Improving aromatic water-contaminant detection with machine-learning classification and regression for simultaneous Absorbance-Transmission Excitation Emission Matrix (A-TEEM) spectroscopy

BTEX公司 偏最小二乘回归 基质(化学分析) 支持向量机 化学 光谱学 分析化学(期刊) 人工智能 二甲苯 环境化学 计算机科学 机器学习 色谱法 物理 量子力学 有机化学
作者
Adam M. Gilmore,Linxi Chen
标识
DOI:10.1117/12.2556434
摘要

Optical detection of aromatic water-contaminants from petroleum or industrial spills is challenging due to background signals from natural and/or man-made components. Further, while target contaminants are regulated at microgram per liter (μg/L) levels, conventional Raman, FTIR and UV-VIS spectroscopy are generally limited to milligram per liter (mg/L) detection ranges. This study reports on patented A-TEEM spectroscopy which primarily uses fluorescence excitation emission matrix data that are corrected for inner-filter effects (IFE) to eliminate spectral distortion. IFE correction improves resolution of low concentration contaminants from higher concentration backgrounds. The multidimensional ATEEM dataset contains spectral information in the UV-VIS range for all chromophoric and fluorescent compounds in the sample matrix. Nevertheless, because the spectra of many compounds overlap or vary in intensity extracting qualitative and quantitative information generally requires multivariate analyses. Importantly, the UV-VIS and EEM data can be analyzed in a 'multi-block' format to leverage the resolution capacity of these simultaneously acquired independent data sets. We evaluated Benzene, Toluene, Ethylbenzene and Xylene (BTEX) as well as naphthalene in filtered (0.45 μm) raw surface water before drinking water treatment. We show that typical methods including Partial Least Squares (PLS) and Parallel Factor Analysis (PARAFAC) exhibit a variety of pitfalls that can confound accurate contaminant detection and quantification. We report that classification and regression using methods including Support Vector Machine (SVM) and especially XGradient Boost (XGB) algorithms can be more effectively validated to rapidly yield lower μg/L detection limits with potential to automate early-warning reporting.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
2秒前
3秒前
4秒前
研友_VZG7GZ应助欣喜沛芹采纳,获得10
5秒前
感动黄豆完成签到,获得积分20
6秒前
7秒前
7秒前
9秒前
11秒前
Owen应助Kikua采纳,获得10
11秒前
juliar完成签到 ,获得积分10
11秒前
12秒前
Bressanone发布了新的文献求助10
12秒前
13秒前
彭于晏应助忐忑的阑香采纳,获得10
13秒前
赘婿应助xiaojian_291采纳,获得10
13秒前
14秒前
郭小宝发布了新的文献求助20
14秒前
糊涂的语兰完成签到,获得积分10
15秒前
浮生若梦完成签到 ,获得积分10
16秒前
CA发布了新的文献求助10
16秒前
忘记时间发布了新的文献求助30
16秒前
王羊补牢完成签到,获得积分10
18秒前
19秒前
潇湘雪月发布了新的文献求助10
24秒前
栗园应助仙都丽娜采纳,获得10
25秒前
严珍珍完成签到 ,获得积分10
25秒前
思源应助郭小宝采纳,获得10
25秒前
无理完成签到 ,获得积分10
26秒前
28秒前
28秒前
无端完成签到 ,获得积分20
28秒前
感动黄豆发布了新的文献求助10
29秒前
量子星尘发布了新的文献求助10
29秒前
30秒前
zwy109发布了新的文献求助10
31秒前
33秒前
立夏完成签到,获得积分10
34秒前
34秒前
35秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989242
求助须知:如何正确求助?哪些是违规求助? 3531393
关于积分的说明 11253753
捐赠科研通 3270010
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136