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United States Department of Agriculture

Agricultural Research Service

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Research Project: A Fundamental Study to Improve the Measurement of Cotton Length Distribution from Fiber Beards (Ci Project # 05-714)

Location: Cotton Structure and Quality Research

2012 Annual Report


1a.Objectives (from AD-416):
The objective of the research is to study fundamental issues on the relationship between the cotton length distribution of the original sample and the length distribution measured from a cotton fiber beard. A component of the objective is to study the feasibility of including a new length parameter characterizing short fibers in the cotton classification system. The results from this research will facilitate the acceptance of High Volume Instrument (HVI) as the base of a universal cotton classification system, which will benefit the global trading and consumption of cotton.


1b.Approach (from AD-416):
1. Constructing fibrograms of the projecting portions of the beards as used in High Volume Instrument (HVI). First, we will obtain signals for constructing fibrograms of beards as those used in HVI length test. This can be done by collecting raw signals either from HVI or from a scanner. We are developing a method for obtaining fibrograms by using image processing techniques to analyze images of fiber beards from a scanner. Then the length distributions (pdf) of the beard (projecting portion) will be calculated from the fibrograms. These fibrograms and length distributions can be compared to those of the projecting portion obtained from Advanced Fiber Information System (AFIS) tests. The potential differences between the projecting portions fibrograms obtained from these two devices will be identified and analyzed.

2. Calculating the length distribution of the original fibers. We will convert the distributions of the scanned projecting portions of the beards to compute the entire length distributions of the original fibers. These original length distributions are obtained from AFIS tests. The conversion can be achieved by implementing the relationship between the distributions (pdf) of HVI beards and those of the original fibers we developed in earlier stages, such as the application of PLS regression algorithm.

3. Verification After deriving the entire length distributions of original fibers from scanning the beards, we then will verify our results by comparing them to AFIS measured fiber length distributions of the original fibers. Based on the verification we will make proper adjustments to our algorithms and models. For this purpose, we are carrying out a larger set of HVI and AFIS tests. AFIS data have been used as references in this study.

4. Implementation As a result, we can provide the industry a method that enables the HVI to obtain the entire length distributions of the original cotton fibers. Implementation of our results includes providing the industry algorithms and equations for computing fiber length distributions and length parameters such as Lower Half Mean Length and Short Fiber Content.


3.Progress Report:

A cotton beard’s staple diagram contains complete information of fiber length distribution. It is directly related to its fibrogram, cumulative function, and probability density function. When using the beard testing method to scan a beard, the length distribution of the scanned projecting portion is different from that of the original sample. ARS scientists at Southern Regional Research Center (SRRC) in New Orleans, LA developed a model to compute the staple diagram of original sample from that of the beard’s projecting portion. We worked on the expanded database and focused on processing the optical signals acquired during beard length measurements by using a data acquisition system. These signals were then used to generate the staple diagrams of the projecting portion of the beard, based on which the staple diagram model was implemented to compute the staple diagram of the original beards. Different fiber length parameters were calculated from the staple diagrams. Results showed good agreements with Advance Fiber Information System (AFIS) generated results, which were used as the reference. The staple diagram method shows very good potential in practical implementation.

ARS scintists at SRRC in New Orleans, LA completed more samples to expand the database of High Volume Instrument (HVI) and AFIS length distributions, which include distributions constructed from AFIS data of individual fiber lengths. For each cotton sample, a set of AFIS length data includes the original fibers before HVI testing, the fibers taken from the HVI fiber beards, the fibers cut from the projecting portion, and the fibers of the hidden portion, as well as data from HVI tests. A larger set of data is essential for validating and refining the algorithm for processing experimental data from HVI signals, the staple diagram model, and the Partial Least Squares Regression (PLS) method. Computer programs have been developed to handle these data.

ARS scientists at SRRC in New Orleans, LA used the five-parameter mixed Weibull function to model the probability density functions of cotton length distributions. We developed the PLS method to compute the set of five parameters of the original beard from that of the projecting portion. We applied this technique on the new experimental data and obtained results of length density functions and length parameters computed from them. Both the above mentioned approaches can be very helpful for developing industrial implementations to enhance the rapid beard testing method.

Cotton fiber length parameters represent different characteristics of cotton length distributions. These parameters’ impacts on yarn properties are different. We investigated the effects of a set of cotton fiber length parameters on yarn properties. Linear regression models involving different number of fiber length parameters and their combinations were developed to predict ring and Open End (OE) spun yarns’ properties, including strength, irregularity, thick places, thin places, neps, ends down, and elongation. Statistical analysis results revealed that different combinations of length parameters are needed to produce the best yarn property models and that the variations of length distributions play a very important role in predicting yarn properties. This emphasizes the importance of obtaining and utilizing the entire length distributions of cotton samples, which enables the computation of different length parameters including variation of length distribution.

The methods used to monitor activities for this agreement were annual reports, technical visits/e-mails/interactions, presentations at scientific and industry meetings, and publications.


Last Modified: 4/20/2014
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