Analytical Methods最新文献

Milk is an indispensable component of the human diet and is therefore an important target matrix for rapid food safety screening. Melamine adulteration of dairy products poses a potential health risk, highlighting the need for simple and reliable analytical methods for rapid screening. Herein, a thymine-derivative-functionalized silver nanoparticle-based colorimetric sensing platform was developed for the rapid detection of melamine in milk. The thymine moieties provide complementary hydrogen-bonding recognition sites for melamine, while the silver nanoparticles serve as plasmonic signal transducers. Melamine-induced interparticle association produces concentration-dependent nanoparticle aggregation and changes in localized surface plasmon resonance, resulting in a readily visible color transition from yellow to purplish red. The sensing response was interrogated through two complementary readout formats: image-based colorimetric screening using visual observation/RGB analysis and ratiometric UV-vis spectroscopy for quantitative determination. Under optimized conditions, the method exhibited excellent linearity over the concentration range of 0.16-0.28 µM (R² = 0.996) and achieved a calculated detection limit of 3.52 nM. A visible response was obtained within 90 s, and common milk-associated interferents at more than 1000-fold molar excess relative to melamine caused negligible changes in the analytical response. For independent validation using three representative commercial milk products, satisfactory recoveries of 98.6-102.9% were obtained, with relative standard deviations of 1.1-3.2%. The proposed platform integrates molecular recognition with a simple optical readout, providing a rapid approach to the screening and quantitative determination of melamine in dairy matrices.

Patulin is a mycotoxin commonly found in apple-derived products, posing significant risks to consumer health. In response to increasing regulatory demands and analytical challenges, this study presents a novel sample preparation strategy for the determination of patulin and its metabolites in apple juice using dispersive magnetic solid-phase microextraction with a magnetic molecularly imprinted polymer, combined with liquid chromatography-high resolution mass spectrometry. The patulin-selective commercial molecularly imprinted polymer was magnetized, yielding a sorbent with high specificity and straightforward handling. The material was characterized by energy dispersive spectroscopy and field emission scanning electron microscopy, confirming successful magnetization and appropriate morphology. The method achieved excellent linearity (1-100 ng mL^-1; R² = 0.989), with detection and quantification limits of 0.32 ng mL^-1 and 1.09 ng mL^-1, respectively, both significantly below the maximum levels established by European legislation. Validation confirmed satisfactory trueness (96-106% recovery) and precision (RSD ≤ 7.4%). Application to ten commercial juices revealed the presence of PAT at 4.8 ± 0.4 ng mL^-1 in one sample. Untargeted high resolution mass spectrometry analysis further enabled the tentative identification of hydroascladiol and ascladiol as patulin metabolites in three juices. The proposed magnetic molecularly imprinted polymer-based workflow represents a significant advancement for both regulatory monitoring and broader mycotoxin derivative screening.

Metal-organic frameworks have emerged as a highly versatile class of crystalline porous materials demonstrating exceptional potential as luminescent sensors for monitoring environmental and food safety contaminants. This review explores various synthetic strategies of MOF fabrication, from conventional solvothermal and hydrothermal routes to advanced green alternatives, such as microwave-assisted, sonochemical, mechanochemical, ionothermal and microfluidic synthesis. Despite their outstanding analytical sensitivity, transitioning these platforms to field-deployable commercial devices faces major technical limitations. Primary challenges include hydrolytic degradation under humid conditions or extreme pH environments, and framework collapse due to thermal or mechanical stress. To overcome these challenges, we discuss optimization strategies designed to enhance the chemical, thermal, and mechanical stability of MOFs. Furthermore, recent progress in engineering MOFs as fluorescent probes for the trace-level detection of environmental pollutants (per- and polyfluoroalkyl substances (PFAS), toxic heavy metals and agricultural pesticides) and food contaminants (mycotoxins and prohibited additives) is summarized. Ultimately, these advancements are expected to bridge the gap between academic research and practical deployment in real-world monitoring applications.

This study demonstrates the application of low-cost laser-induced graphene (LIG) electrodes for the determination of azithromycin using a batch injection analysis (BIA) system. The LIG/BIA platform provided a sensitive and rapid electroanalytical response, with excellent linearity (R² = 0.999), low detection limit (LOD = 0.024 µmol L^-1) and linear range of 1.0 to 80.0 µmol L^-1, together with satisfactory precision and accuracy across different matrices. The method was successfully applied to pharmaceutical formulations, synthetic urine, and environmental water samples, providing satisfactory recovery results under the investigated sample conditions. In addition to its analytical performance, the proposed approach offers key practical advantages, including simple electrode fabrication, low sample consumption, minimal sample manipulation, and rapid successive measurements. These results highlight the potential of LIG-based sensors as a versatile and accessible alternative for routine monitoring of azithromycin in complex samples.

Pancreatic cancer (PC) remains one of the most lethal malignancies due to the lack of reliable tools for early-stage diagnosis. Therefore, developing highly sensitive and robust detection strategies that accurately quantify CA19-9 in complex biological samples is crucial for improving early diagnostic outcomes. A Cu/SiO₂ nanozyme, which exhibits intrinsic peroxidase-mimetic activity, has been utilized for the first time in an immunodetection assay for the sensitive and selective analysis of PC-biomarker CA19-9. Cu/SiO₂ nanozyme oxidizes 3,3',5,5'-tetramethylbenzidine (TMB) with hydrogen peroxide (H₂O₂), producing blue-colored oxTMB with absorbance at 650 nm. An indirect ELISA was performed using a mouse anti-CA19-9 monoclonal antibody (MAC IgG) and a goat anti-mouse IgG secondary antibody (GAM IgG) conjugated to a Cu/SiO₂ nanozyme. Cu/SiO₂ nanozyme kinetics were performed using Michaelis-Menten and Lineweaver-Burk plots. The obtained parameters were 7.43 × 10^-3 M, 8.65 × 10^-7 M s^-1, and 3.67 × 10^-4 s^-1 for K_M, V_max, and K_cat, respectively. The developed colorimetric immunoassay demonstrated high sensitivity with a limit of detection (LOD) of 0.000771 U mL^-1. The applicability of the developed method was evaluated using human serum samples, achieving 100.18-103.15% recovery. Software-assisted analysis was utilized for rapid detection, achieving an outstanding LOD of 0.00139 U mL^-1, respectively. Overall, this developed colorimetric immunoassay demonstrates its applicability for sensitive and clinically relevant detection of the PC biomarker CA19-9.

Direct sample extraction methods suitable for lipids such as desorption electrospray ionization (DESI), rapid evaporative ionization mass spectrometry (REIMS), and liquid extraction surface analysis (LESA) are combined with ionization or direct sample delivery to a mass spectrometer. Here, we describe three types of off-line direct sample extraction methods and their combination with multiple reaction monitoring (MRM) profiling to survey lipids present on the surface of beef and chicken meat as proof-of-concept samples. A collection of 679 MRMs selected from over 3200 MRMs related to lipids was profiled using a conventional capillary binary pump, autosampler and a triple quadrupole mass spectrometer system. The surface extraction experiments were performed manually by simply dispensing solvent onto the surface of the samples and recovering it immediately (liquid surface extraction - LSE), or by using solvent loaded into a large orifice tip to contact the sample for 5 seconds without dispensing (solvent "touch" extraction - STE). The third approach, surface vaporization extraction (SVE), was based on extracting vapors from the surface of the sample created using the tip of a wood-burning tool, collecting the vapors in a microtube, and diluting them for flow injection. LSE was used to evaluate the effect of different solvent compositions on the number of MRMs detected. Solvent acidification was also evaluated. Compared to the Bligh & Dyer extraction method using homogenized beef samples, LSE provided a consistently lower number of MRMs detected and the most informative solvent for surface extraction was pure acetonitrile. Since meat samples' surfaces are not completely flat and also present natural irregularities, the solvent volume recovered was variable. Therefore, we turned to STE to evaluate whether the lipid composition of beef brown spots was different from the red regions and to probe changes in composition due to one freeze-thaw cycle. Also, the reproducibility of STE between three users was evaluated. The third surface extraction approach, SVE, was applied to study the lipid profiles of vapors collected from raw and cooked meat. Overall, the three surface methods were able to provide informative discrimination for the different aspects assayed.

Although water quality monitoring using optical sensors has been extensively studied, it remains a frontline research area owing to its simplicity and adaptability. In this study, we continue to develop a kinetic-based sensing strategy based on multiple optical measurements during an indicator reaction. The effect on the reaction rate makes it possible to observe the effect of sample components that do not otherwise manifest themselves in photometry and fluorimetry. The oxidation of calcein (fluorescein-methyleneiminodiacetic acid) with hypochlorite or hydrogen peroxide and riboflavin with H₂O₂ in the presence of Cu(II) or Fe(II) were used as indicator reactions. Fluorescence intensity and absorbance/reflectance of reaction mixtures were measured by periodic photography of 96-well plates. Optical data were processed using supervised chemometric methods: linear discriminant analysis (LDA), logistic regression (LR) and others. To demonstrate the sensitivity of the indicator systems to compounds of various natures, six transition metal ions (Cu(II), Ni(II), Mn(II), Co(II), Fe(II), and Cr(III)) and five odor-causing organic compounds (trimethylamine, phenol, 2-chlorophenol, o-cresol, and mercaptoethanol), selected as model pollutants, were discriminated based on their nature and concentration with an accuracy of up to 95-100%. Eleven water samples were differentiated by sample and by water type (spring, well, borehole, pond, and tap) using the same indicator reactions. Municipal sewage water diluted up to 100 000 times was discriminated from river water, enabling the identification of unexpected cases of water source contamination. The obtained results surpass the benchmarks of previous studies and should serve as an incentive for further research aimed at the application of kinetic optical fingerprinting in water quality monitoring.

The concentration of iron ions is a crucial indicator for assessing water quality. In this study, nitrogen-doped carbon dots (NCDs) were synthesized using a microwave-assisted method with citric acid and urea as precursors, thereby establishing a fluorescence sensing platform for the detection of alkaline pH and Fe³⁺. During Fe³⁺ detection, the fluorescence intensity of NCDs was specifically quenched as the concentration of Fe³⁺ increased, demonstrating good linearity across the ranges of 1-10 µM and 10-100 µM, with a detection limit of 0.55 µM. By integrating smartphone-based image analysis, visual semi-quantitative detection of alkaline pH and Fe³⁺ was achieved. To enhance prediction accuracy across a broad concentration range, a machine learning model was introduced to develop a high-precision quantitative analysis method for Fe³⁺. The spiked recovery rates in actual water samples ranged from 99.26% to 101.14%, with relative standard deviations below 3%. This platform combines fluorescence sensing, smartphone imaging, and machine learning technologies, offering the advantages of simple operation and low cost, thus providing a novel strategy for the on-site rapid detection of Fe³⁺ in water environments.

This study presents the design of a multicommuted flow analysis system that utilizes an air stream to separate ethanol from wine samples. The separated ethanol is then oxidized with a dichromate solution, followed by spectrophotometric determination. The design adheres to the principles of green analytical chemistry and focuses on integrating the benefits of multicommuted flow analysis with an emphasis on versatility, robustness, minimal reagent usage, and waste reduction. The experimental setup, controlled by an Arduino Due board with electronic interfaces, was operated using custom software designed for this study. Ethanol molecules were extracted from the sample using an air stream, trapped in a reaction chamber, and subjected to derivatization with potassium dichromate (K₂Cr₂O₇), followed by photometric detection with an LED-based photometer. After optimizing the control parameters, the method's efficacy was confirmed by analyzing ethanol levels in red wine samples. The system demonstrated several advantages, including a linear response range of 2.5-25.0% (r² = 0.999), a detection limit of 0.59% (v/v) ethanol, a coefficient of variation of 1.78% (n = 10) for a 10.0% (v/v) ethanol standard solution, a sampling rate of 36 determinations per hour, a waste generation of 1.0 mL per determination, and a reagent consumption of 17 mg of K₂Cr₂O₇ per determination. Based on the AGREE - Analytical GREEnness Metric Approach, the analytical greenness of the proposed method is estimated to be 66%.

Serotonin (5-hydroxytryptamine, 5-HT) is a critical biomarker for carcinoid tumor diagnosis, yet its precise quantification in clinical settings remains challenging. Herein, we develop a high-performance optical fiber localized surface plasmon resonance (LSPR) biosensor functionalized with in situ grown gold nanoflowers (AuNFs). The hierarchical branched architecture of AuNFs concentrates electromagnetic fields into dense nanoscale "hotspots", delivering a ∼2.2-fold signal amplification compared to spherical AuNPs. Integrated with a specific 5-HT aptamer, the sensor achieves a wide linear range (10-1000 ng mL^-1) and a low limit of detection (7.06 ng mL^-1), seamlessly covering physiological to pathological concentrations. Validation in human serum confirms excellent accuracy with recoveries of 99.9-102.9%. This label-free, rapid, and cost-effective platform offers a highly promising tool for point-of-care carcinoid screening and neuroendocrine biomarker monitoring.