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
刚刚
刚刚
Jolin发布了新的文献求助10
刚刚
1秒前
启明完成签到,获得积分10
1秒前
1秒前
英俊的铭应助耍酷白筠采纳,获得10
1秒前
科目三应助lw采纳,获得10
1秒前
1秒前
大模型应助LONELY采纳,获得10
2秒前
orixero应助renlangfen采纳,获得10
2秒前
无花果应助linye采纳,获得10
2秒前
赵Zhao完成签到,获得积分10
2秒前
2秒前
HU发布了新的文献求助30
2秒前
3秒前
waaliyh完成签到,获得积分10
3秒前
YG发布了新的文献求助10
4秒前
jksg完成签到,获得积分10
4秒前
完美世界应助江晚正愁与采纳,获得10
4秒前
生生不息完成签到,获得积分20
4秒前
恰药蚊香发布了新的文献求助10
4秒前
hanhou发布了新的文献求助10
5秒前
xlz_0226完成签到,获得积分10
5秒前
虚拟的代灵完成签到,获得积分10
5秒前
5秒前
Hello完成签到,获得积分10
5秒前
6秒前
6秒前
一二三四发布了新的文献求助10
6秒前
immm发布了新的文献求助10
7秒前
闪闪的夜阑完成签到,获得积分10
7秒前
7秒前
满增明完成签到,获得积分10
7秒前
7秒前
支臻发布了新的文献求助10
7秒前
7秒前
李根苗完成签到,获得积分10
7秒前
淡定沛珊发布了新的文献求助10
9秒前
司南发布了新的文献求助50
9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
Picture this! Including first nations fiction picture books in school library collections 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1500
Cowries - A Guide to the Gastropod Family Cypraeidae 1200
Quality by Design - An Indispensable Approach to Accelerate Biopharmaceutical Product Development 800
ON THE THEORY OF BIRATIONAL BLOWING-UP 666
Signals, Systems, and Signal Processing 610
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 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6391965
求助须知:如何正确求助?哪些是违规求助? 8207410
关于积分的说明 17372941
捐赠科研通 5445467
什么是DOI,文献DOI怎么找? 2879014
邀请新用户注册赠送积分活动 1855449
关于科研通互助平台的介绍 1698579