Fusion of Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy for Mineral Identification Based on Machine Learning

激光诱导击穿光谱 拉曼光谱 光谱学 融合 鉴定(生物学) 矿物 材料科学 化学 分析化学(期刊) 光学 物理 环境化学 生物 冶金 植物 语言学 哲学 量子力学
作者
Yujia Dai,Ziyuan Liu,Shangyong Zhao
出处
期刊:Molecules [MDPI AG]
卷期号:29 (14): 3317-3317 被引量:1
标识
DOI:10.3390/molecules29143317
摘要

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares–discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals.
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
PSCs发布了新的文献求助10
刚刚
QWJ完成签到,获得积分10
刚刚
1秒前
1秒前
1秒前
zxy完成签到,获得积分10
2秒前
sober完成签到,获得积分10
2秒前
2秒前
mmknnk完成签到,获得积分20
2秒前
cc2064完成签到 ,获得积分10
2秒前
调皮冰旋发布了新的文献求助10
3秒前
西哈哈完成签到,获得积分20
3秒前
3秒前
3秒前
3秒前
Orange应助幸福胡萝卜采纳,获得10
3秒前
SHDeathlock完成签到,获得积分10
4秒前
习习发布了新的文献求助100
5秒前
Jolene66完成签到,获得积分10
5秒前
研友_8RlQ2n发布了新的文献求助10
5秒前
6秒前
852应助Pangsj采纳,获得10
6秒前
Song完成签到 ,获得积分10
6秒前
6秒前
7秒前
大胆夜绿发布了新的文献求助10
7秒前
Dr终年完成签到,获得积分10
7秒前
katharsis完成签到,获得积分10
7秒前
Ricardo发布了新的文献求助10
8秒前
歪歪象发布了新的文献求助10
8秒前
zeno123456完成签到,获得积分10
8秒前
陈某某发布了新的文献求助10
8秒前
9秒前
he完成签到,获得积分10
9秒前
9秒前
科研小民工应助忍冬半夏采纳,获得30
9秒前
小马甲应助年华采纳,获得10
9秒前
9秒前
CipherSage应助开放的听枫采纳,获得10
9秒前
Never stall发布了新的文献求助10
9秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527469
求助须知:如何正确求助?哪些是违规求助? 3107497
关于积分的说明 9285892
捐赠科研通 2805298
什么是DOI,文献DOI怎么找? 1539865
邀请新用户注册赠送积分活动 716714
科研通“疑难数据库(出版商)”最低求助积分说明 709678