Detection of Pesticides in Fruits by Surface-Enhanced Raman Spectroscopy Coupled with Gold Nanostructures

Rapid and specific detection of thiabendazole: enzymatic digestion-enabled fluorescent aptasensor

Thiabendazole, a widely used broad-spectrum fungicide in agriculture, poses risks to human health. To monitor its presence in water, we propose a fluorescent aptasensor utilizing Escherichia coli exonuclease I (Exo I). The findings demonstrate a linear correlation between thiabendazole concentrations and digestion percentage, with a detection limit (LOD) exceeding 1 µM and a determination coefficient (R2) of 0.959. This aptamer-based fluorescence spectroscopy detection system holds promise for a rapid, specific, and sensitive analysis of thiabendazole in environmental waters and food matrices.

Enhanced sensing performance of carbaryl pesticide by employing a MnO2/GO/e-Ag-based nanoplatform: role of graphene oxide as an adsorbing agent in the SERS analytical performance

A functional ternary substrate was developed for surface-enhanced Raman scattering (SERS) sensing systems. MnO2 nanosheets were synthesized by a simple and controllable hydrothermal method, followed by the integration of graphene oxide (GO) nanosheets. Subsequently, MnO2/GO nanostructures were decorated with plasmonic Ag nanoparticles (e-AgNPs). The MnO2/GO/e-Ag substrate could enhance the SERS sensing signal for organic chemicals without the assistance of chemical bonds between those analytes and the semiconductor within the ternary substrate, which have been proven to promote charge transfer and elevate the SERS enhancement in previous studies. Instead, GO nanosheets acted as a carpet also supporting the MnO2 nanosheets and e-AgNPs to form a porous structure, allowing the analytes to be well-adsorbed onto the ternary substrate, which improved the sensing performance of the SERS platform, compared to pure e-AgNPs, MnO2/e-Ag, and GO/e-Ag alone. The GO content in the nanocomposite was also considered to optimize the SERS substrate. With the most optimal GO content of 0.1 wt%, MnO2/GO/e-Ag-based SERS sensors could detect carbaryl, a pesticide, at concentrations as low as 1.11 × 10-8 M in standard solutions and 10-7 M in real tap water and cucumber extract.

Advancing Mycotoxin Detection: Multivariate Rapid Analysis on Corn Using Surface Enhanced Raman Spectroscopy (SERS)

Mycotoxin contamination on food and feed can have deleterious effect on human and animal health. Agricultural crops may contain one or more mycotoxin compounds; therefore, a good multiplex detection method is desirable to ensure food safety. In this study, we developed a rapid method using label-free surface-enhanced Raman spectroscopy (SERS) to simultaneously detect three common types of mycotoxins found on corn, namely aflatoxin B1 (AFB1), zearalenone (ZEN), and ochratoxin A (OTA). The intrinsic chemical fingerprint from each mycotoxin was characterized by their unique Raman spectra, enabling clear discrimination between them. The limit of detection (LOD) of AFB1, ZEN, and OTA on corn were 10 ppb (32 nM), 20 ppb (64 nM), and 100 ppb (248 nM), respectively. Multivariate statistical analysis was used to predict concentrations of AFB1, ZEN, and OTA up to 1.5 ppm (4.8 µM) based on the SERS spectra of known concentrations, resulting in a correlation coefficient of 0.74, 0.89, and 0.72, respectively. The sampling time was less than 30 min per sample. The application of label-free SERS and multivariate analysis is a promising method for rapid and simultaneous detection of mycotoxins in corn and may be extended to other types of mycotoxins and crops.

Glyphosate-Exonuclease Interactions: Reduced Enzymatic Activity as a Route to Glyphosate Biosensing

N-phosphonomethyle-glycine (glyphosate) is the most widely used pesticide worldwide due to its effectiveness in killing weeds at a moderate cost, bringing significant economic benefits. However, owing to its massive use, glyphosate and its residues contaminate surface waters. On site, fast monitoring of contamination is therefore urgently needed to alert local authorities and raise population awareness. Here the hindrance of the activity of two enzymes, the exonuclease I (Exo I) and the T5 exonuclease (T5 Exo) by glyphosate, is reported. These two enzymes digest oligonucleotides into shorter sequences, down to single nucleotides. The presence of glyphosate in the reaction medium hampers the activity of both enzymes, slowing down enzymatic digestion. It is shown by fluorescence spectroscopy that the inhibition of ExoI enzymatic activity is specific to glyphosate, paving the way for the development of a biosensor to detect this pollutant in drinking water at suitable detection limits, i.e., 0.6 nm.

Recent development of surface-enhanced Raman scattering for biosensing

Surface-Enhanced Raman Scattering (SERS) technology, as a powerful tool to identify molecular species by collecting molecular spectral signals at the single-molecule level, has achieved substantial progresses in the fields of environmental science, medical diagnosis, food safety, and biological analysis. As deepening research is delved into SERS sensing, more and more high-performance or multifunctional SERS substrate materials emerge, which are expected to push Raman sensing into more application fields. Especially in the field of biological analysis, intrinsic and extrinsic SERS sensing schemes have been widely used and explored due to their fast, sensitive and reliable advantages. Herein, recent developments of SERS substrates and their applications in biomolecular detection (SARS-CoV-2 virus, tumor etc.), biological imaging and pesticide detection are summarized. The SERS concepts (including its basic theory and sensing mechanism) and the important strategies (extending from nanomaterials with tunable shapes and nanostructures to surface bio-functionalization by modifying affinity groups or specific biomolecules) for improving SERS biosensing performance are comprehensively discussed. For data analysis and identification, the applications of machine learning methods and software acquisition sources in SERS biosensing and diagnosing are discussed in detail. In conclusion, the challenges and perspectives of SERS biosensing in the future are presented.

Ex Situ Characterization of 1T/2H MoS2 and Their Carbon Composites for Energy Applications, a Review

The growing interest in the development of next-generation net zero energy systems has led to the expansion of molybdenum disulfide (MoS₂) research in this area. This activity has resulted in a wide range of manufacturing/synthesis methods, controllable morphologies, diverse carbonaceous composite structures, a multitude of applicable characterization techniques, and multiple energy applications for MoS₂. To assess the literature trends, 37,347 MoS₂ research articles from Web of Science were text scanned to classify articles according to energy application research and characterization techniques employed. Within the review, characterization techniques are grouped under the following categories: morphology, crystal structure, composition, and chemistry. The most common characterization techniques identified through text scanning are recommended as the base fingerprint for MoS₂ samples. These include: scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy. Similarly, XPS and Raman spectroscopy are suggested for 2H or 1T MoS₂ phase confirmation. We provide guidance on the collection and presentation of MoS₂ characterization data. This includes how to effectively combine multiple characterization techniques, considering the sample area probed by each technique and their statistical significance, and the benefit of using reference samples. For ease of access for future experimental comparison, key numeric MoS₂ characterization values are tabulated and major literature discrepancies or currently debated characterization disputes are highlighted.

3D flexible compositing resonant cavity system for high-performance SERS sensing

Arrayed resonant cavity with outstanding optical trapping ability have received increasing attention in surface-enhanced Raman spectroscopy (SERS). Here, a three-dimensional (3D) composite AgNPs-Al₂O₃/Au/inverted patterned sapphire substrate PMMA (IPSSPMMA) flexible resonant cavity system is theoretically and experimentally investigated as a flexible SERS sensor. With the help of an effective plasma coupling (localized surface plasmons (LSPs) and surface plasmon polaritons (SPPs)), as shown by the Finite Element Method, a resonant cavity between IPSSPMMA and a particle-film nanostructure is created. Moreover, the proposed fabrication scheme can be easily used for large-scale fabrication. To measure the performance of IPSSPMMA, Rhodamine 6 G (R6G) and Crystalline violet (CV) were used as probe molecules with limit of detection (LOD) of 6.01 × 10^-12 M and 5.36 × 10^-10 M, respectively, and enhancement factors (EF) of R6G up to 8.6 × 10⁹. Besides, in-situ detection of CV on the surface of aquatic products with a LOD of 3.96 × 10^-5 M, enables highly sensitive in-situ detection of surface analytes. The Raman performance and in-situ detection results demonstrate that the proposed flexible compositing resonant cavity system has the advantages of ultra-sensitivity, stability, uniformity, and reproducibility, and has great potential for applications in the food safety field.

A novel portable Raman scattering platform for antibiotic screening in pig urine

Background and Aim

Public health and food safety are gaining attention globally. Consumer health can be protected from chemical residues in meat by early detection or screening for antibiotic residues before selling the meat commercially. However, conventional practices are normally applied after slaughtering, which leads to massive business losses. This study aimed to use portable surface-enhanced Raman spectroscopy (SERS) equipped with multivariate curve resolution-alternation least squares (MCR-ALS) to determine the concentrations of enrofloxacin, oxytetracycline, and neomycin concentrations. This approach can overcome the problems of business loss, costs, and time-consumption, and limit of detection (LOD).

Materials and Methods

Aqueous solutions of three standard antibiotics (enrofloxacin, oxytetracycline, and neomycin) with different concentrations were prepared, and the LOD for each antibiotic solution was determined using SERS. Extracted pig urine was spiked with enrofloxacin at concentrations of 10, 20, 50, 100, and 10,000 ppm. These solutions were investigated using SERS and MCR-ALS analysis. Urine samples from pigs at 1 and 7 days after enrofloxacin administration were collected and investigated using SERS and MCR-ALS to differentiate the urinary enrofloxacin concentrations.

Results

The LOD of enrofloxacin, oxytetracycline, and neomycin in aqueous solutions were 0.5, 2.0, and 100 ppm, respectively. Analysis of enrofloxacin spiking in pig urine samples demonstrated the different concentrations of enrofloxacin at 10, 20, 50, 100, and 10,000 ppm. The LOD of spiking enrofloxacin was 10 ppm, which was 10 times lower than the regulated value. This technique was validated for the first time using urine collected on days 1 and 7 after enrofloxacin administration. The results revealed a higher concentration of enrofloxacin on day 7 than on day 1 due to consecutive administrations. The observed concentration of enrofloxacin was closely correlated with its circulation time and metabolism in pigs.

Conclusion

A combination of SERS sensing platform and MCR-ALS is a promising technique for on-farming screening. This platform can increase the efficiency of antibiotic detection in pig urine at lower costs and time. Expansion and fine adjustments of the Raman dataset may be required for individual farms to achieve higher sensitivity.

Quantitative Analysis of Acetone in Transformer Oil Based on ZnO NPs@Ag NWs SERS Substrates Combined with a Stoichiometric Model

Acetone is an essential indicator for determining the aging of transformer insulation. Rapid, sensitive, and accurate quantification of acetone in transformer oil is highly significant in assessing the aging of oil-paper insulation systems. In this study, silver nanowires modified with small zinc oxide nanoparticles (ZnO NPs@Ag NWs) were excellent surface-enhanced Raman scattering (SERS) substrates and efficiently and sensitively detected acetone in transformer oil. Stoichiometric models such as multiple linear regression (MLR) models and partial least square regressions (PLS) were investigated to quantify acetone in transformer oil and compared with commonly used univariate linear regressions (ULR). PLS combined with a preprocessing algorithm provided the best prediction model, with a correlation coefficient of 0.998251 for the calibration set, 0.997678 for the predictive set, a root mean square error in the calibration set (RMSECV = 0.12596 mg/g), and a prediction set (RMSEP = 0.11408 mg/g). For an acetone solution of 0.003 mg/g, the mean absolute percentage error (MAPE) was the lowest among the three quantitative models. For a concentration of 7.29 mg/g, the MAPE was 1.60%. This method achieved limits of quantification and detections of 0.003 mg/g and 1 μg/g, respectively. In general, these results suggested that ZnO NPs@Ag NWs as SERS substrates coupled with PLS simply and accurately quantified trace acetone concentrations in transformer oil.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

Silver Nanoparticle Films Obtained by Convective Self-Assembly for Surface-Enhanced Raman Spectroscopy Analyses of the Pesticides Thiabendazole and Endosulfan

Pesticides pose a great threat to human health and their rapid detection has become an urgent public safety issue engaging the scientific community to search for fast and reliable detection techniques. In this context, Surface Enhanced Raman Spectroscopy (SERS) has emerged as a valuable detection and analysis tool due to its high sensitivity and selectivity, proving its suitability for the food industry and environmental monitoring applications. Here, we report on the fabrication of colloidal silver nanoparticle (AgNP) films by convective self-assembly (CSA) on solid planar substrate and their use for the SERS analyses of two types of pesticides, the fungicide thiabendazole (TBZ) and the insecticide α-endosulfan (α-ES). Electron microscopy shows that these nanoparticle films are dense, highly compact, and uniform across several mm² areas. The SERS efficiency of the fabricated AgNP films is evaluated using a well-known Raman probe, p-aminothiophenol, for multiple excitation laser lines (532 nm, 633 nm, and 785 nm). The films exhibit the largest SERS enhancement factors for 785 nm excitation, reaching values larger than 10⁵. Thiabendazole could be readily adsorbed on the AgNPs without any sample surface functionalization and detected down to 10⁻⁶ M, reaching the sub-ppm range. Endosulfan, a challenging analyte with poor affinity to metal surfaces, was captured near the metal surface by using self-assembled alkane thiol monolayers (hexanethiol and octanethiol), as demonstrated by the thorough vibrational band analysis, and supported by density functional theory (DFT) calculations. In addition, principal component analysis (PCA) based on SERS spectra offers significant leverage in discrimination of the molecules anchored onto the metallic nanostructured surface. This present study demonstrates the utility of self-assembled colloidal nanoparticle films as SERS substrates for a broad range of analytes (para-aminothiophenol, thiabendazole, α-endosulfan, and alkanethiols) and contributes to the development of SERS-based sensors for pesticides detection, identification and monitoring.

Combining multilayered wrinkled polymer SERS substrates and spectral data processing for low concentration analyte detection

A series of thermally shrinkable polymer surface-enhanced Raman scattering (SERS) substrates were prepared with bimetallic Au and Ag (oxidized or not) films and with Au nanoparticles (AuNPs) located at different places in the layered structure to evaluate the synergistic effect of different known SERS amplification methods to enhance the Raman signal for low concentration dopamine detection. A bimetallic Au and Ag layered structure improved the Raman signal by 5 and 2 times compared to the single-layered Au and Ag films. Oxidizing the Ag layer prior to deposition of Au further improved the signal by a factor of 2, while adding AuNP on wrinkled films increased another 10 times the intensity of the Raman signal. It was found that the enhancement was another 10 times stronger when using AuNPs in combination with other means of enhancement such as with a silver underlayer or surface wrinkling. Wrinkling alone only gave a few-fold increase compared to a flat film, but the combination of wrinkling with AuNPs and a silver underlayer improved the SERS intensity by more than 3 orders of magnitude, showing the synergistic effect of these enhancement methods. The optimized sensors were then tested in dynamic SERS with low concentration dopamine solutions, where the signal showed characteristics of a digital SERS response. Raman spectra preprocessing and sorting software was developed to triage the SERS-active spectra from the null spectra, to count the detection events such as the ones observed in single molecule experiments.

Study of the Fabrication of Gold Nanoparticle-Graphene-Arrayed Micro/Nanocavities as SERS Substrates Compared to Two Different Angles of Triangular Pyramid Tips

Surface-enhanced Raman scattering (SERS) has attracted attention because of its enormous potential to detect molecules with low concentrations. The method of fabricating SERS substrates is of great importance for improving the detection resolution. However, SERS substrates with different triangular pyramid tips fabricated by using the tip-based nanoindentation method has not been reported. Here, we prepared arrayed micro/nanocavities on copper-based graphene using the continuous indentation method with a Berkovich tip and a cube-corner tip, which have different face angles. Gold nanoparticles were then sputtered onto the graphene-copper micro/nanocavities to form the Au@GR@Cu micro/nanocavities SERS substrates. The substrates formed using the Berkovich tip and cube-corner tip were labeled B2-B9 and C2-C9, respectively, in which the numbers indicate the machining feed. Rhodamine 6G (R6G) was employed, and the Raman intensities of R6G on the differently arrayed Au@GR@Cu micro/nanocavities were measured. The Raman intensities of R6G were stronger on the pile-ups than on the inverted triangular pyramid cavities. The Raman intensities of R6G were highest on the C2 and B2 structures and lowest on the C9 and B9 structures. The Raman intensities of R6G on the arrayed Au@GR@Cu micro/nanocavities fabricated by the cube-corner tip were stronger than those on the arrayed Au@GR@Cu micro/nanocavities fabricated using the Berkovich tip with the same machining feed. In addition, the electric field intensity and distribution of the B9 and C9 arrayed Au@GR@Cu were simulated using Comsol software. Au@GR@Cu structures fabricated by the cube-corner tip were generated with higher electric field intensities. Furthermore, the relative standard deviations at 1362 cm^-1 of R6G were 6.19 and 6.62% on the C2 and C4 surfaces, respectively, showing good homogeneity. The SERS spectra of 10^-9 mol/L malachite green solution and 10^-6 mol/L carbaryl solution were recognized on the C1, C2, and C4 surfaces on day 1 and after 3 months, respectively. After storage at room temperature for 3 months, the reductions in the Raman intensities were less than 10%, indicating excellent stability. The results showed that the arrayed Au@GR@Cu micro/nanocavities fabricated using the cube-corner tip performed better than those fabricated using the Berkovich tip and exhibited excellent uniformity, availability, and stability, providing great potential for detecting pesticides at low concentrations.

Engineering a local acid-like environment in alkaline medium for efficient hydrogen evolution reaction

Tuning the local reaction environment is an important and challenging issue for determining electrochemical performances. Herein, we propose a strategy of intentionally engineering the local reaction environment to yield highly active catalysts. Taking Pt^δ− nanoparticles supported on oxygen vacancy enriched MgO nanosheets as a prototypical example, we have successfully created a local acid-like environment in the alkaline medium and achieve excellent hydrogen evolution reaction performances. The local acid-like environment is evidenced by operando Raman, synchrotron radiation infrared and X-ray absorption spectroscopy that observes a key H₃O⁺ intermediate emergence on the surface of MgO and accumulation around Pt^δ− sites during electrocatalysis. Further analysis confirms that the critical factors of the forming the local acid-like environment include: the oxygen vacancy enriched MgO facilitates H₂O dissociation to generate H₃O⁺ species; the F centers of MgO transfers its unpaired electrons to Pt, leading to the formation of electron-enriched Pt^δ− species; positively charged H₃O⁺ migrates to negatively charged Pt^δ− and accumulates around Pt^δ− nanoparticles due to the electrostatic attraction, thus creating a local acidic environment in the alkaline medium.

While catalysts have intrinsic activities toward reactions, such performances often require further optimization. Here, authors engineer an acid-like environment in alkaline media by fine-tuning the reaction environment of platinum nanoparticles on oxide nanosheets for H₂ evolution electrocatalysis.

Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN

Since electric vehicles (EVs) could reduce the growing concerns on environmental pollution issues and relieve the social dependency of fossil fuels, the EVs market is fast increased in recent years. However, a large growth in the number of EVs will bring a great challenge to the present traffic system; thus, an acceptable model is necessary to forecast the sales of EVs in order to better plan the appropriate supply of necessary facilities (e.g., charging stations and sockets in car parks) as well as the electricity required on the road. In this study, we propose a model to predict the sales volume and increase rate of EVs in the world and China, using both statistics and machine learning methods by combining principle component analysis and a general regression neural network, based on the previous 11 years of sales data of EVs. The results indicate that a continuing growth in the sales of EVs will appear in both the world and China in the coming eight years, but the sales increase rate is slowly and continuously deceasing because of the persistent growth of the basic sales volume. The results also indicate that the increase rate of sales of EVs in China is higher than that of the world, and the proportion of sales of EVs in China will increase gradually and will be above 50% in 2025. In this case, large accessory facilities for EVs are required in China in the coming few years.

Raman Spectroscopy Detects Changes in Carotenoids on the Surface of Watermelon Fruits During Maturation

A non-invasive and non-destructive technique, Raman spectroscopy, was explored to distinguish different maturity stages (20, 30, 40, and 50 days after anthesis) of watermelon (Citrullus lanatus) fruits from four cultivars: Fascination, Orange Crisp, Amarillo and Crimson Sweet. Spectral acquisition from the fruit surface was carried out at the wavelength range of 400-2,000 cm^-1 using a handheld Raman spectrometer equipped with 830 nm laser excitation source. The spectra were normalized at 1,438 cm^-1 which was assigned to CH₂ and CH₃ vibration. Detecting changes in the spectral features of carotenoids on the surface of watermelon fruits can be used as a marker to monitor the maturity of the fruit. The spectral analysis confirmed the presence of two major carotenoids, lutein and β-carotene, and their intensity decreased upon maturity on the fruit surface. Identification of these pigments was further confirmed by resonance Raman spectra and high-performance liquid chromatography analysis. Results of partial least square discriminant analysis of pre-processed spectra have demonstrated that the method can successfully predict the maturity of watermelon samples with more than 85% accuracy. Analysis of Variance of individual Raman bands has revealed a significant difference among the stages as the level of carotenoids was declined during the ripening of the fruits. Thus, Raman spectral signatures can be used as a versatile tool for the non-invasive determination of carotenoid changes on the watermelon fruits' surface during ripening, thereby enabling effective monitoring of nutritional quality and maturity indices before harvesting the watermelon.

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

SERS for antibiotic resistance diagnosis.

Surface Enhanced Raman Spectroscopy: Applications in Agriculture and Food Safety

Recent global warming has resulted in shifting of weather patterns and led to intensification of natural disasters and upsurges in pests and diseases. As a result, global food systems are under pressure and need adjustments to meet the change—often by pesticides. Unfortunately, such agrochemicals are harmful for humans and the environment, and consequently need to be monitored. Traditional detection methods currently used are time consuming in terms of sample preparation, are high cost, and devices are typically not portable. Recently, Surface Enhanced Raman Scattering (SERS) has emerged as an attractive candidate for rapid, high sensitivity and high selectivity detection of contaminants relevant to the food industry and environmental monitoring. In this review, the principles of SERS as well as recent SERS substrate fabrication methods are first discussed. Following this, their development and applications for agrifood safety is reviewed, with focus on detection of dye molecules, melamine in food products, and the detection of different classes of pesticides such as organophosphate and neonicotinoids.

Shell thickness-controlled synthesis of Au@Ag core-shell nanorods structure for contaminants sensing by SERS

The accessibility of contaminants detection methods is urgently required for environmental and food safety control. In this report, we developed the Au@Ag core-shell nanorod structures for contaminants sensing by surface-enhanced Raman spectroscopy (SERS). The silver shell thickness and the corresponding plasmon wavelength of Au@Ag core-shell nanorods were tuned by changing the coating time and the silver precursor amount. Moreover, these structures exhibit ultra-sensitive detection ability for Nile blue A dye and Fenobucarb pesticide sensing by SERS. Interestingly, the highest Raman enhancement factor is obtained for the Au@Ag core-shell sample with a minimal silver shell thickness leaded by the optimal enhancement of the electromagnetic field of bimetallic structures. Hence, our report demonstrates that the combination of unique features of two plasmonic metals into core-shell structures promises potential applicability in SERS-based analysis.

Rapid field trace detection of pesticide residue in food based on surface-enhanced Raman spectroscopy

Surface-enhanced Raman spectroscopy is an alternative detection tool for monitoring food security. However, there is still a lack of a conclusion of SERS detection with respect to pesticides and real sample analysis, and the summary of intelligent algorithms in SERS is also a blank. In this review, a comprehensive report of pesticides detection using SERS technology is given. The SERS detection characteristics of different types of pesticides and the influence of substrate on inspection are discussed and compared by the typical ways of classification. The key points, including the progress in real sample analysis and Raman data processing methods with intelligent algorithm, are highlighted. Lastly, major challenges and future research trends of SERS analysis of pesticide residue are also addressed. SERS has been proven to be a powerful technique for rapid test of residue pesticides in complex food matrices, but there still is a tremendous development space for future research.

Rapid detection of pesticide in milk, cereal and cereal based food and fruit juices using paper strip-based sensor

The study was aimed to validate paper strip sensors for the detection of pesticide residues in milk, cereal-based food, and fruit juices in comparison with GC-MS/MS under field conditions. The detection limit of pesticide using rapid paper strip sensor for organophosphate, carbamate, organochlorine, fungicide, and herbicide group ranges from 1 to 10, 1-50, 250-500, 1-50, and 1 ppb, respectively in milk and milk product, cereal-based food and fruit juices. Among 125 samples of milk samples collected from the market 33 milk samples comprising 31 raw milk and 2 pasteurized milk found positive for pesticide using the strip-based sensor. In cereal based food and fruit juice samples, 6 cereal flours and 4 fruit juices were found positive for pesticide residues. The pesticide positive samples were further evaluated quantitatively using GC-MS/MS wherein 7 samples comprised of raw milk, pasteurized milk, rice flour, wheat flour, maize flour, apple juice, and pomegranate juice have shown the presence of chlorpyrifos, chlorpyrifos-methyl, α-endosulfan, β-endosulfan DDD and DDT at trace level as well as at above MRL level. It is envisaged that the developed paper strip sensor can be a potential tool in the rapid and cost-effective screening of a large number of food samples for pesticide residues.

AgNPs decorated 3D bionic silicon nanograss arrays pattern with high-density hot-spots for SERS sensing via green galvanic displacement without additives

Surface-enhanced Raman scattering (SERS) sensing has always been considered as a kind of high-efficiency analysis technique in different areas. Herein, we report a AgNPs decorated 3D bionic silicon (Si) nanograss SERS substrate with higher sensitivity and specificity by green galvanic displacement. The Si nanograss arrays are directly grown on a Si substrate via catalyst-assisted vapor–liquid–solid (VLS) growth and subsequent plasma interaction. AgNPs were rapidly immobilized on Si nanograss arrays without any organic reagents, and avoiding the interference signal of additives. The AgNPs decorated 3D bionic silicon nanograss arrays not only possess a larger specific surface area (loading more reporter molecules), but also provide a potential distribution and arrangement for plentiful hot spots. Using Rhodamine 6G (R6G) as a probe molecule, the prepared SERS substrates exhibited great potential for high-sensitivity SERS sensing, and pushed the limit of detection (LOD) down to 0.1 pM. A higher Raman analytical enhancement factor (AEF, 3.3 × 10⁷) was obtained, which was two magnitudes higher than our previous Ag micro–nano structures. Additionally, the practicality and reliability of our 3D bionic SERS substrates were confirmed by quantitative analysis of the spiked Sudan I in environmental water, with a wide linear range (from 10⁻¹⁰ M to 10⁻⁶ M) and low detection limit (0.1 nM).

The Si nanograss arrays are directly grown on Si substrate via catalyst-assisted VLS growth and subsequent plasma interaction. AgNPs were rapidly immobilized on Si nanograss arrays for SERS sensing, without any organic reagents and additives.

In situ and rapid determination of acetamiprid residue on cabbage leaf using surface-enhanced Raman scattering

BACKGROUND

Pesticide residues in agricultural products and foods pose a serious threat to human health, and therefore a simple, rapid and direct method is urgently needed for pesticide residue detection. In addition to realizing the detection of acetamiprid in cabbage extract solution, the main target of this study was to establish an in situ surface-enhanced Raman scattering (SERS) method, which could directly detect acetamiprid residue on cabbage leaf without the need for extraction. Acetamiprid was first used to contaminate the surface of fresh cabbage leaf, and then bimetallic silver-coated gold nanoparticles (Au@AgNPs) were added on the contaminated spots and dried for SERS measurement.

RESULTS

Results suggested that acetamiprid can be detected in cabbage extract and on cabbage leaf surface in situ using the SERS method based on the Au@AgNPs substrate. The limit of detection was 0.08 μg mL^-1 in cabbage extract and 0.14 mg kg^-1 on cabbage leaf, the recovery ranged from 80.5% to 105.5% and the relative standard deviation was in the range 4.37-10.63%.

CONCLUSIONS

The proposed SERS method provides an in situ, nondestructive and rapid way to detect acetamiprid residue on the surface of fruits and vegetables, which could serve as an auxiliary approach for early screening of contaminated produce in field or on site in the future. © 2020 Society of Chemical Industry.

Sensing Atrazine Herbicide Degradation Products through Their Interactions with Humic Substances by Surface-Enhanced Raman Scattering

In this work, we have developed a simple method to carry out the quantitative analysis of deethylhydroxyatrazine (DEHA), the most abundant metabolite of atrazine herbicide (ATZ), based on the surface-enhanced Raman scattering technique. Since this ATZ product can undergo pH-dependent tautomerization, a previous characterization of the DEHA vibrational spectrum was accomplished. This study consisted of the Raman scattering study, both experimental and theoretical, of the enolic and ketonic tautomers of this molecule. SERS spectra were recorded at different pH in order to assess the effect of the metal surface in nanoparticles along with the pH on the structure of DEHA and to find the optimal experimental conditions of the quantitative detection of DEHA. Additionally, the interaction of DEHA with two types of humic acid reference standards, the Elliot humic and leonardite humic ones, was also performed by SERS. This interaction was conducted with two different objectives: to evaluate the interaction mechanism of the ATZ degradation product with humic substances and to check if this interaction can modify the sensitivity of the SERS detection of DEHA. The results presented in this study have clearly demonstrated that SERS spectroscopy is a very powerful technique for characterizing DEHA and other triazine sub-products at a very low concentration in water and also for analyzing the interaction of these important pollutants with humic substances.

Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices

It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (R_p) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10⁻⁶, and the limit of detection (LOD) is 2.3610 × 10⁻⁵ g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.

Onsite/on-field analysis of pesticide and veterinary drug residues by a state-of-art technology: A review

Pesticides and veterinary drugs are generally employed to control pests and insects in crop and livestock farming. However, remaining residues are considered potentially hazardous to human health and the environment. Therefore, regular monitoring is required for assessing and legislation of pesticides and veterinary drugs. Various approaches to determining residues in various agricultural and animal food products have been reported. Most analytical methods involve sample extraction, purification (cleanup), and detection. Traditional sample preparation is time-consuming labor-intensive, expensive, and requires a large amount of toxic organic solvent, along with high probability for the decomposition of a compound before the analysis. Thus, modern sample preparation techniques, such as the quick, easy, cheap, effective, rugged, and safe method, have been widely accepted in the scientific community for its versatile application; however, it still requires a laboratory setup for the extraction and purification processes, which also involves the utilization of a toxic solvent. Therefore, it is crucial to elucidate recent technologies that are simple, portable, green, quick, and cost-effective for onsite and infield residue detections. Several technologies, such as surface-enhanced Raman spectroscopy, quantum dots, biosensing, and miniaturized gas chromatography, are now available. Further, several onsite techniques, such as ion mobility-mass spectrometry, are now being upgraded; some of them, although unable to analyze field sample directly, can analyze a large number of compounds within very short time (such as time-of-flight and Orbitrap mass spectrometry). Thus, to stay updated with scientific advances and analyze organic contaminants effectively and safely, it is necessary to study all of the state-of-art technology.

Raman Signal Enhancement Tunable by Gold-Covered Porous Silicon Films with Different Morphology

The ease of fabrication, large surface area, tunable pore size and morphology as well surface modification capabilities of a porous silicon (PSi) layer make it widely used for sensoric applications. The pore size of a PSi layer can be an important parameter when used as a matrix for creating surface-enhanced Raman scattering (SERS) surfaces. Here, we evaluated the SERS activity of PSi with pores ranging in size from meso to macro, the surface of which was coated with gold nanoparticles (Au NPs). We found that different pore diameters in the PSi layers provide different morphology of the gold coating, from an almost monolayer to 50 nm distance between nanoparticles. Methylene blue (MB) and 4-mercaptopyridine (4-MPy) were used to describe the SERS activity of obtained Au/PSi surfaces. The best Raman signal enhancement was shown when the internal diameter of torus-shaped Au NPs is around 35 nm. To understand the role of plasmonic resonances in the observed SERS spectrum, we performed electromagnetic simulations of Raman scattering intensity as a function of the internal diameter. The results of these simulations are consistent with the obtained experimental data.

Rapid Quantitative Detection of Deltamethrin in Corydalis yanhusuo by SERS Coupled with Multi-Walled Carbon Nanotubes

With the increase in demand, artificially planting Chinese medicinal materials (CHMs) has also increased, and the ensuing pesticide residue problems have attracted more and more attention. An optimized quick, easy, cheap, effective, rugged and safe (QuEChERS) method with multi-walled carbon nanotubes as dispersive solid-phase extraction sorbents coupled with surface-enhanced Raman spectroscopy (SERS) was first proposed for the detection of deltamethrin in complex matrix Corydalis yanhusuo. Our results demonstrate that using the optimized QuEChERS method could effectively extract the analyte and reduce background interference from Corydalis. Facile synthesized gold nanoparticles with a large diameter of 75 nm had a strong SERS enhancement for deltamethrin determination. The best prediction model was established with partial least squares regression of the SERS spectra ranges of 545~573 cm⁻¹ and 987~1011 cm⁻¹ with a coefficient of determination (R²) of 0.9306, a detection limit of 0.484 mg/L and a residual predictive deviation of 3.046. In summary, this article provides a new rapid and effective method for the detection of pesticide residues in CHMs.

Raman spectroscopy: A rapid method to assess the effects of pasture feeding on the nutritional quality of butter

The animal diet is a critical variable affecting the composition and functionality of dairy products. As "Grass-Fed" labeling becomes more prominent on the market, rapid and label-free methods for verification of feeding systems are required. This work proposes the use of Raman spectroscopy to study the effects of 3 different experimental cow feeding systems-perennial ryegrass pasture, perennial ryegrass with white clover pasture, and an indoor total mixed ration diet (TMR)-on the nutritional quality of sweet cream butter. The results demonstrate that Raman spectroscopy coupled with multivariate analysis is a promising approach to distinguish butter derived from pasture or conventional TMR feeding systems. A Pearson correlation analysis confirmed high positive correlations between the spectral bin at 1,657 cm^-1, ascribed to the stretching vibrations of C=C bonds, and concentrations of α-linolenic acid and conjugated linolenic acid (CLA) in butter, and in general with the concentration of n-3 and n-3+CLA fatty acids and polyunsaturated fatty acids in the samples. The yellow color indicative of the presence of carotenoids in butter, which has previously been suggested as a biomarker of pasture or "Grass-Fed" feeding, was also positively correlated with the data obtained from the Raman spectra. Raman spectroscopy could also be used to accurately predict indicators of the nutritional quality of butter, such as the thrombogenic index, which showed a strong negative correlation with the spectral bin at 3,023 cm^-1.

Rapid detection of ziram residues in apple and pear fruits by SERS based on octanethiol functionalized bimetallic core-shell nanoparticles

Existing approaches for the screening of unsafe materials in food matrices are time-consuming, tiresome and destructive in nature. Therefore, in the current study, a surface-enhanced Raman spectroscopy (SERS) method based on octanethiol-functionalized core-shell nanoparticles (Oct/Au@AgNPs) was established for rapid detection of ziram in apple and pear fruits. The morphology of substrate was evaluated using high-resolution TEM images and superimposed HAADF-STEM-EDS elemental mapping images, which confirmed that Au@AgNPs having gold (Au) core size of 28 nm in diameter and silver (Ag) shell of 5.5 nm in thickness were successfully grafted with octanethiol. The SERS method with the sensitive nanoparticles could detect ziram of up to 0.015 and 0.016 ppm in apple and pear with high coefficients of determination (R²) of 0.9987 and 0.9993, respectively. Furthermore, satisfactory recoveries (80-106%) were also accomplished for the fungicide in real samples. This work demonstrated that the functionalized silver-coated gold nanoparticles were easy to prepare and could be used as sensitive SERS platforms for monitoring of other agrochemicals in foods.

Surface Imprinted Layer of Cypermethrin upon Au Nanoparticle as a Specific and Selective Coating for the Detection of Template Pesticide Molecules

The detection of specific pesticides on food products is essential as these substances pose health risks due to their toxicity. The use of surface-enhanced Raman spectroscopy (SERS) takes advantage of the straightforward technique to obtain fingerprint spectra of target analytes. In this study, SERS-active substrates are made using Au nanoparticles (NPs) coated with a layer of polymer and followed by imprinting with a pesticide–Cypermethrin, as a molecularly imprinted polymer (MIP). Cypermethrin was eventually removed and formed as template cavities, then denoted as Au NP/MIP, to capture the analogous molecules. The captured molecules situated in-between the areas of high electromagnetic field formed by plasmonic Au NPs result in an effect of SERS. The formation of Au NP/MIP was, respectively, studied through morphological analysis using transmission electron microscopy (TEM) and compositional analysis using X-ray photoelectron spectroscopy (XPS). Two relatively similar pesticides, Cypermethrin and Permethrin, were used as analytes. The results showed that Au NP/MIP was competent to detect both similar molecules despite the imprint being made only by Cypermethrin. Nevertheless, Au NP/MIP has a limited number of imprinted cavities that result in sensing only low concentrations of a pesticide solution. Au NP/MIP is thus a specific design for detecting analogous molecules similar to its template structure.

Antibody-free rapid diagnosis of malaria in whole blood with surface-enhanced Raman Spectroscopy using Nanostructured Gold Substrate

PURPOSE

The aim of this study is to establish a rapid antibody-free diagnostic method of malaria infection with Plasmodium falciparum and Plasmodium vivax in whole blood with Surface-enhanced Raman Spectroscopy using Nanostructured Gold Substrate.

MATERIALS AND METHODS

The blood samples collected from patients were first lysed and centrifuged before dropping on the gold nano-structure (AuNS) substrate. Malaria diagnosis was performed by detecting Raman peaks from Surface Enhanced Raman Spectroscopy (SERS) with a 532 nm laser excitation.

RESULTS

Raman peaks at 1370 cm-1, 1570 cm-1, and 1627 cm-1, known to have high specificity against interference from other mosquito-borne diseases such as Dengue and West Nile virus infection, were selected as the fingerprint markers associated with P. falciparum and P. vivax infection. The limit of detection was 10-5 dilution, corresponding to the concentration of parasitized blood cells of 100/mL. A total number of 25 clinical samples, including 5 from patients with P. falciparum infection, 10 with P. vivax infection and 10 from healthy volunteers, were evaluated to support its clinical practical use. The whole assay on malaria detection took 30 min to complete.

CONCLUSIONS

While the samples analyzed in this work have strong clinical relevance, we have clearly demonstrated that sensitive malaria detection using AuNS-SERS is a practical direction for rapid in-field diagnosis of malaria infection.

Flexible PET/ITO/Ag SERS Platform for Label-Free Detection of Pesticides

We show a new type of elastic surface-enhanced Raman spectroscopy (SERS) platform made of poly(ethylene terephthalate) (PET) covered with a layer of indium tin oxide (ITO). This composite is subjected to dielectric barrier discharge (DBD) that develops the active surface of the PET/ITO foil. To enhance the Raman signal, a modified composite was covered with a thin layer of silver using the physical vapor deposition (PVD) technique. The SERS platform was used for measurements of para-mercaptobenzoic acid (p-MBA) and popular pesticides, i.e., Thiram and Carbaryl. The detection and identification of pesticides on the surface of fruits and vegetables is a crucial issue due to extensive use of those chemical substances for plant fungicide and insecticide protection. Therefore, the developed PET/ITO/Ag SERS platform was dedicated to quantitative analysis of selected pesticides, i.e., Thiram and Carbaryl from fruits. The presented SERS platform exhibits excellent enhancement and reproducibility of the Raman signal, which enables the trace analysis of these pesticides in the range up to their maximum residues limit. Based on the constructed calibration curves, the pesticide concentrations from the skin of apples was estimated as 2.5 µg/mL and 0.012 µg/mL for Thiram and Carbaryl, respectively. Additionally, the PET/ITO/Ag SERS platform satisfies other spectroscopic properties required for trace pesticide analysis e.g., ease, cost-effective method of preparation, and specially designed physical properties, especially flexibility and transparency, that broaden the sampling versatility to irregular surfaces.

What are the Main Sensor Methods for Quantifying Pesticides in Agricultural Activities? A Review

In recent years, there has been an increase in pesticide use to improve crop production due to the growth of agricultural activities. Consequently, various pesticides have been present in the environment for an extended period of time. This review presents a general description of recent advances in the development of methods for the quantification of pesticides used in agricultural activities. Current advances focus on improving sensitivity and selectivity through the use of nanomaterials in both sensor assemblies and new biosensors. In this study, we summarize the electrochemical, optical, nano-colorimetric, piezoelectric, chemo-luminescent and fluorescent techniques related to the determination of agricultural pesticides. A brief description of each method and its applications, detection limit, purpose—which is to efficiently determine pesticides—cost and precision are considered. The main crops that are assessed in this study are bananas, although other fruits and vegetables contaminated with pesticides are also mentioned. While many studies have assessed biosensors for the determination of pesticides, the research in this area needs to be expanded to allow for a balance between agricultural activities and environmental protection.

Generalized regression neural network association with terahertz spectroscopy for quantitative analysis of benzoic acid additive in wheat flour

Investigations were initiated to develop terahertz (THz) techniques associated with machine learning methods of generalized regression neural network (GRNN) and back-propagation neural network (BPNN) to rapidly measure benzoic acid (BA) content in wheat flour. The absorption coefficient exhibited a maximum absorption peak at 1.94 THz, which generally increased with the content of BA additive. THz spectra were transformed into orthogonal principal component analysis (PCA) scores as the input vectors of GRNN and BPNN models. The best GRNN model was achieved with three PCA scores and spread value of 0.2. Compared with the BPNN model, GRNN model to powder samples could be considered very successful for quality control of wheat flour with a correlation coefficient of prediction (r p) of 0.85 and root mean square error of prediction of 0.10%. The results suggest that THz technique association with GRNN has a significant potential to quantitatively analyse BA additive in wheat flour.

Photoelectrochemical determination of malathion by using CuO modified with a metal-organic framework of type Cu-BTC

A photoelectrochemical (PEC) sensor was constructed for the detection of non-electroactive malathion. It is based on the use of a hierarchical CuO material derived from a Cu-BTC metal-organic framework (where BTC stands for benzene-1,3,5-tricarboxylic acid). The modified CuO was obtained by calcination of Cu-BTC at a high temperature (300 °C) and possesses a high photocurrent conversion efficiency. Under irradiation with visible light and in the presence of malathion, the formation of the CuO-malathion complex on the CuO gave rise to an increase in steric hindrance. This results in a decrease in photocurrent. This novel PEC detection method has a lower detection limit of 8.6 × 10^-11 mol L^-1 and a wide linear range (1.0 × 10^-10 ~ 1.0 × 10^-5 mol L^-1). Graphical abstract Schematic presentation of the Cu-BTC MOF derived photoelectrochemical sensor for non-electroactive malathion detection.