Quantitative analysis of textile delusterant based on terahertz spectral and data fusion strategies

太赫兹辐射 织物 融合 传感器融合 计算机科学 材料科学 遥感 人工智能 光电子学 地质学 复合材料 语言学 哲学
作者
Xianhua Yin,Huicong Chen,An Li,Wei Mo
出处
期刊:Infrared Physics & Technology [Elsevier BV]
卷期号:125: 104293-104293 被引量:7
标识
DOI:10.1016/j.infrared.2022.104293
摘要

• Spectral data fusion and partial least squares were used to determine the delusterant content in textiles. • High-level data fusion was the most effective way to model the quantification of delusterant. • It lays the valuable foundation for the testing of textile additives. Titanium dioxide is a delusterant and an important component in the manufacturing of polyester fiber. For the need of fast, accurate and nondestructive detection of matting agents in textiles, a quantitative analysis method based on terahertz absorption spectroscopy and derivative spectroscopy, combined with chemometrics and data fusion strategy is proposed. This experiment was used two spectra for fusion. The terahertz absorption spectra were obtained in the band of 0.2–1.9 THz by optical parameter extraction. The derivative spectrum was derived from the first-order derivative of the absorption spectrum. Partial least squares (PLS) and data fusion were used to construct a prediction model for titanium dioxide concentration in polyester fiber. Low-level data fusion was the direct combination of two spectral data; The successive projections algorithm (SPA) and Monte Carlo uninformative variable elimination (MCUVE) were employed by mid-level data fusion for feature selection, after which the feature variables were fused; multiple linear regression was used for fusion by high-level data fusion. The prediction accuracy of the high-level data fusion model is higher than that of other models, which the correlation coefficient of cross-validation (Rcv) and correlation coefficient of prediction (Rp) are 0.9229 and 0.9227. The mean relative error (MRE) is 0.2654. The results show that terahertz spectroscopy combined with chemometric methods and high-level data fusion strategies can achieve rapid, accurate and non-destructive detection of titanium dioxide in polyester fiber, which can lay the theoretical foundation for terahertz spectroscopy detection methods for textile additives.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
我是老大应助安静的芝麻采纳,获得10
刚刚
2秒前
讨厌下雨完成签到,获得积分20
4秒前
涛1118发布了新的文献求助10
4秒前
4秒前
吴志亮完成签到,获得积分10
5秒前
Muya发布了新的文献求助10
6秒前
klj发布了新的文献求助10
6秒前
突突突突突完成签到,获得积分10
6秒前
山高水长完成签到,获得积分10
6秒前
6秒前
7秒前
7秒前
szx233完成签到 ,获得积分10
8秒前
jmlx发布了新的文献求助10
8秒前
10秒前
10秒前
AAA发布了新的文献求助10
11秒前
lx发布了新的文献求助10
11秒前
在水一方应助淇淇采纳,获得10
14秒前
zhangwuhui完成签到,获得积分10
16秒前
丘比特应助独特的鹅采纳,获得10
16秒前
16秒前
夜轩岚发布了新的文献求助10
16秒前
17秒前
椰子水完成签到,获得积分10
18秒前
英姑应助岛王采纳,获得10
19秒前
19秒前
21秒前
21秒前
SciGPT应助小刚采纳,获得10
22秒前
22秒前
zhangwuhui发布了新的文献求助10
22秒前
tomorrow发布了新的文献求助10
23秒前
pluto应助徐远忠采纳,获得10
24秒前
张张发布了新的文献求助10
24秒前
刘星星发布了新的文献求助10
25秒前
动听的雪卉完成签到,获得积分10
25秒前
wellscurry完成签到,获得积分20
25秒前
zhai发布了新的文献求助10
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Reducing Compassion Fatigue, Secondary Traumatic Stress and Burnout 600
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Mammalian Synthetic Biology 500
Auslegungsgeschichte 500
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7639066
求助须知:如何正确求助?哪些是违规求助? 9212206
关于积分的说明 19761593
捐赠科研通 7205836
什么是DOI,文献DOI怎么找? 3275955
关于科研通互助平台的介绍 2437529
邀请新用户注册赠送积分活动 2273219