不同仪器间SERS谱变换的函数回归

IF 3.6 3区 化学 Q2 CHEMISTRY, ANALYTICAL
Analyst Pub Date : 2024-12-04 DOI:10.1039/D4AN01177E
Tao Wang, Yanjun Yang, Haoran Lu, Jiaheng Cui, Xianyan Chen, Ping Ma, Wenxuan Zhong and Yiping Zhao
{"title":"不同仪器间SERS谱变换的函数回归","authors":"Tao Wang, Yanjun Yang, Haoran Lu, Jiaheng Cui, Xianyan Chen, Ping Ma, Wenxuan Zhong and Yiping Zhao","doi":"10.1039/D4AN01177E","DOIUrl":null,"url":null,"abstract":"<p >Surface-enhanced Raman spectroscopy (SERS) holds remarkable potential for the rapid and portable detection of trace molecules. However, the analysis and comparison of SERS spectra are challenging due to the diverse range of instruments used for data acquisition. In this paper, a spectra instrument transformation framework based on the penalized functional regression model (SpectraFRM) is introduced for cross-instrument mapping with subsequent machine learning classification to compare transformed spectra with standard spectra. In particular, the nonparametric forms of the functional response, predictors, and coefficients employed in SepctraFRM allow for efficient modeling of the nonlinear relationship between target spectra and standard spectra. In the leave-one-out training and test of 20 analytes across four instruments, the results demonstrate that SpectraFRM can provide interpretable corrections to peaks and baseline spectra, leading to approximately 11% error reduction, compared with original spectra. With an additional feature extraction step, the transformed spectra outperform the original spectra by 10% in analytes identification tasks. Overall, the proposed method is shown to be flexible, robust, accurate, and interpretable despite varieties of analytes and instruments, making it a potentially powerful tool for the standardization of SERS spectra from various instruments.</p>","PeriodicalId":63,"journal":{"name":"Analyst","volume":" 3","pages":" 460-469"},"PeriodicalIF":3.6000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/an/d4an01177e?page=search","citationCount":"0","resultStr":"{\"title\":\"Functional regression for SERS spectrum transformation across diverse instruments†\",\"authors\":\"Tao Wang, Yanjun Yang, Haoran Lu, Jiaheng Cui, Xianyan Chen, Ping Ma, Wenxuan Zhong and Yiping Zhao\",\"doi\":\"10.1039/D4AN01177E\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Surface-enhanced Raman spectroscopy (SERS) holds remarkable potential for the rapid and portable detection of trace molecules. However, the analysis and comparison of SERS spectra are challenging due to the diverse range of instruments used for data acquisition. In this paper, a spectra instrument transformation framework based on the penalized functional regression model (SpectraFRM) is introduced for cross-instrument mapping with subsequent machine learning classification to compare transformed spectra with standard spectra. In particular, the nonparametric forms of the functional response, predictors, and coefficients employed in SepctraFRM allow for efficient modeling of the nonlinear relationship between target spectra and standard spectra. In the leave-one-out training and test of 20 analytes across four instruments, the results demonstrate that SpectraFRM can provide interpretable corrections to peaks and baseline spectra, leading to approximately 11% error reduction, compared with original spectra. With an additional feature extraction step, the transformed spectra outperform the original spectra by 10% in analytes identification tasks. Overall, the proposed method is shown to be flexible, robust, accurate, and interpretable despite varieties of analytes and instruments, making it a potentially powerful tool for the standardization of SERS spectra from various instruments.</p>\",\"PeriodicalId\":63,\"journal\":{\"name\":\"Analyst\",\"volume\":\" 3\",\"pages\":\" 460-469\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.rsc.org/en/content/articlepdf/2025/an/d4an01177e?page=search\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analyst\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://pubs.rsc.org/en/content/articlelanding/2025/an/d4an01177e\",\"RegionNum\":3,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analyst","FirstCategoryId":"92","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2025/an/d4an01177e","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0

摘要

表面增强拉曼光谱(SERS)在快速便携式检测微量分子方面具有显著的潜力。然而,由于用于数据采集的仪器范围不同,SERS光谱的分析和比较具有挑战性。本文提出了一种基于惩罚函数回归模型(SpectraFRM)的光谱仪器转换框架,用于跨仪器映射和后续机器学习分类,将转换后的光谱与标准光谱进行比较。特别是,SepctraFRM中采用的功能响应、预测因子和系数的非参数形式可以有效地模拟目标光谱和标准光谱之间的非线性关系。在4台仪器上对20个分析物进行了“留一”训练和测试,结果表明,SpectraFRM可以对峰和基线光谱进行可解释的修正,与原始光谱相比,误差降低了约11%。通过额外的特征提取步骤,转换后的光谱在分析物识别任务中比原始光谱高出10%。总体而言,尽管分析物和仪器多种多样,但所提出的方法具有灵活性,鲁棒性,准确性和可解释性,使其成为各种仪器SERS光谱标准化的潜在强大工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Functional regression for SERS spectrum transformation across diverse instruments†

Functional regression for SERS spectrum transformation across diverse instruments†

Surface-enhanced Raman spectroscopy (SERS) holds remarkable potential for the rapid and portable detection of trace molecules. However, the analysis and comparison of SERS spectra are challenging due to the diverse range of instruments used for data acquisition. In this paper, a spectra instrument transformation framework based on the penalized functional regression model (SpectraFRM) is introduced for cross-instrument mapping with subsequent machine learning classification to compare transformed spectra with standard spectra. In particular, the nonparametric forms of the functional response, predictors, and coefficients employed in SepctraFRM allow for efficient modeling of the nonlinear relationship between target spectra and standard spectra. In the leave-one-out training and test of 20 analytes across four instruments, the results demonstrate that SpectraFRM can provide interpretable corrections to peaks and baseline spectra, leading to approximately 11% error reduction, compared with original spectra. With an additional feature extraction step, the transformed spectra outperform the original spectra by 10% in analytes identification tasks. Overall, the proposed method is shown to be flexible, robust, accurate, and interpretable despite varieties of analytes and instruments, making it a potentially powerful tool for the standardization of SERS spectra from various instruments.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Analyst
Analyst 化学-分析化学
CiteScore
7.80
自引率
4.80%
发文量
636
审稿时长
1.9 months
期刊介绍: "Analyst" journal is the home of premier fundamental discoveries, inventions and applications in the analytical and bioanalytical sciences.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信