基于高光谱成像系统的纺织品成分定性鉴别

针对目前对纺织品成分鉴别快速、无损、在线检测的需求，提出了一种以高光谱成像系统结合化学计量学方法鉴别纺织品成分的方法。以常用的10类纺织品为鉴别目标，分析比较了数据预处理及样本集挑选方法的优劣，建立偏最小二乘法判别分析模型进行鉴别，最终提出高光谱成像系统进行纺织品成分定性鉴别的技术路线。研究结果表明：一阶导数处理能消除由纺织品加工工艺和测试条件等因素造成的基线漂移现象，提高鉴别模型的泛化性能，降低训练样本代表性的要求；通过所建立的判别分析模型，经过不同加工工艺的纺织品均能得到鉴别，且鉴别准确率达到96.78%，证实了高光谱成像技术应用于纺织品成分定性鉴别中的可行性。

Qualitative identification of textile chemical compositionbased on hyperspectral imaging system

In view of the current need of rapid, nondestructive and on-site? an identification of textile composition, an identification methodusing hyperspectral imaging system combined withchemometry was proposed. The hyperspectral images of 10 categories of commonly used textile were captured and spectral data were extracted after hyperspectral image calibration. On the basis of comparative analysis, comparative analysis of data pretreatment and sample selection methods, a partial least squares discriminant analysis model was established toidentifytextile, and a technical route of identifying textile with hyperspectral imaging technique was put forward. The results show that the first derivative pretreatment eliminates the baseline drifts caused by textile processing and test conditions, thus improving the generalization performance of the identification model and reducing the representativeness requirements of training samples. Taking advantage of the built identification model, textile suffered from various processings can be identified with theaccuracy rate of 96.78%, which confirms the feasibility of hyperspectral imaging technology in textile components qualitative identification.