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Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

  • Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

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Metadaten
Author of HS ReutlingenHauler, Otto; Ostertag, Edwin; Al Ktash, Mohammad; Brecht, Marc
URN:urn:nbn:de:bsz:rt2-opus4-29120
DOI:https://doi.org/10.1255/jsi.2020.a18
ISSN:2040-4565
Erschienen in:Journal of Spectral Imaging
Publisher:IM Publications Open LLP
Place of publication:Chichester
Document Type:Article
Language:English
Year of Publication:2020
Tag:NIR spectroscopy; UV-Vis spectroscopy; cotton; hyperspectral imaging; principal component analysis; pushbroom imaging
Volume:9
Page Number:11
First Page:1
Last Page:11
Article Number:a18
DDC classes:570 Biowissenschaften, Biologie
Open Access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International