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Smart Agriculture ›› 2021, Vol. 3 ›› Issue (4): 42-52.doi: 10.12133/j.smartag.2021.3.4.202106-SA002

• Topic--Agricultural Products Processing and Testing • Previous Articles     Next Articles

Rapid Detection of Imazalil Residues in Navel Orange Peel Using Surface-Enhanced Raman Spectroscopy

ZHANG Sha1,2(), LIU Muhua1,2, CHEN Jinyin2, ZHAO Jinhui1,2()   

  1. 1.College of Engineering, Jiangxi Agricultural University/Key Laboratory of Modern Agricultural Equipment in Jiangxi Province, Nanchang 330045, China
    2.Jiangxi Provincial Collaborative Innovation Center of Key Technologies and Quality and Safety in Post-Harvest Processing of Fruits and Vegetables, Nanchang 330045, China
  • Received:2021-06-02 Revised:2021-06-24 Online:2021-12-30 Published:2022-01-24
  • corresponding author: ZHAO Jinhui E-mail:1175794826@qq.com;9115328@ qq.com


Imazalil, a preservative for navel orange in the process of postharvest processing, is easy to seep into the flesh through the peel and produce residues in the flesh, which is vulnerable to cause endanger to human body if it was eaten accidentally. Base on this, a fast detection method of imazalil residues in navel orange peel ,namely surface-enhanced Raman spectroscopy (SERS) was proposed in this study. Firstly, the SERS detection conditions of imazalil residues in navel orange peel were optimized, and the optimal detection conditions were determined as follows: Reaction time of 2 min, gold colloid of 400 μL, NaBr as electrolyte solution, NaBr dosage of 25 μL. Based on the above optimal conditions, 6 groups of spectral data processed by adaptive iterative penalized least squares (air PLS), air PLS combination with normalization, air PLS combination with baseline correction, air PLS combination with first derivative, air PLS combination with standard normal distribution (SNV), air PLS combination with multiplicative scatter correction (MSC) were used to establish support vector regression (SVR) models and compare the models prediction performance. And air PLS method was selected as the spectral pretreatment method, because the value of correlation coefficient computed value of prediction set (RP) is the largest, and the value of root mean square error calculated value of the prediction set (RMSEP) is the smallest. Then, principal component analysis (PCA) was used to extract the features from spectral data, and the first seven principal component scores were selected as the input values of SVR prediction model. SVR, multiple linear regression (MLR) and partial least squares regression (PLSR) were used to analyze and compare the prediction performances. The RP value of prediction set of SVR prediction model could reach 0.9156, the RMSEP value of their prediction set was 4.8407 mg/kg, and the relative standard deviation computation value (RPD) was 2.3103, which indicated that the closer the predicted value of imazalil residue on navel orange surface based on SVR algorithm was to the measured value, the more effective the prediction accuracy of the model could be. The above data indicated that the speedy detection of imazalil residues in navel orange peel could be emploied by SERS coupled with PCA and SVR modeling method.

Key words: navel orange, imazalil, surface-enhanced Raman spectroscopy, support vector regression, multiple linear regression, partial least squares regression

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