近红外光谱法预测福尔马林中甲醛和甲醇残留量

IF 1.6 4区 化学 Q3 CHEMISTRY, APPLIED Journal of Near Infrared Spectroscopy Pub Date : 2022-03-03 DOI:10.1177/09670335221078355
R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho
{"title":"近红外光谱法预测福尔马林中甲醛和甲醇残留量","authors":"R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho","doi":"10.1177/09670335221078355","DOIUrl":null,"url":null,"abstract":"Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r2). The best results obtained a r2 above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":"30 1","pages":"160 - 168"},"PeriodicalIF":1.6000,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy\",\"authors\":\"R. Magalhães, N. Paiva, J. Ferra, F. Magalhães, J. Martins, L. Carvalho\",\"doi\":\"10.1177/09670335221078355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r2). The best results obtained a r2 above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.\",\"PeriodicalId\":16551,\"journal\":{\"name\":\"Journal of Near Infrared Spectroscopy\",\"volume\":\"30 1\",\"pages\":\"160 - 168\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2022-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Near Infrared Spectroscopy\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1177/09670335221078355\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Near Infrared Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1177/09670335221078355","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 2

摘要

氨基树脂主要由两种工艺生产:强酸工艺和碱酸工艺。两者在配方中都使用甲醛和碱(如氢氧化钠)。在这项工作中,前向区间偏最小二乘法被应用于创建预测模型,以确定氨基树脂生产中使用的甲醛和残留甲醇(存在于甲醛溶液中)的浓度。近红外(NIR)光谱是在两个不同的温度下获得的:18和35°C。用参考方法(即亚硫酸钠(甲醛)和气相色谱法(甲醇))的测量值建立了偏最小二乘校准模型。使用交叉验证的均方根误差(RMSECV)和预测决定系数(r2)比较了最佳模型的性能。最佳结果得到的r2高于0.994。对于甲醛和甲醇浓度,获得的RMSECV值分别为0.063%(m/m)和0.031%(m/s)。使用不同的甲醛溶液样品进行外部验证。与参考方法相比,本工作中提出的近红外方法被证明是有效的,并且能够显著缩短甲醛和甲醇浓度测量的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy
Amino resins are produced by two main processes: the strong acid process and the alkaline-acid process. Both use formaldehyde and a base (e.g. sodium hydroxide) in their formulation. In this work, Forward Interval Partial Least Squares methodology was applied to create prediction models for the determination of the concentration of formaldehyde and residual methanol (that is present in the formaldehyde solution) used in the production of amino resins. Near infrared (NIR) spectra were acquired at two different temperatures: 18 and 35°C. A Partial Least Squares calibration models were established with the measured values from reference methods: namely, sodium sulfite (formaldehyde) and gas chromatography (methanol). The performances of the best models were compared using the root mean square error of cross validation (RMSECV) and coefficient of determination for prediction (r2). The best results obtained a r2 above 0.994. The RMSECV values obtained were 0.063% (m/m) and 0.031% (m/m) for the formaldehyde and methanol concentration, respectively. External validation was performed using different formaldehyde solution samples. The NIR methodology presented in this work proved to be effective and enables a significant time reduction, when compared to the reference methods, in the measurement of formaldehyde and methanol concentrations.
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
CiteScore
3.30
自引率
5.60%
发文量
35
审稿时长
6 months
期刊介绍: JNIRS — Journal of Near Infrared Spectroscopy is a peer reviewed journal, publishing original research papers, short communications, review articles and letters concerned with near infrared spectroscopy and technology, its application, new instrumentation and the use of chemometric and data handling techniques within NIR.
期刊最新文献
Non-linear machine learning coupled near infrared spectroscopy enhanced model performance and insights for coffee origin traceability Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils Detection and classification of spongy tissue disorder in mango fruit during ripening by using visible-near infrared spectroscopy and multivariate analysis A method to standardize the temperature for near infrared spectra of the indigo pigment in non-dairy cream based on symbolic regression Moisture content of Panax notoginseng taproot predicted using near infrared spectroscopy
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1