Low-resolution mid-infrared reflection analysis for discernment of contaminants in seed cotton

Authors: WENBIN, JIANG - New Mexico State University; Whitelock, Derek; HUGHS, S - Retired ARS Employee; RAYSON, GARY - New Mexico State University

Submitted to: International Journal of Analytical and Bioanalytical Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2018
Publication Date: 11/22/2018
Citation: Wenbin, J., Whitelock, D.P., Hughs, S.E., Rayson, G. 2018. Low-resolution mid-infrared reflection analysis for discernment of contaminants in seed cotton. International Journal of Analytical and Bioanalytical Methods. 1:1-13.

Interpretive Summary: Cotton contamination from plastic has increased considerably over the past 10 years and is a serious issue for the U.S. cotton industry. Of particular concern are black plastic film used as mulch in fields and yellow and pink plastic film used for round module wrap. Researchers from the USDA-ARS cotton ginning laboratory and New Mexico State University in Las Cruces, New Mexico collaborated to design and construct an instrument to distinguish cotton samples from common plastic contaminants. The device that uses infrared light was 100 percent accurate at differentiating plastics from cotton when analyzing pure samples. When the plastic samples were placed on a background of cotton, 11 to 60 percent coverage of the instrument’s field-of-view area by the plastic was necessary for positive identification of the plastic contaminant, depending on the type of plastic. These results will aid researchers as they work toward developing a system to detect and extract plastic contaminants from cotton at the cotton gin, helping U.S. cotton to keep its “Contamination Free” reputation.

Technical 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-1) 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 Resolution). Simulation of low resolution spectra was undertaken to demonstrate feasibility of these wavelengths. Cotton and contaminants samples were analyzed in triplicate using a corresponding instrument constructed in-house. Respective reflectance data were processed using an in-house derived algorithm. Contaminants were successfully differentiated from cotton in a 33.2 mm2 field of view with 100% accuracy. When mixed, the detection limit for positive identification of contaminant was observed when foreign material comprised 11% to 60% of the field of view.