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Dynamic surface-enhanced Raman spectroscopy for the detection of acephate residue in rice by using gold nanorods modified with cysteamine and multivariant methods

文献类型： 外文期刊

作者： Weng, Shizhuang 1 ; Zhu, Wenxiu 1 ; Li, Pan 2 ; Yuan, Hecai 1 ; Zhang, Xueyan 1 ; Zheng, Ling 1 ; Zhao, Jinling 1 ; Huang 1 ;

作者机构： 1.Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, 111 Jiulong Rd, Hefei, Peoples R China

2.Chinese Acad Sci, Hefei Inst Phys Sci, 350 Shushanhu Rd, Hefei 230031, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Minist Agr Beijing, Agr Prod Qual & Safety Risk Assessment Lab, Beijing, Peoples R China

关键词： Dynamic surface-enhanced Raman spectroscopy; Acephate; Gold nanorods modified with cysteamine; Residue level analysis; Multivariant methods

期刊名称：FOOD CHEMISTRY （ 影响因子：7.514； 五年影响因子：7.516 ）

ISSN： 0308-8146

年卷期： 2020 年 310 卷

页码：

收录情况： SCI

摘要： Dynamic surface-enhanced Raman spectroscopy (D-SERS) was employed for the rapid detection of acephate in rice with simply regulated gold nanorods. Gold nanorods modified with cysteamine were prepared to circumvent the weak affinity of acephate molecules to the gold surface for a gigantic and stable enhancement. D-SERS was adopted to measure spectra of acephate residue at a range of 100.2-0.5 mg/L in rice samples, and the low residue of 0.5 mg/L can be still detected. Multivariant methods in machine or deep learning were used to develop the regression models for the automatic analysis of acephate residue level. Partial least squares regression and principal component analysis obtained the optimal performance with the root-mean-square error (RMSE) of validation of 5.4776, coefficient of determination (R-2) of validation of 0.9560, RMSE of prediction of 6.2845, and R-2 of prediction of 0.9541. Thus, the proposed method provides accurate and sensitive detection for acephate in rice.

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