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

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Towards solving the reproducibility crisis in surface-enhanced Raman spectroscopy-based pesticide detection

Growing concerns about pesticide residues in agriculture are pushing the scientific community to develop innovative and efficient methods for detecting these substances at low concentrations down to the molecular level. In this context, surface-enhanced Raman spectroscopy (SERS) is a powerful analytical method that has so far already undergone some validation for its effectiveness in pesticide detection. However, despite its great potential, SERS faces significant difficulties obtaining reproducible and accurate pesticide spectra, particularly for some of the most widely used pesticides, such as malathion, chlorpyrifos, and imidacloprid. Those inconsistencies can be attributed to several factors, such as interactions between pesticides and SERS substrates and the variety of substrates and solvents used. In addition, differences in the equipment used to obtain SERS spectra and the lack of standards for control experiments further complicate the reproducibility and reliability of SERS data. This review systematically discusses the problems mentioned above, including a comprehensive analysis of the challenges in precisely evaluating SERS spectra for pesticide detection. We not only point out the existing limitations of the method, which can be traced in previous review works, but also offer practical recommendations to improve the quality and comparability of SERS spectra, thereby expanding the potential applications of the method in such an essential field as pesticide detection.

A stable and flexible Au@Ag NPs/PVA SERS platform for thiram residue detection on rough surface

Flexible and transparent surface-enhanced Raman scattering (SERS) substrates have gained great attention in analysis field as they offer a fast, non-destructive, and highly sensitive platform for in-situ detection. In this work, we present a facile one-pot strategy for synthesizing gold-cored silver shell nanoparticles (Au@Ag NPs) in the polyvinyl alcohol (PVA) colloid. With no other reducing agents, PVA can serve as both reducing and stabilizing agents for forming Au@Ag NPs. Besides, PVA acts as a scaffold to maintain SERS "hot-spots" by preventing nanoparticle aggregation. By using this flexible Au@Ag NPs/PVA colloid, the analytes can be extracted from rough surfaces for SERS measurements with excellent sensitivity, repeatability and stability. The SERS activity of the Au@Ag NPs/PVA remained at 89.8% even after 120 days of storage at room temperature in sealed air atmosphere. The selective detection of thiram residues on the surface of fruits and vegetables was successfully achieved. The limits of detection for thiram residues on apple and tomato surfaces were measured to be 0.58 and 0.56 ng cm-2, respectively, with recovery rate ranging from 91% to 107%. This work demonstrates the immense application potential of SERS colloid platform in the fields of food safety and environmental analysis.

Sandwich-like CuNPs@AgNPs@PSB SERS substrates for sensitive detection of R6G and Forchlorfenuron

The development of a highly sensitive, synthetically simple and economical SERS substrate is technically very important. A fast, economical, sensitive and reproducible CuNPs@AgNPs@ Porous silicon Bragg reflector (PSB) SERS substrate was prepared by electrochemical etching and in situ reduction method. The developed CuNPs@AgNPs@PSB has a large specific surface area and abundant "hot spot" region, which makes the SERS performance excellent. Meanwhile, the successful synthesis of CuNPs@AgNPs can not only modulate the plasmon resonance properties of nanoparticles, but also effectively prolong the time stability of Cu nanoparticles. The basic performance of the substrate was evaluated using rhodamine 6G (R6G). (Detection limit reached 10-15 M, R2 = 0.9882, RSD = 5.3 %) The detection limit of Forchlorfenuron was 10 μg/L. The standard curve with a regression coefficient of 0.979 was established in the low concentration range of 10 μg/L -100 μg/L. This indicates that the prepared substrates can accomplish the detection of pesticide residues in the low concentration range. The prepared high-performance and high-sensitivity SERS substrate have a very promising application in detection technology.

Intelligent Rapid Detection Techniques for Low-Content Components in Fruits and Vegetables: A Comprehensive Review

Fruits and vegetables are an important part of our daily diet and contain low-content components that are crucial for our health. Detecting these components accurately is of paramount significance. However, traditional detection methods face challenges such as complex sample processing, slow detection speed, and the need for highly skilled operators. These limitations fail to meet the growing demand for intelligent and rapid detection of low-content components in fruits and vegetables. In recent years, significant progress has been made in intelligent rapid detection technology, particularly in detecting high-content components in fruits and vegetables. However, the accurate detection of low-content components remains a challenge and has gained considerable attention in current research. This review paper aims to explore and analyze several intelligent rapid detection techniques that have been extensively studied for this purpose. These techniques include near-infrared spectroscopy, Raman spectroscopy, laser-induced breakdown spectroscopy, and terahertz spectroscopy, among others. This paper provides detailed reports and analyses of the application of these methods in detecting low-content components. Furthermore, it offers a prospective exploration of their future development in this field. The goal is to contribute to the enhancement and widespread adoption of technology for detecting low-content components in fruits and vegetables. It is expected that this review will serve as a valuable reference for researchers and practitioners in this area.

Silver-nanoparticle-coated Fe3O4/chitosan core-shell microspheres for rapid and ultrasensitive detection of thiram using surface magnetic solid-phase extraction-surface-enhanced Raman scattering (SMSPE-SERS)

We report a surface magnetic solid-phase extraction-surface-enhanced Raman scattering (SMSPE-SERS) method based on silver-nanoparticle-coated Fe3O4/chitosan (Fe3O4/CS@Ag) microspheres as the substrate, and this method integrates all steps from sample pretreatment to detection. Fe3O4/CS was synthesized by a one-step solvothermal method in which chitosan (CS) was used as a surface modifier and adsorbent. Fe3O4/CS@Ag microspheres exhibit both adsorption ability and SERS activity. Therefore, we used the SMSPE-SERS method to detect pesticide residues on fruit peel. The procedures of capturing, separating and enriching pesticides, as well as detection, are all integrated. In addition, the SERS substrate allows label-free detection of thiram pesticide in both fruit peel and apple juice. Owing to the uniform distribution of Ag NPs and the adsorption ability of CS, the thiram-detection sensitivity was sufficiently high to detect the lowest concentration of 1.2 ng/cm2, which was significantly lower than the maximum thiram residue limit (7 μg/cm2) in fruits. The method was comparable to high-performance liquid chromatography with recovery ranging from 86.60 to 109.69 %.

Current Trends of Raman Spectroscopy in Clinic Settings: Opportunities and Challenges

Early clinical diagnosis, effective intraoperative guidance, and an accurate prognosis can lead to timely and effective medical treatment. The current conventional clinical methods have several limitations. Therefore, there is a need to develop faster and more reliable clinical detection, treatment, and monitoring methods to enhance their clinical applications. Raman spectroscopy is noninvasive and provides highly specific information about the molecular structure and biochemical composition of analytes in a rapid and accurate manner. It has a wide range of applications in biomedicine, materials, and clinical settings. This review primarily focuses on the application of Raman spectroscopy in clinical medicine. The advantages and limitations of Raman spectroscopy over traditional clinical methods are discussed. In addition, the advantages of combining Raman spectroscopy with machine learning, nanoparticles, and probes are demonstrated, thereby extending its applicability to different clinical phases. Examples of the clinical applications of Raman spectroscopy over the last 3 years are also integrated. Finally, various prospective approaches based on Raman spectroscopy in clinical studies are surveyed, and current challenges are discussed.

A three-dimensional gold nanoparticles spherical liquid array for SERS sensitive detection of pesticide residues in apple

High-performance plasmonic substrates have recently attracted much research attention. Herein, a three-dimensional gold nanoparticles (AuNPs) spherical liquid array (SLA) with high "hot spots" and tunable nanometer gap by optimizing the proportion of AuNPs colloids over chloroform was synthesized based on a water-oil interfacial self-assembly strategy. The substrate demonstrated excellent surface-enhanced Raman scattering (SERS) performance using tetrathiafulvalene and rhodamine 6G (R6G) as probe molecules. With a simple extraction and soaking pretreatment process, the SLA exhibited high sensitivity for analysing triazophos on apple peels, with a limit of detection (LOD) of 0.005 µg/mL and recovery ranging from 96 to 110 %. Particularly, the chloroform produced an inherent characteristic peak at 665 cm-1, which was used as the internal standard to correct SERS signal fluctuation, leading to an improvement of the corresponding coefficient R2 from 0.97 to 0.99, thus improving the reproducibility. Therefore the SLA substrate possesses the potential for quantitative analysis of food contaminants.

Trace analysis of food by surface-enhanced Raman spectroscopy combined with molecular imprinting technology: Principle, application, challenges, and prospects

Surface-enhanced Raman spectroscopy (SERS) is a rapid detection method with high sensitivity and simple pretreatment, but can be affected by interference from matrix components. By incorporating molecularly imprinted polymers (MIPs) that recognize specific targets, MIP-SERS sensors effectively overcome the interference of complex matrices and offer improved stability and sensitivity. This review provides a comprehensive understanding of the applications of MIP-SERS sensors for the detection of trace toxic substances in food. The underlying mechanism and development of SERS technology and the principle and classification of MIPs technology are discussed. Furthermore, the types of MIP-SERS sensors are introduced, with their advantages and disadvantages systematically illustrated. Recent advances in MIP-SERS technology for the detection of mycotoxins, additives, prohibited dyes, pesticides, veterinary drug residues, and other hazardous substances in food are highlighted. Finally, this review discusses the challenges associated with MIP-SERS technology and proposes future development prospects.

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

A facile heat-treatment solid phase microextraction method for SERS detection of isocarbophos in tea using a hand-held Raman spectrometer

A facile sensor system based on heat-treatment solid phase microextraction and Surface-Enhanced Raman Scattering (HT-SPME-SERS) was established for in-situ detection of isocarbophos in complex tea matrix. Starting from the action optimization of temperature control unit and air flow control unit, pesticide molecules volatilizing from solution are efficiently captured by substrate and generate real-time SERS signals by a hand-held Raman spectrometer, and the sensor system based on HT-SPME-SERS was finally established. A novel SERS substrate of Cu@rGO@Ag was developed as HT-SPME-SERS material, where reduced graphene oxide (rGO) enriched pesticide molecules by π-π stacking. A superior detection sensitivity brought by the ultra-high enhancement effect of Cu@rGO@Ag substrate was obtained. A good linear relationship between Raman intensity and isocarbophos concentration was obtained and the limit of detection (LOD) was as low as 0.00451 ppm. The detection results obtained from the sensor system have been verified by gas chromatography-mass spectrometer (GC-MS), showing its great application potential for the safety of agricultural products.

An autofocusing method for dynamic surface-enhanced Raman spectroscopy detection realized by optimized hill-climbing algorithm with long time stable hotspots

In the manual dynamic surface-enhanced Raman spectroscopy (D-SERS) detection process, it is difficult to focus on sample drop due to the constantly changing hotspot and easy judgment method. In this paper, we proposed an automatic focusing method based on long time stable hotspot with aid of optimization of hill-climbing algorithm and achieved on a designed device. First, set up a high temperature accelerating evaporation process to obtain hotspot and then cool to a low temperature rapidly to maintain it. Then, the spectral intensity was used as a focus of feedback signal in optimized hill-climbing algorithm to drive the sample stage to move up and down to adjust the depth of the laser on the samples to realize automatic focusing. As a result, the hotspot can be maintained for 5 min, and the autofocusing result can be achieved within 9 s, while the sensitivity was improved with two orders of magnitude in D-SERS detection of crystal violet (CV) compared with manual focusing.

Recent Progress in the Application of Metal Organic Frameworks in Surface-Enhanced Raman Scattering Detection

Metal-organic framework (MOF) compounds are centered on metal ions or metal ion clusters, forming lattices with a highly ordered periodic porous network structure by connecting organic ligands. As MOFs have the advantages of high porosity, large specific surface area, controllable pore size, etc., they are widely used in gas storage, catalysis, adsorption, separation and other fields. SERS substrate based on MOFs can not only improve the sensitivity of SERS analysis but also solve the problem of easy aggregation of substrate nanoparticles. By combining MOFs with SERS, SERS performance is further improved, and tremendous research progress has been made in recent years. In this review, three methods of preparing MOF-based SERS substrates are introduced, and the latest applications of MOF-based SERS substrates in biosensors, the environment, gases and medical treatments are discussed. Finally, the current status and prospects of MOF-based SERS analysis are summarized.

On-site Simultaneous Determination of Neonicotinoids, Carbamates, and Phenyl Pyrazole Insecticides in Vegetables by QuEChERS Extraction on Nitrogen and Sulfur co-doped Carbon Dots and Portable Mass Spectrometry

In food safety monitoring, on-site and simultaneous detection of a variety of insecticides with different concentrations in the same matrix is necessary. However, the task remains challenging. In this study, a novel nitrogen and sulfur co-doped carbon dot (N, S-CD) was synthesized and used as a QuEChERS clean-up reagent to reduce matrix interferences in the determination of insecticides in vegetables. In addition, a portable mass spectrometer (µ-MS) was employed, without chromatography separation, to directly determine neonicotinoids, carbamates, and benzopyrazole insecticides (with acetamiprid, imidacloprid, thiamethoxam, fipronil, and carbofuran as models) in the pretreated samples. The N,S-CD µ-MS method exhibited effective clean-up performance with satisfactory matrix effects between -15.2% and 15.7%. The recoveries of spiked vegetable samples ranged from 82.2% to 109.7% for the five target insecticides, and the relative standard deviations (RSDs) ranged from 3.8% to 16.5%. The linear ranges were from 2.0 to 5.0 ng/g, with low detection limits (LOD) from 0.5 to 1.0 ng/g. Moreover, the total pretreatment and detection time was within 20 min. Thus, the incorporation of N,S-CD with QuEChERS extraction, together with the portable µ-MS system, could be a promising and feasible strategy for on-site, rapid, and simultaneous detection of various insecticides in vegetables.

Pesticide Residues and Unauthorized Dyes as Adulteration Markers in Chilli Pepper and Tomato

To assess the contamination of processed chilli pepper and tomatoes, a report over the past four decades since the establishment of the Rapid Alert System for Food and Feed (RASFF) was retrieved and analysed. Out of the 887 notification reports assessed for eligibility, 446 were found regarding chilli pepper and tomato contamination. This study identified India as the country of origin with the highest number of reported cases relating to chilli pepper contamination. Italy and Türkiye were the countries with the highest number of reported cases regarding the exportation of adulterated tomatoes to other countries according to the RASFF report. Unauthorized dyes such as Sudan I, III, IV, orange II, rhodamine B, and para red were reported to have been detected in either chilli pepper or tomato in the supply chain. Almost all unauthorized dyes in this study were found to be more than the range (0.5 to 1 mg/kg) of the detection limit of Sudan dye and other related dyes using analytical methods set by the European Union. Unapproved pesticides by the European Union (EU) found in this study were acetamiprid, chlorothalonil, chlorpyrifos, dimethoate, methomyl, monocrotophos, omethoate, oxamyl, and thiophanate methyl. The present study indicates the persistence of chilli pepper and tomato contamination with harmful dyes and pesticide residues despite the ban on the use of certain chemicals in the food chain.

Membrane technology for pesticide removal from aquatic environment: Status quo and way forward

The noxious side effects of pesticides on human health and environment have prompted the search of effective and reliable treatment techniques for pesticide removal. The removal of pesticides can be accomplished through physical, chemical and biologicals. Physical approaches such as filtration and adsorption are prevailing pesticide removal strategies on account of their effectiveness and ease of operation. Membrane-based filtration technology has been recognized as a promising water and wastewater treatment approach that can be used for a wide range of organic micropollutants including pesticides. Nanofiltration (NF), reverse osmosis (RO) and forward osmosis (FO) have been increasingly explored for pesticide removal from aquatic environment owing to their versatility and high treatment efficiencies. This review looks into the remedial strategies of pesticides from aqueous environment using membrane-based processes. The potentials and applications of three prevailing membrane processes, namely NF, RO and FO for the treatment of pesticide-containing wastewater are discussed in terms of the development of advanced membranes, separation mechanisms and system design. The challenges in regards to the practical implementation of membrane-based processes for pesticide remediation are identified. The corresponding research directions and way forward are highlighted. An in depth understanding of the pesticide nature, water chemistry and the pesticide-membrane interactions is the key to achieving high pesticide removal efficiency. The integration of membrane technology and conventional removal technologies represents a new dimension and the future direction for the treatment of wastewater containing recalcitrant pesticides.

A super-hydrophobic perfluoropolyether coated polytetrafluoroethylene sheets substrate for detection of acetamiprid surface-enhanced Raman spectroscopy

In this paper, a hydrophobic substrate as concentrators including an inner layer of polytetrafluoroethylene (PTFE) and an outer layer covered a thin layer of perfluoropolyether (PFPE) was constructed to achieve a higher sensitivity for acetamiprid (AC) SERS detection. The condensation effect of the PTFE-PFPE hydrophobic substrate-induced aggregation of gold nanoparticles (Au NPs) result ''hot spots'' for SERS. The hydrophobic substrate is better reproducibility (RSD < 5%) compared with that on a conventional silicon wafer. A further application of the hydrophobic substrate was demonstrated by the detection of AC in tea samples within a detection range of 0.03 mg/L to 3 mg/L. The hydrophobic substrate eliminates the problem of solution diffusion to avoid the "coffee ring" effect (When a droplet adheres to a solid surface, the suspended molecular particles usually deposit on the edge of the droplet to form a ring).

Development of sensitive colorimetric aptasensor based on short DNA aptamer and its application to screening for paraquat residues in agricultural soil

Paraquat is a widely used herbicide for controlling weeds and grasses in agriculture, and its contaminated residues in agricultural areas are of increasing concern. This work reports the development of the sensitive and easy-to-use colorimetric aptasensor for screening paraquat residues in agricultural soil. The short DNA fragments derived from the original aptamer were analyzed for their capability to interact with paraquat by molecular dynamic simulation. The paraquat-aptasensor was developed using the selected DNA fragment and gold nanoparticles. Its limit of detection (LOD) for paraquat is 2.76 nM, which is more sensitive than the aptasensor with long-length aptamer (LOD = 12.98 nM). The developed aptasensor shows the selectivity to paraquat, but not to other tested herbicides; ametryn, atrazine, difenzoquat, 2,4-D-dimethyl ammonium, and glufosinate. The recovery rates of paraquat detection in the spiked soil samples were in a range of 99.5%-105.1%, with relative standard deviation values of <4%. The developed aptasensor was used to screen for paraquat residues in agricultural soils, and three out of 23 soil samples were tested positive for paraquat, which was confirmed by a high-performance liquid chromatography analysis. These results suggested the potential application of the developed aptasensor to detect paraquat residues in agricultural sites.

Sensitive and handy detection of pesticide residue on fruit surface based on single microsphere surface-enhanced Raman spectroscopy technique

HYPOTHESIS

Surface-enhanced Raman spectroscopy (SERS) has become an emerging and reliable tool for detecting pesticide residues due to its high sensitivity, fast testing speed and easy sample handling. SERS active substrates are the key to achieve efficient and sensitive detection. However, for the most widely used noble metal nanoparticles, there are problems of high noble metal nanoparticle usage and random aggregation. The micron-scale Raman spot is focused on multiple randomly aggregated nanoparticles during the test, resulting in poor reproducibility. Therefore, the development of micron-scale cost-effective SERS substrates with good reproducibility and simple detecting method is of great significance in practical detection.

EXPERIMENTS

Through deposition of silver nanoparticles (Ag-NPs) by chemical reduction on the surface of monodisperse sulfonated polystyrene (SPS) microspheres, micron-sized PS@Ag-NPs core-shell microspheres were prepared with excellent SERS activity. After that, two simple protocols (Method I and Method II) were explored for the determination of thiram on apple epidermis.

FINDINGS

Based on our developed strategy of the single microsphere SERS technique, we successfully fabricated uniform PS@Ag-NPs substrate with high SERS activity and excellent detection sensitivity. The single microsphere SERS technique possesses the capability of anti-dilutability and the utilization of ultra-low PS@Ag-NPs microsphere dosage, realizing qualitative and quantitative detection of thiram on apple with detection limits far below the standard stipulated by China and the European Union.

Biosensor Based on Covalent Organic Framework Immobilized Acetylcholinesterase for Ratiometric Detection of Carbaryl

A ratiometric electrochemical biosensor based on a covalent organic framework (COF_Thi-TFPB) loaded with acetylcholinesterase (AChE) was developed. First, an electroactive COF_Thi-TFPB with a two-dimensional sheet structure, positive charge and a pair of inert redox peaks was synthesized via a dehydration condensation reaction between positively charged thionine (Thi) and 1,3,5-triformylphenylbenzene (TFPB). The immobilization of AChE on the positively charged electrode surface was beneficial for maintaining its bioactivity and achieving the best catalytic effect; therefore, the positively charged COF_Thi-TFPB was an appropriate support material for AChE. Furthermore, the COF_Thi-TFPB provided a stable internal reference signal for the constructed AChE inhibition-based electrochemical biosensor to eliminate various effects which were unrelated to the detection of carbaryl. The sensor had a linear range of 2.2–60 μM with a detection limit of 0.22 μM, and exhibited satisfactory reproducibility, stability and anti-interference ability for the detection of carbaryl. This work offers a possibility for the application of COF-based materials in the detection of low-level pesticide residues.

Advances in the Application of Exosomes Identification Using Surface-Enhanced Raman Spectroscopy for the Early Detection of Cancers

Exosomes are small nanoscale vesicles with a double-layered lipid membrane structure secreted by cells, and almost all types of cells can secrete exosomes. Exosomes carry a variety of biologically active contents such as nucleic acids and proteins, and play an important role not only in intercellular information exchange and signal transduction, but also in various pathophysiological processes in the human body. Surface-enhanced Raman Spectroscopy (SERS) uses light to interact with nanostructured materials such as gold and silver to produce a strong surface plasmon resonance effect, which can significantly enhance the Raman signal of molecules adsorbed on the surface of nanostructures to obtain a rich fingerprint of the sample itself or Raman probe molecules with ultra-sensitivity. The unique advantages of SERS, such as non-invasive and high sensitivity, good selectivity, fast analysis speed, and low water interference, make it a promising technology for life science and clinical testing applications. In this paper, we briefly introduce exosomes and the current main detection methods. We also describe the basic principles of SERS and the progress of the application of unlabeled and labeled SERS in exosome detection. This paper also summarizes the value of SERS-based exosome assays for early tumor diagnosis.