Determination of Ciprofloxacin in Fish by Surface-Enhanced Raman Scattering Using a Liquid-Liquid Self-Assembled Gold Nanofilm

Surface-enhanced Raman scattering detection of thiram and ciprofloxacin using chitosan-silver coated paper substrates

Fast detection of contaminants of emerging concern (CECs) in water resources is of great environmental interest. Ideally, sustainable materials should be used in water quality monitoring technologies implemented for such purposes. In this regard, the application of bio-based materials aimed at the fabrication of analytical platforms has become of great importance. This research merges both endeavors by exploring the application of chitosan-coated paper, decorated with silver nanoparticles (AgNPs), on surface-enhanced Raman scattering (SERS) spectroscopy studies of two distinct types of CECs dissolved in aqueous samples: an antibiotic (ciprofloxacin) and a pesticide (thiram). Our results indicate the superior SERS performance of biocoated substrates compared to their non-coated paper counterparts. The detection limits achieved for thiram and ciprofloxacin using the biocoated substrates were 0.024 ppm and 7.7 ppm, respectively. The efficient detection of both analytes is interpreted in terms of the role of the biopolymer in promoting AgNPs assemblies that result in local regions of enhanced SERS activity. Taking advantage of these observations, we use confocal Raman microscopy to obtain Raman images of the substrates using ciprofloxacin and thiram as molecular probes. We also demonstrate that these biobased substrates can be promising for on-site analysis when used in conjunction with portable Raman instruments.

In-situ SERS detection of quinolone antibiotic residues in aquaculture water by multifunctional Fe3O4@mTiO2@Ag nanoparticles

Antibiotic residues in aquaculture environments disrupt the ecosystem balance and pose a potential hazard to human health when entering the food chain. Therefore, ultra-sensitive detection of antibiotics is necessary. In this study, a multifunctional Fe3O4@mTiO2@Ag core-shell nanoparticle (NP), synthesized using a layer-by-layer method, was demonstrated to be useful as an enhanced substrate for in-situ surface-enhanced Raman spectroscopy (SERS) detection of various quinolone antibiotics in aqueous environments. The results showed that the minimum detectable concentrations of the six investigated antibiotics were 1 × 10-9 mol/L (ciprofloxacin, danofloxacin, enoxacin, enrofloxacin, and norfloxacin) and 1 × 10-8 mol/L (difloxacin hydrochloride) under the enrichment and enhancement of Fe3O4@mTiO2@Ag NPs. Additionally, there was a good quantitative relationship between the antibiotics concentrations and SERS peak intensities within a certain detection range. The results of the spiked assay of actual aquaculture water samples showed that the recoveries of the six antibiotics ranged from 82.9% to 113.5%, with relative standard deviations ranging from 1.71% to 7.24%. In addition, Fe3O4@mTiO2@Ag NPs achieved satisfactory results in assisting the photocatalytic degradation of antibiotics in aqueous environments. This provides a multifunctional solution for low concentration detection and efficient degradation of antibiotics in aquaculture water.

Magnetic Fe3O4@COF@Ag SERS substrate combined with machine learning algorithms for detection of three quinolone antibiotics: Ciprofloxacin, norfloxacin and levofloxacin

Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levofloxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 × 10^-9M, LEV: 1.44 × 10^-8M, NFX: 1.56 × 10^-8M. This provides a new method for the detection of antibiotics in dairy products.

Solar Light-Assisted Oxidative Degradation of Ciprofloxacin in Aqueous Solution by Iron(III) Chelated Cross-Linked Chitosan Immobilized on a Glass Plate

The massive worldwide use of antibiotics leads to water pollution and increasing microbial resistance. Hence, the removal of antibiotic residues is a key issue in water remediation. Here, we report the solar light-assisted oxidative degradation of ciprofloxacin (CPF), using H2O2 in aqueous solution, catalyzed by iron(III) chelated cross-linked chitosan (FeIII-CS-GLA) immobilized on a glass plate. The FeIII-CS-GLA catalyst was characterized by FTIR and 57Fe-Mössbauer spectroscopies as well as X-ray diffraction, revealing key structural motifs and a high-spin ferric character of the metal. Catalytic degradation of CPF was investigated as a function of solar light irradiation time, solution pH, concentration of H2O2 and CPF, as well as cross-linker dosage and iron(III) content in FeIII-CS-GLA. The system was found to serve as an efficient catalyst with maximum CPF degradation at pH 3. The specific ·OH scavenger mannitol significantly reduces the degradation rate, indicating that hydroxyl radicals play a key role. The mechanism of catalytic CPF degradation was evaluated in terms of pseudo-first-order and Langmuir-Hinshelwood kinetic models; adsorption of CPF onto the FeIII-CS-GLA surface was evidenced by field emission scanning electron microscopy coupled with energy dispersive X-ray spectroscopy. FeIII-CS-GLA can be reused multiple times with only minor loss of catalytic efficiency. Antimicrobial activity tests performed against both Gram-negative (Escherichia coli DH5α, Salmonella typhi AF4500) and Gram-positive bacteria (Bacillus subtilis RBW) before and after treatment confirmed complete degradation of CPF. These results establish the immobilized FeIII-CS-GLA as a rugged catalyst system for efficient photo-Fenton type degradation of antibiotics in aqueous solutions.

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.

Simultaneous and Accurate Quantification of Multiple Antibiotics in Aquatic Samples by Surface-Enhanced Raman Scattering Using a Ti3C2Tx/DNA/Ag Membrane Substrate

Rapid and accurate analysis of multiple targets in complex samples is still a big challenge in the fast detection field. Herein, we developed a rapid and accurate strategy for simultaneous quantification of trace multiple antibiotic residues in complex aquatic samples by surface-enhanced Raman scattering (SERS) using a Ti₃C₂T_x/DNA/Ag membrane substrate. This membrane substrate was proven to have good uniformity, reproducibility, stability, and SERS activity by a series of characterizations. Also, this substrate combined excellent electromagnetic enhancement and chemical enhancement effects, which endowed it with good sensitivity and selectivity during SERS analysis. It achieved the integration of multitarget separation, enrichment, and in situ detection, which significantly improved the selectivity, sensitivity, accuracy, and detection throughput by membrane substrate coupling with SERS for real-sample analysis. Finally, this rapid SERS analysis strategy was successfully applied to the simultaneous quantification of trace nitrofurantoin (NFT) and ofloxacin (OFX) in aquatic samples. It was observed that trace NFT and OFX were actually detected and simultaneously quantified to be 8.0-13.7 and 42.6-49.1 μg/kg in aquatic samples, respectively, with good recoveries of 88.0-107% and relative standard deviations of 0.3-5.5%. The results were verified by a traditional high-performance liquid chromatography method with relative errors of -9.8 to 5.3%. This strategy provided a methodological reference for accurate SERS quantification of multiple targets in complex samples.

Technological Advancements for the Detection of Antibiotics in Food Products

Antibiotics, nowadays, are not only used for the treatment of human diseases but also used in animal and poultry farming to increase production. Overuse of antibiotics leads to their circulation in the food chain due to unmanaged discharge. These circulating antibiotics and their residues are a major cause of antimicrobial resistance (AMR), so comprehensive and multifaceted measures aligning with the One Health approach are crucial to curb the emergence and dissemination of antibiotic resistance through the food chain. Different chromatographic techniques and capillary electrophoresis (CE) are being widely used for the separation and detection of antibiotics and their residues from food samples. However, the matrix present in food samples interferes with the proper detection of the antibiotics, which are present in trace concentrations. This review is focused on the scientific literature published in the last decade devoted to the detection of antibiotics in food products. Various extraction methods are employed for the enrichment of antibiotics from a wide variety of food samples; however, solid-phase extraction (SPE) techniques are often used for the extraction of antibiotics from food products and biological samples. In addition, this review has scrutinized how changing instrumental composition, organization, and working parameters in the chromatography and CE can greatly impact the identification and quantification of antibiotic residues. This review also summarized recent advancements in other detection methods such as immunological assays, surface-enhanced Raman spectroscopy (SERS)-based assays, and biosensors which have emerged as rapid, sensitive, and selective tools for accurate detection and quantification of traces of antibiotics.

Controllable Self-Assembly of SERS Hotspots in Liquid Environment

Controllable synthesis of novel metal nanoparticles and effective capture of hotspots are of great significance for SERS (surface-enhanced Raman spectroscopy) detection. Therefore, in this paper, a green controllable synthesis method of gold nanoparticle was achieved via epigallocatechin gallate reduction. Different morphologies of gold nanoparticles were synthesized just by changing the solution pH values, and the growth kinetics of AuNPs (gold nanoparticles) were systematically studied. The synthetic AuNPs were put in a droplet to study dynamic variations of self-assembly SERS hotspots from the liquid sol state to the solid dry state. The addition of halogen ions in the droplet can controllably regulate the self-assembly three-dimensional hotspot model of gold nanoparticles in the evaporation process of a droplet, during which the most enhancement effect can be easily captured. The dynamically changing images of nanoparticles in the process were graphically described based on the internal interaction forces of a droplet. Two stronger areas in the changes of SERS intensity were achieved with a high concentration of halogen ions, while only one maximum intensity area was obtained with a low concentration of halogen ions added. This method can effectively avoid complex and unpredictable microenvironments of SERS substrates in the liquid drop, further improving the reproducibility of SERS detection as well as broadening it to biological applications.