Investigation of Fourier transform infrared spectroscopy potential in cotton fiber micronaire measurement and distribution

Authors: Liu, Yongliang; Delhom, Christopher

Submitted to: Textile Research Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/11/2024
Publication Date: 4/13/2024
Citation: Liu, Y., Delhom, C.D. 2024. Investigation of Fourier transform infrared spectroscopy potential in cotton fiber micronaire measurement and distribution. Textile Research Journal. 94(19–20):2178–2188. https://doi.org/10.1177/00405175241235398.
DOI: https://doi.org/10.1177/00405175241235398.

Interpretive Summary: Cotton fiber micronaire (MIC) is an essential fiber quality and characterizes both fiber maturity and fineness components. Almost all cottons produced in the U.S. are classed or graded following official standards and standardized procedures by using high volume instruments (HVI) on a conditioned fiber sample with a constant weight. Cotton fibers still attached to cotton seeds (or seed cotton) have to be harvested first in field and then ginned at gin facilities for fiber testing. There is an increasing interest in rapid and accurate analysis of cotton MIC using low-cost and portable systems with the least fiber preparation steps. Previous studies investigated the potential of using portable and small near infrared (NIR) instruments to measure fiber MIC. Fiber MIC measurement in seed cotton may meet some difficulties, as seed cotton harvested by cotton mechanical harvesters contains some amounts of foreign or non-lint materials and also multiple cotton bolls are required for routine MIC test. As a different approach, attenuated total reflection Fourier transform infrared (ATR FT-IR) was attempted, because of micro-sampling ability without initial removal of both cotton seed and visible trash as well as of sub-sampling representation in a naturally variable sample. ATR FT-IR spectra of clean and well-defined 104 cotton materials were explored for fiber MIC prediction, then the models were applied to predict the MIC property of independent seed cotton locule fibers. Compared to regression models, algorithmic IR approach indicated a similarity in coefficient of determination, bias, and percentage of samples within the 95% agreement range between validation samples and independent samples. In particular, algorithmic approach avoided the need to re-calibrate the model with new samples. The results could provide cotton scientists an alternative and rapid tool for monitoring the fiber MIC distribution within one seed cotton sample or between seed cotton samples during the early MIC testing in remote / breeding locations.

Technical Abstract: Cotton micronaire (MIC) is an essential fiber quality attribute that characterizes both fiber maturity and fineness components. MIC and other fiber attributes are measured on fiber lint routinely in laboratories under controlled environmental conditions following a well-established high volume instruments (HVI®) protocol. In this study, the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was explored for fiber MIC assessment, especially for seed cotton locule fibers that were mingled with non-lint materials and varied in fiber maturity within a naturally variable sample. Partial least squares (PLS) multivariate regression models and algorithmic infrared maturity (MIR) approach were developed and then applied to predict MIC values of validation samples and independent seed cotton samples. Compared to PLS models, algorithmic MIR approach indicated a similarity in coefficient of determination (Rv2, RT2), bias, and percentage of samples within the 95% agreement range between the validation samples and independent samples. In particular, algorithmic MIR approach avoided the need to re-calibrate the model with new samples. Therefore, the development of a robust and effective FT-IR technique combined with MIR approach for rapid laboratory MIC assessment and distribution demonstrated a great potential for its extension to the early MIC testing in remote / breeding locations.