Ag-coated tetrapod gold nanostars (Au@AgNSs) for acetamiprid determination in tea using SERS combined with microfluidics.

Acetamiprid is an organic and highly toxic compound. Despite being widely used as a pesticide agent on a large scale, acetamiprid poses numerous health risks to living organisms, particularly humans. Herein, a strategy for the detection of acetamiprid in tea employing surface-enhanced Raman scattering (SERS) technology incorporated with a microfluidic chip was developed. Significantly, a seed-mediated growth approach was utilized to engineer Ag-coated tetrapod gold nanostars (core-shell Au@AgNSs) with four sharp tips. The synthesized Au@AgNSs showed an enhancement factor of 7.2 × 106. Solid works was used to figure out the two-channel microfluidic chip featuring four circular split hybrid structures, and COMSOL (Software for Multiphysics Simulation) was utilized to model the fusion effect between the substrate (Au@AgNSs) and the sample (acetamiprid). For the first time, the core-shell Au@AgNSs and acetamiprid were fused in the microfluidic channel to facilitate the detection of acetamiprid using SERS. The outcomes pointed out that the standard curve correlation coefficient between SERS intensity (876 cm-1) and the concentration of acetamiprid in tea specimens was calculated as 0.991, while the limit of detection (LOD) was 0.048 ng mL-1, which is well below the minimum limit set by the European Union (10 ng mL-1). Thus, the developed technique combining SERS and microfluidics demonstrated high potential for the rapid and efficient detection of acetamiprid in tea.

Recent and emerging trends of metal-organic frameworks (MOFs)-based sensors for detecting food contaminants: A critical and comprehensive review.

Interest in the use of sensors based on metal-organic frameworks (MOFs) to detect food pollutants has been growing recently due to the desirable characteristics of MOFs, including uniform structures, large surface area, ultrahigh porosity and easy-to-functionalize surface. Fundamentally, this review offers an excellent solution using MOFs-based sensors (e.g., fluorescent, electrochemical, electrochemiluminescence, surface-enhanced Raman spectroscopy, and colorimetric sensors) to detect food contaminants such as pesticide residues, mycotoxins, antibiotics, food additives, and other hazardous candidates. More importantly, their application scenarios and advantages in food detection are also introduced in more detail. Therefore, this systematic review analyzes detection limits, linear ranges, the role of functionalities, and immobilized nanoparticles utilized in preparing MOFs-based sensors. Additionally, the main limitations of each sensing type, along with the enhancement mechanisms of MOFs in addressing efficient sensing are discussed. Finally, the limitations and potential trends of MOFs-based materials in food contaminant detection are also highlighted.

Dual function of magnetic nanocomposites-based SERS lateral flow strip for simultaneous detection of aflatoxin B1 and zearalenone.

Aflatoxin B1 (AFB1) and zearalenone (ZEN) are two mycotoxins that often co-occur in corn. A surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-LFIA) that can simultaneously detect AFB1 and ZEN in corn samples was developed employing the core-interlayer-satellite magnetic nanocomposites (Fe3O4@PEI/AuMBA@AgMBA) as dual-functional SERS tags. Under the optimal conditions, the detection ranges of AFB1 and ZEN in corn samples were 0.1-10 µg/kg and 4-400 µg/kg, respectively. Moreover, the test results for two mycotoxins in contaminated corn samples employing the suggested SERS-LFIA was in line with those of the HPLC technique. In view of its satisfactory sensitivity, accuracy, precision and short testing time (20 min), the developed system has a promising application prospect in the on-site simultaneous detection of AFB1 and ZEN.

Raman spectroscopy-based microfluidic platforms: A promising tool for detection of foodborne pathogens in food products.

Rapid and sensitive detection of foodborne pathogens in food products is paramount for ensuring food safety and public health. In the ongoing effort to tackle this issue, detection methods are continually researched and upgraded to achieve rapidity, sensitivity, portability, and cost-effectiveness. This review addresses the critical need for improved technique by focusing on Raman spectroscopy-based microfluidic platforms, which have shown potential in revolutionizing the field of foodborne pathogen analysis offering point-of-care diagnosis and multiplex detection. The key problem lies in the persistent threat of compromised food quality and public health due to inadequate pathogen detection. The review elucidates the various trapping strategies employed in a microfluidic platform, including optical trapping, electrical trapping, mechanical trapping, and acoustic trapping for the capture of microbial cells. Subsequently, the review delves into the key aspects of the application of microbial detection in food products, highlighting recent advances and challenges in the field. The integrated technique allows point-of-care application assessment, which is an attractive quality for in-line and real-time detection of foodborne pathogens. However, the application of the technique in food products is limited and requires further research to combat the complexity of the food matrix, reduced costs of production, and ensure real-time use for diverse pathogens. Ultimately, this review aims to propel advancements in microbial detection, thus promoting enhanced food safety through state-of-the-art technologies.

A film-like SERS aptasensor for sensitive detection of patulin based on GO@Au nanosheets.

Patulin (PAT) commonly contaminates fruits, posing a significant risk to human health. Therefore, a highly effective and sensitive approach in identifying PAT is warranted. Herein, a SERS aptasensor was constructed based on a two-dimensional film-like structure. GO@Au nanosheets modified with SH-cDNA were employed as capture probes, while core-shell Au@Ag nanoparticles modified with 4-MBA and SH-Apt were utilized as signal probes. Through the interaction between capture probes and signal probes, adjustable hotspots were formed, yielding a significant Raman signal. During sensing, the GO@Au-cDNA competitively attached to Au@AgNPs@MBA-Apt, resulting in an inverse relationship between PAT levels and SERS intensity. The acquired results exhibited linear responses to PAT within the range of 1-70 ng/mL, with a calculated limit of detection of 0.46 ng/mL. In addition, the SERS aptasensor exhibited satisfactory recoveries in apple samples, which aligned closely with HPLC. With high sensitivity and specificity, this method holds significant potential for PAT detection.

Fast Nondestructive Detection Technology and Equipment for Food Quality and Safety.

Fast nondestructive detection technology in food quality and safety evaluation is a powerful support tool that fosters informatization and intelligence in the food industry, characterized by its rapid processing, convenient operation, and seamless online inspection [...].

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics.

In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing-partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse's sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.

Determination of Dicofol in Tea Using Surface-Enhanced Raman Spectroscopy Coupled Chemometrics.

Dicofol is a highly toxic residual pesticide in tea, which seriously endangers human health. A method for detecting dicofol in tea by combining stoichiometry with surface-enhanced Raman spectroscopy (SERS) technology was proposed in this study. AuNPs were prepared, and silver shells were grown on the surface of AuNPs to obtain core-shell Au@AgNPs. Then, the core-shell Au@AgNPs were attached to the surface of a PDMS membrane by physical deposition to obtain a Au@AgNPs/PDMS substrate. The limit of detection (LOD) of this substrate for 4-ATP is as low as 0.28 × 10-11 mol/L, and the LOD of dicofol in tea is 0.32 ng/kg, showing high sensitivity. By comparing the modeling effects of preprocessing and variable selection algorithms, it is concluded that the modeling effect of Savitzky-Golay combined with competitive adaptive reweighted sampling-partial least squares regression is the best (Rp = 0.9964, RPD = 10.6145). SERS technology combined with stoichiometry is expected to rapidly detect dicofol in tea without labels.

Development of a Sensitive SERS Method for Label-Free Detection of Hexavalent Chromium in Tea Using Carbimazole Redox Reaction.

Tea plants absorb chromium-contaminated soil and water and accumulate in tea leaves. Hexavalent chromium (Cr6+) is a very toxic heavy metal; excessive intake of tea containing Cr6+ can cause serious harm to human health. A reliable and sensitive surface-enhanced Raman spectroscopy (SERS) method was developed using Au@Ag nanoparticles as an enhanced substrate for the determination of Cr6+ in tea. The Au@AgNPs coated with carbimazole showed a highly selective reaction to Cr6+ in tea samples through a redox reaction between Cr6+ and carbimazole. The Cr6+ in the contaminated tea sample reacted with methimazole-the hydrolysate of carbimazole-to form disulfide, which led to the decrease in the Raman intensity of the peak at 595 cm-1. The logarithm of the concentration of Cr6+ has a linear relationship with the Raman intensity at the characteristic peak and showed a limit of detection of 0.945 mg/kg for the tea sample. The carbimazole functionalized Au@AgNPs showed high selectivity in analyzing Cr6+ in tea samples, even in the presence of other metal ions. The SERS detection technique established in this study also showed comparable results with the standard ICP-MS method, indicating the applicability of the established technique in practical applications.

Mesoporous silica coated core-shell nanoparticles substrate for size-selective SERS detection of chloramphenicol.

With the growing popularity of the non-destructive technique, surface-enhanced Raman spectroscopy (SERS) demands a highly sensitive and reproducible plasmonic nanoparticles substrate. In this study, a novel bimetallic core-shell nanoparticles (Au@Ag@mSiO2NP) substrate consisting of a gold core, silver shell, and a mesoporous silica coating was synthesized. The mesoporous coating structure was created by employing template molecules such as surfactant and their subsequent removal allowing selective screening based on the size of analyte molecules. Results showed that the plasmonic substrate could selectively enhance small molecules by preventing large macromolecules to reach the exciting zone of the substrate core, achieving the detection of chloramphenicol in milk samples with a detection limit of 6.68 × 10-8 M. Moreover, the mesoporous coating provided additional stability to the Au@Ag nanoparticles, leading to the reusability of the substrate. Thus, this work offered a simple and smart Au@Ag@mSiO2NP substrate for effective SERS detection of analytes.

Surface-enhanced Raman spectroscopy combined with stable isotope probing to assess the metabolic activity of Escherichia coli cells in chicken carcass wash water.

Rapid evaluation of the metabolic activity of microorganisms is crucial in the assessment of the disinfection ability of various antimicrobial agents in the food industry. In this study, surface-enhanced Raman spectroscopy combined with isotope probing was employed for the analysis of the disinfection of single bacterial cells in the chicken carcass wash water. The Raman signals from single Escherichia coli O157:H7 cells were enhanced by in situ synthesis of silver nanoparticles. The ΔCD of the cells grown in presence of 0.5% hydrogen peroxide and 50 ppm chlorine was 5.86 ± 1.86% and 5.1 ± 2.3%, respectively, which showed significant reduction compared with cells grown in the absence of disinfecting agents (19.86 ± 2.51%) after 2 h of incubation. The study proved that the proposed method had the potential to assess the metabolic activity of microorganisms in other food products and optimize the disinfection process.

Analyzing macromolecular composition of E. Coli O157:H7 using Raman-stable isotope probing.

Metabolic dynamics of bacterial cells is needed for understanding the correlation between changes in environmental conditions and cell metabolic activity. In this study, Raman spectroscopy combined with deuterium labelling was used to analyze the metabolic activity of a single Escherichia coli O157:H7 cell. The incorporation of deuterium from heavy water into cellular biomolecules resulted in the formation of carbon-deuterium (CD) peaks in the Raman spectra, indicating the cell metabolic activity. The broad vibrational peaks corresponding to CD and CH peaks encompassed different specific shifts of macromolecules such as protein, lipids, and nucleic acid. The utilization of tryptophan and oleic acid by the cell as the sole carbon source led to changes in cell lipid composition, as indicated by new peaks in the second derivative spectra. Thus, the proposed method could semi-quantitatively determine total metabolic activity, macromolecule specific identification, and lipid and protein metabolism in a single cell.

Detection of Bioactive Metabolites in Escherichia Coli Cultures Using Surface-Enhanced Raman Spectroscopy.

Detection of bioactive metabolites produced by bacteria is important for identifying biomarkers for infectious diseases. In this study, a surface-enhanced Raman spectroscopy (SERS)-based technique was developed for the detection of bioactive metabolite indole produced by Escherichia coli (E. coli) in biological media. The use of highly sensitive Au@Ag core-shell nanoparticles resulted in the detection of indole concentration as low as 0.0886 mM in standard solution. The supplementation of growth media with 5 mM of exogenous tryptophan resulted in the production of a maximum yield of indole of 3.139 mM by E. coli O157:H7 at 37 °C. The growth of bacterial cells was reduced from 47.73 × 108 to 1.033 × 106 CFU/mL when the cells were grown in 0 and 10 mM exogenous tryptophan, respectively. The amount of indole in the Luria-Bertani (LB) media had an inverse correlation with the growth of cells, which resulted in a three-log reduction in the colony-forming unit when the indole concentration in the media was 20 times higher than normal. This work demonstrates that SERS is an effective and highly sensitive method for rapid detection of bioactive metabolites in biological matrix.

Recent developments in Raman spectral analysis of microbial single cells: Techniques and applications.

The conventional microbial cell analyses are mostly population-averaged methods that conceal the characteristics of single-cell in the community. Single-cell analysis can provide information on the functional and structural variation of each cell, resulting in the elimination of long and tedious microbial cultivation techniques. Raman spectroscopy is a label-free, noninvasive, and in-vivo method ideal for single-cell measurement to obtain spatially resolved chemical information. In the current review, recent developments in Raman spectroscopic techniques for microbial characterization at the single-cell level are presented, focusing on Raman imaging of single cells to study the intracellular distribution of different components. The review also discusses the limitation and challenges of each technique and put forward some future outlook for improving Raman spectroscopy-based techniques for single-cell analysis. Raman spectroscopic methods at the single-cell level have potential in precision measurements, metabolic analysis, antibiotic susceptibility testing, resuscitation capability, and correlating phenotypic information to genomics for cells, the integration of Raman spectroscopy with other techniques such as microfluidics, stable isotope probing (SIP), and atomic force microscope can further improve the resolution and provide extensive information. Future focuses should be given to advance algorithms for data analysis, standardized reference libraries, and automated cell isolation techniques in future.