DETERMINATION OF COPPER,ZINC,CADMIUM AND LEAD IN WATER USING CO-PRECIPITATION METHOD AND RAMAN SPECTROSCOPY

Mn co-precipitation method combined with Raman spectroscopy were used to detenmine trace heavy metals(copper,zinc,cadmium and lead)in water sample.Different concentrations of heavy metals including copper,zinc,cadmiun and lead in water samples were separated and enriched by Mn^(2+)-phen SCN-ternary complex 0o-precipitation procedure.The Raman spectra of co-precipitation sediments were collected using confocal micro-Raman spectrometry.Different preprocessing treatments and regression calibration methods were compared.The best models using partial least squares regression(PLS)of copper,zinc,cadmium and lead were built with a correlation cofficient of prediction(R_(p))of 0.979,0.964,0.956 and 0972,respectively,and the root mean square error of prediction(RMSEP)of 6.587,9.046,9.998 and 7.751 pug/kg,respectively.The co-precipitation procedure combined with Raman spectroscopy method are feasible to detect the amount of heavy metals in water.

Quantitative of pesticide residue on the surface of navel orange by confocal microscopy Raman spectrometer

The potential of Confocal micro Raman spectroscopy in the quantitative analysis of pesticide(Chlorpyrifos,Omethoate)residues on orange surface is investigated in this work.Quantitative analysis models were established by partial least squares(PLS)using different preprocessing methods(Smoothing,First derivative,MSC,Baseline)for pesticide residues.For pesticide resi-dues,the higher correlation coefficients(r)is 0.972 and 0.943,the root mean square error of prediction(RMSEP)is 2.05%and 2.36%,respectively.It is therefore clear that Confocal micro-Raman spectroscopy techniques enable rapid,nondestructive and reliable measurements,so Raman spectrometry appears to be a prormising tool for pesticide residues.

Online quantitative analysis of soluble solids content in navel oranges using visible-nearinfrared spectroscopy and variable selection methods

Variable selection is applied widely for visible-near infrared(Vis-NIR)spectroscopy analysis of internal quality in fruits.Different spectral variable selection methods were compared for online quantitative analysis of soluble solids content(SSC)in navel oranges.Moving window partial least squares(MW-PLS),Monte Carlo uninformative variables elimination(MC-UVE)and wavelet transform(WT)combined with the MC-UVE method were used to select the spectral variables and develop the calibration models of online analysis of SSC in navel oranges.The performances of these methods were compared for modeling the Vis NIR data sets of navel orange samples.Results show that the WT-MC-UVE methods gave better calibration models with the higher correlation cofficient(r)of 0.89 and lower root mean square error of prediction(RMSEP)of 0.54 at 5 fruits per second.It concluded that Vis NIR spectroscopy coupled with WT-MC-UVE may be a fast and efective tool for online quantitative analysis of SSC in navel oranges.

Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing

The study aimed to investigate the fast and nondestructive method for detecting carbon and nitrogen content in citrus canopy.The multispectral imagery of Tarocco blood orange(Citrus sinensis L.Osbeck)plant canopy was obtained by a multispectral camera array mounted at an eight-rotor unmanned aerial vehicle(UAV)flying at an altitude of 100 m above the canopy in Wanzhou District of Chongqing Municipality,China.Average spectral reflectance data of the whole canopy,mature leaf areas and young leaves areas were extracted from the imagery.Two spectral pre-processing methods,multiplicative scatter correction(MSC)and standard normal variable(SNV),and two modeling methods,the partial least squares(PLS)and the least squares support vector machine(LS-SVM),were adopted and compared for their prediction accuracy of total content of nitrogen,soluble sugar and starch in the leaves.The results showed that,based on the spectral data extracted from the mature leaves in the multispectral imagery,the PLS model based on the original spectrum obtained a Rp(correlation coefficient)of 0.6469 and RMSEP(root mean squares error of prediction)of 0.1296,suggested that it was the best for the prediction of total nitrogen content;the PLS model based on MSC(multiplicative scatter correction)spectrum pre-processing was the best for predicting total soluble sugar content(Rp=0.6398 and RMSEP=8.8891);and the LS-SVM model based on MSC was the best for the starch content prediction(Rp=0.6822 and RMSEP=14.9303).The prediction accuracy for carbon and nitrogen contents based on the spectral data extracted from the whole canopy and the young leaves were lower than that from the mature leaves.The results indicate that it is feasible to estimate the carbon and nitrogen contents by low-altitude airborne multispectral images.

Detection of pesticides on navel orange skin by surface-enhanced Raman spectroscopy coupled with Ag nanostructures

Residual pesticides such as phosmet and chlorpyrifos in fruit have become a public concern problem in recent years.In this study,surface-enhanced Raman spectroscopy(SERS)was used to detect and characterize pesticides extracted from navel orange surfaces.Silver colloid was prepared for getting the SERS of phosmet and chlorpyrifos.Enhanced Raman signals of phosmet over a concentration range of 5 mg/L to 30 mg/L and chlorpyrifos over a concentration range of 5 mg/L to 20 mg/L were acquired.Partial least squares(PLS)regression combined with different data preprocessing methods was used to develop quantitative models.With the second derivative data preprocessing,the best prediction model of phosmet pesticide was achieved with a correlation coefficient(r)of 0.852 and the root mean square error of prediction(RMSEP)of 5.177 mg/L.The best prediction model of chlorpyrifos pesticide was achieved with r of 0.843 and the RMSEP of 2.992 mg/L using the multiplicative scatter correction(MSC)and first derivative data preprocessing.This study indicated that SERS coupled with Ag nanostructures is a potential tool for analysis of phosmet and chlorpyrifos pesticide residues.