Screening pesticide residues on fruit peels using portable Raman spectrometer combined with adhesive tape sampling

SERS detection of triazole pesticide residues on vegetables and fruits using Au decahedral nanoparticles

Triazole pesticides are widely used in modern agricultural practices to improve agricultural production quality. Simultaneously, unreasonable and standardized use of triazole pesticides could induce a series of potential diseases of humans. Surface-enhanced Raman spectroscopy has attracted enormous research attention because of its label-free and fingerprint detection capability to noninvasively trace extremely low concentration analytes. To the best of our knowledge, there is a lack of systematic comparison regarding the Raman spectral information of triazole pesticides in existing literatures. In this work, we successfully captured the characteristic peaks of six different triazole pesticides individually and simultaneously using Au decahedral nanoparticles. The proposed method exhibited remarkable detection sensitivity, a wide dynamic range, and the capability for in-situ detection of multiple pesticide residues on bean, apple, and vegetable surfaces with satisfactory recovery rates. Therefore, our proposed SERS platform have great applications in agricultural products safety, environmental monitoring and other fields.

Identification of liquor adulteration by Raman spectroscopy method based on ICNAFS

The health of consumers can be impacted by the additives placed into the liquor. To address the issues of poor accuracy, low reliability, and complex operational procedures in identifying adulteration in existing liquor, an improved convex non-negative matrix factorization (ICNAFS) with an adaptive graph constraint for unsupervised feature extraction is proposed in this paper, with the goal of achieving rapid identification of adulteration in liquor by Raman spectroscopy through dimensionality reduction. For the sake to streamline the calculation process for effective feature extraction and increase the accuracy of the analyzed model, the proposed ICNAFS method incorporates two fundamental models, such as ridge regression and convex non-negative matrix factorization (NMF). In particular, dimensionality reduction of the original spectrum is initially conducted using Principal Component Analysis (PCA), Sequential Projection Algorithm (SPA), Convex Non-Negative Matrix Factorization with an Adaptive Graph Constraint (CNAFS), and ICNAFS respectively. k-means is subsequently employed to merge the four models for clustering analysis. The results suggest that the accuracy of the presented ICNAFS-assisted k-means model is higher than the other techniques, with a clustering accuracy of 98.67%, exhibiting a 4% improvement over the existing CNAFS, through examination of 150 sets of tainted liquor data from five categories of samples. This demonstrates the potency of the proposed ICNAFS-assisted k-means clustering model in conjunction with Raman spectroscopy as a method for detecting tainted liquor.

Research Status in the Use of Surface-Enhanced Raman Scattering (SERS) to Detect Pesticide Residues in Foods and Plant-Derived Chinese Herbal Medicines

Surface-enhanced Raman scattering (SERS) technology has unique advantages in the rapid detection of pesticides in plant-derived foods, leading to reduced detection limits and increased accuracy. Plant-derived Chinese herbal medicines have similar sources to plant-derived foods; however, due to the rough surfaces and complex compositions of herbal medicines, the detection of pesticide residues in this context continues to rely heavily on traditional methods, which are time consuming and laborious and are unable to meet market demands for portability. The application of flexible nanomaterials and SERS technology in this realm would allow rapid and accurate detection in a portable format. Therefore, in this review, we summarize the underlying principles and characteristics of SERS technology, with particular focus on applications of SERS for the analysis of pesticide residues in agricultural products. This paper summarizes recent research progress in the field from three main directions: sample pretreatment, SERS substrates, and data processing. The prospects and limitations of SERS technology are also discussed, in order to provide theoretical support for rapid detection of pesticide residues in Chinese herbal medicines.

Surface-Enhanced Raman Scattering in Silver-Coated Suspended-Core Fiber

In this paper, the silver-coated large-core suspended-core fiber (LSCF) probe was fabricated by the dynamic chemical liquid phase deposition method for surface-enhanced Raman scattering (SERS) sensing. The 4-mercaptophenylboronic acid (4-MPBA) monolayer was assembled in the LSCF as the recognition monolayer. Taking advantage of the appropriate core size of the LSCF, a custom-made Y-type optical fiber patch cable was utilized to connect the semiconductor laser, Raman spectrometer, and the proposed fiber SERS probe. The SERS signal is propagated in the silver-coated air channels, which can effectively reduce the Raman and fluorescence background of the silica core. Experiments were performed to measure the Raman scattering spectra of the 4-MPBA in the silver-coated LSCF in a non-enhanced and enhanced case. The experiment results showed that the Raman signal strength was enhanced more than 6 times by the surface plasmon resonance compared with the non-enhanced case. The proposed LSCF for SERS sensing technology provides huge research value for the fiber SERS probes in biomedicine and environmental science. The combination of SERS and microstructured optical fibers offers a potential approach for SERS detection.

pH-responsive switchable deep eutectic solvents to mediate pretreatment method for trace analysis of triazole fungicides in peel wastes

The existing QuEChERS-combined analytical pretreatment methods are limited by large reagent consumption, high environmental burden, and mediocre effects. To provide an efficient and green pretreatment method, this study developed pH-responsive switchable deep eutectic solvents (SDESs) to extract triazole fungicides (TFs) from fruit peel wastes, which could enhance the preconcentration effect of target compounds in food waste samples with complex matrices. The mechanisms of pH-induced phase transition and hydrophobicity-hydrophilicity conversion of pH-responsive SDESs were investigated by pH phase diagrams and chemical characterization techniques, respectively. We validated the established method by high-performance liquid chromatography-diode array detector (HPLC-DAD), and lower LOD (0.089-0.351 ng mL^-1), LOQ (0.297-1.172 ng mL^-1), RSD (≤8.8 %) and satisfactory recoveries (90.6 %-110.9 %) and preconcentration factors (389-512) were obtained in rotting grape peel, watermelon peel, and orange peel samples. Our findings highlight the potential of pH-responsive SDESs in the extraction and analysis of various natural food products.

Scalable fabrication of silver covered polyurethane nanofibers as flexible SERS nanosensors for aflatoxin detection

Flexible surface enhanced Raman spectroscopy (SERS) nanosensors, constructed by integration of plasmonic nanostructures with polymeric substrates, have received increasing research interests for recent decades. When compared to abundant works on optimization of the plasmonic nanostructures, the research involving the influence of polymeric substrates on analytical performance of resultant flexible SESR nanosensors is unexpectedly limited. Herein, the ultra-thin silver layer has been deposited on the electrospun polyurethane (ePU) nanofibrous membranes via vacuum evaporation to prepare the flexible SRES nanosensors. Interestingly, we find that the molecular weight and polydispersion index of synthesized PU play important roles in regulating the fine morphology of electrospun nanofibers, which in turns determine the Raman enhancement of resultant flexible SERS nanosensors. Specifically, the optimized SERS nanosensor, obtained by evaporating 10 nm silver layer on top of nanofibers derived from electrospinning of PU with a weight-average molecular weight of 140,354 and polydispersion index of 1.26, enables label-free detection of the carcinogen of aflatoxin down to 0.1 nM. Thanks to its scalable fabrication and good sensitivity, the current work opens new way for design of cost-effective flexible SERS nanosensors for environmental monitoring and food security applications.

Integration of solid-phase microextraction and surface-enhanced Raman spectroscopy for in-vivo screening of polybrominated diphenyl ether

The monitoring of polybrominated diphenyl ethers (PBDEs) is of great significance owing to their high persistence, bioaccumulation, and toxicity to humans and animals. In this study, a sensitive and reproducible probe that integrates solid-phase microextraction and surface-enhanced Raman spectroscopy (SPME-SERS) was developed for screening PBDEs in multiphase specimens, including live fish, water, and electrical products. A roughed Cu fiber with an Ag layer was fabricated with dual functions. BDE-15 was readily extracted and detected on the SPME-SERS probe consisting of propanethiol-modified Ag nanoplates on a Cu wire. A clear linear relationship (R² = 0.988) was established between the SERS intensity at 782 cm^-1 and the logarithmic concentrations (from 100 ppb to 100 ppm), with a detection limit of 15 ppb. This proposed method enables continuous in vivo monitoring in fish without complicated pretreatments. The results obtained by this SPME-SERS approach were validated by high-performance liquid chromatography and showed good agreement. This "extracting and detecting" SPME-SERS method provides a potential tool to monitor the occurrence, formation, and migration of PBDEs.

Open nanocavity-assisted Ag@PDMS as a soft SERS substrate with ultra-sensitivity and high uniformity

To achieve high sensitivity and uniformity simultaneously in a surface-enhanced Raman scattering (SERS) substrate, this paper presents the preparation of a flexible and transparent three-dimensional (3D) ordered hemispherical array polydimethylsiloxane (PDMS) film. This is achieved by self-assembling a single-layer polystyrene (PS) microsphere array on a silicon substrate. The liquid-liquid interface method is then used to transfer Ag nanoparticles onto the PDMS film, which includes open nanocavity arrays created by etching the PS microsphere array. An open nanocavity assistant soft SERS sample, "Ag@PDMS," is then prepared. For electromagnetic simulation of our sample, we utilized Comsol software. It has been experimentally confirmed that the Ag@PDMS substrate with silver particles of 50 nm in size is capable of achieving the largest localized electromagnetic hot spots in space. The optimal sample, Ag@PDMS, exhibits ultra-high sensitivity towards Rhodamine 6 G (R6G) probe molecules, with a limit of detection (LOD) of 10^-15 mol/L, and an enhancement factor (EF) of ∼10¹². Additionally, the substrate exhibits a highly uniform signal intensity for probe molecules, with a relative standard deviation (RSD) of approximately 6.86%. Moreover, it is capable of detecting multiple molecules and can perform real detection on non-flat surfaces.

Deep eutectic solvent-based adhesive tape extraction combined with enzyme inhibition assay for the determination and distinction of dithiocarbamate pesticides in food samples

A simple, green extraction method of dithiocarbamate (DTC) pesticides in food samples was developed using adhesive tapes and a green deep eutectic solvent (DES). A rapid and convenient determination and distinction method of DTC pesticides was established using tyrosinase inhibition assay. First, DTC pesticides were extracted by pasting and peeling off the adhesive tape, then eluted by the DES synthesized from xylitol and ethylene glycol. Second, determination of DTC pesticides was conducted by inhibiting the activity of tyrosinase which can catalyze the oxidation of catechol. Less colored products were generated in the reaction system (tyrosinase, catechol, and 4-aminoantipyrine), leading to weak absorbance. In addition, different DTC pesticides (ziram, propineb, zineb, mancozeb, thiram, metiram, and ferbam) were successfully distinguished by sensor arrays (tyrosinase, phenolic compounds, and 4-aminoantipyrine) through principal component analysis. The limit of detection was found to be 0.2 μg kg^-1, and the limit of quantification was 0.6 μg kg^-1. The recoveries ranging from 89.4% to 103.8% were obtained in vegetable, fruit, and cereal, with a relative standard deviation of less than 4.2%. The method is simple, rapid, and convenient and shows good application prospects in the determination of pesticides in a variety of food samples.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

HPTLC+SRES screening of pesticide for point-of-care application as shown with thiram in juice

In this study, a HPTLC-platformed SERS detection was established for screening thiram in juice. After a simple extraction, the sample liquid was separated on HPTLC plates, which resulted in a specific zone for the analyte. Following infiltration with atomize water, the band of interest was easily scraped off and eluted. In parallel, a flexible and SERS-active substrate was fabricated by the in-situ synthesis of gold nanoparticles within cotton fabrics. Under optimized conditions, fingerprint-like signal at 1376 cm^-1 of the analyte were easily recorded by a hand-held Raman spectrometer with enough LOD (0.5 mg/L), LOQ (0.9 mg/L) and reproducibility (<11.7%). The optimized screening system was further validated with pear, apple and mango juice by determining the spike-and-recovery rates (75.6 to 112.8%). It was demonstrated that this method could be a facile point-of-care testing system tailored for pesticide screening.

An adhesive SERS substrate based on a stretched silver nanowire-tape for the in situ multicomponent analysis of pesticide residues

Two essential factors in powerful surface-enhanced Raman spectroscopy analysis of trace pesticide residues are viz., high sensitivity and efficient sampling. Herein, owing to elastic properties, a stretched Ag nanowire (Ag NW)-tape under the strain of 15% formed a wrinkled structure with periodic microridges and microgrooves, where abundant nanogaps were generated by the aggregated Ag NWs. Compared with the unstretched Ag NW-tape substrate, an appreciable signal enhancement of the modified 4-mercaptobenzoic acid (4-MBA) molecules with a ratio of 2.6 was discerned from the sophisticated SERS substrate due to the electromagnetic enhancement induced by the relatively high density of "hot spots" around the Ag NW aggregates. The as-fabricated Ag NW-tape substrate performed admirably in detecting 4-MBA and demonstrated an enhancement factor of 1.16 × 10⁶. Moreover, for the in situ detection of tetramethylthiuram disulfide, thiabendazole, and their mixture, the relatively high recovery rates of over 88% were favorably realized by the Ag NW-tape substrate with superior sensitivity, distinct flexibility, and adhesiveness. This fascinating SERS substrate, dependent on the flexible and adhesive Ag NW-tape, is promising for application in SERS analysis of trace residues on various practical surfaces.

Simultaneous, Label-Free and High-throughput SERS Detection of Multiple Pesticides on Ag@Three-Dimensional Silica Photonic Microsphere Array

Rapid identification and quantitative simultaneous analysis for multiple pesticide in real samples based on surface-enhanced Raman spectroscopy (SERS) is still a challenge because of sample complexity, reproducibility, and stability of SERS substrate. With use of colloidal silver nanoparticles loaded three-dimensional (3D) silica photonic microspheres (SPMs) array as the analytical platform, a SERS-based array assay for multiple pesticides was developed in this work. The silver nanoparticles were fixed into the gaps formed by the self-assembled nanospheres of the 3D SPMs to produce "hot spots", on which the Raman enhanced effect was up to 9.86 × 10⁷ and the maximum electric field enhancement effect reached to 9.75 times, ensuring the target pesticides on the surface of the SERS-substrate integrated SPM can be detected sensitively. Using 2,4-dichlorophenoxyacetic acid (2,4-D), glyphosate, and imidacloprid as the testing pesticides, the label-free and high-throughput SERS assay for simultaneous detection of the pesticides was established, giving good linear detection ranges (0.1-204.8 μg/mL for 2,4-D, 0.3-247.9 μg/mL for glyphosate, and 0.2-204.8 μg/mL for imidacloprid) and low detection limits (3.03 ng/mL for 2,4-D, 3.14 ng/mL for glyphosate, and 8.82 ng/mL for imidacloprid). The spiked recovery rates in the real samples were measured in the range of 82-112%, which was consistent with that of the classical standard methods. The label-free 3D SERS array analytical platform provides a powerful tool for high-throughput and low-cost screening of multiple pesticide residues in real samples.

Metrological traceability in process analytical technologies and point-of-need technologies for food safety and quality control: not a straightforward issue

Traditional techniques for food analysis are based on off-line laboratory methods that are expensive and time-consuming and often require qualified personnel. Despite the high standards of accuracy and metrological traceability, these well-established methods do not facilitate real-time process monitoring and timely on-site decision-making as required for food safety and quality control. The future of food testing includes rapid, cost-effective, portable, and simple methods for both qualitative screening and quantification of food contaminants, as well as continuous, real-time measurement in production lines. Process automatization through process analytical technologies (PAT) is an increasing trend in the food industry as a way to achieve improved product quality, safety, and consistency, reduced production cycle times, minimal product waste or reworks, and the possibility for real-time product release. Novel methods of analysis for point-of-need (PON) screening could greatly improve food testing by allowing non-experts, such as consumers, to test in situ food products using portable instruments, smartphones, or even visual naked-eye inspections, or farmers and small producers to monitor products in the field. This requires the attention of the research community and devices manufacturers to ensure reliability of measurement results from PAT strategy and PON tests through the demonstration and critical evaluation of performance characteristics. The fitness for purpose of methods in real-life conditions is a priority that should not be overlooked in order to maintain an effective and harmonized food safety policy.

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 plasmonic AgNP decorated heterostructure substrate for synergetic surface-enhanced Raman scattering identification and quantification of pesticide residues in real samples

Rapid and on-site Raman spectroscopic identification and quantification of pesticide residues have been restricted to the low instrumental sensitivity of a portable Raman instrument, and no ideal platforms have been reported for analyzing pesticides on real sample surfaces. An efficient method to improve the detection sensitivity is to fabricate a highly sensitive surface-enhanced Raman scattering (SERS) substrate. Here, we present a MOF-derived ZnO@TiO₂ heterostructure combined with plasmonic AgNPs as a SERS sensor to achieve synergetic EM and CM enhancement, exhibiting high sensitivity, excellent signal reproducibility (RSD < 5.9%) and superior stability for analysis of model molecules. The SERS sensor achieved a low detection concentration of 10^-8 M for both CV and R6G molecular solutions on a portable Raman device. As a proof of concept, we modelled a pesticide residue on real samples of dendrobium leaves. Thiram, triazophos and fonofos solutions were selected as analytes for mimicking the function of on-site analysis. The SERS analytical platform showed not only high sensitivity for single- and multi-component identification, but also quantitative detection of pesticide residues on dendrobium leaves. These preliminary investigations indicate that this SERS analytical platform will allow the development and potential applications in rapid and on-site pesticide analysis.

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.

Rapid Trace Detection of Pesticide Residues on Tomato by Surface-Enhanced Raman Spectroscopy and Flexible Tapes

The non-judicious use of pesticides in agro-food poses a severe threat to food safety and human health. As an emerging chromatographic fingerprint provider, surface-enhanced Raman spectroscopy analysis (SERS) sheds bright light on sensitive and nondestructive detection of pesticide residues. This research proposed a novel strategy to detect three-pesticide residues (thiabendazole, carbendazim, and chlorpyrifos) on tomato peel based on the flexible and sticky SERS substrate. After selecting the best commercial adhesive tape (3M9080), the SERS substrate was constructed by optimizing the parameters in the preparation process of AuNPs. Therefore, a new simple “tape-wrapped SERS” way for pesticide residue analysis was established with a simple procedure of “absorption, separation, and drop addition.” Based on chemometrics method, the limit of semiquantitative detection was 20, 36, and 80 ng/cm2 for thiabendazole, carbendazim, and chlorpyrifos, respectively, on tomato surface, which indicated that the proposed method could meet the requirement of actual application with a large prospect in agro-food safety detection.

Boosting bacteria differentiation efficiency with multidimensional surface-enhanced Raman scattering: the example of Bacillus cereus

Surface-enhanced Raman scattering (SERS) is a powerful tool for constructing biomolecular fingerprints, which play a vital role in differentiation of bacteria. Due to the rather subtle differences in the SERS spectra among different bacteria, artificial intelligence is usually adopted and enormous amounts of spectral data are required to improve the differentiation efficiency. However, in many cases, large volume data acquisition on bacteria is not only technical difficult but labour intensive. It is known that surface modification of SERS nanomaterials can bring additional dimensionality (difference) of the SERS fingerprints. Here in this work, we show that the concept could be used to improve the bacteria differentiation efficiency. Ag NPs were modified with 11-mercaptoundecanoic acid, 11-mercapto-1-undecanol, and 1-dodecanethiol to provide additional dimensionality. The modified NPs then were mixed with cell lysate from different strains of Bacillus cereus (B. cereus). Even by applying a simple PCA process to the resulting SERS spectra data, all the three modified Ag NPs showed superior differentiation results compared with bare Ag NPs, which could only separate Staphylococcus aureus (S. aureus) and B. cereus. It is believed that the multidimensional SERS could find great potential in bacteria differentiation.

Hybridizing aggregated gold nanoparticles with a hydrogel to prepare a flexible SERS chip for detecting organophosphorus pesticides

A simple method has been developed to hybridize aggregated gold nanoparticles with a hydrogel for novel hydrogel SERS chips with high sensitivity, good repeatability, long-term stability, and strong anti-interference ability.

Nanotechnology in food and water security: on-site detection of agricultural pollutants through surface-enhanced Raman spectroscopy

Agricultural pollutants are harmful components threatening human health, wildlife, the environment, and the ecosystem. To avoid their exposure, developing prevention and detection systems with high sensitivity and selectivity is required. Most conventional methods, including molecular and chromatographic techniques, cannot be adopted for outdoor on-site detection even though they can provide sensitive and selective detection. Thus, detection platforms that can provide on-site detection via miniaturized and high throughput systems should be developed. As an alternative method, surface-enhanced Raman scattering (SERS) provides unique information about the substances in the presence of plasmonic nanostructures, and it can be portable with the use of portable detection systems and spectrometers. In this study, on-site detection of agricultural pollutants through SERS is reviewed. Three different types of agricultural pollutants were pointed out. On-site detection of biological pollutants, including bacteria and viruses, is reviewed as the first type of pollutant. As a second type, the detection of pesticides, antibiotics, and additives are focused on as chemical pollutants. The third group includes the detection of microplastics and also nanoparticles from the environment.

Recent advances in plasmonic Prussian blue-based SERS nanotags for biological application

The reliability and reproducibility of surface-enhanced Raman scattering (SERS) technology is still a great challenge in bio-related analysis. Prussian blue (PB)-based SERS tags have attracted increasing interest for improving these deficiencies due to its unique Raman band (near 2156 cm-1) in the Raman-silent region, providing zero-background bio-Raman labels without interference from endogenous biomolecules. Moreover, the stable PB shell consisting of multiple layers of CN- reporters ensure a stable and strong Raman signal output, avoiding the desorption of the Raman reporter from the plasmonic region by the competitive adsorption of the analyte. More importantly, they possess outstanding multiplexing potential in biological analysis owing to the adjustable Raman shift with unique narrow spectral widths. Despite more attention having been attracted to the structure and preparation of PB-based SERS tags for their better biological applications over the past five years, there is still a great challenge for SERS suitable for applications in the actual environment. The biological applications of PB-based SERS tags are comprehensively recounted in this minireview, mainly focusing on quantification analysis, multiple-spectral analysis and cell-imaging joint phototherapy. The prospects of PB-based SERS tags in clinical diagnosis and treatment are also discussed. This review aims to draw attention to the importance of SERS tags and provide a reference for the design and application of PB-based SERS tags in future bio-applications.

Quantitative detection of 6-thioguanine in body fluids based on a free-standing liquid membrane SERS substrate

The adverse reactions caused by 6-thioguanine (6-TG) in anti-cancer treatment are closely related to the dose, leading to the urgent need for clinical monitoring of its concentration. In this work, a highly reproducible free-standing liquid membrane (FLM) surface-enhanced Raman spectroscopy (SERS) substrate was developed to detect 6-TG in human urine and serum quantitatively. Briefly, a prepared sample was adjusted to pH 2 and mixed with concentrated core-shell bimetallic nanoparticle (Ag_coreAu_shell NP) suspension. The Au/Ag ratio of the Ag_coreAu_shell NPs was optimized. Then the mixture was formed into an FLM using a custom mold. The relative standard deviation (RSD) of the experimental results can be stabilized below 10% (n ≥ 10). The R² of the calibration curve in the range of 10 ~ 100 μg kg^-1 was 0.988. In addition, the limit of detection (LOD) (3σ/k) of 6-TG was 5 μg kg^-1. The FLM SERS platform has been successfully applied to the rapid and reliable analysis of 6-TG spiked in body fluids.

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.

Sensitive, Reusable, Surface-Enhanced Raman Scattering Sensors Constructed with a 3D Graphene/Si Hybrid

Surface-enhanced Raman scattering (SERS) substrates based on graphene and its derivatives have recently attracted attention among those interested in the detection of trace molecules; however, these substrates generally show poor uniformity, an unsatisfactory enhancement factor, and require a complex fabrication process. Herein, we design and fabricate three-dimensional (3D) graphene/silicon (3D-Gr/Si) heterojunction SERS substrates to detect various types of molecules. Notably, the detection limit of 3D-Gr/Si can reach 10^-10 M for rhodamine 6G (R6G) and rhodamine B (RB), 10^-7 M for crystal violet (CRV), copper(II) phthalocyanine (CuPc), and methylene blue (MB), 10^-8 M for dopamine (DA), 10^-6 M for bovine serum albumin (BSA), and 10^-5 M for melamine (Mel), which is superior to most reported graphene-based SERS substrates. Besides, the proposed 3D-Gr/Si heterojunction SERS substrates can achieve a high uniformity with relative standard deviations (RSDs) of less than 5%. Moreover, the 3D-Gr/Si SERS substrates are reusable after washing with ethyl alcohol to remove the adsorbed molecules. These excellent SERS performances are attributed to the novel 3D structure and abundantly exposed atomically thin edges, which facilitate charge transfer between 3D-Gr and probe molecules. We believe that the 3D-Gr/Si heterojunction SERS substrates offer potential for practical applications in biochemical molecule detection and provide insight into the design of high-performance SERS substrates.

Towards a point-of-care SERS sensor for biomedical and agri-food analysis applications: a review of recent advancements

The growing demand for reliable and robust methodology in bio-chemical sensing calls for the continuous advancement of sensor technologies. Over the last two decades, surface-enhanced Raman spectroscopy (SERS) has emerged as one of the most promising analytical techniques for sensitive and trace analysis or detection in biomedical and agri-food applications. SERS overcomes the inherent sensitivity limitation associated with Raman spectroscopy, which provides vibrational "fingerprint" spectra of molecules that makes it unique and versatile among other spectroscopy techniques. This paper comprehensively reviews the recent advancements of SERS for biomedical, food and agricultural applications over the last 6 years, and we envision that, in the near future, some of these platforms have the potential to be translated as a point-of-care and rapid sensor for real-life end-user applications. The merits and limitations of various SERS sensor designs are analysed and discussed based on critical features such as sensitivity, specificity, usability, repeatability and reproducibility. We conclude by highlighting the opportunities and challenges in the field while stressing the technological gaps to be addressed in realizing commercially viable point-of-care SERS sensors for practical biomedical and agri-food technological applications.

Inkjet printed silver nanoparticles on hydrophobic papers for efficient detection of thiram

Silver nanoparticles coated paper (AgNPs-paper) substrates were prepared by inkjet printing Ag ink on four different wettability papers. Scanning electron microscope and contact angle analyzer were used to characterize their surface morphology and wettability. AgNPs-paper substrates were used to detect the surface-enhanced Raman scattering (SERS) spectra of thiram aqueous solution. Relationships between the surface wettability, surface morphology and SERS activities of the substrates were systematically studied. The silver nanoparticles deposited on the hydrophobic papers (photographic paper, graph paper, and weighing paper) were evenly and densely arranged. While in-homogeneous distribution was observed on the hydrophilic printing paper. It can be found that the AgNPs-photographic paper with the maximum contact angle exhibited the highest SERS enhancement. The detection limit for thiram adsorbed on the AgNPs-photographic paper was 10^-10 mol/L, which was lower than the others. Good linear responses (R² = 0.9918, 0.9897) between the SERS intensities and logarithmic concentrations were obtained from 10⁴ to 10^-10 mol/L. Moreover, the substrate had good uniformity and reproducibility with relative standard deviation values of 4.20% and 4.90% measured by eight points and ten substrates, respectively. The AgNPs-photographic paper exhibited high stability within eight months.

Multifunctional Flexible SERS Sensor on a Fixate Gel Pad: Capturing, Derivation, and Selective Picogram Indirect Detection of Explosive 2,2',4,4',6,6'-Hexanitrostilbene

2,2',4,4',6,6'-Hexanitrostilbene (HNS) is an explosive with better explosion performance than the well-known 2,4,6-trinitrotoluene (TNT). Surprisingly, unlike other nitroaromatic explosives, there are limited reports regarding the detection of the HNS, let alone sensing reports on surface residues. In this work, a multifunctional flexible SERS sensor was proposed for the indirect detection of HNS based on the transparent fixate gel pads. The sticky and flexible gel pad can effectively collect any HNS surface residues. The inherent amine groups within the gel pad of which the main ingredient is polyurethane can react with HNS to form the orange Meisenheimer-alike complex. The modification of Ag NPs with halide ions was screened for the best SERS performance. KI-modified-citrate-reduced Ag NPs showed selective but enormous SERS enhancement for the HNS derivative. The detection of HNS in the solution phase was explored, and a linear range of 0.01-25 ppm was achieved. The lowest detectable amount (LDA) of HNS was found to be 50 pg, making it one of the most sensitive methods in literature. It was successfully utilized for the HNS residues sensing on fingerprints and bags with LDAs of 5 and 200 ng, respectively. In addition, other explosives including TATB, LLM-105, RDX, HMX, FOX-7, and TNT were also examined to assess the selectivity of the fixate. It was found that the fixate showed excellent selectivity for HNS.

Applications of surface-enhanced Raman spectroscopy in detection fields

Surface-enhanced Raman spectroscopy (SERS) is a Raman spectroscopy technique that has been widely used in food safety, environmental monitoring, medical diagnosis and treatment and drug monitoring because of its high selectivity, sensitivity, rapidness, simplicity and specificity in identifying molecular structures. This review introduces the detection mechanism of SERS and summarizes the most recent progress concerning the use of SERS for the detection and characterization of molecules, providing references for the later research of SERS in detection fields.

Unsupported liquid-state platform for SERS-based determination of triazophos

A highly reproducible surface-enhanced Raman scattering (SERS) unsupported liquid-state platform (ULP) was developed for accurate quantitative determination of triazophos. Herein, citrate-reduced Ag NPs suspension was concentrated and placed in a stainless steel perforated template to form the SERS ULP. The relative standard deviation of the SERS measurements was less than 5% (n ≥ 10), and the R² of the calibration curve was 0.994. The developed SERS ULP was applied for determination of triazophos in spiked agricultural products (rice, cabbage, and apple). Experiment results showed that the coefficient of variation ranged from 5.3 to 6.2% for intra-day and from 5.5 to 6.3% for inter-day (n = 3), which proved excellent SERS reproducibility. Moreover, the results were in good agreement with those from HPLC analysis. As a liquid-state SERS substrate, the highly reproducible ULP can perform precision quantitative analysis without surface modification of NPs, which is a significant improvement. This method provides a new perspective for quantitative SERS analysis of pesticide residues. Graphical abstract.

Copper foam in situ loaded with precious metal nanoparticles as transmission SEIRAS substrate for rapid detection of dithiocarbamate pesticides

A transmission surface-enhanced infrared absorption spectroscopy (SEIRAS) substrate based on copper foam (CF) was developed for the rapid detection of dithiocarbamate (DTC) pesticides. The transmission SEIRAS substrate was prepared by immersing CF in a mixed solution of a precious metal solution and a polyvinyl pyrrolidone (PVP) solution. Then CF could be in situ loaded with precious metal nanoparticles (MNPs) by PVP-modified displacement reaction in seconds. PVP could be helpful for the uniform distribution of MNPs. Experimental parameters were evaluated with 11-mercaptoundecanoic acid (MUA) as a probe molecule, including the concentration of the precious metal solution, the amount of PVP, and the reaction time. Taking CF loaded with gold nanoparticles (Au NPs-CF) as an example, the signal intensity of DTC pesticide (Ziram) could be enhanced by two orders of magnitude, and the R² of the calibration curve was 0.999. In addition, the Pt NPs-CF substrate was applied to the rapid detection of other DTC pesticides (Ferbam). In summary, we have developed a new method to prepare the transmission SEIRAS substrate and apply it to the rapid detection of DTC pesticides in liquid.

Preparation of Hydrophobic Film by Electrospinning for Rapid SERS Detection of Trace Triazophos

For real application, it is an urgent demand to fabricate stable and flexible surface-enhanced Raman scattering (SERS) substrates with high enhancement factors in a large-scale and facile way. Herein, by using the electrospinning technique, a hydrophobic and flexible poly(styrene-co-butadiene) (SB) fibrous membrane is obtained, which is beneficial for modification of silver nanoparticles (Ag NPs) colloid in a small region and then formation of more "hot spots" by drying; the final SERS substrate is designated as Ag/SB. Hydrophobic Ag/SB can efficiently capture heterocyclic molecules into the vicinity of hot spots of Ag NPs. Such Ag/SB films are used to quantitatively detect trace triazophos residue on fruit peels or in the juice, and the limit of detection (LOD) of 2.5 × 10^-8 M is achieved. Ag/SB films possess a capability to resist heat. As a case, 6-mercaptopurine (6MP) that just barely dissolves in 90 °C water is picked for conducting Ag/SB-film-based experiments.

Flexible paper-based SERS substrate strategy for rapid detection of methyl parathion on the surface of fruit

Herein, we reported a simple, flexible and sensitive surface-enhanced Raman scattering (SERS) substrate to detect methyl parathion residues in real life. The substrate was fabricated by filter paper and gold nanoparticles (Au NPs) with excellent reproducibility and stability. First, Au NPs were synthesized by the seed mediated growth method and assembled to the filter paper through immersion. The Raman probe molecule 4-MBA was used to evaluate performance of the substrate for an optimized signal using a portable Raman spectrometer coupled with 785 nm laser. Then, the paper-based substrate was applied to detect methyl parathion standard solution whose detection limit was down to 0.011 μg/cm², and the linear range was between 0.018 μg/cm² and 0.354 μg/cm². Afterwards, actual sample (apple) spiked with methyl parathion was taken to verify the practicality of the substrate by a simple way of "press-peel off". The recovery rate was ranged from 94.09% to 98.72%, indicating that this method is reliable in actual sample detection without complicated pretreatment steps. This work demonstrates that the flexible paper-based substrate combined with portable Raman instruments can be potentially applied to on-site detection of hazardous substances in the field of food safety.

Paper-based SERS substrate and one-class classifier to monitor thiabendazole residual levels in extracts of mango peels

The assessment of pesticide residue levels demands fast, low cost and easy-to-use procedures which are not found in conventional methods. In this work, SERS substrates based on the deposition of gold nanoparticles (GNPs) on common office paper were prepared using a wax printer. These substrates combined with Data Driven Soft Independent Modelling of Class Analogies (DD-SIMCA), a one-class classifier algorithm, were used for detection of pesticide residues in water extracts of mango peels. Paper-based substrates made sample collection easier compared with conventional SERS methods, since few microliters of the pesticide aqueous extract from fruit peels needed to be deposited onto the substrate. Moreover, one-class classifiers dismiss the need for quantification or calibration curves. Classification of a fruit with residue levels in accordance to regulatory bodies' limits is based on a mathematical threshold. Just as in an authentication problem, all the possibilities for a given analysed fruit are now restricted to agreeing or not agreeing with current regulations. The performance of the one-class model was demonstrated by detecting thiabendazole (TBZ) residues at various mango samples, with all results being confirmed by HPLC-DAD analysis. The final model could distinguish samples with TBZ levels above the ones allowed by the Brazilian Health Regulatory Agency with 94% of selectivity and 92% of sensitivity, even in the presence of other pesticides.

Fruit Seeds as Sources of Bioactive Compounds: Sustainable Production of High Value-Added Ingredients from By-Products within Circular Economy

The circular economy is an umbrella concept that applies different mechanisms aiming to minimize waste generation, thus decoupling economic growth from natural resources. Each year, an estimated one-third of all food produced is wasted; this is equivalent to 1.3 billion tons of food, which is worth around US$1 trillion or even $2.6 trillion when social and economic costs are included. In the fruit and vegetable sector, 45% of the total produced amount is lost in the production (post-harvest, processing, and distribution) and consumption chains. Therefore, it is necessary to find new technological and environmentally friendly solutions to utilize fruit wastes as new raw materials to develop and scale up the production of high value-added products and ingredients. Considering that the production and consumption of fruits has increased in the last years and following the need to find the sustainable use of different fruit side streams, this work aimed to describe the chemical composition and bioactivity of different fruit seeds consumed worldwide. A comprehensive focus is given on the extraction techniques of water-soluble and lipophilic compounds and in vitro/in vivo functionalities, and the link between chemical composition and observed activity is holistically explained.