Analysis of Sticky Cotton by Near-Infrared Spectroscopy

Barton, Franklin E.; Bargeron, J. D.; Gamble, Gary R.; McAlister, D. L.; Héquet, Eric

doi:10.1366/000370205774783214

Cited by 13 publications

(15 citation statements)

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“…Instead of using some Priori method [10][11][12][13], we used a convenient and reliable signal processing method with the Unscrambler software [14][15][16] to exclude the disturbances and select the correlated pairs. Initially 1-3 correlated pairs were selected manually from the data in Fig.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…A multi-wavelength fiber laser in the near-infrared with narrow spectral lines, which can be designed to match absorption lines of target gases well, is used to enhance the sensitivity and the selectivity, and the Unscrambler software [14][15][16] is applied to improve the efficiency of the signal processing. Furthermore, because of the wide wavelength tunable range of multi-wavelength fiber laser, the present technique can measure multi-gas simultaneously even if the absorption peaks of the gases are relatively far away from each other.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Gas Concentration by Correlation Spectroscopy Using a Multi-Wavelength Fiber Laser

Wang¹,

Somesfalean²,

Mei³

et al. 2011

PIER

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Abstract-A correlation spectroscopy (COSPEC) based on a multiwavelength fiber laser is first proposed for the detection of gas concentration. The lasing wavelengths are selected to match several characteristic absorption peaks of the gas under test, and the gas concentration is easily measured by correlating it with the reference gas. The present method is immune from the instability of the light source and the influence of other gases. The concentration measurement of C 2 H 2 is demonstrated in the experiment in its nearinfrared dominant absorption region. The technique has prospects for simultaneous detection of multiple gases, and the measurement of mixed gases of C 2 H 2 and CO 2 is also analyzed.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Gas Concentration by Correlation Spectroscopy Using a Multi-Wavelength Fiber Laser

Wang¹,

Somesfalean²,

Mei³

et al. 2011

PIER

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show abstract

“…the presence of sugars on cellulose cotton fibres). 12 However, due to the strong absorption of wavelengths above 1100 nm by water, assessment of intact biological products is generally undertaken using the wavelength range below 1100 nm (shortwave NIR or SWNIR). Instruments operating in this wavelength range are generally smaller and less expensive, features suited to an application that involves field assessments.…”

Section: Introductionmentioning

confidence: 99%

Quality Estimation of Agave Tequilana Leaf for Bioethanol Production

Rijal

Walsh

Subedi

et al. 2016

Journal of Near Infrared Spectroscopy

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Agave tequilana is a potential biofuel crop, for which the characters of juice total soluble sugar content (TSS), dry matter content (DM), cellulose , hemicellulose and lignin content are quality criteria. Spectra of leaves were obtained using a hand-held silicon photodiode array (Si PDA)-based spectrometer with a wavelength range of 300-1100 nm and an InGaAs-based Fourier transform near infrared (FT-NIR) spectrometer with a wavelength range of 1100-2500 nm. Fresh leaves were harvested at different maturity stages, in different seasons and from two locations in Queensland during 2012-2014. Partial least square regression models were developed for DM and TSS of fresh leaf, and for cellulose, hemicellulose and lignin of dried material, with models tested on populations of independent samples collected in different years, seasons and locations. Prediction statistics for DM of fresh leaf using the Si PDA spectrometer (729-975 nm) were r 2 = 0.49-0.87 and root mean square error of prediction (RMSEP) = 2.36-1.44%, while with the use of the FT-NIR spectrometer, the prediction statistics were r 2 = 0.53-0.66 and RMSEP = 2.63-2.18% (across different years, seasons and locations). Prediction statistics for TSS in fresh leaf using the Si PDA spectrometer (729-975 nm) were r 2 = 0.53-0.69 and RMSEP = 1.70-1.91%, with poorer results obtained using the FT-NIR spectrometer (r 2 = 0.33-0.56; RMSEP = 1.88-2.45%). With increased sample diversity in the calibration set, NIR technology is recommended for estimation of DM and TSS in fresh Agave leaves. FT-NIR-based prediction of cellulose , hemicellulose or lignin of independent sets (of different years or cultivars) was unsatisfactory, with r 2 < 0.75 and bias >10% of mean. These results may be improved with increased sample range, and attention to laboratory (reference method) error. However, leaf cellulose and hemicellulose content may be more easily estimated through correlation to leaf DM level (R 2 of 0.77 across all sampling events).

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“…4 Silverleaf Whitefly (Bemisia tabaci) and Cotton Aphid (Aphis gossypii) honeydew deposits have been reported to be responsible for most cases of cotton fiber stickiness, due to their high amounts of entomologically produced carbohydrates. 4,5 Melezitose is the dominant insect sugar produced from aphids, and trehalulose is the dominant insect sugar produced by the whitefly. 4 Distinguishing the ratio of melezitose and trehalulose allows for an identification of the insect responsible for the contamination.…”

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confidence: 99%

“…4 The physical testing methods are used because they are simple and are direct measures of the stickiness phenomenon and because some stakeholders believe that using IC to determine the surface carbohydrates of a small sample taken from a cotton bale may not necessarily reveal whether or not a bale will have an actual stickiness processing issue. 5 The mechanical instruments that have been developed to evaluate the stickiness of cotton are the Minicard (MC) instrument, the Lintronics Fiber Contamination Tester (FCT), the Sticky Cotton Thermodetector (SCT) and the High Speed Stickiness Detector, among others (some of which are no longer commercially available). 8 The MC and Thermodetector are two techniques used by the United States Department of Agriculture Agricultural Research Service (USDA ARS) cotton processing facility in New Orleans, LA; both result in rating systems that give gradations of stickiness from ''non-sticky'' or ''none'' to ''strong'' or ''heavy'' stickiness.…”

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confidence: 99%

Comparisons of Minicard ratings with ion chromatography sugar profiles of water extracts of cotton fibers and those of Minicard sticky spot materials

Peralta

Fortier

Zumba

et al. 2016

Textile Research Journal

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Specific levels of the carbohydrates melezitose and trehalulose deposited on the surface of cotton fibers are indicators of whitefly or aphid contamination. These deposits could cause stickiness problems during cotton ginning and textile processing. Cotton stickiness is highly complex, but surface carbohydrates may play the largest role in manifesting an issue. We utilized ion chromatography (IC) to identify and quantify nine sugars of interest present in the water extracts of 25 cotton samples to create sugar profiles for each sample: inositol, trehalose, glucose, fructose, trehalulose, sucrose, melezitose, raffinose and maltose. We compared the sugar profiles to the respective Minicard ratings of either NONE, LIGHT, MODERATE or HEAVY to draw correlations between the IC data and the rating. Trehalulose and melezitose in water extracts highly and positively correlate to Minicard ratings, confirming past researchers' attribution of cotton stickiness to insect sugars. Trehalose and maltose also highly correlated, possibly due to their marker content in honeydew. Glucose and fructose moderately correlated to the ratings. IC studies of the collected Minicard sticky spot material found trehalulose and melezitose were the most prevalent sugars in HEAVY rated samples. Glucose and fructose were present in larger amounts in the MODERATE versus HEAVY rated samples. This result may indicate that the Benedict Test, which attributes these reducing sugars to stickiness, may not be sufficient for conjecturing a stickiness issue. When comparing the averages of the nine sugars present in water extracts versus those sugars contained in Minicard sticky spots, the overall distributions were very similar.Over the years, the production rates and efficiency of cotton fiber processing technologies have changed and their performance has improved, thereby changing the requirements for a higher quality cotton fiber. The spinning of yarn performance is affected by the fiber characteristics, such as fiber morphology (convolutions), geometric features (length, fineness), static electrical forces, waxes, salts and sugars. 1 The sugars present on the fiber are usually a combination of plant metabolic sugars and entomological sugars. If the cotton is over contaminated with insect sugars, they should be detected prior to the beginning of the cotton fiber processing. 2,3 This is because cotton stickiness if a very serious problem in textile areas, including the growing, ginning and spinning sectors, and can cause major problems during opening, carding and through to spinning, many times resulting in financial losses. 4 Silverleaf Whitefly (Bemisia tabaci) and Cotton Aphid (Aphis gossypii) honeydew deposits have been

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Analysis of Sticky Cotton by Near-Infrared Spectroscopy

Cited by 13 publications

References 1 publication

Detection of Gas Concentration by Correlation Spectroscopy Using a Multi-Wavelength Fiber Laser

Detection of Gas Concentration by Correlation Spectroscopy Using a Multi-Wavelength Fiber Laser

Quality Estimation of Agave Tequilana Leaf for Bioethanol Production

Comparisons of Minicard ratings with ion chromatography sugar profiles of water extracts of cotton fibers and those of Minicard sticky spot materials

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