Rapid detection of sulfonamide antibiotics residues in swine urine by surface-enhanced Raman spectroscopy

Sulfadiazine detection in aquatic products using upconversion nanosensor based on photo-induced electron transfer with imidazole ligands and copper ions

Contamination of aquatic products with sulfonamide antibiotics poses a threat to consumer health and can lead to the emergence of drug-resistant bacteria. Common methods to detect such compounds are slow and require expensive instruments. We developed a sensitive sulfadiazine (SDZ) detection method based on the photoinduced electron transfer between UCNPs and Cu2+. The surface-modified upconversion nanoparticles bind to Cu2+ by electrostatic adsorption, causing fluorescence quenching. The quenched fluorescence was subsequently recovered by the addition of imidazole and SDZ to the detection system, which formed a complex with Cu2+. The sensor showed excellent linearity over a wide concentration range (0.05-1000 ng/mL), had a low limit of detection (0.04 ng/mL), was selective, and was not affected by common substances present in aquatic media. This indicates that the sensor has great potential for application in the detection of SDZ residues in aquatic products.

Investigation of biodistribution by liquid-phase microextraction: Using a fatal diphenidol poisoning case

Liquid-phase microextraction (LPME) possesses a high potential to isolate organic substances from different sample matrices. In this work, LPME was applied for the first time to investigate the biodistribution of diphenidol in different biofluids, organs, and brain regions using a fatal poisoning case. Since the LPME of diphenidol hasn't been reported, the effect of supported liquid membrane (SLM), acceptor and donor phases, and extraction time on LPME performance was investigated first. The solvents of 2-nonanone and 2-nitrophenyl octyl ether (NPOE) were found to be stable and efficient SLMs for LPME of diphenidol from biofluids and tissue samples, respectively. At steady state, the LPME recoveries for different sample matrices were in the range of 87 %-91 %. Due to the clean-up capability of LPME and the relatively high concentration of diphenidol in the fatal poisoning case, the proposed LPME systems were validated with related sample matrices using HPLC-UV for the determination. The methods displayed good linearity (R² ≥ 0.9943), and the limits of detection were 0.30 mg L-1, 0.28 mg L-1, and 2.7 μg g-1 for blood, urine, and liver samples, respectively. Meanwhile, the precision (≤13%), accuracy (90-110%), and matrices effect (±15%) were satisfactory at low, medium, and high concentrations. In addition, the stability, carryover, and dilution integrity met the requirements of ASB Standard 036. Finally, the proposed method was successfully applied to evaluate the biodistribution of diphenidol in five different biofluids, five organs, and six brain regions from a fatal poisoning case. Generally, the distribution of diphenidol in biofluids was lower than that in the organs and brain regions, and the highest concentration of diphenidol was observed in the liver, which is very important for the selection of inspection samples in forensic toxicological analysis. Therefore, LPME was proved to be a powerful tool for the investigation of biodistribution and postmortem redistribution in the fields of forensics.

SERS as a tool for determination of structurally related compounds: The case of sulfanilamide class antibiotics

Analysis of real objects based on surface-enhanced Raman spectroscopy (SERS) often utilizes new SERS substrates and/or complex analysis procedures, and they are optimized for only the determination of a single analyte. Moreover, analysis simplicity and selectivity are often sacrificed for maximum (sometimes unnecessary) sensitivity. Consequently, this trend limits the versatility of SERS analysis and complicates its practical implementation. Thus, we have developed a universal, but simple SERS assay suitable for the determination of structurally related antibiotics (five representatives of the sulfanilamide class) in complex objects (human urine and saliva). The assay involves only mixing of acidified analyzed solution with co-activating agent (polydiallyldimethylammonium chloride - PDDA) and SERS substrate (standard colloidal silver nanoparticles). Acidification promotes the generation of SERS spectra with maximum similarity and intensity, which is explained by the favorable enhancement of the protonated sulfanilamide moiety (a structurally similar part of the studied antibiotics) as a result of its strong electrostatic interaction with the SERS-active surface. Meanwhile, the addition of PDDA improves analysis selectivity by reducing background signal from body fluids, enabling to simplify sample pretreatment (dilution for urine; mucin removal and dilution for saliva). Therefore, the assay allows for rapid (≤10 min), precise, and accurate class-specific determination of sulfanilamides within concentration ranges suitable for non-invasive therapeutic drug monitoring in urine (40-600 μM) and saliva (10-30 μM). We also believe that thorough investigation of structurally related analytes and accompanying effects (e.g., high spectral similarity) is a promising direction to improve the understanding of SERS in general and expand its capabilities as an analytical tool.

Rapid Sample Pretreatment Facilitating SERS Detection of Trace Weak Organic Acids/Bases in Simple Matrices

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for highly sensitive qualitative and quantitative analyses of trace targets. However, sensitive SERS detection can only be facilitated with a suitable sample pretreatment in fields related to trace amounts for food safety and clinical diagnosis. Currently, the sample pretreatment for SERS detection is normally borrowed and improved from the ones in the lab, which yields a high recovery but is tedious and time-consuming. Rapid detection of trace targets in a complex environment is still a considerable issue for SERS detection. Herein, we proposed a liquid-liquid extraction method coupled with a back-extraction method for sample pretreatment based on the pH-sensitive reversible phase transition of the weak organic acids and bases, where the lowest detectable concentrations were identical before and after the pretreatment process. The sensitive (μg L-1 level) and rapid (within 5 min) SERS detection of either koumine, a weak base, or celastrol, a weak acid, was demonstrated in different drinking water samples and beverages. Furthermore, target generality was demonstrated for a variety of weak acids and bases (2 < pKa < 12), and the hydrophilicity/hydrophobicity of the target determines the pretreatment efficiency. Therefore, the LLE-BE coupled SERS was developed as an easy, rapid, and low-cost tool for the trace detection of the two types of targets in simple matrices, which paved the way toward trace targets in complex matrices.

Smart optical sensing of multiple antibiotic residues from wastewater effluents with ensured specificity using SERS assisted with multivariate analysis

Surface-enhanced Raman spectroscopy offers great potential for rapid and highly sensitive detection of pharmaceuticals from environmental sources. Herein, we investigated the feasibility of label-free sensing of antibiotic residues from wastewater effluents with high specificity by combining with multivariate analysis. Highly ordered silver nanoarrays with ∼34 nm roughness have been fabricated using a cost-effective electroless deposition technique. As-fabricated Ag arrays showed superior LSPR effects with an enhancement factor of 8 × 107. Excellent reproducibility has also been noticed with RSD values within 11%, whilst the sensor showed good stability and reusability characteristics for being used as a low-cost and reusable sensor. SERS studies demonstrated that antibiotics-spiked wastewater effluents can be detected with high efficiency in a label-free method. The molecular fingerprint bands of antibiotics such as sulfamethoxazole, sulfadiazine, and ciprofloxacin were well analyzed in effluent, tap, and deionized water. It has been found that antibiotics can be detected near picomolar levels; meanwhile, liquid chromatography-mass spectrometry (LC-MS) exhibited a detection limit within nanomolar concentrations only. Furthermore, the specificity of SERS sensing has been further analyzed using a multivariate analysis method, principal component analysis followed by linear discriminant analysis (PCA-LDA); which showed prominent discrimination to distinguish each antibiotic residue from wastewater effluents. The current study presented the potential of Ag nanoarray sensors for rapid, highly specific, and cost-effective analysis of pharmaceutical products for environmental remediation applications.

Molecularly imprinted polymers by reflux precipitation polymerization for selective solid-phase extraction of quinolone antibiotics from urine

Molecularly imprinted polymers (MIPs) possess high specific cavities towards the template molecules, thus solid-phase extraction (SPE) based on MIPs using the target as the template has been widely used for selective extraction. However, the performance of SPE depends strongly on the shape and the distribution of the MIP sorbents, and rapid synthesis of MIPs with uniform particles remains a challenge. Our previous studies have shown that reflux precipitation polymerization (RPP) was a simple and rapid method for the synthesis of uniform MIPs. However, synthesis of MIPs by RPP for a group of targets using only one of the targets as the template has rarely been reported. In this work, MIPs with specific recognition capability for a group of quinolone antibiotics were synthesized for the first time via RPP with only ofloxacin as the template. The synthesized MIPs displayed good adsorption performance and selectivity (IF > 3.5) towards five quinolones, and subsequently were used as SPE adsorbents. Based on this MIPs-SPE, after systematic optimization of the SPE operation parameters during loading, washing and elution, an efficient and sensitive enough SPE method for separation and enrichment of the five quinolones in urine was developed and evaluated in combination with LC-MS/MS. The results showed that MIPs-SPE-LC-MS/MS has a good correlation (R2 ≥ 0.9961) in the linear range of 1-500 μg L-1. The limit of detection (LOD) and limit of quantification (LOQ) for the five quinolones were 0.10-0.14 μg L-1 and 0.32-0.48 μg L-1, respectively. In addition, the proposed method demonstrated good reproducibility (≤ 13 %) and high accuracy (92 %-113 %). We are confident that this method holds significant promise for the analysis of quinolones within the contexts of forensic medicine, epidemiology, and environmental chemistry.

SERS combined with QuEChERS using NBC and Fe3O4 MNPs as cleanup agents to rapidly and reliably detect chlorpyrifos pesticide in citrus

The surface-enhanced Raman spectroscopy (SERS) technique is being increasingly used for the detection of pesticide residues in agricultural products. However, there are large amounts of fluorescence-producing substances in agricultural products, which seriously affect the Raman signal of the analyte. In this paper, the QuEChERS method was used to remove interfering fluorescent substances in the analyte, and the purification effects of different doses of nano bamboo charcoal (NBC) and Fe3O4 magnetic nanoparticle (Fe3O4 MNP) adsorbents were studied. Meanwhile, the Raman spectral acquisition conditions (AuNPs, test solution, and NaCl) were optimized based on the orthogonal test method. The results showed that 300 µL AuNPs, 40 µL test solution, and 100 µL 1.5% NaCl gave the best SERS response effect. 12.5 mg NBC combined with 10 mg Fe3O4 MNPs could effectively remove the interfering substances from citrus. The Raman spectra of chlorpyrifos molecules were theoretically modeled using density-functional theory (DFT). By comparing the DFT results with the actual tests, five feature peaks, at 338, 522, 558, 672, and 1600 cm-1, were obtained for the detection of chlorpyrifos pesticide residues in citrus. Based on the Raman feature peak intensity at 672 cm-1, the concentration of chlorpyrifos in citrus showed a good linear relationship (R2 = 0.9979) in the concentration range of 3-20 mg kg-1. The recovery rate was 92.12% to 98.38%, and the relative standard deviation (RSD) was 1.77% to 5.29%. The lowest detection concentration was about 3 mg kg-1, and the detection time of a single sample could be completed within 15 min. This study showed that the combination of SERS and QuEChERS preprocessing methods could achieve rapid detection of chlorpyrifos pesticide residues in citrus.

Binding Mechanism and Surface-Enhanced Raman Scattering of the Antimicrobial Sulfathiazole on Gold Nanoparticles

A combined study using the surface-enhanced Raman scattering (SERS) technique and quantum chemical calculations was carried out to elucidate the adsorption behavior of sulfathiazole, an antibiotic drug, on gold nanoparticles. The tetrahedral Au20 cluster was used as a simple model to mimic a nanostructured gold surface. Computations using density functional theory with the PBE functional were performed in both the gas phase and aqueous medium using a continuum model. The drug is found to bind to the Au metals via the nitrogen of the thiazole ring. The interaction is also partially stabilized by the ring-surface π coupling rather than a sideway adsorption as previously proposed. In an aqueous solution, the drug molecule mainly exists as a deprotonated form, which gives rise to a much greater affinity toward Au nanoparticles as compared to the neutral forms. The drug adsorption further induces a significant alteration on the energy gap of the gold cluster Aun, which could result in an electrical noise. Notable SERS signals below 1600 cm-1, which result from a coupling of several vibrations including the ring breathing, C-C stretching, and N-H bending, could be employed for both qualitative and quantitative detection and assessment of sulfathiazole at trace concentrations.

Raman spectral pattern recognition of breast cancer: A machine learning strategy based on feature fusion and adaptive hyperparameter optimization

Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.

Exploring Surface-Enhanced Raman Spectroscopy (SERS) Characteristic Peaks Screening Methods for the Rapid Determination of Chlorpyrifos Residues in Rice

Surface-enhanced Raman spectroscopy (SERS), coupled with characteristic peak screening methods, was developed for analyzing chlorpyrifos (CM) pesticide residues in rice. Au nanoparticles (AuNPs) were prepared as Raman signal enhancement. Magnesium sulfate (MgSO₄), primary secondary amine (PSA), and C₁₈ were used to purify the rice extraction. A successive projections algorithm (SPA) was performed to identify the optimal characteristic peaks of CM in rice from full Raman spectroscopy. Support vector machine (SVM) and partial least squares (PLS) were implemented to investigate the quantitative analysis models. The results demonstrated that six Raman peaks such as 671, 834, 1016, 1114, 1436, and 1444 cm^-1 were selected by the SPA and SVM models and had better performance using six peaks (only 0.92% of the full spectra variables) with R²_p = 0.97, RMSEP = 2.89 and RPD = 4.26, and the experiment time for a sample was accomplished within 10 min. Recovery for five unknown concentration samples was 97.45-103.96%, and T-test results also displayed no obvious differences between the measured value and the predicted value. The study stated that SERS, combined with characteristic peak screening methods, can be applied to rapidly monitor the chlorpyrifos residue in rice.

A novel portable Raman scattering platform for antibiotic screening in pig urine

Background and Aim

Public health and food safety are gaining attention globally. Consumer health can be protected from chemical residues in meat by early detection or screening for antibiotic residues before selling the meat commercially. However, conventional practices are normally applied after slaughtering, which leads to massive business losses. This study aimed to use portable surface-enhanced Raman spectroscopy (SERS) equipped with multivariate curve resolution-alternation least squares (MCR-ALS) to determine the concentrations of enrofloxacin, oxytetracycline, and neomycin concentrations. This approach can overcome the problems of business loss, costs, and time-consumption, and limit of detection (LOD).

Materials and Methods

Aqueous solutions of three standard antibiotics (enrofloxacin, oxytetracycline, and neomycin) with different concentrations were prepared, and the LOD for each antibiotic solution was determined using SERS. Extracted pig urine was spiked with enrofloxacin at concentrations of 10, 20, 50, 100, and 10,000 ppm. These solutions were investigated using SERS and MCR-ALS analysis. Urine samples from pigs at 1 and 7 days after enrofloxacin administration were collected and investigated using SERS and MCR-ALS to differentiate the urinary enrofloxacin concentrations.

Results

The LOD of enrofloxacin, oxytetracycline, and neomycin in aqueous solutions were 0.5, 2.0, and 100 ppm, respectively. Analysis of enrofloxacin spiking in pig urine samples demonstrated the different concentrations of enrofloxacin at 10, 20, 50, 100, and 10,000 ppm. The LOD of spiking enrofloxacin was 10 ppm, which was 10 times lower than the regulated value. This technique was validated for the first time using urine collected on days 1 and 7 after enrofloxacin administration. The results revealed a higher concentration of enrofloxacin on day 7 than on day 1 due to consecutive administrations. The observed concentration of enrofloxacin was closely correlated with its circulation time and metabolism in pigs.

Conclusion

A combination of SERS sensing platform and MCR-ALS is a promising technique for on-farming screening. This platform can increase the efficiency of antibiotic detection in pig urine at lower costs and time. Expansion and fine adjustments of the Raman dataset may be required for individual farms to achieve higher sensitivity.

A Sensitive and Selective Colorimetric Method Based on the Acetylcholinesterase-like Activity of Zeolitic Imidazolate Framework-8 and Its Applications

In this study, a simple colorimetric method was established to detect copper ion (Cu²⁺), sulfathiazole (ST), and glucose based on the acetylcholinesterase (AChE)-like activity of zeolitic imidazolate framework-8 (ZIF-8). The AChE-like activity of ZIF-8 can hydrolyze acetylthiocholine chloride (ATCh) to thiocholine (TCh), which will further react with 5,5′-dithiobis (2-nitrobenzoic acid) (DTNB) to generate 2-nitro-5-thiobenzoic acid (TNB) that has a maximum absorption peak at 405 nm. The effects of different reaction conditions (buffer pH, the volume of ZIF-8, reaction temperature and time, and ATCh concentration) were investigated. Under the optimized conditions, the value of the Michaelis-Menten constant (K_m) is measured to be 0.83 mM, which shows a high affinity toward the substrate (ATCh). Meanwhile, the ZIF-8 has good storage stability, which can maintain more than 80.0% of its initial activity after 30 days of storage at room temperature, and the relative standard deviation (RSD) of batch-to-batch (n = 3) is 5.1%. The linear dependences are obtained based on the AChE-like activity of ZIF-8 for the detection of Cu²⁺, ST, and glucose in the ranges of 0.021–1.34 and 5.38–689.66 µM, 43.10–517.24 µM, and 0.0054–1.40 mM, respectively. The limit of detections (LODs) are calculated to be 20.00 nM, 9.25 µM, and 5.24 µM, respectively. Moreover, the sample spiked recoveries of Cu²⁺ in lake water, ST in milk, and glucose in strawberry samples were measured, and the results are in the range of 98.4–115.4% with the RSD (n = 3) lower than 3.3%. In addition, the method shows high selectivity in the real sample analysis.

Nanozyme based on ZIF-8 for the colorimetric detection of sulfonamides in cow milk

A simple and time-saving colorimetric method was developed to quantify sulfonamides (SAAs) in milk via inhibition of the human carbonic anhydrase II (hCAII)-like activity of ZIF-8 that can hydrolyze p-nitrophenyl acetate (pNPA) to p-nitrophenol (pNP), following the color change from yellow to colorless. Effects of different reaction conditions, including pH, temperature, amount of ZIF-8, and incubation time, were investigated. The value of Michaelis-Menten constant (K_m) is measured to be 0.15 mM, which exhibits high affinity to pNPA. The IC₅₀ (0.17, 0.24, and 0.60 mM) and inhibition constant (K_i) (0.09, 0.13, and 0.33 mM) of sulfamethazine (SD), sulfadimethoxine (SDM), and sulfathiazole (ST) on ZIF-8 were measured, respectively. Moreover, the activity of ZIF-8 remains more than 90.0% of its initial activity after 30 days' storage. The colorimetric method for SD, SDM, and ST determination was established at the linear ranges of 6.3-750.0 μM (1.75-208.75 mg/kg), 6.3-750.0 μM (1.96-232.75 mg/kg), and 5.0-1250.0 μM (1.28-319.15 mg/kg) with limit of detection of 4.3, 3.2, and 3.9 mΜ (1.2, 0.99, and 0.96 mg/kg), respectively. In addition, the spiked recoveries of SAAs in milk sample are in the range of 81.6%-106.7% with RSD less than 6.5%. In short, the developed colorimetric method can achieve rapid analysis of SAAs in milk with simple operations.