Fast And Accurate Determination Of Chlormequat Chloride Residues Based On SERS

In recent years,the food safety problem caused by pesticide residues have attracted much attention.Surface-enhanced Raman spectroscopy(SERS)has unique advantages in the field of pesticide residue detection due to its fast analysis speed,high detection sensitivity and low water phase interference.In this paper,the method of fast and accurate determination of chlormequat chloride residue using SERS was mainly discussed.Thro?gh the detection of chlormequat residues in three objects including cherry tomato peel,wheat granules and soil,a detection research system from the outside to the inside and gradually deepening was formed.And,an analysis software for the SERS spectrum of chlormequat chloride residue was designed.The main research contents are as follows:(1)The possibility of fast and accurate determination of Chlormequat Chloride residues on cherry tomato peel using SERS with chemometrics method was studied.Firstly,pure chlormequat powder was dissolved in a mixed solution of ethanol and water(1:1)to prepare standard samples with different concentrations(10-0.25 mg/L).To simulate actual residue on peel,the labelled region of cherry tomato peel was gradually dropped with chlormequat chloride solution(10-0.5 mg/L).Then,gold nanorods(GNRs)was used as the active substrate for SERS detection,and spectra were collected by a portable Raman spectrometer equipped with 785 nm laser of 150 mW,and then was initially baseline-corrected and normalized.For chlormequat chloride,the spectra of 644-687,702-730 and 839-866 cm-1 were selected as spectra of characteristic ranges for subsequent analysis.Regression models for chlormequat chloride residue determination were developed by partial least squares regression(PLSR)and random forest(RF)with spectra of 600-1800 cm-1 and characteristic ranges.Prediction performance of models was evaluated based on root mean square error of cross-validation(RMSECV)which was calculated by 10-fold cross-validation method.Then,two supervised variable selection algorithms as competitive adaptive reweighted sampling(CARS)and random frog were used to select variables in the PLSR models for further improving the prediction accuracy.The experiments showed that the regression model,which was established by RF combined with the spectra of characteristic range,had the best prediction results with RMSECV of 0.0503 mg/L.Predictive recovery rate was in range of 93.2%-99.3%,and standard deviation of 0.032 mg/L to 0.878 mg/L was small for the residue of different concentration.These results indicated that the method developed using SERS,RF with spectra of characteristic range was an effective and feasible approach for determination of chlormequat chloride residue on cherry tomato peel at concentration ranging from 0.5 mg/L to 10 mg/L.(2)A simple and sensitive method for fast and accurate determination of chlormequat chloride residue in wheat granules using SERS was developed.Firstly,the standard sample(20-0.25 mg/L)was prepared by pretreating the wheat grains.Additionally,the spiked sample was prepared by wheat grains powder was spiked with chlormequat to yield final residue at 10-0.25 lig/g.SERS spectra were collected based on GNRs and corrected with baseline subsequently.The spectra of 644-687,702-730 and 839-866 cm-1 were selected as spectra of characteristic ranges for subsequent analysis.Furtherly,multiple linear regression(MLR)and PLSR were used for accurate and quantitative determination.Kernel principal component analysis(KPCA)was applied to extract the feature of spectral data for obtaining main information and reducing the dimension.Support vector regression(SVR)combined with KPCA was used to develop the regression model.Results proved that SVR and KPCA with a of 8000 was the optimal model,and RMSECV of the model was 0.0268 mg/L.The predictive recovery was in the range of 94.7%-104.6%,and standard deviation was from 0.007 mg/L to 0.066 mg/L.In addition,an unbiased estimation for generalization of the model was conducted by an independent testing set that was spectra of wheat extraction with 15,8,4 and 2 mg/L obtained through remeasurement.The RMSECV of the new testing set with the optimal model was about 0.2110 mg/L.The above results indicated that concentration of residue could be accurately predicted,and the model established by SVR and KPCA had good generality.(3)A new method for the preparation of substrates was developed to improve the detection limit of chlormequat and to determine the residues of chlormequat in soil.The GNRs were modified with 10-6 M cysteamine to obtain a novel substrate.Then,the extraction solution obtained after soil was pretreated and the chlormequat power was dissolved to prepare standard samples of 5-0.05 mg/L.Concurrently,after different concentrations of chlormequat were sprayed into the soil,the spiked samples of 5-0.1?g/g were prepared.Comparing the SERS spectra of the GNRs and the new substrate,it could be found that the enhancement effect of the new substrate was about 1 time higher than that of the GNRs.The minimum concentration of chlormequat residues in soil detected by SERS combined with a new substrate was 0.1 ?g/g.In addition,a PLSR model was established to predict chlormequat residues in soil.The RMSECV was 0.1245 mg/L,the predictive recovery was 98.4%-110.7%,and the standard deviation was 0.016 mg/L-0.156 mg/L.These results showed that the established regression model could achieve fast prediction of chlormequat residues in soil.The above results indicated that fast and accurate determination of chlormequat residues in three objects could be achieved using SERS combined with chemometrics methods.It could be also applied to detection multiple pesticide residues in other objects.In addition,according to the above detection methods,a multi-object analysis software for SERS spectra of chlormequat pesticide residues was designed,which could be used to import spectral data,select object,process data,display prediction results and feature peak positions,and save results.