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利用可见/近红外光谱法对黄瓜中的农药残留进行无损检测。

Non-destructive detection of pesticide residues in cucumber using visible/near-infrared spectroscopy.

作者信息

Jamshidi Bahareh, Mohajerani Ezeddin, Jamshidi Jamshid, Minaei Saeid, Sharifi Ahmad

机构信息

a Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO) , Karaj , Iran.

出版信息

Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2015;32(6):857-63. doi: 10.1080/19440049.2015.1031192. Epub 2015 Apr 14.

Abstract

The feasibility of using visible/near-infrared (Vis/NIR) spectroscopy was assessed for non-destructive detection of diazinon residues in intact cucumbers. Vis/NIR spectra of diazinon solution and cucumber samples without and with different concentrations of diazinon residue were analysed at the range of 450-1000 nm. Partial least squares-discriminant analysis (PLS-DA) models were developed based on different spectral pre-processing techniques to classify cucumbers with contents of diazinon below and above the MRL as safe and unsafe samples, respectively. The best model was obtained using a first-derivative method with the lowest standard error of cross-validation (SECV = 0.366). Moreover, total percentages of correctly classified samples in calibration and prediction sets were 97.5% and 92.31%, respectively. It was concluded that Vis/NIR spectroscopy could be an appropriate, fast and non-destructive technology for safety control of intact cucumbers by the absence/presence of diazinon residues.

摘要

评估了使用可见/近红外(Vis/NIR)光谱法对完整黄瓜中敌敌畏残留进行无损检测的可行性。在450 - 1000纳米范围内分析了敌敌畏溶液以及不含和含有不同浓度敌敌畏残留的黄瓜样品的Vis/NIR光谱。基于不同的光谱预处理技术建立了偏最小二乘判别分析(PLS-DA)模型,以分别将敌敌畏含量低于和高于最大残留限量(MRL)的黄瓜分类为安全和不安全样品。使用一阶导数法获得了最佳模型,其交叉验证标准误差最低(SECV = 0.366)。此外,校准集和预测集中正确分类样品的总百分比分别为97.5%和92.31%。得出的结论是,Vis/NIR光谱法可能是一种适用于通过敌敌畏残留的有无对完整黄瓜进行安全控制的快速无损技术。

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