation of two handheld near-ir spectrometers for direct forensic examination of fibres in-situ

Rashed, Hamad S.; Mishra, Puneet; Nordon, Alison; Palmer, David Scott; Baker, Matthew J.

doi:10.1016/j.vibspec.2020.103205

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…1 Used cotton, viscose and lyocell fibers are important raw materials for chemical recycling and are challenging to identify quickly and accurately. Most recent studies on non-destructive optical methods for textile identification have focused on classifying synthetic and natural fibers, such as polyester, [8][9][10][11] cotton, [8][9][10][11] viscose, [9][10][11] and wool. 8,9,12 These results are important for developing automated textile identification for efficient separation and sorting once the upcoming EU regulation on textile collection will be enforced.…”

Section: Introductionmentioning

confidence: 99%

“…10 The intrinsic viscosities of cotton and viscose fibers are in the range 150-2000 mg l −1 , 13 and improving classification accuracy could enable sorting fiber raw materials for controlling fiber dosing and subsequent dope viscosity for fiber spinning. Rashed et al 11 further compared two handheld NIR sensors for textile identification using random forests and obtained 94-96% classification accuracies for cotton and viscose based on a test set separated from 14 and 10 cotton and viscose samples, respectively. Saito et al 14 reported classification of natural and regenerated cellulose fibers.…”

Section: Introductionmentioning

confidence: 99%

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Identification of cellulose textile fibers

Mäkelä

Rissanen

Sixta

2021

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of cellulose textile fibers

Mäkelä

Rissanen

Sixta

2021

Analyst

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“…Over the last decade, the miniaturisation and on-site portability of benchtop spectrometers have shown great prospects in many fields, such as food safety [17], materials analysis [18] and pharmacology [19]. The use of portable NIRS spectra from both solid organic surfaces and liquids coupled with custom machine-learning algorithms to investigate the classification/regression of target components under different conditions has previously been demonstrated [20] [21].…”

Section: Introductionmentioning

confidence: 99%

Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics

et al. 2023

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There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infra-red (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-infrared (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-infrared spectroscopy and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco's modified eagle medium (DMEM), were collected in long-term and short-term experiments. The spectra were pre-processed, and analysed by partial least squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on-site and low-cost virus pre-screening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.

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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%

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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A comparative investigation of two handheld near-ir spectrometers for direct forensic examination of fibres in-situ

Cited by 18 publications

References 30 publications

Identification of cellulose textile fibers

Identification of cellulose textile fibers

Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics

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

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