Application of surface-enhanced Raman spectroscopy in fast detection of toxic and harmful substances in food

Au octahedrons monolayer film SERS substrate coupled with a hybrid metaheuristic algorithm-optimized ELM model: An analytical strategy for rapid and label-free detection of zearalenone in corn oil

This study developed a rapid, label-free analytical strategy for quantifying zearalenone (ZEN) in corn oil. A highly sensitive Au octahedrons (Ohs) monolayer film was synthesized as the surface-enhanced Raman spectroscopy (SERS) substrate. A hybrid metaheuristic algorithm that combines the particle swarm optimization (PSO) algorithm and the grey wolf optimizer (GWO) algorithms, was used to optimize an extreme learning machine (ELM) model (i.e., the PSOGWO-ELM model). The PSOGWO-ELM model analyzed the collected SERS spectra to determine ZEN contents in corn oil. The results demonstrated that the analytical strategy possessed excellent performance: the root mean squared error of the prediction set (RMSEP) = 0.2297 μg/mL, the coefficient of determination of the prediction set (RP2) = 0.9907, and the ratio of performance to deviation of the prediction set (RPDP) = 10.3695. The proposed analytical approach shows considerable promise for the rapid, label-free, and accurate detection of trace levels of ZEN in corn oil.

Intermediate data fusion improves the accuracy of near-infrared spectroscopy and Raman spectroscopy for the detection of aflatoxin B1 in peanuts

This study developed a convolutional neural network (CNN) model based on feature-level data fusion for quantitatively detecting aflatoxin B1 (AFB1) in peanuts. Using a portable near-infrared (NIR) spectrometer and a Raman spectrometer, NIR and Raman spectra were collected from peanut samples with varying levels of fungal contamination. The spectral data were then enhanced and preprocessed, and individual CNN models were constructed for each type of spectrum. Building on the single-spectrum models, data-level and feature-level fusion of the NIR and Raman spectra were performed, and corresponding CNN models were developed for the quantitative detection of AFB1 in peanuts. Experimental results demonstrated that the CNN models with data fusion significantly improved detection performance and generalization ability compared to single-spectrum CNN models, particularly those using feature-level fusion. The feature-level fusion CNN model yielded the best performance, with a root mean square error of prediction of 19.7787 μg·kg-1, a prediction correlation coefficient of 0.9836 for test set 1 (containing augmented spectra), and 0.9890 for test set 2 (containing only raw spectra), with a relative prediction deviation of 7.6506. Overall, this study demonstrated the superiority of data fusion and the feasibility of applying CNNs in spectral detection, providing a reference for quantitatively detecting mycotoxins.

Multifunctional surface-enhanced Raman scattering imaging for detection and visualization of pesticide residues in crops

Investigating pesticide residues holds paramount significance for ensuring food safety and promoting sustainable agricultural practices. Nevertheless, high-sensitivity analytical techniques are urgent needed due to intricate pesticide combinations, low concentration levels, and complexities in sample preparation. Here, silver nanoparticles synthesized by sodium borohydride reduction created a sprayable multifunctional uniformly dispersed surface-enhanced Raman scattering (SERS) substrate that demonstrated exceptional sensitivity, enabling high-quality signal acquisition of various pesticide residues on different backgrounds for the first-time using SERS imaging strategy. The substrate exhibited an enhancement factor of 108 and a detection limit lower than 10-10 M (0.1 ppb) for pesticides dispersed in colloids. Moreover, the study showcased distinctive detection and quantitative analysis of mixed pesticides, exhibiting excellent linearity (r2 = 0.9983). In addition, SERS imaging technology combined with vertex component analysis and Euclidean distance methods can directly observe the distribution of pesticides outside and inside crops. Overall, this study highlights the potential of SERS and its intuitive imaging approach in pesticide residue detection and distribution visualization, expanding its application in the field of green chemistry.

Uniformly aligned Ag NPs/graphene paper for enhanced SERS detection of pesticide residue

The surface-enhanced Raman scattering (SERS) technique provides a quick and reliable method for detecting pesticide residues. In this study, flexible substrates, composed of orderly arranged silver nanospheres (Ag NPs) films on graphene paper, were fabricated through a simple, low-cost Ag NP self-assembly process at a liquid-liquid interface, followed by transfer of the films onto the graphene paper. The SERS performance of the fabricated substrates was evaluated using a portable Raman spectrometer, with rhodamine 6G (R6G) serving as the probe molecule. The results indicate that the bilayer Ag NP films-covered graphene paper exhibits optimal overall performance, characterized by high sensitivity and high uniformity. The limit of detection (LOD) for the R6G molecule is as low as 8.73 × 10-9 M, demonstrating the strong signal amplification capability of the SERS substrate. Moreover, the relative standard deviation (RSD) of the Raman intensity at 1508 cm-1 for different selected points on the substrate is 5.018 %, indicating high uniformity of the SERS substrate. Finally, the performance of the SERS substrate was further evaluated by detecting thiram in fresh orange juice, demonstrating the capability to detect concentrations as low as 10-6 M. This result highlights the significant potential of the developed SERS substrate for practical applications in food safety and quality control.

Novel analysis based on Raman spectroscopy in nutrition science

Modern research in nutrition science is transitioning from classical methodologies to advanced analytical strategies, in which Raman spectroscopy plays a crucial role. Raman spectroscopy and its derived techniques are gaining recognition in nutrition science for their features, such as high-speed, non-destructive analysis, label-free multiple detection and high sensitivity. Raman-enhancing techniques have further improved the sensitivity of Raman spectroscopy and widely extended its detection and imaging applications in nutrient analysis, as well as in ancillary tasks for nutrition research, such as nutrient status evaluation, nutrient interaction and metabolism studies. Further development of Raman-based analytical approaches lies in the improvement of instruments with higher precision, as well as the incorporation of other analytical techniques and advanced data analysis tools. This paper provides a comprehensive review of the application of nanoscience and nanotechnology, with a specific focus on Raman technology, in the field of food and nutrition science research. Instead of delving into the quantitative or qualitative detection capabilities of Raman technology, we highlight the remarkable food analysis and nutrition research methods established by this technology. Generally, this review introduces the characteristics and applications of Raman technology in nutrition analysis and discusses the limitations and future prospects of Raman spectroscopy for nutrition monitoring.

Ultrasensitive Recognition of Trace Nerve Agents Enabled via a Thermally Activated Delayed Fluorescence-Based Fluorescent Probe

The development of fluorescent probes for the detection of nerve agents has been a significant focus of research due to their lethal toxicity to humans. Inspired by the excited state properties of thermally activated delayed fluorescence (TADF), we designed two visualized fluorescence probes, PT and PPT, that exhibit characteristics of delayed fluorescence and aggregation-induced emission. These probes are intended for the rapid and highly sensitive detection of diethyl chlorophosphate (DCP). Upon exposure to DCP vapors, the PT and PPT probes demonstrated rapid fluorescence quenching in less than 5 s, which was accompanied by a color change from yellow to red. The limits of detection for the probes were determined to be 3.0 and 2.9 ppb. Furthermore, we demonstrate that the reduction of acid interference through the use of dispersed SiO2 is an important step in the fabrication of N-heterocyclic nerve agent probes. Importantly, we also constructed a portable fluorescence detector that incorporates these films as key components, validating its applicability through the successful detection of nerve agents.

Multifunctional Surface Enhanced Raman Scattering Substrate Fe 3O4 @AgNPs@MIL-101 for Pretreatment and Rapid Detection of Pesticide Residues on the Surface of Fruit Peels

A multifunctional surface-enhanced Raman scattering substrate Fe3O4@AgNPs@MIL-101 was prepared. Rapid SERS detection of pesticide residues was realized by direct pre-enrichment and separation on the peel surface. MIL-101 has an ortho-octahedral framework and large pore size, which endowed Fe3O4@AgNPs@MIL-101 with the ability to rapidly adsorb and separate positively charged targets. The introduction of tannic acid realized the in situ growth of AgNPs on the backbone, to modulate the electromagnetic enhancement. Pesticide molecules were adsorbed onto the surface of AgNPs mediated by central S atoms, accompanied by the interaction between pesticide molecules and AgNPs, the corresponding SERS signals of different pesticides were observed. Together with the introduction of magnetic coating Fe3O4, the molecules were enriched in the hotspot and separated to further enhance the SERS performance. Magnet instead of centrifugation was used to simultaneously perform surface extraction and sample separation for a noninvasive, rapid, immediate, and portable assay. The method was accomplished in measurement of thiram and thiabendazole on apple and tangerine epidermis, and the limits of detection (LODs) were 20 ng/cm2 and 4 μg/cm2, respectively. The recovery was reasonable, and it showed that the procedure is valuable for the rapid and nondestructive surface analysis of residual chemicals.

Design and fabrication of self-calibration colorimetric/fluorescence/SERS tri-modal optical sensor for highly rapid and accurate detection of mercury ions in foods

The improvement of detection accuracy without loss of rapidity and sensitivity by optical sensors in complex food analysis is still full of challenges owing to the matrix interference. Herein, a novel and simple self-calibration colorimetric/fluorescence/surface-enhanced Raman spectroscopy (SERS) tri-modal optical sensor based on aminated Rhodamine 6G (R6G-NH2) was developed for highly rapid, sensitive, and accurate detection of Hg2+ in food samples. The high recognition specificity of R6G-NH2 for Hg2+ can be achieved through the metal chelation interaction between Hg2+ and -NH2, -COOH groups in R6G-NH2 with formation of R6G-NH2-Hg2+-R6G-NH2 complex. The DFT and FDTD simulations were adopted to confirm the theoretical feasibility in Hg2+ detection by tri-modal optical. Under the optimum conditions, the analytical method based on self-calibration tri-modal optical sensor for Hg2+ detection was established with promising properties (rapidity, linearity, linear range, LOD, and LOQ), providing a strategy in rapid, selective, sensitive, and accurate detection for food safety.

Surface-enhanced Raman scattering-based strategies for tumor markers detection: A review

The presence of malignant tumors poses a significant threat to people's life and well-being. As biochemical parameters indicate the occurrence and development of tumors, tumor markers play a pivotal role in early cancer detection, treatment, prognosis, efficient monitoring, and other aspects. Surface-enhanced Raman scattering (SERS) is considered a potent tool for the detection of tumor markers owing to its exceptional advantages encompassing high sensitivity, superior selectivity, rapid analysis speed, and photobleaching resistance nature. This review aims to provide a comprehensive understanding of SERS applications in the detection of tumor markers. Firstly, we introduce the SERS enhancement mechanism, classification of active substrates, and SERS detection techniques. Secondly, the latest research progress of in vitro SERS detection of different types of tumor markers in body fluids and the application of SERS imaging in biomedical imaging are highlighted in sections of the review. Finally, according to the current status of SERS detection of tumor markers, the challenges and problems of SERS in biomedical detection are discussed, and insights into future developments in SERS are offered.

Raman Spectroscopy Monitoring of Duck Egg Brining Process

Salted duck eggs are a popular food in China and a key ingredient in pastries such as mooncakes, valued for their unique flavors. In this study, we examined the influence of brining processes on duck eggs, focusing on salt concentration and the effect of added wine. Four experimental groups were established: 18% salt, 25% salt, and 18% or 25% salt with added wine. The results from egg yolks suggest that increasing the salt concentration or adding 10% wine (53% alcohol) accelerates the brining process, while the Raman spectra of egg whites remain remarkably stable throughout brining. Our findings suggest that the traditional 30-day brining period can be reduced to 20-25 days with a higher salt concentration or the addition of wine, after which the egg yolk structure becomes largely stable.

Helical au nanostructure for SERS detection of hazardous molecular and chiral enantiomers

In recent years, incidents of pesticide pollution and abuse of feed additives have occurred frequently, which pose a great threat to human health. Raman spectroscopy has become an important method in the field of food safety due to its rapidity, simplicity and sensitivity. It is important to obtain complex structure to promote surface-enhanced Raman scattering (SERS) effect. In this study, gold helical nanoparticles with rich surface structure were synthesized using cysteine as induce agent. Notably, the complex helical structure and tip led to an excellent electromagnetic enhancement property. The helical structure showed ultra-sensitive detection of hazardous molecular, such as thiram and ractopamine. Interestingly, the D/L-Au structure had significant chiral optical activity and could be used as an unlabeled SERS platform for enantiomer identification. This study provided an effective strategy for the detection of pesticides and feed additives, which could be applied in other aspects of food safety in the future.

Farm to fork applications: how vibrational spectroscopy can be used along the whole value chain?

Vibrational spectroscopy is a nondestructive analysis technique that depends on the periodic variations in dipole moments and polarizabilities resulting from the molecular vibrations of molecules/atoms. These methods have important advantages over conventional analytical techniques, including (a) their simplicity in terms of implementation and operation, (b) their adaptability to on-line and on-farm applications, (c) making measurement in a few minutes, and (d) the absence of dangerous solvents throughout sample preparation or measurement. Food safety is a concept that requires the assurance that food is free from any physical, chemical, or biological hazards at all stages, from farm to fork. Continuous monitoring should be provided in order to guarantee the safety of the food. Regarding their advantages, vibrational spectroscopic methods, such as Fourier-transform infrared (FTIR), near-infrared (NIR), and Raman spectroscopy, are considered reliable and rapid techniques to track food safety- and food authenticity-related issues throughout the food chain. Furthermore, coupling spectral data with chemometric approaches also enables the discrimination of samples with different kinds of food safety-related hazards. This review deals with the recent application of vibrational spectroscopic techniques to monitor various hazards related to various foods, including crops, fruits, vegetables, milk, dairy products, meat, seafood, and poultry, throughout harvesting, transportation, processing, distribution, and storage.

Recent trends and impact of localized surface plasmon resonance (LSPR) and surface-enhanced Raman spectroscopy (SERS) in modern analysis

An optical biosensor is a specialized analytical device that utilizes the principles of optics and light in bimolecular processes. Localized surface plasmon resonance (LSPR) is a phenomenon in the realm of nanophotonics that occurs when metallic nanoparticles (NPs) or nanostructures interact with incident light. Conversely, surface-enhanced Raman spectroscopy (SERS) is an influential analytical technique based on Raman scattering, wherein it amplifies the Raman signals of molecules when they are situated near specific and specially designed nanostructures. A detailed exploration of the recent ground-breaking developments in optical biosensors employing LSPR and SERS technologies has been thoroughly discussed along with their underlying principles and the working mechanisms. A biosensor chip has been created, featuring a high-density deposition of gold nanoparticles (AuNPs) under varying ligand concentration and reaction duration on the substrate. An ordinary description, along with a visual illustration, has been thoroughly provided for concepts such as a sensogram, refractive index shift, surface plasmon resonance (SPR), and the evanescent field, Rayleigh scattering, Raman scattering, as well as the electromagnetic enhancement and chemical enhancement. LSPR and SERS both have advantages and disadvantages, but widely used SERS has some advantages over LSPR, like chemical specificity, high sensitivity, multiplexing, and versatility in different fields. This review confirms and elucidates the significance of different disease biomarker identification. LSPR and SERS both play a vital role in the detection of various types of cancer, such as cervical cancer, ovarian cancer, endometrial cancer, prostate cancer, colorectal cancer, and brain tumors. This proposed optical biosensor offers potential applications for early diagnosis and monitoring of viral disease, bacterial infectious diseases, fungal diseases, diabetes, and cardiac disease biosensing. LSPR and SERS provide a new direction for environmental monitoring, food safety, refining impurities from water samples, and lead detection. The understanding of these biosensors is still limited and challenging.

Acoustofluidic one-step production of plasmonic Ag nanoparticles for portable paper-based ultrasensitive SERS detection of bactericides

SERS measurements for monitoring bactericides in dairy products are highly desired for food safety problems. However, the complicated preparation process of SERS substrates greatly impedes the promotion of SERS. Here, we propose acoustofluidic one-step synthesis of Ag nanoparticles on paper substrates for SERS detection. Our method is economical, fast, simple, and eco-friendly. We adopted laser cutting to cut out appropriate paper shapes, and aldehydes were simultaneously produced at the cutting edge in the pyrolysis of cellulose by laser which were leveraged as the reducing reagent. In the synthesis, only 5 μL of Ag precursor was added to complete the reaction, and no reducing agent was used. Our recently developed acoustofluidic device was employed to intensely mix Ag+ ions and aldehydes and spread the reduced Ag nanoparticles over the substrate. The SERS substrate was fabricated in 1 step and 3 min. The standard R6G solution measurement demonstrated the excellent signal and prominent uniformity of the fabricated SERS substrates. SERS detection of the safe concentration of three bactericides, including tetracycline hydrochloride, thiabendazole, and malachite green, from food samples can be achieved using fabricated substrates. We take the least cost, time, reagents, and steps to fabricate the SERS substrate with satisfying performance. Our work has an extraodinary meaning for the green preparation and large-scale application of SERS.

Reports of tropane alkaloid poisonings and analytical techniques for their determination in food crops and products from 2013 to 2023

Food safety is crucial to attaining food security and sustainability. Unsafe foods for human and animal consumption lead to product recalls and rejection, negatively impacting the global economy and trade. Similarly, climate change can adversely affect the availability of safe and nutritious food at the table. The changing climatic conditions and global food trade and transport can make the movement of toxic plants possible, resulting in food crops being increasingly invaded by some species of plants that produce toxic secondary metabolites, such as tropane alkaloids (TAs). Datura stramonium from the Solanaceae plant family is an invasive and virulent plant that produces high amounts of two TAs, atropine and scopolamine. Various food poisoning events following accidental or deliberate ingestion of foods contaminated by atropine and scopolamine from seeds of D. stramonium have been recorded in different locations globally. Due to these incidents, regulatory agencies require the development of plant toxin detection methods that can be used in the food chain as early as possible. This systematic review thus focuses on the TA determination techniques in food and feeds published between 2013 and 2023. A particular focus was given to the sample preparation methods, the improvements of each technique claimed, and data to support the performance of each method, especially the ability to measure at or below the maximum level. The review concludes with other technological advancements, including rapid spectroscopy, electrophoresis, and colorimetric methods, as well as the possibility of coupling with smartphones for use in on-farm detection and the challenges in applying them.

Superhydrophobic nanocellulose-based self-assembled flexible SERS substrates for pesticide detection

Flexible surface-enhanced Raman scattering (SERS) substrates that provide simple sampling are helpful for the on-site detection of explosive contamination, pesticide residues on food surfaces, and water pollution in public spaces. Using superhydrophobic nanocellulose-based film as the support, 2D flexible SERS substrates that integrated sampling, enrichment, and detection were successfully fabricated via the solvent-induced evaporation method. This approach enabled the co-loading of two plasmonic nanoparticles with different sizes and shapes. A uniform and dense distribution of two-dimensional "hot spots" was created by the plasmonic nanoparticles' self-assembly on the hydrophobic substrate. By adjusting the loading ratio of Au-core/Ag-shell nanocubes and gold nanospheres, their synergistic effect optimized the "hot spots" structure and significantly increased the SERS signal intensity. Additionally, the hydrophobic property of the substrate allowed the target analytes to be concentrated throughout the drying process, significantly increasing the sensitivity of SERS detection. This flexible substrate can sensitively and accurately detect the pesticide residues of phosphorus and methyl parathion on apple peel with the detection limit of 10-7 g/L and relative standard deviation (RSD) less than 10 %. The high-performance SERS substrate has great potential for in-situ detection applications such as food safety and environmental monitoring.

Surface-enhanced Raman scattering based on noble metal nanoassemblies for detecting harmful substances in food

Residues of harmful substances in food can severely damage human health. The content of these substances in food is generally low, making detection difficult. Surface-enhanced Raman scattering (SERS), based on noble metal nanomaterials, mainly gold (Au) and silver (Ag), has exhibited excellent capabilities for trace detection of various substances. Noble metal nanoassemblies, in particular, have extraordinary flexibility and tunable optical properties, which cannot be offered by single nanoparticles (NPs). These nanoassemblies, with their various morphologies synthesized using NPs through artificially induced self-assembly or template-driven preparation, can significantly enhance the local electric field and create "hot spots" due to the gaps between adjacent NPs. Consequently, the SERS properties of NPs become more prominent, leading to improved performance in the trace detection of various substances and detection limits that are considerably lower than the current relevant standards. Noble metal nanoassemblies show promising potential in ensuring food safety. This review discusses the synthesis methods and SERS properties of noble metal nanoassemblies and then concentrates on their application in detecting biotoxins, drug residues, illegal additives, and heavy metals. The study provides valuable references for further research into the application of nanoassemblies in food safety detection.

Extralong hot-spots sensor for SERS sensitive detection of phthalate plasticizers in biological tear and serum fluids

Phthalate plasticizers (PAEs) illegally used in food pose a great threat to human health. A new and efficient sensing platform for the sensitive detection of the PAE residues in biological fluids needs to be designed and developed. Here, we report a simple and reliable surface-enhanced Raman spectroscopy (SERS) active platform with extralong hot spots of Au nanobipyramids@Ag nanorods (Au NBPs@Ag NRs) for the rapid and sensitive detection of PAEs in biological fluids. To achieve high activity, Au NBPs@Ag NRs with different shell lengths were fabricated by controlling the synthesis conditions, and the corresponding SERS properties were investigated by using crystal violet (CryV) and butyl benzyl phthalate (BBP). The experimental results showed that a longer shell length correlated to greater Raman activity, which was confirmed by finite-difference time-domain (FDTD) electromagnetic simulation. More importantly, the extralong hot spots of the Au NBPs@Ag NR SERS-active substrate showed excellent homogeneity and reproducibility for the CryV probe molecules (6.21%), and the detection limit was 10-9 M for both BBP and diethylhexyl phthalate (DEHP). Furthermore, through the standard addition method, an extralong hot spots SERS substrate could achieve highly sensitive detection of BBP and DEHP in serum and tears fluids, and the detection limit was as low as 3.52 × 10-8 M and 2.82 × 10-8 M. Therefore, the Au NBPs@Ag NR substrate with an extraordinarily long surface is efficient and versatile, and can potentially be used for high-efficiency sensing analysis in complex biological fluids.

Toward Sensitive and Reliable Immunoassays of Marine Biotoxins: From Rational Design to Food Analysis

Marine biotoxins are metabolites produced by algae that can accumulate in shellfish or fish and enter organisms through the food chain, posing a serious threat to biological health. Therefore, accurate and rapid detection is an urgent requirement for food safety. Although various detection methods, including the mouse bioassay, liquid chromatography-mass spectrometry, and cell detection methods, and protein phosphatase inhibition assays have been developed in the past decades, the current detection methods cannot fully meet these demands. Among these methods, the outstanding immunoassay virtues of high sensitivity, reliability, and low cost are highly advantageous for marine biotoxin detection in complex samples. In this work, we review the recent 5-year progress in marine biotoxin immunodetection technologies such as optical immunoassays, electrochemical immunoassays, and piezoelectric immunoassays. With the assistance of immunoassays, the detection of food-related marine biotoxins can be implemented for ensuring public health and preventing food poisoning. In addition, the immunodetection technique platforms including lateral flow chips and microfluidic chips are also discussed. We carefully investigate the advantages and disadvantages for each immunoassay, which are compared to demonstrate the guidance for selecting appropriate immunoassays and platforms for the detection of marine biotoxins. It is expected that this review will provide insights for the further development of immunoassays and promote the rapid progress and successful translation of advanced immunoassays with food safety detection.

Application of Surface-Enhanced Raman Spectroscopy Combined with Immunoassay for the Detection of Adrenoceptor Agonists

Rapid, sensitive, and accurate detection of adrenoceptor agonists is a significant research topic in the fields of food safety and public health. Immunoassays are among the most widely used methods for detecting adrenoceptor agonists. In recent years, surface-enhanced Raman spectroscopy combined with immunoassay (SERS-IA) has become an effective technique for improving detection sensitivity. This review focuses on the innovation of Raman reporter molecules and substrate materials for the SERS-IA of adrenoceptor agonists. In addition, it also investigates the challenges involved in potentially applying SERS-IA in the detection of adrenoceptor agonists. Overall, this review provides insight into the design and application of SERS-IA for the detection of adrenoceptor agonists, which is critical for animal-derived food safety and public health.

RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components

Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.

Integrated surface-enhanced Raman spectroscopy and convolutional neural network for quantitative and qualitative analysis of pesticide residues on pericarp

Pesticide residue poses a significant global public health concern, necessitating improved detection methods. Here, a novel platform was introduced based on surface-enhanced Raman spectroscopy (SERS) to detect ten distinct types of pesticides. Notably, the sensitivity of this approach is exemplified by detecting trace amounts of 50 pM (10 ppt) thiabendazole. The correlation between the characteristic peak intensity of coexisting pesticides and their concentrations displays an exceptional linear relationship (R2 = 0.9999), underscoring its utility for quantitative mixed pesticide detection. Additionally, qualitative analysis of five mixed pesticides was conducted leveraging distinctive peak labeling. Harnessing machine learning techniques, a model for classifying and predicting pesticides on pericarps was developed. Remarkably, the convolutional neural network achieved classification accuracy of 100 % and prediction accuracy of 99.62 %. This innovative approach accurately identifies and quantifies diverse pesticides, thus offering a feasible scheme for in-situ detection of pesticide residues. Ultimately, this strategy contributes to ensuring food safety and public health.

SERS substrate based on COF@Ag for detecting amoxicillin in honey and lake water

This study presents the design of a Surface-enhanced Raman scattering (SERS) substrate, COF@Ag, for the sensitive detection of Amoxicillin (AMX) in lake water and honey. Furthermore, the study investigates the role of covalent organic frameworks (COFs) in SERS detection. The characterization results demonstrate the capability of COFs to efficiently enrich Ag nanoparticles (AgNPs), resulting in a more concentrated distribution of hotspots and an enhanced electromagnetic field on the substrate. By employing density functional theory (DFT) simulation, the frontier electronic orbitals of COFs and AMX were analyzed, and the chemical bonds and weak interactions in the system were examined using the Interaction Region Indicator (IRI) method to propose potential enhancement mechanisms. In aqueous solutions, the linear range is 1 μg/L-30 μg/L, with a limit of detection (LOD) 0.279 μg/L. In lake water, the linear range span from 100 μg/L to 500 μg/L, with a detection limit of 8.244 μg/L. For honey, the linear range extend from 20 ng/g to 100 ng/g, with a detection limit of 2.917 ng/g. This method holds key significance in facilitating the rapid detection of amoxicillin and advancing the application of COFs in SERS.

Advances, challenges, and opportunities for food safety analysis in the isothermal nucleic acid amplification/CRISPR-Cas12a era

Global food safety stands out as a prominent public concern, affecting populations worldwide. The recurrent challenge of food safety incidents reveals the need for a robust inspection framework. In recent years, the integration of isothermal nucleic acid amplification with CRISPR-Cas12a techniques has emerged as a promising tool for molecular detection of food hazards, presenting next generation of biosensing for food safety detection. This paper provides a comprehensive review of the current state of research on the synergistic application of isothermal nucleic acid amplification and CRISPR-Cas12a technology in the field of food safety. This innovative combination not only enriches the analytical tools, but also improving assay performance such as sensitivity and specificity, addressing the limitations of traditional methods. The review summarized various detection methodologies by the integration of isothermal nucleic acid amplification and CRISPR-Cas12a technology for diverse food safety concerns, including pathogenic bacterium, viruses, mycotoxins, food adulteration, and genetically modified foods. Each section elucidates the specific strategies employed and highlights the advantages conferred. Furthermore, the paper discussed the challenges faced by this technology in the context of food safety, offering insightful discussions on potential solutions and future prospects.

Highly sensitive detection of tryptophan based on Schiff base reaction and surface-enhanced Raman spectroscopy

In this study, a simple, rapid and sensitive method combining surface-enhanced Raman spectroscopy and Schiff base reaction was developed for the detection of tryptophan. This method does not require product separation to obtain a significant Raman signal of the derivatized product, and the derivatization reaction can be controlled by experimental parameters such as reaction temperature, time, concentration of derivatization reagent and concentration of sodium nitrite. The characteristic peak of the derivative of tryptophan (1620 cm-1) was selected for quantitative analysis, and the intensity of the characteristic Raman spectrum peak showed a linear relationship with the concentration of tryptophan (10-8-10-4 mol/L) in the range of with a correlation coefficient R2 of 0.9922. This assay combines surface-enhanced Raman spectroscopy and Schiff base reaction, which is characterized by high sensitivity and easy operation, and has good application prospects in the detection of tryptophan in food.

Advances in electrochemical-optical dual-mode biosensors for detection of environmental pathogens

Electrochemical techniques are commonly used to analyze and screen various environmental pathogens. When used in conjunction with other optical recognition methods, it can extend the sensing range, lower the detection limit, and offer mutual validation. Nowadays, electrochemical-optical dual-mode biosensors have ensured the accuracy of test results by integrating two signals into one, indicating their potential use in primary food safety quantitative assays and screening tests. Particularly, visible optical signals from electrochemical/colorimetric dual-mode biosensors could meet the demand for real-time screening of microbial pathogens. While electrochemical-optical dual-mode probes have been receiving increasing attention, there is limited emphasis on the design approaches for sensors intended for microbial pathogens. Here, we review the recent progress in the merging of optical and electrochemical techniques, including fluorescence, colorimetry, surface plasmon resonance (SPR), and surface enhanced Raman spectroscopy (SERS). This study particularly emphasizes the reporting of various sensing performances, including sensing principles, types, cutting-edge design approaches, and applications. Finally, some concerns and upcoming advancements in dual-mode probes are briefly outlined.

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.

Computer vision based deep learning approach for toxic and harmful substances detection in fruits

Formaldehyde (CH₂O) is one of the significant chemicals mixed with different perishable fruits in Bangladesh. The fruits are artificially preserved for extended periods by dishonest vendors using this dangerous chemical. Such substances are complicated to detect in appearance. Hence, a reliable and robust detection technique is required. To overcome this challenge and address the issue, we introduce comprehensive deep learning-based techniques for detecting toxic substances. Four different types of fruits, both in fresh and chemically mixed conditions, are used in this experiment. We have applied diverse data augmentation techniques to enlarge the dataset. The performance of four different pre-trained deep learning models was then assessed, and a brand-new model named "DurbeenNet," created especially for this task, was presented. The primary objective was to gauge the efficacy of our proposed model compared to well-established deep learning architectures. Our assessment centered on the models' accuracy in detecting toxic substances. According to our research, GoogleNet detected toxic substances with an accuracy rate of 85.53 %, VGG-16 with an accuracy rate of 87.44 %, DenseNet with an impressive accuracy rate of 90.37 %, and ResNet50 with an accuracy rate of 91.66 %. Notably, the proposed model, DurbeenNet, outshone all other models, boasting an impressive accuracy rate of 96.71 % in detecting toxic substances among the sample fruits.

Qualitative and quantitative studies of phthalates in extra virgin olive oil (EVOO) by surface-enhanced Raman spectroscopy (SERS) combined with long short term memory (LSTM) neural network

Phthalates are commonly used plasticizers in the plastics industry, and have received extensive attention due to their reproductive toxicity. Since phthalates are lipophilic solutions, phthalates can easily migrate from packaging to edible oils. This study synthesized stable and sensitive Gold Nanostars as SERS substrates to conduct qualitative and quantitative analysis of two common phthalates, dibutyl phthalate and di(2-ethylhexyl) phthalate. Two ethanol standard solutions and actual oil solutions of phthalates at different concentrations (10, 5, 1, 0.5, 0.1, 0.02 mg/kg) were prepared. After dimension reduction, LSTM achieved the accuracy of 98% for pure EVOO and EVOO adulterated with different types of phthalates. In terms of quantification, LSTM demonstrates great predictive performance with Rp2 greater than 0.97 and the ratio of performance to deviation greater than 5. These results have certain guiding significance for the analysis of plasticizers in edible oil.

Dual-mode colorimetric and fluorescence biosensors for the detection of foodborne bacteria

Due to the growing demand for detection technologies, there has been significant interest in the development of integrated dual-modal sensing technologies, which involve combining two signal transduction channels into a single technique, particularly in the context of food safety. The integration of two detection signals not only improves diagnostic performance by reducing assumptions, but also enhances diagnostic functions with increased application flexibility, improved accuracy, and a wider detection linear range. The top two output signals for emerging dual-modal probes are fluorescent and colorimetric, due to their exceptional advantages for real-time sensitive sensing and point-of-care applications. With the rapid progress of nanotechnology and material chemistry, the integrated colorimetric/fluorimetric dual-mode systems show immense potential in sensing foodborne pathogenic bacteria. In this comprehensive review, we present a detailed summary of various colorimetric and fluorimetric dual-modal sensing methods, with a focus on their application in detecting foodborne bacteria. We thoroughly examine the sensing methodologies and the underlying principles of the signal transduction systems, and also discuss the challenges and future prospects for advancing research in this field.

Application of DNA-fueled molecular machines in food safety testing

Food is consumed by humans, which is indispensable to human life. Therefore, considerable attention of the whole society has been paid to food safety. Over the last few years, dramatic social development has brought new challenges to food safety, making developing new and quick methods for on-site food safety testing an important necessity. As a result, DNA-fueled molecular machines, characterized by high efficiency, accuracy, and sensitivity in testing, have come into the spotlight, based on which sensors can be constructed to detect toxic and harmful substances in food products. This study reviewed recent research on several DNA-fueled molecular machines, including DNA tweezers, DNA walkers, and DNA origami, for rapidly detecting toxic and harmful substances. Based on the above studies, the sensitivity and timeliness of several DNA molecular machines were summarized and compared, and the development prospect of DNA fuel molecular machines in the field of food safety detection was prospected.

Ratiometric Lanthanide Metal‐Organic Frameworks (MOFs) for Smartphone‐Assisted Visual Detection of Food Contaminants and Water: A Review

Developing a reliable portable biosensor is crucial for ensuring food safety and human health. This involves accurately detecting contaminants in food and water at their source. Smartphone cameras have recently become useful for capturing color or fluorescence changes that occur when a probe interacts with specific molecules on paper or in a chemical solution. Ratiometric designs, which self‐calibrate and minimize the impact of environmental changes, are gaining popularity. These designs rely on color changes or fluorescence shifts, which are easily assessable with smartphones. This overview highlights advances in ratiometric optical sensing using Metal‐organic frameworks (MOFs) with lanthanide components coupled with smartphones. These advancements allow contaminants in food and water to be visually identified. The article explains the principles, properties, and applications of color changes for visual detection in food safety. Using lanthanide metal‐organic frameworks with smartphones offers a potent method to detect contaminants, enhancing food safety and safeguarding human health.

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.

MoS2 quantum dots-based optical sensing platform for the analysis of synthetic colorants. Application to quinoline yellow determination

In this work, a novel fluorescence sensor has been designed to solve the actual need of new fast and inexpensive sensing platforms for the analysis of synthetic colorants. It is based on MoS2 quantum dots obtained by a hydrothermal method and incorporated as fluorophore into the matrix of PVC membranes, which are deposited on quartz substrates by spin-coating. It was proven, as in these conditions, MoS2 quantum dots maintain the fluorescent properties that they present in solution. Experiments carried out in solution displayed a maximum emission when they were excited under 310 nm. This initial fluorescence decreases linearly in presence of increasing concentrations of various synthetic colorants namely quinoline yellow, tartrazine, sunset yellow, allura red, ponceau 4R and carmoisine. The two possible mechanisms that can explain this quenching effect, colorants absorbing photons emitted by quantum dots and/or competing with the nanomaterial for photons coming from the excitation source, were evaluated. The most pronounced effect was observed with quinoline yellow, as a result of a mixed mechanism. The optimized methodology developed for the determination of quinoline yellow showed a linear concentration range between 5.4 and 55.0 µg with a limit of detection of 1.6 µg. The sensor was applied to the determination of quinoline yellow in a food colour paste obtaining results in good agreement with those obtained by HPLC-UV-vis measurements.

A review of recent progress in the application of Raman spectroscopy and SERS detection of microplastics and derivatives

The global environmental concern surrounding microplastic (MP) pollution has raised alarms due to its potential health risks to animals, plants, and humans. Because of the complex structure and composition of microplastics (MPs), the detection methods are limited, resulting in restricted detection accuracy. Surface enhancement of Raman spectroscopy (SERS), a spectral technique, offers several advantages, such as high resolution and low detection limit. It has the potential to be extensively employed for sensitive detection and high-resolution imaging of microplastics. We have summarized the research conducted in recent years on the detection of microplastics using Raman and SERS. Here, we have reviewed qualitative and quantitative analyses of microplastics and their derivatives, as well as the latest progress, challenges, and potential applications.

Recent advancements in nanomaterial based optical detection of food additives: a review

Food additives have become a critical component in the food industry. They are employed as preservatives to decelerate the negative effects of environmental and microbial factors on food quality. Currently, food additives are used for a variety of purposes, including colorants, flavor enhancers, nutritional supplements, etc., owing to improvements in the food industry. Since the usage of food additives has increased dramatically, the efficient monitoring of their acceptable levels in food products is quite necessary to mitigate the problems associated with their inappropriate use. The traditional methods used for detecting food additives are generally based on standard spectroscopic and chromatographic techniques. However, these analytical techniques are limited by their high instrumentation cost and time-consuming procedures. The emerging field of nanotechnology has enabled the development of highly sensitive and specific sensors to analyze food additives in a rapid manner. The current article emphasizes the need to detect various food additives owing to their potential negative effects on humans, animals, and the environment. In this article, the role of nanomaterials in the optical sensing of food additives has been discussed owing to their high accuracy, ease-of-use, and excellent sensitivity. The applications of nanosensors for the detection of various food additives have been elaborated with examples. The current article will assist policymakers in developing new rules and regulations to mitigate the adverse effects of toxic food additives on humans and the environment. In addition, the prospects of nanosensors for the optical detection of food additives at a commercial scale have been discussed to combat their irrational use in the food industry.

Surface-Enhanced Raman Spectroscopy (SERS) Activity of Gold Nanoparticles Prepared Using an Automated Loop Flow Reactor

This study used automatic control methods to prepare gold nanoparticles (AuNPs) as the substrate and rhodamine 6G molecule as the probe to investigate the enhancement effect, stability, and consistency of surface-enhanced Raman spectroscopy (SERS). The gold nanosols were prepared via automatic control using loop flow-reactor technology, and the synthesis of nanoparticles with different sizes was precisely controlled by optimizing the ratio of the solution required for the reaction between sodium citrate and chloroauric acid during the preparation process. The morphology, structure, and optical properties of the prepared AuNPs were investigated using field-emission scanning electron microscopy, transmission electron microscopy, and ultraviolet visible spectroscopy. Using the proposed method, AuNPs with average particle sizes of 72, 85, 93, and 103 nm were synthesized in a precisely controlled manner. The 93 nm particles exhibited good SERS activity for rhodamine 6G under 785 nm excitation with a detection limit of 2.5 × 10-10 M. The relative standard deviation of the SERS spectra synthesized multiple times was <3.5%, indicating their good sensitivity and reproducibility. The results showed that the AuNPs prepared by the automatic control of the loop-flow method have high sensitivity, stability, and reproducibility. Moreover, they exhibited notable potential for in situ measurement and quantitative analysis using SERS.

Raman Spectroscopy Combined with Malaria Protein for Early Capture and Recognition of Broad-Spectrum Circulating Tumor Cells

Early identification of tumors can significantly reduce the mortality rate. Circulating tumor cells (CTCs) are a type of tumor cell that detaches from the primary tumor and circulates through the bloodstream. Monitoring CTCs may allow the early identification of tumor progression. However, due to their rarity and heterogeneity, the enrichment and identification of CTCs is still challenging. Studies have shown that Raman spectroscopy could distinguish CTCs from metastatic cancer patients. VAR2CSA, a class of malaria proteins, has a strong broad-spectrum binding effect on various tumor cells and is a promising candidate biomarker for cancer detection. Here, recombinant malaria VAR2CSA proteins were synthesized, expressed, and purified. After confirming that various types of tumor cells can be isolated from blood by recombinant malaria VAR2CSA proteins, we further proved that the VAR2CSA combined with Raman spectroscopy could be used efficiently for tumor capture and type recognition using A549 cell lines spiked into the blood. This would allow the early screening and detection of a broad spectrum of CTCs. Finally, we synthesized and purified the malaria protein fusion antibody and confirmed its in vitro tumor-killing activity. Herein, this paper exploits the theoretical basis of a novel strategy to capture, recognize, and kill broad-spectrum types of CTCs from the peripheral blood.

Label-free detection of trace level zearalenone in corn oil by surface-enhanced Raman spectroscopy (SERS) coupled with deep learning models

Surface-enhanced Raman spectroscopy (SERS) and deep learning models were adopted for detecting zearalenone (ZEN) in corn oil. First, gold nanorods were synthesized as a SERS substrate. Second, the collected SERS spectra were augmented to improve the generalization ability of regression models. Third, five regression models, including partial least squares regression (PLSR), random forest regression (RFR), Gaussian progress regression (GPR), one-dimensional convolutional neural networks (1D CNN), and two-dimensional convolutional neural networks (2D CNN), were developed. The results showed that 1D CNN and 2D CNN models possessed the best prediction performance, i.e., determination of prediction set (R_P²) = 0.9863 and 0.9872, root mean squared error of prediction set (RMSEP) = 0.2267 and 0.2341, ratio of performance to deviation (RPD) = 6.548 and 6.827, limit of detection (LOD) = 6.81 × 10^-4 and 7.24 × 10^-4 μg/mL. Therefore, the proposed method offers an ultrasensitive and effective strategy for detecting ZEN in corn oil.

Design, Fabrication, and Applications of SERS Substrates for Food Safety Detection: Review

Sustainable and safe food is an important issue worldwide, and it depends on cost-effective analysis tools with good sensitivity and reality. However, traditional standard chemical methods of food safety detection, such as high-performance liquid chromatography (HPLC), gas chromatography (GC), and tandem mass spectrometry (MS), have the disadvantages of high cost and long testing time. Those disadvantages have prevented people from obtaining sufficient risk information to confirm the safety of their products. In addition, food safety testing, such as the bioassay method, often results in false positives or false negatives due to little rigor preprocessing of samples. So far, food safety analysis currently relies on the enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), HPLC, GC, UV-visible spectrophotometry, and MS, all of which require significant time to train qualified food safety testing laboratory operators. These factors have hindered the development of rapid food safety monitoring systems, especially in remote areas or areas with a relative lack of testing resources. Surface-enhanced Raman spectroscopy (SERS) has emerged as one of the tools of choice for food safety testing that can overcome these dilemmas over the past decades. SERS offers advantages over chromatographic mass spectrometry analysis due to its portability, non-destructive nature, and lower cost implications. However, as it currently stands, Raman spectroscopy is a supplemental tool in chemical analysis, reinforcing and enhancing the completeness and coverage of the food safety analysis system. SERS combines portability with non-destructive and cheaper detection costs to gain an advantage over chromatographic mass spectrometry analysis. SERS has encountered many challenges in moving toward regulatory applications in food safety, such as quantitative accuracy, poor reproducibility, and instability of large molecule detection. As a result, the reality of SERS, as a screening tool for regulatory announcements worldwide, is still uncommon. In this review article, we have compiled the current designs and fabrications of SERS substrates for food safety detection to unify all the requirements and the opportunities to overcome these challenges. This review is expected to improve the interest in the sensing field of SERS and facilitate the SERS applications in food safety detection in the future.

Nucleic acid aptamer-based biosensors and their application in thrombin analysis

Thrombin is a multifunctional serine protease that plays an important role in coagulation and anticoagulation processes. Aptamers have been widely applied in biosensors due to their high specificity, low cost and good biocompatibility. This review summarizes recent advances in thrombin quantification using aptamer-based biosensors. The primary focus is optical sensors and electrochemical sensors, along with their applications in thrombin analysis and disease diagnosis.

Applications of Delayed Luminescence for tomato fruit quality assessment across varied Sicilian cultivation zones

The food industry places significant emphasis on ensuring quality and traceability as key components of a healthy diet. To cater to consumer demands, researchers have prioritized the development of analytical techniques that can rapidly and non-invasively provide data on quality parameters. In this study, we propose to use the Delayed Luminescence (DL), an ultra-weak and photo-induced emission of optical photons, as a tool for a rapid evaluation of quality profile associated with fruit ripening, in support of traditional analysis methods. Delayed Luminescence measurements have been performed on cherry tomatoes, with and without the PGI "Pomodoro di Pachino" certification, harvested from two different growing areas of south-eastern Sicily (Italy). Then, DL emissions were correlated with soluble solid content and titratable acidity values, which are known to affect the flavor, the commerciality and the maturity degree of tomato fruits. In addition, we evaluated the changes in the DL parameters with respect to the geographical origin of the cherry tomatoes, with the aim of testing the possibility of applying the technique for identification purposes. The signals of Delayed Luminescence appeared to be good indicators of the macromolecular structure of the biological system, revealing structural changes related to the content of total soluble solids present in the juice of tomatoes analyzed, and they appeared unsuitable for authenticating vegetable crops, since the differences in the photon yields emitted by tomato Lots were not related to territory of origin. Thus, our results suggest that DL can be used as a nondestructive indicator of important parameters linked to tomato fruit quality.

Aggregation-induced bimodal excitation of nitrogen-doped carbon dots for ratiometric sensing of new coccine and solid-state multicolor lighting

Trace detection of foodstuff pigments have gained increasing attention because of their close association with biological and environmental processes. Herein, we propose an innovative bimodal excitation nitrogen-doped carbon dots (N-CDs) for ratiometric sensing of new coccine (NC) pigment, which are synthesized by using melamine and o-phenylenediamine as precursors via solvothermal treatment. With the increase of the N-CDs concentration, N-CDs exhibit not only a concentration-dependent tunable color behavior, but also a novel aggregation-induced bimodal excitation phenomenon. Considering this distinctive bimodal excitation behavior, a ratiometric sensor based on N-CDs has been developed for the detection of the NC in different organic solvents due to the inner filter effect and fluorescence resonance energy transfer. The intensity ratio of two excitation signals is linear with the NC concentration in the range of 0.032-100 µM, and the limit of detection is as low as 32.1 nM. Meanwhile, we realize the design of multicolor-emission N-CDs/polymer films. All in all, this work presents a novel kind view of the mechanism of distinctive bimodal excitation of N-CDs, and further proposes an innovative ratiometric method for the screening analysis of NC in food samples and environmental pollutants.

Carbon nanomaterial-based molecularly imprinted polymer sensors for detection of hazardous substances in food: recent progress and future trends

The presence of various harmful substances in food is significantly risky to human health. Therefore, simple, rapid, and selective food hazard analysis tools have become a focus of sensing research. At present, molecularly imprinted polymers (MIPs) have attracted more and more attention because of their easy preparation and high selectivity. Due to their simple preparation, low cost, large specific surface area, and high conductivity, carbon nanomaterial can be used as sensing substrate carriers. Therefore, the combination of carbon nanomaterial with MIPs has attracted great attention. This paper summarizes the development, composition, and preparation methods of MIPs, as well as the latest research progress in carbon nanomaterials for the detection of various food hazards using sensors. In addition, the practical applications of carbon nanomaterial-based MIP sensors, their current challenges and future trends, and the ongoing efforts devoted to developing new and efficient carbon nanomaterial-based MIP sensing platforms are also introduced.

Conventional and advanced detection techniques of foodborne pathogens: A comprehensive review

Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results.

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.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

MIPs-SERS Sensor Based on Ag NPs Film for Selective Detection of Enrofloxacin in Food

The quinolone antibiotics represented by enrofloxacin (ENRO) are harmful to the ecological environment and human health due to illegal excessive use, resulting in increasing food residues and ENRO levels in the environment. To this end, we developed a MIPs-SERS method using surface-enhanced Raman spectroscopy (SERS) and molecularly imprinted polymers (MIPs) to detect ENRO in food matrices. Firstly, a layer of silver nanoparticles (Ag NPs) with the best SERS effect was synthesized on the surface of copper rods as the enhancing material by in situ reductions, and then MIPs targeting ENRO were prepared by the native polymerization reaction, and the MIPs containing template molecules wrapped on the surface of silver nanoparticle films (Ag NPs-MIPs) were obtained. Our results showed that the Ag NPs-MIPs could specifically identify ENRO from the complex environment. The minimum detection limit for ENRO was 0.25 ng/mL, and the characteristic peak intensity of ENRO was linearly correlated to the concentration with a linear range of 0.001~0.1 μg/mL. The experimental results showed that in comparison to other detection methods, the rapid detection of ENRO in food matrices using Ag NPs-MIPs as the substrate is reliable and offers a cost-effective, time-saving, highly selective, and sensitive method for detecting ENRO residues in real food samples.