Quantitative Detection of Isofenphos-Methyl in Corns Using Surface-Enhanced Raman Spectroscopy (SERS) with Chemometric Methods

Flexible 3D Plasmonic Web Enables Remote Surface Enhanced Raman Spectroscopy

Nanoplasmonic materials concentrate light in specific regions of dramatic electromagnetic enhancement: hot spots. Such regions can be employed to perform single molecule detection via surface-enhanced Raman spectroscopy. However, this phenomenon is challenging since hot spots are expected to be highly intense/abundant and positioning of molecules within such hot spots is crucial to manage with ultrasensitive SERS. Herein, it is discovered that a 3D plasmonic web embedded within a biohybrid (3D-POWER) exhibits plasmonic transmission, spontaneously absorbs the analyte, and meets these so much needed criteria in ultrasensitive SERS. 3D-POWER is built with nanopaper and self-assembled layers of graphene oxide and gold nanorods. According to in silico experiments, 3D-POWER captures light in a small region and performs plasmonic field transmission in a surrounding volume, thereby activating a plasmonic web throughout the simulated volume. The study also provides experimental evidence supporting the plasmonic field transport ability of 3D power, which operates as a SERS signal carrier (even beyond the apparatus field of view), and the ultrasensitive behavior of this ecofriendly and flexible material facilitating yoctomolar limit of detection. Besides, 3D-POWER is proven useful in food and biofluids analysis. It is foreseen that 3D-POWER can be employed as a valuable platform in (bio)analytical applications.

A Review of Detection of Antibiotic Residues in Food by Surface-Enhanced Raman Spectroscopy

Antibiotics, as veterinary drugs, have made extremely important contributions to disease prevention and treatment in the animal breeding industry. However, the accumulation of antibiotics in animal food due to their overuse during animal feeding is a frequent occurrence, which in turn would cause serious harm to public health when they are consumed by humans. Antibiotic residues in food have become one of the central issues in global food safety. As a safety measure, rapid and effective analytical approaches for detecting these residues must be implemented to prevent contaminated products from reaching the consumers. Traditional analytical methods, such as liquid chromatography, liquid chromatography mass spectrometry, and capillary electrophoresis, involve time-consuming sample preparation and complicated operation and require expensive instrumentation. By comparison, surface-enhanced Raman spectroscopy (SERS) has excellent sensitivity and remarkably enhanced target recognition. Thus, SERS has become a promising alternative analytical method for detecting antibiotic residues, as it can provide an ultrasensitive fingerprint spectrum for the rapid and noninvasive detection of trace analytes. In this study, we comprehensively review the recent progress and advances that have been achieved in the use of SERS in antibiotic residue detection. We introduce and discuss the basic principles of SERS. We then present the prospects and challenges in the use of SERS in the detection of antibiotics in food. Finally, we summarize and discuss the current problems and future trends in the detection of antibiotics in food.

Surface-enhanced Raman spectroscopywith gold nanorods modified by sodium citrate and liquid-liquid interface self-extraction for detection of deoxynivalenol in Fusarium head blight-infected wheat kernels coupled with a fully convolution network

Surface-enhanced Raman spectroscopy (SERS) and deep learning network were adopted to develop a detection method for deoxynivalenol (DON) residues in Fusarium head blight (FHB)-infected wheat kernels. First, the liquid-liquid interface self-extraction was conducted for the rapid separation of DON in samples. Then, the gold nanorods modified with sodium citrate (Cit-AuNRs) were prepared as substrate for a gigantic enhancement of SERS signal. Results showed that the spectral characteristic peaks for DON residues of 99.5-0.5 mg/L were discernible with the relative standard deviation of 4.2%, with the limit of detection of 0.11 mg/L. Meanwhile, the fully convolutional network for the spectra of matrix input form was developed and obtained the optimal quantitative performance, with a root-mean-square error of prediction of 4.41 mg/L and coefficient of determination of prediction of 0.9827. Thus, the proposed method provides a simple, sensitive, and intelligent detection for DON in FHB-infected wheat kernels.

Rapid detection of chlorpyrifos pesticide residue in tea using surface-enhanced Raman spectroscopy combined with chemometrics

Surface enhanced Raman spectroscopy based on rapid pretreatment combined with Chemometrics was used to determine chlorpyrifos residue in tea. Au nanoparticles were used to as enhance substrate. Different dosages of PSA and NBC were investigated to eliminate the tea substrate influence. Competitive adaptive reweighted sampling (CARS) was used to optimize the characteristic peaks, and compared to full spectra variables and the experiment selected variables. The results showed that PSA of 80 mg and NBC of 20 mg was an excellent approach for rapid detecting. CARS - PLS had better accuracy and stability using only 1.7% of full spectra variables. SVM model achieved better performance with R²_p = 0.981, RMSEP = 1.42 and RPD = 6.78. Recoveries for five unknown concentration samples were 98.47 ~ 105.18% with RSD - 1.53% ~ 5.18%. T-test results showed that t value was 0.720, less than t0.05,4 = 2.776, demonstrating that no clear difference between the real value and predicted value. The detection time of a single sample is completed within 15 min. This study demonstrated that SERS coupled with Chemometrics and QuEChERS may be employed to rapidly examine the chlorpyrifos residue in tea towards its quality and safety monitoring.

Fast and Low-Cost Surface-Enhanced Raman Scattering (SERS) Method for On-Site Detection of Flumetsulam in Wheat

The pesticide residues in agri-foods are threatening people’s health. This study aims to establish a fast and low-cost surface-enhanced Raman scattering (SERS) method for the on-site detection of flumetsulam in wheat. The two-step modified concentrated gold nanoparticles (AuNPs) acted as the SERS substrate with the aid of NaCl and MgSO₄. NaCl is served as the activator to modify AuNPs, while MgSO₄ is served as the aggregating agent to form high-density hot spots. The activation and aggregation are two essential collaborative procedures to generate remarkable SERS enhancement and achieve the trace-level detection of flumetsulam. This method exhibits good enhancement effect with an enhancement factor of 10⁶ and wide linear range (5–1000 μg/L). With simple pretreatment, the flumetsulam residue in real wheat samples can be successfully detected with the limit of detection (LOD) down to 0.01 μg/g, which is below the maximum residue limit of flumetsulam in wheat (0.05 μg/g) set in China. The recovery of flumetsulam residue in wheat ranges from 88.3% to 95.6%. These results demonstrate that the proposed SERS method is a powerful technique for the detection of flumetsulam in wheat, which implies the great application potential in the rapid detection of other pesticide residues in various agri-foods.

Supercritical Fluid Extraction of Pesticides and Insecticides from Food Samples and Plant Materials

The principal intention of this study is presenting the attempts carried out for extracting, separating, and determining of the pesticide and insecticide residues existing in food and plant samples. In this regard, a set of content, including the explanations about the supercritical fluid extraction (SFE), supercritical fluid chromatography, and various types of pesticides are indicated. Besides, the parameters affecting the pesticides extraction composed of temperature, pressure, modifier, drying agent, and so on are discussed. Also, examples of insecticides extraction by SFE technique as an important subset of pesticides are indicated. Along with these items, some interesting works, concerning the innovations implemented in the field of SFE of pesticide and insecticide residues from foodstuff and plants are depicted.

Detection of Pirimiphos-Methyl in Wheat Using Surface-Enhanced Raman Spectroscopy and Chemometric Methods

Pesticide residue detection is a hot issue in the quality and safety of agricultural grains. A novel method for accurate detection of pirimiphos-methyl residues in wheat was developed using surface-enhanced Raman spectroscopy (SERS) and chemometric methods. A simple pretreatment method was conducted to extract pirimiphos-methyl residue from wheat samples, and highly effective gold nanorods were prepared for SERS measurement. Raman peaks assignment was calculated using density functional theory. The Raman signal of pirimiphos-methyl can be detected when the concentrations of residue in wheat extraction solution and contaminated wheat is as low as 0.2 mg/L and 0.25 mg/L, respectively. Quantification of pirimiphos-methyl was performed by applying regression models developed by partial least squares regression, support vector machine regression and random forest with principal component analysis using different preprocessed methods. As for the contaminated wheat samples, the relative deviation between gas chromatography-mass spectrometry value and predicted value is in the range of 0.10%-6.63%, and predicted recovery is 94.12%-106.63%, ranging from 23.93 mg/L to 0.25 mg/L. Results demonstrated that the proposed SERS method is an effective and efficient analytical tool for detecting pirimiphos-methyl in wheat with high accuracy and excellent sensitivity.

Rapid Detection of Pesticide Residues in Paddy Water Using Surface-Enhanced Raman Spectroscopy

Pesticide residue in paddy water is one of the main factors affecting the quality and safety of rice, however, the negative effect of this residue can be effectively prevented and reduced through early detection. This study developed a rapid detection method for fonofos, phosmet, and sulfoxaflor in paddy water through chemometric methods and surface-enhanced Raman spectroscopy (SERS). Residue from paddy water samples was directly used for SERS measurement. The obtained spectra from the SERS can detect 0.5 mg/L fonofos, 0.25 mg/L phosmet, and 1 mg/L sulfoxaflor through the appearance of major characteristic peaks. Then, we used chemometric methods to develop models for the intelligent analysis of pesticides, alongside the SERS spectra. The classification models developed by K-nearest neighbor identified all of the samples, with an accuracy of 100%. For the quantitative analysis, the partial least squares regression models obtained the best predicted performance for fonofos and sulfoxaflor, and the support vector machine model provided optimal results, with a root-mean-square error of validation of 0.207 and a coefficient of determination of validation of 0.99952, for phosmet. Experiments for actual contaminated samples also showed that the above models predicted the pesticide residue values with high accuracy. Overall, using SERS with chemometric methods provided a simple and convenient approach for the detection of pesticide residues in paddy water.

Synthesized Au NPs@silica composite as surface-enhanced Raman spectroscopy (SERS) substrate for fast sensing trace contaminant in milk

With increased concerns on milk safety issues, the development of a simple and sensitive method to detect 2,4-dichlorophenoxyacetic acid (2,4-D), a common contaminant in milk, becomes relevant in safeguarding human health threats that results from its consumption. Surface-enhanced Raman spectroscopy (SERS) shows excellent ability for various targets analysis but its usage for rapid and accurate determination of analyte via SERS presents challenges. This study attempted the quantification of 2,4-dichlorophenoxyacetic acid (2,4-D) residue in milk using a novel SERS active substrate- decorated silica films with Au nanoparticles (Au NPs@ silica) coupled to chemometric algorithms. Au NPs@ silica composite was synthesized as a SERS sensor through self-assembly. Thereafter, the SERS spectrum of 2,4-D extract from milk with different concentrations based on the developed SERS sensor was collected and the spectra were analyzed by partial least squares (PLS), and variable selection algorithms - genetic algorithm-PLS (GA-PLS), competitive-adaptive reweighted sampling-PLS (CARS-PLS) and ant colony optimization-PLS (ACO-PLS), to develop quantitative models for 2,4-D prediction. The results obtained showed that the CARS-PLS model gave the optimum result with LOD of 0.01 ng/mL realized and a determination coefficient in the prediction set of (R_P) = 0.9836 within a linear range of 10^-2 to 10⁶ ng/mL was achieved. Au NPs@ silica SERS sensor combined with CARS-PLS may be employed for rapid quantification of 2,4-D extract from milk towards its quality and safety monitoring.

Superhydrophobic SERS substrates based on silver dendrite-decorated filter paper for trace detection of nitenpyram

In the present work, highly sensitive Raman detection of nitenpyram using superhydrophobic filter paper as substrates is introduced. The process is simple, and efficient. By sequentially coating silver dendrites and Octyltrimethoxysilane (OTMOS) on filter paper, we produced highly active surface-enhanced Raman scattering (SERS) substrates which show advancing and receding water contact angles of θ_A/θ_R = 159°/156°. Nitenpyram, a type of pesticides popularly used in agriculture, can be easily detected with the detection limit as low as 1 nM using the superhydrophobic filter paper as SERS substrates, which validates their use in Raman applications.

Quantitative Determination of Chlormequat Chloride Residue in Wheat Using Surface-Enhanced Raman Spectroscopy

A simple and sensitive method for detection of chlormequat chloride residue in wheat was developed using surface-enhanced Raman spectroscopy (SERS) coupled with chemometric methods on a portable Raman spectrometer. Pretreatment of wheat samples was performed using a two-step extraction procedure. Effective and uniform active substrate (gold nanorods) was prepared and mixed with the sample extraction solution for SERS measurement. The limit of detection for chlormequat chloride in wheat extracting solutions and wheat samples was 0.25 mg/L and 0.25 μg/g, which was far below the maximum residual value in wheat of China. Then, support vector regression (SVR) and kernel principal component analysis (KPCA), multiple linear regression, and partial least squares regression were employed to develop the regression models for quantitative analysis of chlormequat chloride residue with spectra around the characteristic peaks at 666, 713, and 853 cm^-1. As for the residue in wheat, the predicted recovery of established optimal model was in the range of 94.7% to 104.6%, and the standard deviation was about 0.007 mg/L to 0.066 mg/L. The results demonstrated that SERS, SVR, and KPCA can provide the accurate and quantitative determination for chlormequat chloride residue in wheat.

Enantioselective analysis and degradation of isofenphos-methyl in vegetables by liquid chromatography-tandem mass spectrometry

The enantioselective degradation of isofenphos-methyl in cowpea, cucumber, and pepper under field conditions was investigated to elucidate the enantioselective environmental behaviors of this pesticide. The concentrations of the enantiomers were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The degradation rates of isofenphos-methyl enantiomers were the fastest in cowpea, followed by cucumber and pepper, with half-lives ranging from 1.48 to 8.06 days. The enantioselective degradation of isofenphos-methyl was characterized by calculating and comparing the values of enantiomer fraction (EF) and enantiomeric selectivity (ES). The degradation rates and enantioselectivities of isofenphos-methyl were different for the three vegetables. (R)-(-)-isofenphos-methyl was degraded faster than (S)-(+)-isofenphos-methyl in cowpea and cucumber, whereas (S)-(+)-isofenphos-methyl underwent preferential degradation in pepper. These results could serve as a reference for the study of enantioselective behavior of isofenphos-methyl in plants and further food safety evaluation, where the enantiomeric differences should be considered in the risk assessment.

Fast and Quantitative Analysis of Ediphenphos Residue in Rice Using Surface-Enhanced Raman Spectroscopy

Detection of residual farm chemicals in agricultural crops is a hot topic in the field of food safety. In this study, ediphenphos residue in rice was detected using surface-enhanced Raman spectroscopy (SERS) on a portable Raman spectrometer. A simple pretreatment method for rice samples was developed, and uniform gold nanorods were used for SERS measurement. Characteristic signals can still be detected when ediphenphos concentration in rice extraction solution was higher than or equal to 0.1 mg/L. Quantitative analysis of ediphenphos was conducted by regression models developed using partial least-squares regression, random forest and kernel principal component analysis, and root-mean-square error of cross validation, coefficient of determination and relative predicted deviation of optimal model were 0.022 mg/L, 0.9967 and 297.45, which indicated the proposed method can predict ediphenphos concentration with high precision. To validate the feasibility of practical application further, rice samples spiked with 10, 5, 1, 0.5, and 0.1 μg/g ediphenphos residue were analyzed using the above method. The predicted recovery was in the range of 93.4% to 102%, and the predicted error was small for residue of each concentration. These results demonstrated that the presented method could be used for accurate and quantitative detection of ediphenphos residue in rice.

PRACTICAL APPLICATION

This study developed a surface-enhanced Raman spectroscopy (SERS) method for detection of ediphenphos in rice coupled with simple extraction protocol and gold nanorods on a portable Raman spectrometer. SERS is a rapid and accurate method which can be applied in agricultural grain safety inspection.