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Submitted to: Textile Research Journal
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 1/15/2013 Publication Date: 5/21/2013 Citation: Liu, Y., Foulk, J.A. 2013. Potential of visible and near infrared spectroscopy in the determination of instrumental leaf grade in lint cottons. Textile Research Journal. 83(9):928-936. Interpretive Summary: Non-lint materials (botanic trashes) co-exist in commercial cotton fibers at various amounts. In order to meet the challenge of assessing the trash content, the USDA Agricultural Marketing Service (AMS) has implemented one index termed leaf grade, which is determined by qualified USDA’s AMS cotton classers via a visual inspection procedure. Lately, the AMS has revised the protocol for cotton leaf grade classification, by replacing the classer’s leaf determination with instrumental leaf measurement from cotton classification HVITM system. This study examined the feasibility of visible and near infrared (NIR) technique in the discrimination of cotton samples with various leaf grade categories, with a acceptable separation of ~ 95.0%. The outcome provides cotton fiber / textile engineers, researchers and regulators a new sight in applying visible and NIR spectroscopy for rapid and routine determination of cotton leaf grade. Technical Abstract: Existence of non-lint materials (or botanic trash) within commercial cotton bales degrades their market value, requires further cleaning process, and compromises the finished product quality. To meet the challenge of assessing the trash content, a number of approaches have been in practice. In the United States, one term to assess the degree of trash amount is leaf grade, which was originally determined by qualified USDA’s AMS cotton classers via a visual inspection procedure. Recently, the AMS has revised the protocol for cotton leaf grade classification, by replacing the classer’s leaf determination with instrumental leaf measurement from cotton classification HVITM system. In this study, visible/NIR spectra were acquired to explore the potential for the discrimination of cotton samples with various leaf grade categories. Seven-class classification models in different spectral regions were developed to optimize the identification efficiency. Results indicated that using the model in the 1105-1700 nm NIR region could reach a acceptable separation of ~ 95.0%, with a 89.9% correct identification in validation set and a 100% success in calibration set. Furthermore, factors of influencing the correct classification were discussed briefly. |
