利用表面增强拉曼光谱(SERS)结合长短期记忆(LSTM)神经网络对特级初榨橄榄油(EVOO)中邻苯二甲酸酯进行定性和定量研究

IF 8.5 1区 农林科学 Q1 CHEMISTRY, APPLIED
Xijun Wu, Zherui Du, Renqi Ma, Xin Zhang, Daolin Yang, Hailong Liu, Yungang Zhang
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引用次数: 0

摘要

邻苯二甲酸酯是塑料工业中常用的增塑剂,因其生殖毒性而受到广泛关注。由于邻苯二甲酸盐是亲脂性溶液,邻苯二甲酸盐可以很容易地从包装转移到食用油中。本研究合成了稳定灵敏的金纳米星作为SERS底物,对两种常见的邻苯二甲酸二丁酯和邻苯二甲酸二(2-乙基己基)酯进行了定性和定量分析。制备了不同浓度(10、5、1、0.5、0.1、0.02 mg/kg)邻苯二甲酸酯乙醇标准溶液和实际油溶液。降维后,LSTM对纯EVOO和掺入不同类型邻苯二甲酸盐的EVOO的准确度达到98%。在量化方面,LSTM表现出较好的预测性能,Rp2大于0.97,性能与偏差之比大于5。这些结果对食用油中增塑剂的分析具有一定的指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Qualitative and quantitative studies of phthalates in extra virgin olive oil (EVOO) by surface-enhanced Raman spectroscopy (SERS) combined with long short term memory (LSTM) neural network

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.

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来源期刊
Food Chemistry
Food Chemistry 工程技术-食品科技
CiteScore
16.30
自引率
10.20%
发文量
3130
审稿时长
122 days
期刊介绍: Food Chemistry publishes original research papers dealing with the advancement of the chemistry and biochemistry of foods or the analytical methods/ approach used. All papers should focus on the novelty of the research carried out.
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