Cotton Fibre Length Measuring Instruments: Merits and Limitations

Last Updated on 20/03/2021

Merits and Limitations of Cotton Fibre Length Measuring Instruments

Dr. N.Balasubramanian
Retd Jt. Director (BTRA) and Consultant
I, Rajeswari, 36, 17th Road, Chembur, Mumbai 400071,
Email: balajamuna@gmail.com

 

Abstract
Baer sorter is the most accurate instrument for measuring short fibre content but is time consuming and requires operator training. HVI fibrograph though much faster suffers from overestimation due to scanning some distance from clamping point. Underestimation as sampling in the clamped beard is not truly length biased and 3 highly unreliable in estimating short fibre content. AFIS is also rapid in measuring fibre length but suffers from fibre breakages during opening. Further fibres are not fully straightened during measurement. Almeter has the merit of giving both frequency distribution and cumulative frequency distribution but short fibres are not fully gripped and move to the centre. Image analyses is more accurate but is time consuming. Considerable divergence is found between the results of various authors about the level of agreement between the results of different fibre length measuring instruments.

Introduction:
Fibre length is one of the most important quality of cotton contributing to higher yarn strength spinnability and reduced end breakages in spinning. Short fibre content has pronounced effect on waste, yarn irregularity, appearance and end breakages Conventional and HVI instruments used for testing fibre length parameters of cottons are briefly reviewed here. Merits and limitations of these instruments, precautions to be taken and the agreement between the results of these instruments are discussed based on research conducted over the years.

Oil plate method
Fibre straightened by liquid paraffin on a glass plate is measured for length by a scale.

  • Merits – Accurate measurement
  • Limitation – Laborious and time consuming.

Balls Sorter
Determines frequency length distribution of fibres from which mean, CV are estimated. Fibres are laid as per their length on a plush table by a moving carriage and fibres falling in different length groups are weighed to give weight length distribution (f(l)). Time consuming and is not commonly used.

Baer Sorter
The fibres are fractionated into different length groups by a set of parallel combs and top comb and uniform array of fibres is prepared in descending order of length to get cumulative fibre length distribution. Effective length, mean length, Upper quarter length and % short fibres are determined. Suter web sorter is a similar American instrument where tufts are weighed instead of being laid on a plush board to prepare a diagram. ASTM Standard D 1447-07, details the Standard Test Method for Length and Length Distribution of Cotton Fibers (Array Method). Cumulative frequency by Baer sorter is q(l) and is related to frequency f(l) of fibres by (balls sorter)

Baer SorterMerits-

  • Effective length is close to Grader’s staple length
  • Provides accurate estimate of short fibre content

Limitations-

  • Time consuming (about 2 hrs per sample)
  • Requires considerable operator skill.

Shirley Photo Electric Stapler
Light is made to fall on a moving tuft of fibres aligned at one end and the reflected light is made to fall on two photo cells. Distance between two maximum gradient points in the current generated in the two photo cells, as the tuft moved on a traversing tape gives staple length.

Peyer Almeter
Fibroliner prepares a beard of aligned fibres held in the needle field of a transfer equipment. The clamped beard is passed between two capacitance plates and the change in capacitance is measured. Both cumulative and histogram of length frequency curve are shown in a printer. Time taken for measurement of length is about 15- 20 min. ASTM D5332- 92 stipulates test method of measuring length by Almeter. Limitation – Finite gauge of Fibroliner does not allow full gripping of short fibres. Fibroliner allows short fibres to move to the centre of beard instead of staying at the aligned end1.

Fibrograph
Beard of fibres is prepared by picking the fibres randomly from the sample by a comb and loose fibres brushed aside. The beard is optically scanned from the base to tip from which a fibrogram is drawn. The comb has 28 needles/inch. Since long fibres have a proportionally higher probability to be caught by comb this results in a length biased sample. The instrument based on this principle was developed by Hertel2. Upper half mean length, mean length and Uniformity index are determined from the fibrogram by drawing tangents to the curve2. ASTM D1447 – 07(2012) e1 gives the Standard Test Method for Length and Length Uniformity of Cotton Fibers by instruments like Fibrograph. Mechanisation of measurement of length in the instrument resulted in servo fibrograph where fibrogram is drawn automatically. Manual models could also be converted to servo automatic model by this conversion3. Time required per test is reduced from 7.5 min to 4 min and operator fatigue reduced. Further improvements were suggested by Tallant to improve accuracy in trace and higher operator speed4. Rouse used 2 dial gauges to determine relative length of fibres and number of fibres in a fibrogram instead of drawing the fibrogram5 for improving accuracy. Ewald and Worley6 developed modifications to enable same fibrograph instrument to work as manual, an automatic curve drawing or an automatic dial or digital type. Hertel and Craven7 introduced the concept of span length measurements which led to Digital fibrograph. Digital counters with push button systems were used in place of dial gauges and the entire operation of length measurement is atomised. 2.5%, 50% span length, uniformity index and short fibre%, short fibre content (SFL) and Floating fibre index are determined in digital fibrograph. Fibro sampler is used in later models to clamp the fibres on the comb. Fibre sample is put inside the cylinder of sampler. Fibre comb, with 13 needles/inch, is rotated around the fibro sampler, with pressure applied on the cotton, during which it picks up fibres projecting from the holes of sampler. Time taken for measurement is reduced to 1 min per sample with reproducibility of .1 to .18 %.

Merits

  1. Simulates beard formed by fibres held by back or front roller nip of a drafting system
  2. Very rapid (about 1 – 4 min per sample)
  3. Does not depend much upon operator skill.

Limitation

1. Because of fibre breakages and length involved in clamping, estimates of length are lower.

2. Holding length calculated mathematically is found to be around 4.06 mm8. Scanning starts at 3.81 mm from comb. As a result, actual scanning starts at 7.87 mm from the comb because of this. The holding length also varies from cotton to cotton. Krowwicki and Ramay9 showed that 50 % span length, 2.5 % span length and Uniformity ratio in Digital Fibrograph are overestimated as scanning starts at 3.81 mm from clamping point.

50 % and 2.5 % span lengths are overestimated to the extent given below:

50% span length = 0.5 L + 1.905
2.5 % span length = 0.975 L + 0.09525

Maximum possible Uniformity Index is increased as a result from 51.28 to 57.6 for 30 mm and 51.28 to 56 for 40 mm cotton. The extent to which fibrogram is affected by scanning at 4 mm from clamp position instead of clamp is shown in Fig 1 and Table 1 below. Both 2.5% and 50 % span length are increased but the effect is more on the latter. As a result, uniformity Index is increased.

Comparison of Fibrograms with scanning from clamp and scanning from 4mm length
Fig 1 : Comparison of Fibrograms with scanning from clamp and scanning from 4mm length

Table-1: Span lengths and uniformity ratios with scanning from clamp and scanning from 4mm length

Span Length Scanning from Clamp (Mm) Scanning 4mm from clamp
2.5 % 31.67 mm 32.05 mm
50 % 12.03 mm  14.32 mm
UI % 40 44.7

3. Crimp in the fibre leads to underestimation.

4. Highly unreliable for estimating short fibre content. Precautions required in fibre sampler for getting reliable beards are discussed by Carpenter10.

  • Even pressure should be applied on the sample inside cylinder
  • The cylinder should be rotated slowly and gently
  • At least one half of perforations should be covered by sample
  • Beard should be uniform without thick and thin places
  • A fresh sample surface should be used for each comb
  • Excessive brushing of beard should be avoided.

Yoakum11 reported a higher fibre length with fibro sampler beards than hand prepared beards. Correlation of yarn strength with 2.5 % span length is as good as the upper quarter length of comb sorter and upper half mean of servo fibrograph. 2.5 % span length by digital fibrograph and short fibre % by comb sorter were better related to ring frame end breakage rate than uniformity ratio by servo fibrograph.

Frequency of fibres of length l, r(l), by fibrograph is a cumulative distribution of Baer sorter diagram and is given by

equation

Relationship between span lengths and mean fibre length are critically influenced by the variability and short fibre content.

Short Fibre Content (SFC)
SFC is indicated in 2 different ways.

  1. % of fibres lower than a certain length (usually 0.5 inch)
  2. % of fibres less than Effective length from Baer sorter.

Balls sorter, Baer Sorter, and Suter web sorter give the most accurate estimate of SFC though they are time consuming. Digital Fibrograph and HVI, though rapid, give unreliable estimate. From fibrograph, baer sorter curve can be obtained by differential method and SFC can be obtained.

Table 2 gives span lengths and UI of fibrogrpah determined by double summation method from original frequency distribution with 3 cottons of nearly same mean length but differing in short fibre content (SFC %). UI is nearly the same for the 3 samples though SFC % is markedly different. This is because both 2.5 % and 50 % span lengths are reduced in the same proportion with increase in SFC %. This amply demonstrates that UI by fibrograph cannot give any indication of short fibre content.

Table 2: Comparison of 50%, 2.5 % span length and UI with short fibre content %

Mean fibre length,mm 26.8 25.6 25.2
CV % 28.9 30.6 32.8
SFC % 7.3 12 14
50 % Span length, mm 16.6 13.6 14
2.5 % Span length 36.6 29.8 29.7
UI % 45.5 45.6 45.6

Cai12 et al found that the sampling in the beard of HVI is not truly length biased. Except in short fibre region the sample is similar to that of original sample. SFC by HVI is lower than of original because of sampling. Chu and Riley13 also found that the sample in the comb prepared by fibre sampler is not a length biased sample. Each fibre irrespective of its length has equal probability of being caught by the comb as a result, mean length and length distribution of fibres in the fibrosampler are close to mean length and length distribution in the original sample. This seems to indicate that fibres are picked by the sampler in clumps rather than individually. Some support to this contention is found in the results of Carpenter10. Actual Fibrogram and that derived from comb sorter results have been compared by Carpenter (Fig 2). Actual fibrogram deviates from that derived from comb sorter, with divergence increasing with length. As a result, fibre length and short fibre content are under estimated.

Actual fbgrogram and that derived from Comb sorter
Fig 2: Actual fbgrogram and that derived from Comb sorter

HVI Testing
High volume Instruments (HVI) have speeded up testing of fibre length apart from other properties substantially. Bale wise testing of cottons is possible and bales with substandard characteristics can be weeded out. HVI determines length characteristics based on Digital Fibrograph principle. ASTM, Standard D 5867-05 specifies the Standard Test Method for Measurement of Physical Properties of Cotton Fibers by High Volume Instruments. Calibration cottons by USDA are used to calibrate the instrument.

Limitations

  1. Fibre breakages occur during comb preparation on HVI as this is done at high speed. Such breakages are more with harsh, entangled cottons.
  2. Further some length of fibre is used in clamping on the comb as in Fibrograph leading to lower estimates of fibre length.
  3. Breakages are also higher with card sliver compared to comber sliver. Over estimation of fibre fractionation is therefore found in HVI.
  4. As mentioned earlier, Cai12 et al and Chu and Riley13 however found that the sampling used in HVI is not length biased but follows original distribution. This is supported by the work of Cui14 et al. The authors found a close agreement between frequency length distribution of cotton of original sample and that of fibres picked by the clamp in fibrosampler. If fibrosampler sample is length biased as assumed by Hertel, then it should agree with length biased mean obtained from the mean and CV of original sample. In Table 3 below mean length of original and Fibrosampler picked sample and length biased mean calculated from mean and CV of original sample from the data given by Cui14 et al are given

Table 3: Comparison of mean fibre length of fibrosampler picked sample with original sample

Sample identification No Mean fiber length of original sample, cm Mean fiber length of Fibro sampler picked sample, cm Length biased mean fiber length calculated from original sample, cm
30 1.666 1.740 2.42
31 1.754 1.782 2.542
33 1.873 1.937 2.266
34 2.041 2.071 2.399
35 2.058 1.999 2.419
36 2.145 2.151 2.553
37 2.176 2.255 2.597
38 2.284 2.324 2.710

Table 3 shows that while mean fibre length of original and fibrosampler picked samples are close to each other, length biased mean estimated from original sample is much higher in all the cases. This would mean that fibrosampler picked sample is not length biased as assumed by Hertel.

Another interesting finding is that projecting portion from the clamp has a higher fibre length than that hidden behind the clamp. Further twice the mean length from projected sample is much higher than mean length of fibrosampler picked sample. It appears that these anomalies have arisen either because of the method followed in collecting projected portion and hidden portion from the clamp or because fibres get dragged forward while being picked in the comb.

To find out the effect of fibre length variability on differences caused in probability of fibre picking in fibrosampler, fibrograms prepared assuming equal probability for fibres is compared against that with length biased probability in fibrosampler are compared for cottons with low and high fibre length variability in Figs 3 and 4.

Comparison of fibrograms
Fig 3: Comparison of fibrograms with equal probability for fibres and length biased probability in fibrosampler beard of high variability cotton, Mean length 26.8 mm, CV = 28.9 %
Comparison of fibrograms with equal probability
Fig 4: Comparison of fibrograms with equal probability for fibres and length biased probability in fibrosampler beard of low variability cotton, Mean length 26.8 mm, CV = 14.5 % %

Table 4 shows that Fibrogram span length with equal probability for fibres is shorter in all length regions than that with length biased probability for fibres in fibrosampler beard. The difference is more pronounced in cotton with high length variability particularly in 50 % span length. 2.5% and 50 % span lengths and Uniformity Index are lower with equal probability than length biased probability. This means that fibrograph estimates are lower than actual because fibrosampler does not pick longer fibres with higher probability as expected by theory by Hertel.

Table 4: Comparison of span lengths with equal probability for fibres and length biased probability in fibrosampler beards

Span length Cotton with high length variability, CV = 28.9 %, Cotton with low length variability CV = 14.5 %
Span length Equal probability for fibres in fibrosampler beard, L1 mm Length biased probability for fibres in fibrosampler beard L2  mm Equal probability for fibres in fibrosampler beard, L1  mm Length biased probability for fibres in fibrosampler beard L2 mm
2.5% 31.71 33.67 .94 27.91 29.39 .95
50 % 12.27 14.15 .87 12.87 13.97 .92
UI 38.7 42 46.1 49.2

Projecting fibre sample
Belmasrour38 et al proposed a method for estimating fibre length distribution of a sample from length distribution of fibres projecting from the comb of fibrograph. Predicted curve gives good agreement with experimental except in short fibre region. CV of fibre length for the projecting fibre sample (from the fibrograph comb) was calculated for constant staple length, low and high length variability original samples are given in Table 5. CV of fibre length is as high as 57.7 % for cut staple and increases marginally to 60 % in low variability sample and to 66% in high variability sample. High variability in beard sample is because of random clamping of fibre which is akin to random breakage of fibre. Variability of fibre length in original sample has only a marginal effect on variability in beard sample. Beard sample with length biased picking has a lower variability than equal probability picking.

Table 5: CV of fibre length for the projecting fibre sample (from the fibrograph comb) for constant staple length, low and high length variability original samples

Cut Staple Low length variability High length variability
Original sample Mean, mm 26 26.8 26.8
CV % 0 14.6 28.8
Equal Probability for fibres Length biased probability for fibres Equal Probability for fibres Length biased probability for fibres Equal Probability for fibres Length biased probability for fibres
Projecting beard length sample Mean 13 13 13.4 14.2 13.4 15
CV % 57.7 57.7 60.1 59.5 66.6 63.1

AFIS (Advanced Fibre Information System)
Fibre samples are opened into individual fibres by feed plate/feed roller and the opened material is aerodynamically presented to electro-optical sensors for measuring length apart from other properties. Apart from mean, distribution is also determined. There are no USDA calibration cottons for AFIS but Uster gives calibration cottons.

Merits

  1. Numerical sample is used as against length biased sample in HVI.
  2. No need to prepare an array as in Baer or Suter web sorter and Almeter. Inaccuracy in fibre alignment and density in array preparation is avoided.
  3. As both length and diameter are measured, both numerical and weight-based distribution can be determined.
  4. Fairly quick as 10000 fibres can be measured in a few minutes.

Limitation

  1. Since fibre breakages occur with longer fibres, length measured is lower.
  2. Further fibres are not straight which leads to under estimation of length.

Image analysis
Fibres manually placed on glass slide are photographed using CCD camera and suitable software is used to make image processing and measurements. Ikiz15 et al study the effect of lighting, resolution, preprocessing and processing algorithms on the accuracy of results. Accuracy of the result is improved with high resolution images. Image processing is more accurate and has higher precision than hand measurement, HVI and AFIS. Y. Xu16 et al developed a method for determining fibre length distribution by preparing an aligned combed tuft and cutting it into number of segments of a known length. The snippets in different segments were scanned by image analysis to get the number of fibres.

Almeter and AFIS give results that are better correlated with sorter results.

Length Biased Mean
The sample from which Baer sorter diagram is prepared is a numerical sample where all fibres have the same probability of occurrence. But the sample in the beard of Fibrograph is a length biased sample (as per Hertel), as probability of fibre being caught is assumed to be proportional to fibre length. If a sliver is clamped across a cross section and loose fibres not held by clamp are combed out, tuft held under the clamp is a length biased sample. Length biased sample frequency can be obtained from the numerical sample by multiplying frequency in each class by the length of fibre. If the fibres in different length groups are weighed as in balls sorter and Suter web sorter, length biased sample will be obtained. Frequency for length in a length biased sample is given by l f(l).

equation2Where, 

lm = Length biased mean

ī = Normal mean

CV = Coefficient of variation of fibre length.

In Table 6 Mean, CV and short fibre content of normal and length biased sample of cotton of an assumed frequency distribution are compared.

Table 6: Comparison of length properties of normal and length biased distributions

Normal Length biased
Mean mm 22.5 24.7
CV % 30.1 % 24.7 %
SFC 10.8 % 4.7 %

Length biased sample has a higher mean but lower SFC and CV.

Comparison of results from different instruments
Nair17 et al found that though 2.5 %length by Baer sorter, HVI and AFIS are correlated, the values by HVI are much lower. 2.5% span length by HVI is about 6-12 mm lower than that of baer sorter and the difference increases with fibre length. AFIS gives results close to Baer sorter with shorter cottons but gives about 2-4 lower values with longer cottons possibly because of fibre breakages.

Audivert and Casteller18 found varying degrees of correlation between span length by Digital fibrograph (SL) and 1. Upper half mean length UHML, upper quartile length (UQL) and mean length from comb sorter and 2. Staple length by Shriley photoelectric stapler. Correlations approach optimum at 30 % SL for comb sorter and 2.5 to 10 % SL for Shirley photoelectric stapler. Jai Prakash19 found close agreement between 2.5 % span length from Digital fibrograph and mean length by Balls sorter with short staple cottons. With increase in length, 2.5 % length was progressively more than mean length by Balls sorter. SFL (< ½ inch) by digital fibrograph was highly correlated with SFL by (< 8/16 inch) byBalls sorter. Ramsey and Beaton20 found a highly significant correlation between HVI Uniformity index (UI) and Comb sorter SFC with US upland standard cottons. But correlation between HVI UI and comb sorter is poor with commercial crops. Likewise, HVI UI has a poor correlation with digital fibrograph UI. UI from HVI is equal to SFC in comb sorter in predicting yarn quality and process performance. UI from HVI has a good correlation with SFC by Almeter and SFC by Almeter gives a good prediction of processing performance and yarn strength. Bargeron21 found mean length by AFIS Almeter to be shorter than that of comb sorter by1.37 mm. SFC by number by Almeter was higher than that of comb sorter by 1.7 %. This may be because Almeter measures length with fibres in relaxed state. 2.5 % and mean length of cotton are lower but SFC is higher in Almeter than Suter web sorter. This is attributed to the fact that fibres are straight with crimp removed in Suter while fibres are in relaxed state in Almeter. However high correlation is found between results of Almeter and comb sorter.

Short fibre content from HVI and AFIS not only differ considerably from that of Baer sorter but also bear no correlation with it. Bragg and Shofner22 incorporated a fibre speed sensor in AFIS to improve the accuracy. With this SFC levels by AFIS come closer to Suter web sorter, though they are still higher by about 8 – 10 units. On the other hand, Cui23 et al found significant differences in SFC between HVI, AFIS and Suter web sorter. Suter sorter gave highest value and AFIS lowest and HVI in between. This is in contradiction to the results of Bragg and Shofner22. Correlation between the instruments for SFC was lower than that for mean length. Calibration and sample non uniformity is mainly responsible for high variation in SFC. Thibodeauk24 et al also found SFC to be highest by Suter web sorter compared to HVI and AFIS. Further Suter web sorter is more accurate to detect difference in SFC between cottons. Higher correlation is found between SFC by HVI and Suter web sorter than that between SFC by AFIS and Suter web sorter. Zeidman et al review mathematical fundamentals on derivation of SFC by number and weight. SFC is related to range and shape of fibre length distribution25. Krifa26 found bimodal frequency length distribution in many cottons, as measured by AFIS and found that the extent of bimodality is correlated to fibre strength and maturity. Apart from the normal mode a second peak of lower amplitude is found at 3-4 mm. Stronger cottons exhibit bimodal distribution while weaker cottons exhibit unimodal distribution. Aggressive opening of the cotton makes it unimodal particularly with weaker cotton because of fibre breakages. Frequency distribution from Ball sorter does not, however, show bimodal distribution. This may be because it is weight length distribution as against number length distribution by AFIS. In a subsequent paper Krifa27 found that the length distribution is close to unimodel at both low and high breakage levels in ginning and processing in spinning i.e. ginned lint and card sliver. Mature cotton exhibits an extended intermediate stage of bimodal distribution compared to immature cottons because of lower breakage level.

Xu16 et al, who used image analysis, however did not find a peak at 3 mm. Landstreet28 derived the fibrogram from number length frequency distribution by double summation, based on a reversal of this technique, Krowicki29 et al compared the fibrogram obtained from frequency length distribution by differential and algorithm methods. Algorithm method gives results close to differential method while taking less time. Equations for fibrogram from the fibre length distribution were determined by Azzouz30 et al assuming fibre length distribution to be normal. Good correlation is found between actual weight length frequency distribution and adjusted to normal distribution. However, number length distribution deviates considerably from normal and the equation is not applicable. Cai31 et al found fibre length distribution plays an important role in prediction of yarn strength and irregularity and it is therefore useful to include fibre length distribution as an important quality parameter of cotton.

Krifa32 emphasized the need for taking fibre length distribution in selection of cotton. Group of bales selected on common HVI properties produces mixing with uncontrolled length distribution variability, caused by fibre breakages, from bales with same but low maturity. Shapiro33 et al discuss mathematical models to examine cotton fibre length distribution under various breakage models. Breakages on unclamped and clamped fibres were analysed. A procedure for testing the validity of model determining effect of breakage on length distribution is proposed.

Krowicki34 examined the effect of lens width on length in Digital fibrograph. Kelly35 et al found that selection in breeding program using HVI and AFIS data gave nearly the same order of improvement in fibre quality.

Hequet36 et al showed that yarn elongation can be predicted from HVI elongation and upper half mean length while yarn strength can be predicted from AFIS mean (by weight), fineness and maturity ratio.

Ureyan and Kadoglu37 found highly significant correlations between fibre properties measured by AFIS and yarn properties.

Cui14 et al showed that a mix of Weibull distributions gives a good fit to the actual fibre length distribution of original sample as well as of the sample picked by clamp of fibro sampler. Mean length and upper half mean from theoretical distribution shows good agreement with actual results. Belmasrour38 et al proposed a method for estimating fibre length distribution of a sample from length distribution of fibres projecting from the comb of fibrograph. Predicted curve gives good agreement with experimental except in short fibre region.

References

  1. K.Q. Robert and L. J. Blanchard, Cotton Cleanability: Part I: ModelingFiber Breakage, Textile Research J, 1997, 67, p 417
  2. K.L. Hertel, A Method of Fibre-Length Analysis Using the Fibrograph, Textile Research J, 1940,10, p 510.
  3. J.D. Tallant, Use of a Servo System for Automatic Operation of the Fibrograph, Textile Research J, 1952, 22, p 617
  4. J.D. Tallant, Improvement on the Servo Conversion of Manual Fibrographs, Textile Research J, 1958, 28, p 815.
  5. J.T. Rouse, The Use of Dial Gauges in Calculating the Results of Fibrograph Length Tests, Textile Research J, 1958, 28, p 505.
  6. P.R. Ewald and S.Worley, JR, Converting the Fibrograph to Automatic Direct- Reading Operation, Textile Research J, 1961, 31, p 602
  7. K.L.Hertel and C.J.Craven, J of Textile Industries, 1960, 124, 7, p 103.
  8. R.S. Krowicki and D. P. Thibodeaux, Holding Length: Effect on Digital Fibrograph Span Length, Textile Research J, 1990, 60, p 383,
  9. S. Krowicki and H. H. Ramey, An Examination of the Digital Fibrograph Length Uniformity Index, Crop Science, 1984, 24, p 378.F
  10. Carpenter, Evaluation of the fibro sampler and the Digital Fibrograph for sampling cotton fibres, and measuring length characteristics. http://ia601601.us.archive.org/5/items/evaluationoffibr775carp/evaluationoffibr775carp.pdf.
  11. Yoakum.Roger L. Preliminary evaluation of the Digital fibrograph and fibro sampler, U.S. Dept. Agr.[Unpublished report.] 1959.
  12. Cai, X. Cui, J. Rodgers, V. Martin andM. Watson, an investigation of the sampling bias of the beard method as used in HVI. J Textile Inst. 2010, 101, p 958.
  13. Y.T.Chu and C. R. Riley, Jr.New Interpretation of the Fibrogram, Textile Research j, 1997, 67, p897
  14. X.L.Cui, J.Rodgers, Y.Cai, L. Li, R.Belmasrour and d S. Pang, Obtaining Cotton Fiber Length Distributions from the Beard Test Method Part 1 – Theoretical Distributions Related to the Beard Method, J of cotton Science, 2009, 13, p265
  15. Ikiz, J. P. Rust, W. J. Jasper and H. J. Trussell, Fiber Length Measurement by Image Processing, Textile Research J, 2001, 71, p 905
  16. W.Xu, B.Xu, W. Li and W. Cui, Snippet Counting for Cotton Length Distribution Measurement Using Image Analysis, Textile Research J, 2008, 78, p 336
  17. A.U.Nair, R.P.Nachane and B.A. Patawardan, Comparative study of different test methods for the measurements of physical properties of cotton, Indian J of fibre and textile research, 2009, 34, p 352.
  18. Audivert and Ma. D. de Casteller, The relations between the fibre length parameters obtained fromthe Digital Fibrograph, the comb sorter, and the Shirley Photo-electric staplerJ, Textile Inst., 1972, 63, p 356.
  19. Jai Prakash, Evaluation of Length Parameters Obtained with the Digital Fibrograph with SpecialReference to Fiber Length Nonuniformity, Textile Reaearch J, 1064, 34, p 857.
  20. H.H. Ramey, JR and P.G. Beaton, Relationships Between Short Fiber Content and HVI Fiber Length Uniformity, Textile Research J, 1989, 59, p 101.
  21. J.D. Bargeron III, Preliminary Investigation of the Length Measurement of Cotton Fibers with the PeyerTexlab System: Comparability and Repeatability, Textile Research J, 1986, 56, p121
  22. K. Bragg and F. M. Shofner, A Rapid, Direct Measurement of Short Fiber Content, Textile Research J, 1993, 63, p 171
  23. X.Cui, T. A. Calamari, K. Q. Robert, and J. B. Price, Measuring the Short Fiber Content of Cotton, Textile Research J, 2003, 73, p891
  24. Thibodeaux, H. Senter, J. L. Knowlton, D. McAlister, and X. Cui, A comparison of Methods for Measuring the Short Fiber content of cotton, J of Cotton science, 2008, 12, p 298.
  25. I. Zeidman, S. K. Batra and P. E. Sasser,Determining Short Fiber Content in Cotton : Part I: Some Theoretical Fundamentals, Textile Research J, 1991, 61, p 21.
  26. M.Krifa, Fiber Length Distribution in Cotton Processing: Dominant Features and Interaction Effects, Textile Research J, 2006, p 426.
  27. M.Krifa, Cotton fiber length distribution modality alteration in ginning and mill processing, J Textile Institute, 2013, 104, p 731.
  28. Landstreet, C. B., The Fibrogram: Its Concept and usein Measuring Fiber Length, Textile Bull. 1961, 87, p54.
  29. R.S. Krowicki, D.P. Thibodeaux and K.E. Duckett, Generating Fiber Length Distribution from the Fibrogram, Textile Research J, 1996, 66­, p 306.
  30. Azzouz, M. B. Hassen, F Sakli,Adjustment of Cotton Fiber Length by the Statistical Normal Distribution: Application to Binary Blends, J of Engineering Fibres and Fabrics, 2008, 3, 3, p 35
  31. Y.Cai, X. Cui, J. Rodgers, D. Thibodeaux, Vi. Martin, M. Watson and S. Pang, A comparative study of the effects of cotton fiber length parameters on modeling yarn properties, Textile Research J, 201383, p 961
  32. M.Krifa, Fiber length distribution variability in cotton bale classification: Interactions among length, maturity, Textile Research J, 2012, 62, p 1244.
  33. H.N. Shapiro, G.Sparer, H.E. Gaffney, R. H. Armitage and J.D. Tallant, Mathematical Aspects of Cotton Fiber Length Distribution under Various Breakage Models, Textile Research J, 1964, 34, p 303.
  34. S. KrowickiThe Effect of Lens Width on Length Measurements by the Digital Fibrograph, J, Textile Institute, 1986, 77, p 223.
  35. C.M. Kelly, E.F. Hequet, and J.K. Dever, Interpretation of AFIS and HVI Fiber Property Measurements in Breeding for Cotton Fiber Quality Improvement, J of cotton science, 2012, 16, p 1.
  36. E. Hequet, N.Abidi, and J. R. Gannaway, Relationships between HVI, AFIS, and yarn tensile properties,http://wcrc.confex.com/wcrc/2007/techprogram/P1794.HTM.
  37. E. Üreyen, and H.Kadoğlu,The Prediction of Cotton Ring Yarn Properties from AFIS Fibre Properties by Using Linear Regression Models, Fibres and Textiles in Eastern Europe, 2007, 15, 4, p 63
  38. R. Belmasrour, L. Li, X. L. Cui, Y.Cai, and J.Rodgers, Obtaining Cotton Fiber Length Distributions from the Beard Test Method Part 2 – A New Approach through PLS Regression, J of cotton Science, 2011, 15, p 73

Share this Article!

Leave a Comment