Reliability of cotton fiber length distributions measured by dual-beard fibrography and advanced fiber information system

Zhou, Jinfeng; Xu, Bugao

doi:10.1007/s10570-020-03611-x

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The consistency check of the fibrograms of the coupled optical algorithm for RBIM and AFIS was conducted by the Bland-Altman method, 27,29,53 which is usually widely used to evaluate the consistency between two instruments or measurement techniques at the 95% confidence level, as shown in Figure 13. Their horizontal and vertical coordinates represent the mean and difference between the relative densities of fibrograms by the coupled optical algorithm for RBIM (M) and that used for the referred AFIS (R), respectively.…”

Section: Accuracy Of Fibrograms Using the Coupled Optical Algorithm F...mentioning

confidence: 99%

“…24,25 It was often employed as a standard method for other methods in the literature. [26][27][28][29] As AFIS can measure colored fiber length in principle, it could also be applied as a reference for the new method.…”

mentioning

confidence: 99%

“…Subsequently, many researchers have conducted studies on this method. 26,29,[36][37][38] However, the applied L-B law only involves absorption light in optical density calculation, resulting in significant errors in colored and thicker fiber beards for the effects of nonnegligible scattered and reflected light.…”

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confidence: 99%

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Reflectivity and fluffiness coupled optical algorithm for colored cotton fibrogram of random-beard image method

et al. 2023

Textile Research Journal

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Insufficient studies of reflectance and fluffiness effects on optical density caused large derivation and limited the application of fibrogram methods in colored cotton fiber length tests for decades. A coupled optical method was proposed here to solve this issue, named reflectivity and fluffiness coupled optical algorithm of the random-beard image method. In this algorithm, a defined theoretical reflectance was declared with an iteration method for optical linear density on the optical analysis of the derived Kubelka–Munk law, which satisfied its assumption of excluding reflected light at the interface with air. Meanwhile, an apparent polynomial relationship was revealed between the actual density and optical densities of fiber assemblies at different fluffiness. Its accuracy on weighted mean fiber length was demonstrated, referred to as advanced fiber information system, using 10 kinds of dyed cotton slivers and 12 kinds of naturally colored lint fibers whose mean and maximum absolute errors are 0.59 mm and 1.6 mm, respectively. Furthermore, their excellent agreement on fibrograms and the weighted mean length was confirmed by Bland–Altman analysis, correlation analysis and the T-test. Finally, an exceptionally high linear correlation (r2 = 0.93) was achieved on their weighted mean length and had no statistical difference with insignificant deviation under a significance level of a = 0.01. This new algorithm, featured fast and low cost, could provide accurate mean fiber length of colored or dark cotton, exhibiting valuable guidance in the potential application of the fiber trade and the quality control of colored cotton fibers or primaries in blending assemblies and yarn.

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Section: Accuracy Of Fibrograms Using the Coupled Optical Algorithm F...mentioning

confidence: 99%

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confidence: 99%

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Reflectivity and fluffiness coupled optical algorithm for colored cotton fibrogram of random-beard image method

et al. 2023

Textile Research Journal

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Evaluating cotton length uniformity through comprehensive length attributes measured by dual-beard fibrography

Zhou

2020

Cellulose

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Predicting cotton fiber properties from fiber length parameters measured by dual-beard fibrograph

Zhou

2023

Cellulose

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Cotton ber properties, although in uenced tremendously by plant growing conditions, are largely dictated by cotton variety, and therefore certain associations inherently exist among the properties. Previous studies have examined cotton properties that have direct impacts on others, e.g., ber maturity on strength, but latent associations between ber properties, such as ber length vs. maturity, have not been explored. This paper focuses on investigating the relationships between ber length and other important properties (e.g., strength and maturity), and creating the regression models to predict these properties in case the testing conditions are unattainable. We rstly selected 117 cotton samples and divided them into a training set (100 samples) and a testing set (17 samples). We then measured the ber length parameters with Dual-beard Fibrograph (DBF) that can generate accurate ber length distributions, and the seven other ber properties, strength, elongation, micronaire, nep, neness, immature-ber-content and maturity-ratio, with High Volume Instrument (HVI) and Advanced Fiber Information System (AFIS). Finally, we performed multivariate regression, Bland-Altman plot, clustering and other statistical analyses to assess the correlations, multicollinearity, agreements and clusters of the ber properties. It was found that ber length parameters had moderate associations (0.3<|r|<0.7) with the seven properties, and the prediction errors on the training set varied from 2.25% (for maturity-ratio) to 14.36% (for nep). The Bland-Altman analysis proved that for any of the seven properties, more than 94.9% of the predicted and actual points were within the 95% limits of agreement and without systematic biases. The regression models based on cotton clusters signi cantly lowered the prediction errors because the cluster centroids better represented the collective features of the ber properties. The analyses on the testing set showed the prediction models can generate comparable results for both the training and testing sets, and demonstrated good generalization over new samples.

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Reliability of cotton fiber length distributions measured by dual-beard fibrography and advanced fiber information system

Cited by 5 publications

References 11 publications

Reflectivity and fluffiness coupled optical algorithm for colored cotton fibrogram of random-beard image method

Reflectivity and fluffiness coupled optical algorithm for colored cotton fibrogram of random-beard image method

Evaluating cotton length uniformity through comprehensive length attributes measured by dual-beard fibrography

Predicting cotton fiber properties from fiber length parameters measured by dual-beard fibrograph

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