Potential of Classifying Cotton Minicard Stickiness through Vis-NIR Spectroscopy as an Analytical Technique with DD-SIMCA as One-Class Classification.

Abstract

Cotton stickiness, mostly resulting from honeydew depositions of whiteflies and aphids, presents a worldwide problem for cotton growers and processors consistently. To meet the challenge of measuring the cotton stickiness, a few direct and indirect techniques exist. Previous study showed that Fourier transform near-infrared (FT-NIR) spectroscopy can be used to detect Minicard stickiness in raw cotton from partial least-squares (PLS) analysis. In the present investigation, visible-NIR (vis-NIR) as an analytical technique was explored for potential classification of four-class Minicard cotton stickiness, in combination mainly with the data-driven version of soft independent modeling of class analogy (DD-SIMCA) as one-class classification. Both PLS prediction-based classification and DD-SIMCA models in different spectral regions were developed to optimize the identification efficiency. Compared to an optimal PLS prediction-based classification model indicating a four-class correct classification of 77.8% in the calibration set and 69.2% in the validation set from the 750-1850 nm NIR region, an optimal DD-SIMCA model from the same spectral region could reach an improved discrimination of >95.0%, with a 98.1% correct identification in the calibration set and a 96.2% success in the validation set. This observation emphasized that vis-NIR spectroscopy with an DD-SIMCA approach could be a rapid and nondestructive tool for screening the Minicard stickiness in cottons.

Plant phenotyping relevance

Vis-NIR分光とDD-SIMCAによって綿花のスティッキネスという作物由来形質を非破壊分類し、モデル開発と検証を行っており、表現型取得・抽出法が中心である。

Code and data availability

The article describes vis–NIR spectra of 80 cotton samples with Minicard stickiness ratings and DD-SIMCA/PLS modeling, but contains no public data deposit, no author code release, and no supplement (pmc-prop-has-supplement no). Only the ORCID profile and CC license URL appear; neither is a paper-specific asset.

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