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For the previous rapid screening methods, antibody, molecularly imprinted polymer, aptamer and receptor are the usually used recognition reagents. It is well known that there are many antibiotic receptors on bacterial surface, so in theory the whole bacterium can be used as recognition reagent for screening of the specific antibiotic. However, such a method has not been reported so far. The aim of the present study is to develop a method for rapid screening of beta-lactam drugs in milk with Escherichia coli as the recognition reagent and with aggregation induced emission luminogen as the signal source. In this study, T4 phage was coupled with Fe3O4 to synthesize a type of magnetic complex that could specifically capture E. coli. Cephalexin was coupled with an aggregation induced emission luminogen (tetraphenylethylene) to synthesize a fluorescent tracer that could specifically bind with the penicillin binding proteins on E. coli surface. The magnetic complex, the E. coli solution and the tracer were mixed with beta-lactam drugs to perform competition. The fluorescent signal from the tracer molecules aggregated on bacterial surface was negatively correlated with the drug concentration. This method could be used for multi-screening of 28 beta-lactam drugs. The operation was simple and rapid, and the sensitivities for these drugs (limits of detection of 0.73-95.19 pg/mL) were improved for 16-959 folds in comparison with fluorescein based fluorescent tracer. The detection results for the real milk samples were consistent with a LC-MS/MS method. This is the first study reporting the use of phage/bacterium system and aggregation induced emission luminogen for detection of antibiotic residues in food sample. Under the guidance of this study, more similar or advanced methods based on bacteria and novel signal sources for detection of antibiotics should be reported in the future.
Alterations in the amine submetabolome are closely associated with cellular metabolic status and are important for understanding metabolic regulation under intervention conditions. In this study, a stable isotope labeling derivatization combined with liquid chromatography-mass spectrometry (LC-MS) was employed to characterize alterations in the cellular amine submetabolome. Optimization of chromatographic separation and cellular metabolic quenching procedures improved the separation performance of amine metabolites while enabling more accurate preservation of the metabolic state at the time of sampling. The established method was applied to a D-lactate treated BEAS-2B cell model to evaluate amine submetabolome alterations under different compound intervention conditions. The results demonstrated that selenomethionine (SeMet) and berberine alleviated D-lactate induced abnormalities in the amine submetabolome and showed metabolic regulatory effects consistent with tumor-suppressive trends observed under in vivo experiments. Overall, this analytical strategy enables characterization of the amine submetabolome in complex biological samples and provides a reliable methodological reference for metabolome-based investigations of cellular metabolic alterations.
Multiple myeloma (MM) is a hematological malignancy associated with high morbidity and mortality. The BBD salvage regimen, consisting of bendamustine (BEN), bortezomib (BOR), and dexamethasone (DEX), has demonstrated efficacy in patients with relapsed or refractory MM. However, no analytical method has been reported for the simultaneous determination of this regimen, and potential pharmacokinetic interactions among its components remain insufficiently explored. In this study, two environmentally friendly, sensitive, and rapid chromatographic methods were developed and validated for the simultaneous quantification of BEN, BOR, and DEX in rat plasma, using sildenafil (SIL) as an internal standard. The methods showed linearity over wide concentration ranges, along with satisfactory accuracy and precision, and were successfully applied to in vivo pharmacokinetic studies following individual and combined administration of the three drugs. The results revealed measurable changes in pharmacokinetic parameters, suggesting potential pharmacokinetic interactions among the components of the BBD regimen. The LC-MS/MS method provided higher sensitivity and selectivity, while the TLC-densitometric method offered a simpler and more environmentally friendly alternative for routine analysis. In addition, the environmental sustainability of the proposed methods was evaluated using multiple greenness assessment tools, confirming their eco-friendly profiles.
The use of two grating monochromators in fluorescence detectors coupled to chromatographic systems has been associated with some unexplained optical effects and anomalous results in the literature. The recently introduced zero-order excitation/emission mode, in which the corresponding grating directs the entire lamp spectrum to the flow cell or the emission spectrum to the photomultiplier tube, introduces additional complexities. The consequences of these optical properties in HPLC-FLDs are investigated. For the tested UV emitters, sensitivity surprisingly dropped up to 10-fold under zero-order diffraction, whereas for the visible fluorophores, sensitivity increased up to 3.6-fold in a nonlinear fashion, depending on the analyte's emission spectrum. This effect is not from a "sag" in photomultiplier gain from scattered light; the rapid increase or decrease in HPLC-FLD signal is caused by (often undisclosed) blazing of holographic diffraction gratings. An optical test based on Rayleigh scattering is developed that predicts the wavelengths at which zero-order diffraction is advantageous versus where it degrades the signal. A one-plus log transform of emission scans revealed a continuous, buried background of higher-order diffraction contributions from monochromators. A robust z-transform tool has been developed in MATLAB®, enabling analysts to view impurities in emission heat maps that would otherwise be invisible. The proposed optical test for assessing the zero-order advantage applies to all HPLC-FLDs under isocratic or gradient conditions. Zero-order diffraction detection can lower the detection limit for visible-light emitters if the grating is blazed for the UV region. Understanding these optical effects enables more reliable ultra-trace fluorescence detection.
For the analysis of pesticides in the complex, high-fat matrix of peanuts, this study developed a high-throughput screening and quantitative method for 215 pesticide residues using gas chromatography-quadrupole Orbitrap high-resolution mass spectrometry (GC-Q-Orbitrap HRMS). The sample preparation workflow was systematically optimized. Peanut samples were extracted with acetonitrile containing 1% acetic acid, followed by salting-out with an acetate buffer salt mixture and purification through a multi-stage filtration column (m-PFC) packed with a novel LPAS adsorbent material. Under the optimized conditions, the method was validated at three spiking levels, delivering average recoveries for the 215 pesticides in the range of 70.7%-118.8%, with relative standard deviations (RSDs) between 1.1% and 9.2%. The distribution and correlation between chemical structures and retention behavior were analyzed to characterize the chromatographic separation capability and patterns for the large number of compounds. The established method is suitable for routine multi-residue monitoring of pesticides in peanuts, and the systematic optimization strategy also provides a reference for analyzing other complex matrices.
Accurate identification of animal-derived feed ingredients is essential for ensuring livestock safety and preventing disease transmission. However, conventional analytical methods (e.g., PCR, microscopy) are often hindered by high costs, low throughput, or limited universality. Furthermore, single-modal approaches face inherent "blind spots": biochemical methods may struggle with biologically homologous species, while visual inspection fails with indistinguishable powders. Therefore, there is a critical need for an intelligent analytical strategy that synergizes multidimensional information for robust classification in complex matrices. Herein, we established a dual-modal deep learning framework integrated with an array-format microstrip isoelectric focusing (mIEF) device. This platform simultaneously captures orthogonal analytical features: microscopic biochemical fingerprints via stable mIEF profiling, and macroscopic physical textures via digital RGB imaging. To maximize identification accuracy, a dual-branch ResNet18 model equipped with a channel attention mechanism was constructed. This algorithm adaptively fuses the two modalities, effectively compensating for the limitations of individual sensors. The proposed model achieved a superior classification accuracy of 96.53% across 15 feed categories, significantly outperforming single-modal baselines (mIEF-only: 83.47%; image-only: 49.33%). Notably, the integrated device supports parallel processing of 12-24 samples per run at an estimated cost of less than $1.5 per sample. This study presents a practical analytical strategy for feed traceability by integrating mIEF-based protein fingerprinting with digital image analysis. The combination of biochemical and visual information improves classification performance compared with either modality alone, while the array-format device supports parallel sample processing at low cost. The proposed platform provides a feasible approach for routine screening of animal-derived feed ingredients, particularly in laboratories where rapid, accessible, and cost-effective methods are needed.
Headspace-based microextraction techniques, such as solid-phase microextraction (SPME) and in-tube extraction (ITEX) are commonly used as pre-concentration steps prior to gas chromatography mass spectrometry (GC-MS) for the analysis of volatile organic compounds. However, these techniques often face limitations when dealing with semi-volatile organic compounds, polar analytes, or complex matrices. Recently, the application of vacuum has emerged as an additional operational parameter capable of overcoming these constraints by enhancing mass transfer while utilizing mild extraction conditions. Vacuum in-tube extraction (V-ITEX), introduced in 2019, combines controlled reduced pressure, dynamic headspace extraction, and sorbent trapping. This tutorial review summarizes the fundamental principles of V-ITEX, explains the underlying theoretical principles, outlines key operational parameters and evaluates performance characteristics and limitations. Representative applications, such as food characterization and metabolomics in human and animal studies are presented. Practical troubleshooting strategies and best-practice recommendations are provided to support implementation of V-ITEX in analytical workflows. Overall, the review highlights the potential of V-ITEX as an alternative approach for headspace extraction, while emphasizing critical parameters that must be controlled for robust analytical performance.
Nucleotide metabolites are key disease biomarkers, but their accurate quantification is hindered by low abundance and severe matrix interference. Magnetic solid-phase extraction is commonly used for enrichment; however, existing adsorbents suffer from limited selectivity and capacity. Therefore, novel adsorbents with high performance are urgently needed. A novel dual-functional pH-responsive magnetic covalent organic framework material (MGO@PEI-COF-Ti4+) was developed by synergistically integrating boronic acid affinity and Ti4+ metal affinity strategies, achieving highly efficient co-adsorption of nucleosides and nucleotides. The unique advantage of this material lies in its pH-regulated adsorption behavior: nucleosides are preferentially adsorbed under weak alkaline conditions, while nucleotides are selectively desorbed under weak acidic conditions, enabling sequential separation of multiple analytes in a single extraction step. Systematic material characterization (SEM, TEM, FT-IR, XRD, XPS) confirmed its well-ordered structure, superparamagnetism, and successful functionalization. Based on this material, we established an analytical method that combines magnetic dispersive solid-phase extraction (MSPE) with ultra-high performance liquid chromatography-ultraviolet detection (UHPLC-UV). The method demonstrated a wide linear range (10-2000 ng mL-1, r > 0.99), low detection limits (1-4 ng mL-1), good precision (RSD ≤9.1%), and satisfactory recovery rates (85.7-102.4%). Furthermore, its environmental friendliness was verified through multiple green assessment tools including GAPI. The method was successfully applied to targeted metabolomic analysis of osteoporotic rat models, providing a simple, low-sample-consumption, and environmental friendliness new analytical tool for efficient monitoring of nucleotide metabolites in complex biological samples.
A pH-regulated analytical strategy was developed for the discrimination and concentration-dependent analysis of metal ions using a single-material fluorescence sensor array based on nitrogen and sulfur co-doped carbon dots (NS-CDs). The NS-CDs were synthesized through an l-cysteine-assisted alkaline reaction, which increased the quantum yield from 3.80% to 9.59% and introduced abundant surface functional groups for metal ion interactions. Under different pH conditions, the same sensing material generated distinct fluorescence response patterns toward Hg2+, Cr6+, and Mn7+, enabling array-based recognition. Mechanistic investigations indicated that the differential responses originated from coordination, redox processes, and inner filter effect-related contributions, which were supported by XPS, FTIR, fluorescence lifetime analysis, and density functional theory calculations. By combining pH modulation with multivariate statistical analysis, including principal component analysis, linear discriminant analysis, and hierarchical cluster analysis, accurate discrimination of the three metal ions was achieved. Leave-one-out cross-validation provided high classification accuracy (>93%), while logistic regression-based machine-learning classification achieved a predictive accuracy of 96.0%. The proposed sensing strategy also enabled concentration-dependent analysis and showed reliable performance in real water samples, affording an overall identification accuracy of 92.6% for unknown samples. This work provides a simple and effective analytical methodology for constructing single-material sensor arrays with tunable response diversity for metal ion detection in practical water samples.
In biomedicine, a promising direction is the use of graphene oxide (GO) as a platform material or as a nanozyme. A key challenge for the application of GO is precise control of its interaction with reactive oxygen species, since this activity may influence the overall therapeutic outcome. Insignificant variations in material origin and processing can translate into measurable differences in its redox behavior. Peroxyl radical (ROO•)-generating assays are of particular interest, as ROO• are major contributors to oxidative stress in vivo, while kinetic chemiluminescence readouts sensitively track antioxidant effects of complex probes. This study addresses the need for a more biologically informed and comparable assessment of aqueous GO dispersions in relation to ROO•. A chemiluminescence (CL) assay based on the well-known AAPH/luminol free radical generation system is presented. Buffer oxygenation is identified as a critical controllable factor: under otherwise identical conditions, oxygenation increased the analytical signal and stabilized the kinetic readout. Platinum-assisted conditioning is introduced as a diagnostic step to suppress peroxide-driven CL enhancement and reveal intrinsic GO behavior. A phenotype-guided strategy is developed and applied. Reproducibly Trolox-like traces are quantified in restricted Trolox-equivalent terms, whereas slow or non-Trolox-like responses are compared using fixed-time reactivity descriptors, enabling comparative series across a diverse set of GO dispersions, including commercial and laboratory-prepared samples as well as fractionated and non-fractionated materials. The combined workflow (buffer saturation & artefact removal & phenotype-guided quantification) improves robustness and interpretability of CL-based peroxyl radical assays for GO dispersions. It enables analytically justified comparison of GO samples differing in origin and processing history, while restricting Trolox-equivalent reporting to kinetically justified cases.
Salmo salar (S. salar) and Oncorhynchus mykiss (O. mykiss) exhibit highly similar morphological characteristics, which frequently leads to market adulteration. This phenotypic resemblance poses significant challenges for accurate on-site species identification using conventional analytical methods. In this study, an amplification-free electrochemical biosensor was constructed by integrating the CRISPR/Cas12a recognition system with a single nanopipette, enabling precise identification of S. salar DNA without the need for complex pretreatment. This strategy is based on target DNA-induced activation of the trans-cleavage activity of Cas12a, which cleaves the DNA reporter molecules immobilized on the inner wall of the nanopipette, leading to alterations in surface charge and enabling electrochemical readout via ion current rectification (ICR). Under optimized conditions, the sensor exhibited a detection limit as low as 0.11 pM, effectively distinguished O. mykiss DNA, and demonstrated favorable reproducibility and stability. This work provides a low-waste, on-site analytical tool for the authentication of aquatic species, enabling direct in situ detection within salmon tissue sections without the need for nucleic acid amplification or complex sample pretreatment, thereby effectively filling a technical gap in rapid and accurate species identification.
One-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. Quantitative 1H Nuclear Magnetic Resonance (1H qNMR) spectroscopy is a powerful tool for analyzing mixtures, offering simple sample preparation and non-destructive measurement. Despite its advantages, a critical bottleneck exists: the manual selection of appropriate quantification peaks. This process, which requires identifying signals with high signal-to-noise ratios and no spectral overlap, is time-consuming, subjective, and heavily reliant on analyst expertise. This challenge is particularly acute for complex mixtures and high-throughput analyses, significantly impeding the efficiency and broader application of the qNMR technique. We construct a library-utilized tool for automated qNMR analysis, named LUMIN. The tool automatically identifies optimal quantification peaks without requiring analysts' pre-defined choices in the reference library and provides two operating modes to balance efficiency with analytical depth. Performance was evaluated in terms of peak-selection reliability and concentration accuracy. In synthetic model mixtures, LUMIN correctly identified over 95% of quantification peaks, fully consistent with expert assignments and without false positives. Quantitative accuracy, benchmarked against manual integration, showed excellent linear correlation with relative errors consistently within 10%. Application to real-world mixtures of both plant and animal origin further demonstrated its robustness and practical applicability. LUMIN translates expert decision-making logic into a robust, automated workflow for 1D 1H qNMR. By eliminating tedious and subjective manual peak selection, it significantly reduces analysis time while enhancing objectivity and reproducibility. This user-friendly tool supports both high-throughput screening and detailed spectral analysis, lowering the barrier for the broader application of qNMR in large-scale studies of complex mixtures.
The forensic analysis of illicit drugs requires analytical methods that are rapid, reliable, and compliant with legal constraints concerning sample integrity. In this study, Fourier Transform Near-Infrared (FT-NIR) spectroscopy and hyperspectral imaging (HSI) were evaluated as non-destructive tools for the identification, classification, and quantification of cocaine and its most common cutting agents, directly through sealed polyethylene packaging. Real samples of cocaine seized in four independent operations, together with six typical adulterants (creatine, caffeine, levamisole, lidocaine, lactose, and mannitol), were analyzed without opening the evidence, ensuring operator safety and full compliance with Article 360 of the Italian Code of Criminal Procedure. FT-NIR spectroscopy combined with chemometric techniques enabled a clear differentiation between cocaine and adulterants through exploratory Principal Component Analysis (PCA) and supervised Soft Independent Modeling of Class Analogy (SIMCA), achieving 100% sensitivity and specificity in cocaine identification. Partial Least Squares (PLS) regression further allowed accurate prediction of cocaine content in seized samples (R2 = 0.95, RMSEP = 2.40%). NIR hyperspectral imaging provided additional advantages by integrating spatial and spectral information, enabling pixel-level classification and visualization of chemical distribution within the samples. Pixel-based SIMCA models applied to NIR-HSI data showed excellent classification performance, selectively identifying cocaine and adulterants while rejecting background and packaging materials without the need for prior masking. Beyond analytical performance, the proposed approach is designed to support green, non-destructive, and operationally safe forensic workflows, enabling rapid screening while minimizing sample handling and operator exposure. Overall, the results demonstrate that FT-NIR spectroscopy and NIR hyperspectral imaging represent powerful, rapid, and legally robust alternatives to conventional destructive techniques for forensic drug analysis, with significant advantages in terms of safety, repeatability, and preservation of evidentiary integrity.
Accurate detection of Fumonisin B1 (FB1) is of great significance for food safety and disease prevention and control. In this work, the construction of dual metal-organic frameworks (MOFs) synergistically modulated electron transfer at the electrode interface, thereby enhancing ECL emission. This approach enabled ultrasensitive detection of fumonisin B1 (FB1). The sensor innovatively introduced two functionalized MOF materials to construct an electrochemiluminescence (ECL) aptasensor. ZnCo-MOF@SnS2 (ZCM@SnS2) was used as a co-reactant accelerator, whose unique bimetallic synergistic effect and three-dimensional porous structure effectively inhibited the aggregation of SnS2 nanosheets. This structure significantly enhanced the catalytic activity and electron transfer efficiency of the electrode interface. Zr-TCBPE-MOF@Au (ZTM@Au) was used as an ECL probe, which significant enhancement of ECL signaling was achieved by rigid immobilization of TPE-based H4TCBPE (1,1,2,2-tetra(4-carboxylbiphenyl)ethylene) by Zr-MOF framework. The ECL performance was further enhanced through systematically optimizing the electrode interface modification process. The experimental results showed that ZCM@SnS2-COOH efficiently catalyzed the co-reactant TPrA to generate a large number of TPrA•+ radicals. ZTM@Au significantly enhanced the luminescence efficiency by shortening the electron transfer pathway. And the synergistic effect of the two resulted in an approximately 2.5-fold enhancement of the ECL signal intensity compared with the single-component modified system. The sensor exhibited excellent selectivity, reproducibility and stability, which provides a new idea for the development of high-performance food safety detection technology based on interface engineering.
Conventional atomic spectrometric techniques provide high sensitivity and accuracy for element detection, yet remain constrained by large instrument size, high gas consumption, and operational complexity. Recent advances in 3D printing technology have enabled new approaches to spectral instrument miniaturization which presents new opportunities for portable, on-site analysis. A fully integrated, 3D-printed hydride generation-capillary point discharge atomic emission spectrometer (HG-CPD-AES) was constructed for on-site arsenic speciation analysis of environmental water samples. A monolithic unit containing the microfluidic reaction chamber, a gas-liquid separator and a coaxial capillary-tungsten electrode excitation source were fabricated by stereolithography and allows direct microsyringe injection of a tiny total sample volume of 150 μL. Hydride vapor confined by the quartz capillary enhances injection efficiency and signal stability. With a total gas flow rate of just 5 mL min-1 (less than 1/20 of a conventional system), the system achieved a limit of detection of 4 μg L-1 with a linear range of 10-3000 μg L-1. The method was validated through analysis of tap water, pond water, and hot spring water samples with 100-110% recoveries and without significant difference in comparison with those by ICP-MS. The cost-effective and open-source design enables rapid replication in various environments, creating a scalable and robust distributed detector platform. This approach facilitates on-site arsenic monitoring in resource-limited settings where conventional analytical infrastructure is unavailable or impractical to deploy.
ENHANCER OF ZESTE: homolog 2 (EZH2), a histone H3K27 trimethyltransferase, is a key epigenetic regulator frequently dysregulated in cancer. To determine its impact on nucleotide biosynthesis and nucleic acid methylation in intact cells requires highly sensitive, isomer-resolving analytical workflows. We developed a targeted ion chromatography-ultra-high-resolution Fourier transform mass spectrometry (IC-UHR-FTMS) workflow with lower limits of quantification down to 9 fmol on-column to determine changes in methylation of DNA, total RNA, and mRNA in A549 cells following EZH2 knockdown (KD). Using dual stable isotope tracers, l-methionine-(methyl-13C) and l-glutamine-(15N2), in a multiplexed stable isotope-resolved metabolomics (SIRM) design, we quantified positionally-resolved 13C/15N labeling of methylated nucleotides and their precursors. EZH2 KD reduced 15N incorporation into deoxynucleotides, indicating impaired de novo synthesis from glutamine. It also attenuated 15N and/or 13C labeling of nucleotides and methylated nucleotides in total RNA and mRNA at various atomic positions, reflecting global losses in biosynthesis and S-adenosylmethionine (SAM)-dependent methylation. Notably, AMP methylation at N6 and 2'-O positions was most responsive to EZH2 KD, implicating reduced capped-RNA translation. Some of the EZH2 KD-induced changes in RNA methylation corresponded with the altered expression of their writer or eraser enzymes. This study demonstrates multiplex stable isotope tracers-coupled IC-UHR-MS as a powerful tool for comprehensive tracing of methylation dynamics in mammalian cells and reveals EZH2's role in metabolic-epitranscriptomic regulation by modulating SAM availability via glutamine-fueled de novo purine biosynthesis and RNA methylation.
Acinetobacter baumannii threatens food safety due to its harsh-condition survival, driving the need for portable, accurate detection. Electrochemiluminescence-based closed bipolar electrode (ECL-BPE) biosensors are attractive for their simplicity and miniaturization, but conventional designs rely solely on a single anodic ECL signal. This single-signal strategy suffers from poor anti-interference ability, limiting detection accuracy. We first developed a dual-signal ratiometric biosensor integrating multimeter voltage and ECL readouts for A. baumannii detection. A dual-functional MOF with DNA immobilization capability and capacitance-like activity was designed to anchor DNA and generate a cathode voltage signal readable by a multimeter. Meanwhile, boron nitride quantum dots (BNQDs) served as a novel co-reactant to establish a luminol-based ECL system, producing an anodic ECL signal captured by a smartphone. Upon target recognition, the increased cathode interfacial resistance allocated a larger voltage to the cathode, enhancing capacitive deposition and yielding a higher multimeter signal, while reducing the anode voltage and thus suppressing the anodic ECL intensity. This inverse correlation between the two signals and target concentration enabled a ratiometric biosensor with significantly improved accuracy, achieving a detection limit of 3.5 CFU g-1, along with high stability, specificity, and anti-interference ability. The biosensor showed 100% concordance with q-PCR results when tested on 12 real samples and 8 positive controls. This work presents a dual-signal ratiometric biosensor on BPE platform and provides a portable, accurate platform for pathogen detection in food industry.
The contamination of surface and ground water by antibiotics has become a pressing environmental concern worldwide. However, the diversified physicochemical properities of antibiotics across different classes impart the major challenge for their broad-spectrum monitoring. Herein, a novel phosphate-rich β-cyclodextrin porous crosslinked polymer named PCD-PCP is successfully synthesized prepared and used for highly efficient extraction of broad-spectrum antibiotics across six classes, covering a wide polarity range spanning over 10 orders of magnitude of logKow values. The PCD-PCP-coated thin-film microextraction (TFME) devices achieved significantly higher enrichment factors to all the six classes of antibiotics compared to commercial HLB coatings. By using the PCD-PCP-coated thin-films for enrichment, an ultra-sensitive TFME-HPLC-MS/MS method is established to simultaneously quantify 18 antibiotics, and the detection limits are all lower than the restricted limits in drinking water set by US EPA. Furthermore, the comprehensive analysis of density functional theory results suggests that the electrostatic attraction, hydrogen bonding, and van der Waals interactions jointly promote the extraction affinities. This study not only provides profound mechanistic insights into the interactions between the adsorbent and antibiotics, but also offers a highly sensitive analytical method for the broad-spectrum enrichment of antibiotics in environmental water samples.
Sulfur dioxide, the most commonly added preservative in wine, serves as antimicrobial and antioxidant functions, but excessive intake poses health risks. Due to the large production of wine, it is essential to develop rapid, sensitive and low-cost methods for detecting total SO2 in wine. A miniaturized optical emission spectrometer was developed by using a point discharge microplasma as an excitation source, in which characteristic optical emission of sulfur at 921.3 nm could be sensitively detected at low concentrations. Simultaneously, when high-concentration SO2 was introduced, the color of the microplasma would be obviously changed from purple to blue, thus the RGB values of the plasma color could be facilely used for quantification of total SO2 in its high concentration range. Therefore, a dual-mode detection strategy for total SO2 was achieved by combining the optical emission spectrometry and colorimetry, thus obtaining improved analytical performance, especially a wider linear dynamic range. Under the selected experimental conditions, a limit of detection (LOD) of 0.8 mg/L was achieved, with a relative standard deviation of 2.2% (10 mg/L, n = 9). The proposed method was successfully applied for determination of total SO2 in wine samples in its aeration process. The proposed method and instrument feature rapid and sensitive detection of total SO2 in a wide dynamic linear range, with additional advantages of compactness, low power consumption and easy operation, showing promising potential for on-site analysis of total SO2 in wine and diverse food and environmental samples.