Detection of foreign materials in cotton using a multi-wavelength imaging method

Jia, Dongfang; Ding, Tianhuai

doi:10.1088/0957-0233/16/6/018

Cited by 16 publications

(5 citation statements)

References 9 publications

(9 reference statements)

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“…The reasons for choosing narrow spectral regions were (1) to compare the model performances from the characteristic bands in different spectral ranges, and (2) to facilitate the development of portable optical and spectral imaging sensors in either the visible or NIR region. 19,20 For each model, the classification efficiency was assessed by the sample numbers corrected classified, and the statistics in calibration and validation sets from seven spectral regions are compared in Table 2.…”

Section: Classification Modelsmentioning

confidence: 99%

Potential of visible and near infrared spectroscopy in the determination of instrumental leaf grade in lint cottons

Liu

Foulk

2013

Textile Research Journal

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Existence of non-lint materials (or botanic trash) within commercial cotton bales degrades their market value, requires a further cleaning process, and compromises finished product quality. To meet the challenge of assessing the trash content, a number of approaches have been in practice. In the US, one term to assess the degree of trash amount is leaf grade, which was originally determined by qualified US Department of Agriculture'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 HVI TM 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 an 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.

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Section: Classification Modelsmentioning

confidence: 99%

Potential of visible and near infrared spectroscopy in the determination of instrumental leaf grade in lint cottons

Liu

Foulk

2013

Textile Research Journal

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“…therefore, researchers have been developing various spectral-based techniques, infrared (Ir), near infrared (nIr), fluorescence and imaging, for use in the detection and identification of trash in lint cotton. [12][13][14][15][16][17][18][19][20][21] among them, nIr spectroscopy is an alternative technique due to the speed, low-cost, ease of use and potential on-line/off-line implementations. It has been successfully applied for the quantification of reducing sugars on the cotton surface and for the prediction of cotton colour and physical attributes.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Recovered Cotton Fibre and Trash Contents in Lint Cotton Waste by Ultraviolet/Visible/Near Infrared Reflectance Spectroscopy

Liu

Gamble

Thibodeaux

2010

Journal of Near Infrared Spectroscopy

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In the cotton industry, each processing stage produces a considerable amount of cotton waste, or trash. these wastes are mixtures of fractions from cotton plants (leaf, stem, seed coat and hull), cotton fibres that mingled in and other components (such as motes and dust). cotton wastes not only present a challenge to environmental management, but also reduce manufacturers' profits, as they contain some degree of cotton fibre with quality similar to that of the cleaned cotton. 1-3 therefore, a great concern in the cotton industry is to strengthen cleaning efficiency before and after the cotton ginning by developing various cleaning machines and procedures. 3-6

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“…Jia and Ding developed a multi-wavelength imaging system for detecting foreign fibers. An image fusion algorithm based on wavelet analysis was created to acquire complete information on foreign fibers [4]. Jia and Ding also used foreign fibers which are difficult to identify by humans in their further experiments.…”

Section: Introductionmentioning

confidence: 99%

Simulating the air flow in the conveying pipe of the foreign fiber detection device

Chen¹,

Wang

Wu³

2009

Front. Chem. Eng. China

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Cotton bales are frequently intermingled with foreign fibers which will interfere in the process of spinning, weaving and dyeing and will worsen the product quality. Nowadays, cotton fibers are sorted manually in most of the cotton textile mills with very low efficiency. There is a great demand for foreign fiber detection devices in Chinese cotton textile mills. The air flow in the conveying pipe of the device has an important effect on the image acquisition, image analysis and removal of foreign fibers. As a primary effort, the air flow in the conveying pipe of the foreign fiber detection device was simulated numerically. The effects of the inlet air velocity on the air turbulence intensity and air velocity along the detecting section were studied.

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Detection of foreign materials in cotton using a multi-wavelength imaging method

Cited by 16 publications

References 9 publications

Potential of visible and near infrared spectroscopy in the determination of instrumental leaf grade in lint cottons

Potential of visible and near infrared spectroscopy in the determination of instrumental leaf grade in lint cottons

Assessment of Recovered Cotton Fibre and Trash Contents in Lint Cotton Waste by Ultraviolet/Visible/Near Infrared Reflectance Spectroscopy

Simulating the air flow in the conveying pipe of the foreign fiber detection device

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