研究目的
To develop a rapid detection method for pesticide residues (fonofos, phosmet, and sulfoxaflor) in paddy water using surface-enhanced Raman spectroscopy (SERS) and chemometric methods.
研究成果
SERS combined with chemometric methods provides a rapid, accurate, and convenient approach for detecting pesticide residues in paddy water, with high classification and quantitative prediction accuracies, as demonstrated in both prepared and actual samples.
研究不足
The study may have limitations in detecting lower concentrations of pesticides beyond the reported limits (e.g., 0.5 mg/L for fonofos), and the method's applicability to other pesticides or complex matrices might require further validation. Reproducibility and interference from other substances in paddy water could be potential areas for optimization.
1：Experimental Design and Method Selection:
The study used SERS for spectral measurement due to its fingerprint characteristics, simple pretreatment, and fast measurement. Chemometric methods (SVM, KNN, RF, NB, PLSR) were employed for qualitative and quantitative analysis of pesticides.
2：Sample Selection and Data Sources:
Fifteen water samples were collected from Feixi rice-base in Hefei, China. Pesticides (fonofos, phosmet, sulfoxaflor) were added at various concentrations. Actual contaminated samples were obtained from the Center of Agricultural Products' Quality and Safety, Anhui Academy of Agricultural Sciences.
3：List of Experimental Equipment and Materials:
Gold nanorods (GNRs) synthesized using a seed-mediated growth method; chemicals like CTAB, hydrogen tetrachloroaurate, etc., from Aladdin Industrial Corporation; portable Raman spectrometer (B&WTEK iRaman-785plus); UV-Vis spectrometer (UV-2600, Shimadzu); SEM microscope (JSM 7500F, JEOL Ltd.); GC-MS instrument (Thermo Fisher, TSQ8000EVO).
4：Experimental Procedures and Operational Workflow:
Samples were centrifuged at 4000 rpm for 3 min; supernatant used for SERS measurement. GNRs colloid was dropped on a silicon chip, dried, then testing water sample added and evaporated. Spectra were collected with the Raman spectrometer (785 nm laser, 150 mW power, 3 scans, 5 s exposure, 2 cm-1 resolution).
5：Data Analysis Methods:
Spectra were baseline-corrected using polynomial fitting. Classification models (SVM, KNN, RF, NB) and regression models (PLSR, SVM, RF) were developed in MATLAB 2013a, with 80% data for calibration and 20% for validation. Performance evaluated using accuracy, RMSE, and R2.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
