Abstract
Contaminants mixed with cotton during harvesting and processing dramatically decreases its quality and economic value. A low resolution mid-infrared reflection instrument using four wavelengths (3100, 2900, 2300, and 1500 cm<sup>-1</sup>) was designed and constructed to distinguish cotton samples from 16 common contaminants (e.g. plastic and grease). These wavelengths were identified from associated high-resolution FT-IR spectra using multivariable analysis (i.e. Principal Component Analysis, Cluster Analysis, and Multiple Curve Regression).
Highlights
Cotton is a very important natural fiber resource
While a classification accuracy > 95% in classifying different trash types was reported, the results indicated the color feature was more reliable because contaminate shape and size varied during cotton processing
The results indicate the potential for successful distinction between cotton and foreign materials using such low-resolution reflectance data
Summary
Cotton is a very important natural fiber resource. Its production has an important impact on economies around the world. Higher cotton quality often increases its value and profitability [1]. Contaminants mixed with cotton fiber during harvesting and processing dramatically decrease cotton’s quality [2]. Cotton in the U.S is widely harvested mechanically and packed using polymeric materials. Common contaminants in the cotton bale include both botanical components (e.g. leaf, stem) and synthetic materials (e.g. plastic bag, module cover) [3]. Identification and removal of contaminants in the cotton is a challenge. Most large-sized cotton trash can be removed after ginning, this process can become more complicated due to the tendency of contaminants to be shredded to small fibers during processing [3,4]
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