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使用拉曼光谱结合多元数据分析快速鉴定和定量甲基丁香酚中的掺假物

Rapid Identification and Quantification of Adulteration in Methyl Eugenol using Raman Spectroscopy Coupled with Multivariate Data Analysis.

作者信息

Anwar Muntaha, Rimsha Gull, Majeed Muhammad Irfan, Alwadie Najah, Nawaz Haq, Majeed Muhammad Zeeshan, Rashid Nosheen, Zafar Fareeha, Kamran Ali, Wasim Muhammad, Mehmood Nasir, Shabbir Ifra, Imran Muhammad

机构信息

Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.

Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

出版信息

ACS Omega. 2024 Feb 8;9(7):7545-7553. doi: 10.1021/acsomega.3c06335. eCollection 2024 Feb 20.

DOI:10.1021/acsomega.3c06335
PMID:38405541
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10882614/
Abstract

Identification of adulterants in commercial samples of methyl eugenol is necessary because it is a botanical insecticide, a tephritid male attractant lure that is used to attract and kill invasive pests such as oriental fruit flies and melon flies on crops. In this study, Raman spectroscopy was used to qualitatively and quantitatively assess commercial methyl eugenol along with adulterants. For this purpose, commercial methyl eugenol was adulterated with different concentrations of xylene. The Raman spectral features of methyl eugenol and xylene in liquid formulations were examined, and Raman peaks were identified as associated with the methyl eugenol and adulterant. Principal component analysis (PCA) and partial least-squares regression analysis (PLSR) have been used to qualitatively and quantitatively analyze the Raman spectral features. PCA was applied to differentiate Raman spectral data for various concentrations of methyl eugenol and xylene. Additionally, PLSR has been used to develop a predictive model to observe a quantitative relationship between various concentrations of adulterated methyl eugenol and their Raman spectral data sets. The root-mean-square errors of calibration and prediction were calculated using this model, and the results were found to be 1.90 and 3.86, respectively. The goodness of fit of the PLSR model is found to be 0.99. The proposed approach showed excellent potential for the rapid, quantitative detection of adulterants in methyl eugenol, and it may be applied to the analysis of a range of pesticide products.

摘要

鉴定市售甲基丁香酚中的掺假物很有必要,因为它是一种植物性杀虫剂,是一种实蝇雄性引诱剂,用于吸引和杀死农作物上的入侵害虫,如东方果实蝇和瓜实蝇。在本研究中,拉曼光谱用于定性和定量评估市售甲基丁香酚及其掺假物。为此,用不同浓度的二甲苯对市售甲基丁香酚进行掺假。研究了甲基丁香酚和二甲苯在液体制剂中的拉曼光谱特征,并确定了与甲基丁香酚和掺假物相关的拉曼峰。主成分分析(PCA)和偏最小二乘回归分析(PLSR)用于定性和定量分析拉曼光谱特征。PCA用于区分不同浓度甲基丁香酚和二甲苯的拉曼光谱数据。此外,PLSR用于建立预测模型,以观察不同浓度掺假甲基丁香酚与其拉曼光谱数据集之间的定量关系。使用该模型计算校准和预测的均方根误差,结果分别为1.90和3.86。发现PLSR模型的拟合优度为0.99。所提出的方法在快速、定量检测甲基丁香酚中的掺假物方面显示出优异的潜力,并且可应用于一系列农药产品的分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/2657ea4e4928/ao3c06335_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/c4616155d2ef/ao3c06335_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/98bd54b966ae/ao3c06335_0002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/cef6657b180b/ao3c06335_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/2657ea4e4928/ao3c06335_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/c4616155d2ef/ao3c06335_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/98bd54b966ae/ao3c06335_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/9651611fa1b9/ao3c06335_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/a48a8aa65b2e/ao3c06335_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/cef6657b180b/ao3c06335_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5bbd/10882614/2657ea4e4928/ao3c06335_0006.jpg

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