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基于表面增强拉曼光谱的高效半定量分析水果中多菌灵的改进 POD 模型。

An Improved POD Model for Fast Semi-Quantitative Analysis of Carbendazim in Fruit by Surface Enhanced Raman Spectroscopy.

机构信息

School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China.

School of Life Sciences, Shanghai University, Shanghai 200444, China.

出版信息

Molecules. 2022 Jun 30;27(13):4230. doi: 10.3390/molecules27134230.

Abstract

The current detection method of carbendazim suffers from the disadvantages of complicated preprocessing and long cycle time. In order to solve the problem of rapid quantitative screening of finite contaminants, this article proposed a qualitative method based on characteristic peaks and a semi-quantitative method based on threshold to detect carbendazim in apple, and finally the method is evaluated by a validation system based on binary output. The results showed that the detection limit for carbendazim was 0.5 mg/kg, and the detection probability was 100% when the concentration was no less than 1 mg/kg. The semi-quantitative analysis method had a false positive rate of 0% and 5% at 0.5 mg/kg and 2.5 mg/kg, respectively. The results of method evaluation showed that when the added concentration was greater than 2.5 mg/kg, the qualitative detection method was consistent with the reference method. When the concentration was no less than 5 mg/kg, the semi-quantitative method is consistent between different labs. The semi-quantitative method proposed in this study can achieve the screening of finite contaminants in blind samples and simplify the test validation process through the detection probability model, which can meet the needs of rapid on-site detection and has a good application prospect.

摘要

目前检测多菌灵的方法存在预处理复杂、周期长的缺点。为解决有限污染物的快速定量筛选问题,本文提出了基于特征峰的定性方法和基于阈值的半定量方法,最终通过基于二值输出的验证系统对方法进行评估。结果表明,多菌灵的检测限为 0.5mg/kg,浓度不低于 1mg/kg 时检测概率为 100%。半定量分析方法在 0.5mg/kg 和 2.5mg/kg 时的假阳性率分别为 0%和 5%。方法评估结果表明,添加浓度大于 2.5mg/kg 时,定性检测方法与参考方法一致。当浓度不低于 5mg/kg 时,不同实验室的半定量方法一致。本研究提出的半定量方法可以通过检测概率模型实现盲样中有限污染物的筛选,简化测试验证过程,满足快速现场检测的需要,具有良好的应用前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e39/9268280/5eb242a478fb/molecules-27-04230-g001.jpg

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