Detection of multi-class pesticide residues with surface-enhanced Raman spectroscopy

Leveraging SERS and Transformer Models for Simultaneous Detection of Multiple Pesticides in Fresh Produce

The widespread use of pesticides in agriculture poses food safety and environmental risks, highlighting the need for rapid detection techniques to mitigate contamination. Surface-enhanced Raman spectroscopy (SERS) coupled with machine learning provides a powerful approach for the detection and quantification of multiple pesticides in agricultural products. This study introduces the SERSFormer-2.0 model, which excels in both multilabel classification and multiregression tasks for pesticide analysis, leveraging the power of transformer-based machine learning architectures. SERSFormer-2.0 employs novel multitask learning approach with task specific feature representation layers, shared multihead attention transformer encoder, and task-specific output layers to detect pesticides and estimate the precise concentrations of each pesticide simultaneously. By utilizing core-shell gold-silver nanoparticles, the model achieves near-perfect performance in identifying and quantifying pesticide residues, with multilabel metrics and regression accuracy demonstrating exceptional reliability (accuracy = 0.999; F1 score = 0.992; precision = 0.990; recall = 0.996). A detailed examination of the Raman spectra reveals the predominant influence of certain pesticides, and the mechanisms behind spectral dominance were elucidated. Our findings underscore the SERSFormer-2.0 model 's robustness and its potential to detect mixed contaminants in agricultural products, enhancing food safety and regulatory practices.

Surface control in the adsorption of tebuthiuron on modified silver surfaces tracked by surface-enhanced Raman scattering spectroscopy

The vibrational assignment of the Raman and surface-enhanced Raman scattering (SERS) spectra of the herbicide tebuthiuron (TBH) was accomplished, which allowed unprecedented propositions for adsorption geometries on the surface of silver nanoparticles (AgNP). Ascribed SERS features allowed suggesting that the adsorption occurred through nitrogen atoms of thiadiazole group, since intense band shift assigned to ring mode was marking of the coordination with the metallic surface. AgNP were treated with different surface modifiers that leaded to substantial changes in TBH adsorption geometries. Spectral changes, as the enhancement of out-of-plane ring modes, were indicative of the presence of tilted thiadiazole geometries in relation to the silver surface. Density Functional Theory (DFT) calculations from TBH molecules, in isolation and in interaction with ten-atom cluster of silver leaded to obtain theoretical spectra that gave support to interpret experimental Raman and SERS spectra.

A "turn-on" fluorescent sensor for herbicide quizalofop-p-ethyl based on cyanostilbene-pyridine macrocycle

Quizalofop-p-ethyl is a widely used herbicide that also poses a risk to human health and environmental safety. However, there is still a lack of simple and in-situ detecting method for quizalofop-p-ethyl so far. In this work, the fluorescent sensor was firstly developed on detection of quizalofop-p-ethyl based on cyanostilbene-pyridine macrocycle (CPM). CPM was prepared by the "1 + 1" condensation of pyridine-substituted cyanostilbene derivative with 4,4'-Bis(chloromethyl)biphenyl in 68 % yield. The weak fluorescence of CPM in aqueous media transferred to strong orange fluorescence after sensing quizalofop-p-ethyl. This sensing behavior exhibited high selectivity among 28 kinds of herbicides and ions. The limitation of detection (LOD) was 2.98 × 10-8 M and the limitation of quantification (LOQ) was 9.94 × 10-8 M (λex = 390 nm, λem = the maximum emission between 512 nm and 535 nm) with a dynamic range of 0.01-0.9 eq. The binding constant (Ka) of quizalofop-p-ethyl to the sensor CPM was 3.2 × 106 M-1. The 1:1 sensing mechanism was confirmed as that quizalofop-p-ethyl was located in the cavity of CPM, which enhanced aggregating effect and reduced the intramolecular rotation of aromatic groups for better AIE effect. The sensing ability of CPM for quizalofop-p-ethyl had been efficiently applied in test paper experiments, agricultural product tests and real water samples, revealing that CPM has good application prospect for simple and in-situ detection of quizalofop-p-ethyl in real environment.

Self-Organized SERS Substrates with Efficient Analyte Enrichment in the Hot Spots

One of the requirements of an efficient surface-enhanced Raman spectroscopy (SERS) substrate is a developed surface morphology with a high density of "hot spots", nm-scale spacings between plasmonic nanoparticles. Of particular interest are plasmonic architectures that could enable self-localization (enrichment) of the analyte in the hot spots. We report a straightforward method of fabrication of efficient SERS substrates that comply with these requirements. The basis of the substrate is a large-area film of tightly packed SiO2 spheres formed by their quick self-assembling upon drop casting from the solution. Thermally evaporated thin Ag layer is converted by quick thermal annealing into nanoparticles (NPs) self-assembled in the trenches between the silica spheres, i.e., in the places where the analyte molecules get localized upon deposition from solution and drying. Therefore, the obtained substrate morphology enables an efficient enrichment of the analyte in the hot spots formed by the densely arranged plasmonic NPs. The high efficiency of the developed SERS substrates is demonstrated by the detection of Rhodamine 6G down to 10-13 mol/L with an enhancement factor of ∼108, as well as the detection of low concentrations of various nonresonant analytes, both small dye molecules and large biomolecules. The developed approach to SERS substrates is very straightforward for implementation and can be further extended to using gold or other plasmonic NPs.

SERS-substrates based on ZnO nanoflowers prepared by green synthesis

ZnO nanoparticles (NPs) with a flower-like morphology, synthesized by an affordable colloidal route using an aqueous fungi extract of Ganoderma lucidum as a reducing agent and stabilizer, are investigated as SERS-substrate. Each "flower" has large effective surface that is preserved at packing particles into a dense film and thus exhibits an advantageous property for SERS and similar sensing applications. The mycoextract used in our low-cost and green synthesis as surface stabilizer allows subsequent deposition of metal NPs or layers. One type of SERS substrates studied here was ZnO NPs decorated in situ in the solution by Ag NPs, another type was prepared by thermally evaporating Ag layer on the ZnO NP film on a substrate. A huge difference in the enhancement of the same analyte in the solution and in the dried form is found and discussed. Detection down to 10-7 M of standard dye analytes such as rhodamine 6G and methylene blue was achieved without additional optimization of the SERS substrates. The observed SERS-activity demonstrate the potential of both the free-standing flower-like ZnO NPs and thereof made dense films also for other applications where large surface area accessible for the external agent is crucial, such as catalysis or sensing.

Electrochemical Determination of Hazardous Herbicide Diuron Using MWCNTs-CS@NGQDs Composite-Modified Glassy Carbon Electrodes

Diuron (DU) abuse in weed removal and shipping pollution prevention always leads to pesticide residues and poses a risk to human health. In the current research, an innovative electrochemical sensor for DU detection was created using a glassy carbon electrode (GCE) that had been modified with chitosan-encapsulated multi-walled carbon nanotubes (MWCNTs-CS) combined with nitrogen-doped graphene quantum dots (NGQDs). The NGQDs were prepared by high-temperature pyrolysis, and the MWCNTs-CS@NGQDs composite was further prepared by ultrasonic assembly. TEM, UV-Vis, and zeta potential tests were performed to investigate the morphology and properties of MWCNTs-CS@NGQDs. CV and EIS measurements revealed that the assembly of MWCNTs and CS improved the electron transfer ability and effective active area of MWCNTs. Moreover, the introduction of NGQDs further enhanced the detection sensitivity of the designed sensor. The MWCNTs-CS@NGQDs/GCE electrochemical sensor exhibited a wide linear range (0.08~12 μg mL-1), a low limit of detection (0.04 μg mL-1), and high sensitivity (31.62 μA (μg mL-1)-1 cm-2) for DU detection. Furthermore, the sensor demonstrated good anti-interference performance, reproducibility, and stability. This approach has been effectively employed to determine DU in actual samples, with recovery ranges of 99.4~104% in river water and 90.0~94.6% in soil. The developed electrochemical sensor is a useful tool to detect DU, which is expected to provide a convenient and easy analytical technique for the determination of various bioactive species.

UV-Nanoimprint Lithography for Predefined SERS Nanopatterns Which Are Reproducible at Low Cost and High Throughput

A controlled and reliable nanostructured metallic substrate is a prerequisite for developing effective surface-enhanced Raman scattering (SERS) spectroscopy techniques. In this study, we present a novel SERS platform fabricated using ultra-violet nanoimprint lithography (UV-NIL) to produce large-area, ordered nanostructured arrays. By using UV-NIL imprinted patterns in resist, we were able to overcome the main limitations present in most common SERS platforms, such as nonuniformity, nonreproducibility, low throughput, and high cost. We simulated and fabricated C-shaped plasmonic nanostructures that exhibit high signal enhancement at an excitation wavelength of 785 nm. The substrates were fabricated by directly coating the imprinted resist with a thin gold layer. Avoiding the need to etch patterns in silicon significantly reduces the time and cost of fabrication and facilitates reproducibility. The functionality of the substrates for SERS detection was validated by measuring the SERS spectra of Rhodamine 6G.

Luminescent Guests Encapsulated in Metal-Organic Frameworks for Portable Fluorescence Sensor and Visual Detection Applications: A Review

Metal-organic frameworks (MOFs) have excellent applicability in several fields and have significant structural advantages, due to their open pore structure, high porosity, large specific surface area, and easily modifiable and functionalized porous surface. In addition, a variety of luminescent guest (LG) species can be encapsulated in the pores of MOFs, giving MOFs a broader luminescent capability. The applications of a variety of LG@MOF sensors, constructed by doping MOFs with LGs such as lanthanide ions, carbon quantum dots, luminescent complexes, organic dyes, and metal nanoclusters, for fluorescence detection of various target analyses such as ions, biomarkers, pesticides, and preservatives are systematically introduced in this review. The development of these sensors for portable visual fluorescence sensing applications is then covered. Finally, the challenges that these sectors currently face, as well as the potential for future growth, are briefly discussed.

A simple mesoporous silica Nanoparticle-based aptamers SERS sensor for the detection of acetamiprid

In this study, we developed a novel, rapid, simple, and sensitive nano sensor based on the controlled release of 4-Aminothiophenol (4-ATP) signal molecules from aptamers (Apts) modified aminated mesoporous silica nanoparticles (MSNs-NH₂) for the quantitative detection of acetamiprid (ACE). Firstly, we synthesized the positively charged MSNs-NH₂ by one-pot method, then loaded 4-ATP signal molecules into the pore, and finally electrostatically adsorbed the Apts onto the MSNs-NH₂, which acts as a gate to control the release of signal molecules. When ACE is added to the system, ACE preferentially and specifically binds to Apts, so the gate opens and 4-ATP signal molecules are released from the pore. Meanwhile, the silver-loaded mesoporous silica nanoparticles (Ag@SiO₂) were prepared by one-pot method as surface-enhanced Raman spectroscopy (SERS) substrate to amplify the signal. The intensity of 4-ATP signal molecules at 1433 cm^-1 position was observed to has a linear relationship with the concentration of ACE by SERS detection. Under the optimized detection conditions, a linear correlation was observed in the range of 5-60 ng/mL (R² = 0.99749), and the limit of detection (LOD) was 2.66 ng/mL. The method has high sensitivity, good selectivity and reproducibility, and can be used for actual sample analysis with the recovery rate of 96.24-103.6 %. This study provides a reference for the rapid and convenient detection of ACE in agricultural products.

Recent Progress in Nanotechnology-Based Approaches for Food Monitoring

Throughout the food supply chain, including production, storage, and distribution, food can be contaminated by harmful chemicals and microorganisms, resulting in a severe threat to human health. In recent years, the rapid advancement and development of nanotechnology proposed revolutionary solutions to solve several problems in scientific and industrial areas, including food monitoring. Nanotechnology can be incorporated into chemical and biological sensors to improve analytical performance, such as response time, sensitivity, selectivity, reliability, and accuracy. Based on the characteristics of the contaminants and the detection methods, nanotechnology can be applied in different ways in order to improve conventional techniques. Nanomaterials such as nanoparticles, nanorods, nanosheets, nanocomposites, nanotubes, and nanowires provide various functions for the immobilization and labeling of contaminants in electrochemical and optical detection. This review summarizes the recent advances in nanotechnology for detecting chemical and biological contaminations in the food supply chain.

A Simple Aptamer SERS Sensor Based on Mesoporous Silica for the Detection of Chlorpyrifos

Chlorpyrifos is an organophosphorus insecticide, which can be used to control a variety of chewing and piercing mouthparts pests in agricultural production. It can destroy the normal nerve impulse conduction by inhibiting the activity of acetylcholinesterase or cholinesterase in the nerves, causing a series of poisoning symptoms. In order to achieve the quantitative analysis of chlorpyrifos residues in agricultural products, an aptamer-controlled signal molecule release method was developed in this study. The signal molecule 4-ATP of surface-enhanced Raman spectroscopy (SERS) was loaded into aminated mesoporous silica nanoparticles (MSNs-NH2) prepared by the one pot method, and then coated with an aptamer of chlorpyrifos through electrostatic interaction. The specific binding of the aptamer and chlorpyrifos led to the release of 4-ATP, and the amount of 4-ATP released was positively correlated with the amount of chlorpyrifos. Finally, the standard curve of chlorpyrifos quantitative detection based on SERS was established. Meanwhile, Ag-carrying mesoporous silica (Ag@MSNs) was prepared as the reinforcement substrate for SERS detection. The results showed that there was a good linear correlation between the Raman intensity and the concentration of chlorpyrifos at 25−250 ng/mL, and the limit of detection (LOD) was 19.87 ng/mL. The recoveries of chlorpyrifos in the apple and tomato samples were 90.08−102.2%, with RSD < 3.32%. This method has high sensitivity, specificity, reproducibility and stability, and can be used for the quantitative detection of chlorpyrifos in the environment and agricultural products.

A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning

Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.

A Novel SERS Substrate Based on Discarded Oyster Shells for Rapid Detection of Organophosphorus Pesticide

Over the past few years, the concern for green chemistry and sustainable development has risen dramatically. Researchers make an effort to find solutions to difficult challenges using green chemical processes. In this study, we use oyster shells as a green chemical source to prepare calcium oxide nanoparticles (CaO-NPs). Transmission electron microscopy (TEM) results showed the CaO-NPs morphology, which was spherical in shape, 40 ± 5 nm in diameter, with uniform dispersion. We further prepared silver/polydopamine/calcium-oxide (Ag/PDA/CaO) nanocomposites as the surface-enhanced Raman scattering (SERS) substrates and evaluated their enhancement effect using the methyl parathion pesticide. The effective SERS detection limit of this method is 0.9 nM methyl parathion, which is much lower than the safety limits set by the Collaborative International Pesticides Analytical Council for insecticide in fruits. This novel green material is an excellent SERS substrate for future applications and meets the goal of green chemistry and sustainable development.

Applications of surface-enhanced Raman spectroscopy in environmental detection

As the human population grows, the anthropogenic impacts from various agricultural and industrial processes produce unwanted contaminants in the environment. The accurate, sensitive and rapid detection of such contaminants is vital for human health and safety. Surface-enhanced Raman spectroscopy (SERS) is a valuable analytical tool with wide applications in environmental contaminant monitoring. The aim of this review is to summarize recent advancements within SERS research as it applies to environmental detection, with a focus on research published or accessible from January 2021 through December 2021 including early-access publications. Our goal is to provide a wide breadth of information that can be used to provide background knowledge of the field, as well as inform and encourage further development of SERS techniques in protecting environmental quality and safety. Specifically, we highlight the characteristics of effective SERS nanosubstrates, and explore methods for the SERS detection of inorganic, organic, and biological contaminants including heavy metals, pharmaceuticals, plastic particles, synthetic dyes, pesticides, viruses, bacteria and mycotoxins. We also discuss the current limitations of SERS technologies in environmental detection and propose several avenues for future investigation. We encourage researchers to fill in the identified gaps so that SERS can be implemented in a real-world environment more effectively and efficiently, ultimately providing reliable and timely data to help and make science-based strategies and policies to protect environmental safety and public health.

Quantitative analysis of carbaryl and thiabendazole in complex matrices using excitation-emission fluorescence matrices with second-order calibration methods

In this paper, a fast and efficient analytical strategy was proposed that chemometrics assisted with excitation-emission fluorescence matrices was used to quantify carbaryl (CAR) and thiabendazole (TBZ) in peach, soil and sewage. Even if there are serious overlapped peaks and unknown interferences in fluorescence analysis, the second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm can be used to analyze CAR and TBZ in peach, soil and sewage. The recoveries of CAR and TBZ in peach are 110.4% and 99.7% and their standard deviations are lower than 2.1% and 0.3%, respectively. In addition, the accuracy of the method was assessed with figures of merit as well as intra-day and inter-day precision. The limit of detection, the limit of quantitation of CAR and TBZ in peach are 1.2 ng mL^-1 and 0.3 ng mL^-1, 3.5 ng mL^-1 and 0.8 ng mL^-1, respectively. And their root-mean-square error of prediction are 17.0 ng mL^-1 and 5.0 ng mL^-1 and there are high sensitivity and selectivity in this method. Meanwhile, the results obtained by ATLD algorithm were compared with those obtained by the self-weighted alternate trilinear decomposition algorithm (SWATLD) and the parallel factor analysis (PARAFAC) algorithm, and statistical methods such as the t-test, F-test and the elliptic joint confidence region were used to evaluate for analysis. There were no significant differences among these methods. At last, high performance liquid chromatography-fluorescence detector (HPLC-FLD) was used to evaluate the accuracy and reliability of the proposed method. These results are satisfactory and indicate that the proposed method can be used for accurate and rapid determination of pesticides in complex systems.

Biophysical matter in a marine estuary identified by the Sentinel-3B OLCI satellite and the presence of terrestrial iron (Fe) nanoparticles

The analysis of marine matter using the Sentinel-3B OLCI (Ocean Land Color Instrument) satellite is the most advanced technique for evaluating: the absorption of colored detrital and dissolved material (ADG_443_NN), total suspended matter concentration (TSM_NN) and of chlorophyll-a (CHL_NN) on a global scale. The objective is to analyze ADG_443_NN, TSM_NN and CHL_NN using the Sentinel-3B OLCI satellite and the presence of Fe-nanoparticles (NPs) + hazardous elements (HEs) in suspended sediments (SSs) in the maritime estuary of the Colombian city of Barranquilla. The study used the unpublished image of the Sentinel-3B OLCI satellite in the evaluation of ADG_443_NN, TSM_NN and CHL_NN in 72 sampled points. Subsequently, 36 samples of SSs were carried out in the Magdalena River, in the identification of Fe-NPs by advanced electron microscopies. The Sentinel-3B satellite revealed particulate accumulations in OCE1 through the intensity of OLCI in ocean. There was also a high Fe-NPs intensity of SSs in the Magdalena channel, spreading contamination to large regions.