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R²=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 pro- poses a method based on particle swarm optimization and backpropagation neural network al- gorithm 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 an- alyzed the Raman spectra of purified preservatives such as benzyl alcohol and phenoxyethanol in skincare products and predicted their vibrational spectra using density functional theory.