摘要
随着消费者对护肤产品成分的关注,以及对天然和无添加产品的偏好,护肤品防腐剂的添加问题成为分析化学的热点领域之一。本文针对护肤品防腐剂的快速无损定量分析问题,提出一种基于粒子群优化反向传播神经网络算法组合表面增强拉曼光谱技术实现护肤品中苯甲醇、苯氧乙醇等防腐剂的定量预测。采集并分析了苯甲醇、苯氧乙醇等防腐剂纯化物及其在护肤品中的拉曼光谱,并通过密度泛函理论预测了苯甲醇、苯氧乙醇等防腐剂的振动光谱。对比了不同纳米颗粒对苯甲醇、苯氧乙醇等防腐剂的增强效应,检测了护肤品中不同浓度防腐剂的拉曼光谱,分别构建了粒子群优化反向传播神经网络算法模型的苯甲醇、苯氧乙醇等防腐剂定量分析模型,通过决定系数和均方根误差评价模型的预测能力,其中PSO-BP模型对测试集的最优预测结果为R^(2)=0.9518,RMSE=7.669×10^(-6)。总之,本文提出的粒子群优化反向传播神经网络算法组合表面增强拉曼光谱的技术方案对量化护肤品防腐剂具有较好的性能表现,该研究可为相关执法部门或质量监督部门提供强有力的技术参考和支撑。
With consumers’increasing attention to the ingredients of skincare products and their preference for natural and additive-free products,the issue of adding preservatives to skincare products has become one of the hot topics in analytical chemistry.This article proposes a method based on particle swarm optimization and backpropagation neural network algorithm combined with surface enhanced Raman spectroscopy(SERS)to achieve quantitative prediction of preservatives such as benzyl alcohol and phenoxyethanol in skincare products for the rapid and non-destructive quantitative analysis of preservatives.We collected and analyzed the Raman spectra of purified preservatives such as benzyl alcohol and phenoxyethanol in skincare products and predicted their vibrational spectra using density functional theory.The enhancement effects of different nanoparticles on preservatives such as benzyl alcohol and phenoxyethanol were compared in this innovative research.The Raman spectra of different concentrations of preservatives in skincare products were detected,and quantitative analysis models for preservatives such as benzyl alcohol and phenoxyethanol were constructed using the novel particle swarm optimization backpropagation neural network algorithm models.The predictive ability of the models was evaluated by the coefficient of determination and root mean square error.Among them,the PSO-BP model had the best prediction results for the test set,with R^(2) of 0.9518 and RMSE of 7.669×10^(-6).In summary,the particle swarm optimization backpropagation neural network algorithm combined with surface enhanced Raman spectroscopy proposed in this article has good performance in quantifying skincare preservatives.This study can provide strong technical reference and support for relevant law enforcement or quality supervision departments.
作者
任甜甜
王静
李静
REN Tiantian;WANG Jing;LI Jing(School of Information Engineering,Xuchang Electrical Vocational College,Xucahng,Henan,461000;School of Information Engineering,Henan Mechanical and Electrical Vocational College,Zhengzhou,Henan,451191;School of Information Engineering,Henan University of Science and Technology,Luoyang,Henan,471000)
出处
《光散射学报》
北大核心
2024年第4期418-426,共9页
The Journal of Light Scattering
基金
践行黄炎培“做学合一”教学观的河南机电“线场”教学模式创新(项目编号:ZJS2022Zd33)
黄炎培职业教育思想研究规划课题(项目编号:ZJS2022Zd33)
中西部地区本科层次职业教育理论与实践研究(项目编号:22GDZY0229)。
关键词
表面增强拉曼光谱
粒子群算法
反向传播神经网络
护肤品
防腐剂
Surface enhanced Raman spectroscopy
Particle swarm optimization algorithm
Backpropagation neural network
Skincare products
Preservative
