偏最小二乘回归
分析物
材料科学
拉曼光谱
表面增强拉曼光谱
基质(水族馆)
分析化学(期刊)
化学
色谱法
计算机科学
拉曼散射
机器学习
海洋学
光学
物理
地质学
作者
Yi Xu,Felix Y.H. Kutsanedzie,Md Mehedi Hassan,Huanhuan Li,Quansheng Chen
标识
DOI:10.1016/j.saa.2018.08.035
摘要
With increased concerns on milk safety issues, the development of a simple and sensitive method to detect 2,4-dichlorophenoxyacetic acid (2,4-D), a common contaminant in milk, becomes relevant in safeguarding human health threats that results from its consumption. Surface-enhanced Raman spectroscopy (SERS) shows excellent ability for various targets analysis but its usage for rapid and accurate determination of analyte via SERS presents challenges. This study attempted the quantification of 2,4-dichlorophenoxyacetic acid (2,4-D) residue in milk using a novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms. Au NPs@ silica composite was synthesized as a SERS sensor through self-assembly. Thereafter, the SERS spectrum of 2,4-D extract from milk with different concentrations based on the developed SERS sensor was collected and the spectra were analyzed by partial least squares (PLS), and variable selection algorithms - genetic algorithm-PLS (GA-PLS), competitive-adaptive reweighted sampling-PLS (CARS-PLS) and ant colony optimization-PLS (ACO-PLS), to develop quantitative models for 2,4-D prediction. The results obtained showed that the CARS-PLS model gave the optimum result with LOD of 0.01 ng/mL realized and a determination coefficient in the prediction set of (RP) = 0.9836 within a linear range of 10-2 to 106 ng/mL was achieved. Au NPs@ silica SERS sensor combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.
科研通智能强力驱动
Strongly Powered by AbleSci AI