The feasibility of using near-infrared reflectance spectroscopy (NIRS) to determine the concentrations of copper (Cu) and zinc (Zn) in Ludwigia prostrata Roxb plants was investigated. Ludwigia prostrata Roxb plants were grown over a full growth cycle under controlled laboratory conditions in soils contaminated with heavy metals. The Cu and Zn concentrations in 72 L. prostrata Roxb samples were analyzed using flame atomic absorption spectrometry, and NIRS spectra were collected in the 1099-2500 nm range. Five mathematical treatments of the spectral data were compared prior to developing the calibration models (n = 48) using partial least squares regression methods. The two calibration models for Cu and Zn concentrations were evaluated according to the correlation coefficient of cross-validation (R(cv)) and root mean squares error of cross-validation. The highest R(cv) and the lowest RMSECV were obtained for Cu (0.9 and 7.24 mg kg(-1)) and Zn (0.94 and 19.17 mg kg(-1)), respectively. The results showed that near-infrared diffuse reflectance spectroscopy can be used for the rapid determination of Cu and Zn in leaves of L. prostrata Roxb plants.
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 di®erent preprocessing methods (Smoothing, First derivative, MSC, Baseline) for pesticide residues. For pesticide residues, the higher correlation coe±cients (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 microRaman spectroscopy techniques enable rapid, nondestructive and reliable measurements, so Raman spectrometry appears to be a promising tool for pesticide residues.
The detection of pesticide residued in fruit is an important concern for consumers. Surface enhanced Raman spectroscopy (SERS) coupled with gold colloid was applied to analyze two kinds of pesticides (phosmet, chlorpyrifos) which were mainly used on the navel orange. The concentration of the phosmet samples of range from 3 to 33 mg/L and chlorpyrifos samples of range from 4 to 34 mg/L. Using Partial least squares (PLS) regression and the different preprocessing method for the spectral data analyses, and different pretreatment methods such as the Savitzky-Golay were compared. The optimal model of phosmet pesticide and chlorpyrifos pesticide were set up. The prediction correlation coefficient (R) and the root mean square error of prediction (RMSEP) of phosmet pesticide were 0.924 and 4.293 mg/L; The R and RMSEP of chlorpyrifos pesticide were 0.715 and 6.646 mg/L. It indicated that SERS technology is a effective method in the field of pesticide residue detection in fruit.
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