外文摘要：Phthalates are commonly used plasticizers in the plastics industry, and have received extensive attention due to their reproductive toxicity. Since phthalates are lipophilic solutions, phthalates can easily migrate from packaging to edible oils. This study synthesized stable and sensitive Gold Nanostars as SERS substrates to conduct qualitative and quantitative analysis of two common phthalates, dibutyl phthalate and di(2-ethylhexyl) phthalate. Two ethanol standard solutions and actual oil solutions of phthalates at different concentrations (10, 5, 1, 0.5, 0.1, 0.02 mg/kg) were prepared. After dimension reduction, LSTM achieved the accuracy of 98% for pure EVOO and EVOO adulterated with different types of phthalates. In terms of quantification, LSTM demonstrates great predictive performance with Rp2 greater than 0.97 and the ratio of performance to deviation greater than 5. These results have certain guiding significance for the analysis of plasticizers in edible oil.
外文关键词：deep learning;olive oil;SERS;Gold Nanostar;Phthalate
作者：Zhang, Xin;Wu, Xijun;Ma, Renqi;Liu, Hailong;Zhang, Yungang;Du, Zherui;Yang, Daolin
作者单位：Yanshan Univ
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：433
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台