Classification of foreign matter embedded inside cotton lint using short wave infrared (SWIR) hyperspectral transmittance imaging

Cotton is an important source of natural fiber around the world. Cotton lint, however, could be contaminated by various types of foreign matter (FM) during harvesting and processing, leading to reduced quality and potentially even defective textile products. Current sensing methods can detect the presence of foreign matter on the surface of cotton lint, but they are not able to efficiently detect and classify foreign matter that is mixed with or embedded inside cotton lint. This study focused on the detection and classification of common types of foreign matter hidden within the cotton lint by a short wave near infrared hyperspectral imaging (HSI) system using the transmittance mode. Fourteen common categories of foreign matter and cotton lint were collected from the field and the foreign matter particles were sandwiched between two thin cotton lint webs. Operation parameters were optimized through a series of experiments for the best performance of the transmittance mode. After acquiring transmittance images of the cotton lint and foreign matter mixture, minimum noise fraction (MNF) rotation was utilized to obtain component images to assist visual detection and mean spectra extraction from a total of 141 wavelength bands. The optimal spectral bands were identified by using the minimal-redundancy-maxi mal-relevance (mRMR)-based feature selection method. Linear discriminant analysis (LDA) and a support vector machine (SVM) were employed to classify foreign matter at the spectral and pixel level, respectively. Over 95% classification accuracies for the spectra and the images were achieved using the selected optimal wavelengths. This study indicated that it was feasible to detect botanical (e.g. seed coat, seed meat, stem, and leaf) and non-botanical (e.g. paper, and plastic package) types of foreign matter that were embedded inside cotton lint using short wave infrared hyperspectral transmittance imaging. (C) 2017 Elsevier B.V. All rights reserved.