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Residual pesticides in agricultural produce pose a threat to wildlife and domestic animals, and are one of the major food safety concerns around the world. Surface-enhanced Raman spectroscopy (SERS) coupled with gold nanoparticles was applied for rapid analysis of phosmet and thiabendazole, two widely used pesticides in apples. Four different diameters of nanoparticles were fabricated to select the optimal SERS substrate for analyzing each pesticide. A simple sample preparation method allowed for extracting the pesticides from apples within 20 min. The minimum detectable concentration was 0.5 μg/g for phosmet and 0.1 μg/g for thiabendazole in apples. Partial least squares regression (PLSR) models were also developed to correlate SERS spectra of apple extracts with the concentrations of each pesticide in apple for quantitative analysis purpose. The coefficient of determination (R 2 cv ) of actual values versus values predicted with PLSR models was 0.936 for phosmet and 0.959 for thiabendazole in apples. This study provides a new, rapid and sensitive approach for analysis of trace amounts of residual pesticides in apples based on SERS coupled with gold nanoparticles, which has great potential for analysis of other trace contaminants.

Surface-enhanced Raman spectroscopy (SERS) is an emerging technology with great potential for sensitive and rapid analysis of trace methylene blue (MB) in fish muscle. Five different SERS substrates including two commercial nanostructured surface substrates and three types of Au nanoparticles (NPs) with different diameters were compared to optimize enhancement effects. The highest sensitivity with the lowest detection limit of 5 ng/mL for MB standard solutions and 10 ng/g for MB in fish muscles was achieved using the 18 ± 2 nm Au NPs. Partial least squares regression models developed for quantitative analysis showed high linear correlation between the actual and predicted values for MB (R2 cv = 0.958, RPD = 4.84, RMSE = 6.97 ng/g) in fish muscle. Since Au NPs are easy to prepare, stable and affordable, the high sensitivity and predictability of this method has the potential to be widely used for trace analysis of prohibited drug residues in fish and potentially other foods or biological samples.

The residue of fishery drugs in aquatic food products has not diminished and has caused worldwide attention. This study aimed to develop a surface-enhanced Raman spectroscopy (SERS) method coupled with gold nanorods (AuNRs) substrates to rapidly detect methylene blue (MB) and malachite green (MG) in fish tissues. Three different aspect ratios (AR) of AuNRs were synthesized by a seed-mediated growth method based on adjusting the amount of 0.1 M of sliver nitrate solution. The 3.5 AR of AuNRs was selected as the optimal substrate because it showed the highest enhancement effect on the probe molecule. MB and MG standard solutions were detected at levels as low as 0.5 and 0.1 ng/mL, respectively. After processing the four fish tissues through a simplified sample preparation method, we compared matrix interference for SERS detection. For the four fish species, the lowest detectable concentration of MB was 5 ng/g for snakehead fish, and 1 ng/g for yellow catfish, black carp, and tilapia fillets. The lowest detectable concentration of MG was 1 ng/g for snakehead fish, 0.5 ng/g for yellow catfish and black carp, and 0.3 ng/g for tilapia fillets. This study shows that SERS coupled with AuNRs could be used for the analysis of trace amounts of contaminants in intricate food matrices as an active, fast and sensitive approach.

Residual pesticides such as phosmet in fruits and vegetables are one of the major food safety concerns for consumers around the world. Surface-enhanced Raman spectroscopy (SERS) with gold-coated substrates was exploited to analyze phosmet (0.5–10 μg/g) in apples. Phosmet could be detected from its SESR spectra at as low as 1 μg/mL in standard solution and 1 μg/g in apple extracts. The calculated limit of detection for phosmet in standard solution and in apple extracts were 1.01 μg/mL and 1.44 μg/g, respectively. There were linear relationships between the concentrations of phosmet solutions and the Raman intensities of four major characteristic peaks, with the coefficient of determination (R2) ranging from 0.905 to 0.984, the root mean square error of prediction (RMSEP) ranging from 0.46 to 1.16 μg/mL, and the ratio performance deviation (RPD) from 3.22 to 8.21. Although the linear regression results for phosmet in apple extracts with Raman intensities were not as good as those for the standard solutions (R2 = 0.866–0.945, RMSEP = 0.84–1.38 μg/g, RPD = 2.65–4.33, which still showed great potential of using SERS for quantitative analysis. The presence of non-targeted compounds in apple extracts affected the sensitivity for qualitative analysis and accuracy for quantitative analysis. This study demonstrated that SERS coupled with gold nanosubstrates could be used for analysis of trace contaminants like phosmet in complex food matrices.