Classification of waste cotton from different countries using the near-infrared technique

Abstract

Cotton fiber is mainly composed of cellulose, which is rarely separated into different kinds. However, the classification and identification of waste cotton from different countries are essential for the customs service of the country. In this study, the near-infrared classification method was introduced to classify and identify cotton fibers. Waste cotton samples from six different countries were collected, and one-fifth of them were used for validation. The near-infrared calibration and prediction models were constructed using both soft independent modeling of class analogy and partial least squares methods. It was found that the optimized model has a high recognition rate, and the prediction accuracy of the model was 99% for six countries. It was demonstrated that the near-infrared model established in this study can be used for fast and accurate identification of waste cotton from different countries.