Tracking Cotton Trash via Fluorescence

Hank Hogan

Cotton producers are plagued with trash -- the bits of leaf, stem, hull, seed coat, bract and other unusable parts of the plant. Such debris determines cotton quality, and cleaner cotton is easier to process into materials such as yarn. Various types of trash require different cleaning and other mitigation strategies to keep processing equipment running efficiently.

Raw cotton contains plant debris, or trash, which is removed during production. The process often pulverizes the debris, making it hard to determine the trash source. Images courtesy of Gary R. Gamble and Jonn A. Foulk, USDA, Agricultural Research Service, Cotton Quality Research Station.

Eliminating the problem is difficult, however, because cotton production itself often makes botanical trash impossible to classify. For example, according to Gary R. Gamble of the US Department of Agriculture’s Cotton Quality Research Station in Clemson, S.C., the mechanical processes with which cotton is picked and ginned turn the trash into small, irregularly positioned pieces that can be covered by cotton fibers and can be light in color. These characteristics make it difficult to categorize the trash by image analysis or other techniques.

Now Gamble and his colleague Jonn A. Foulk have demonstrated that fluorescence spectroscopy can sort through the trash. Gamble noted that the technique needs real-world testing but that, if such tests are successful, the method will be commercially valuable. “It will become an important tool in helping the remaining American textile industry be competitive from the standpoint of being able to optimize production based on knowledge of trash types and quantities.”

In attacking this problem, the researchers used fluorescence spectroscopy because they believed that the types and concentrations of fluorophores found in various plant tissues would make the approach worthwhile. They collected cotton from a number of sources, prepared trash samples from the cotton and extracted those with a solution of dimethyl sulfoxide for the capture of fluorescence spectra.

After extraction using dimethyl sulfoxide, researchers had samples of each of six cotton trash sources. They then captured the fluorescence spectra and revealed the trash source.

In making measurements, they used a fluorometer made by Jobin-Yvon of Edison, N.J., that had a 300-nm excitation source to acquire data from 320 to 750 nm. The result was a series of spectral curves with peaks that ranged from ∼350 nm for bract to close to 700 nm for leaf. The curves had much overlap, and no region in the entire range was unique to any one of the six trash components. The researchers, therefore, developed a calibration model based on the spectra from 128 samples with varying concentrations and types of trash in them. They used this model to predict the concentration of trash components for the remaining 127 samples.

They found good correlation for leaf and hull trash. The model explained more than 93 percent of the observed results, an outcome that Gamble did not foresee. “The predictive power of the model was higher than I expected.”

As for the other components, additional samples from other areas could make the predictions based on fluorescence spectroscopy highly accurate. The scientists plan to collect that data and to use the technique in a pilot study that could lead to commercial applications.

Journal of Agricultural and Food Chemistry, June 27, 2007, pp. 4940-4943.