Analytical Methods最新文献

Synthetic corticosteroids such as dexamethasone and prednisolone are widely used for the treatment of various conditions including tumors and skin disorders. Chronic exposure to synthetic corticosteroids leads to adverse health effects such as osteoporosis, hyperglycemia and hypertension. Several dietary and health supplements have recently been reported to be adulterated with synthetic corticosteroids. Conventional analytical techniques such as high-performance liquid chromatography (HPLC), thin-layer chromatography (TLC) and immunochromatography used for detection of these corticosteroids are time-consuming and require trained personnel, necessitating the development of rapid and cost-effective screening methods to ensure food safety. Here, a rapid and cost-effective colorimetric recognition chemistry has been developed for detection of synthetic corticosteroids in dietary supplements. The method employs spectrophotometric quantification of the chromogenic end product at 410 nm, which was further assessed using RGB-based image analysis. Selectivity and interference studies showed no significant matrix interference from the tested structurally similar steroids and common dietary supplement samples. The assay demonstrated a limit of detection (LOD) of 0.04 ± 0.008% (w/v) and a limit of quantification (LOQ) of 0.12 ± 0.027% (w/v) for prednisolone. The developed method is suitable for on-site detection of synthetic corticosteroids, and can be used by regulatory agencies and industries to ensure food safety and human health.

This work presents the efficient recognition of the hazardous chemical cyanide ions (CN^- ions) using a newly designed organic molecule-based fluorescent receptor, (E)-4-(4-(diphenylamino)styryl)-1-propylquinolin-1-ium (TAME). CN^- ion detection occurs via nucleophilic addition to the styryl group, inducing distinct electronic perturbations as well as blocking of the intramolecular charge transfer (ICT) process. Absorption spectral titration studies reveal a notable blue shift (517-507 nm) in the presence of CN^- ions, while steady-state fluorescence measurements demonstrate significant fluorescence quenching (∼82%) with a strong red shift from 553 nm to 576 nm following the interaction of CN^- ions with the receptor TAME. The lowest limit of detection (LOD) for this fluorescence-based sensing strategy was calculated to be 1.48 nM, which is much lower than the WHO-permissible limit for cyanide in drinking water. Furthermore, the probe was successfully employed as a paper strip, enabling the portable and effective recognition of CN^- ions. Additionally, this sensing system illustrates a notable colour-changing behaviour, which was further exploited for CN^- ion detection through a smartphone-based RGB color variation analysis technique. The real-world utility of the receptor TAME was established through its ability for the effective quantification of hazardous CN^- ions in various real water and food samples.

Easy-to-use, highly-sensitive, and quantitative assays over a large dynamic range are needed to improve patient care and disease monitoring. These factors are challenging to address simultaneously using classical methods. The laboratory ELISA assay is relevant for complex matrices over a linear quantification range, whereas immunoassay implementation in commercial lateral flow assays (LFA) remains mostly qualitative. In the case of myocardial infarction (MI), the cardiac biomarker troponin I (cTnI) is present at low levels in the blood of a healthy patient but increases by several orders of magnitude at the onset of symptoms. Here, we aim to detect cTnI as a proof of concept for developing a sample preparation microfluidic system for the immuno loop-mediated isothermal amplification (immuno-LAMP) method. Immuno-LAMP combines the sensitivity and selectivity of antibody recognition with the wide dynamic range allowed through nucleic acid amplifications. cTnI immuno-LAMP assays are firstly validated in a well-plate, and then optimized and integrated into microfluidic cartridges to improve reliability and enable automation. A network of valves and chambers controls a sample preparation module that handles magnetic beads coupled to capture antibodies. Detection antibodies coupled to short oligonucleotides, used to form the immuno-complex, hybridize with an oligonucleotide structure called a dumbbell. This structure is isothermally amplified in tubes to allow the quantification of cTnI. Our work has demonstrated the ability of our automated sample preparation platform to quantify cTnI spiked in human blood plasma at sub-picomolar concentrations. This prototype opens perspectives for integrating the whole immuno-LAMP method from human plasma samples in a microfluidic device including the LAMP amplification and detection.

Rapid analysis of food and agricultural products is important for evaluating quality and detecting contaminants, but the chemical complexity and physical heterogeneity of these matrices can make traditional workflows challenging and time-consuming. In our previous work, a touch sensor for sheath-flow probe electrospray ionization (sfPESI) was developed to enable automated analysis of liquid samples by detecting displacement currents upon surface contact. By coupling the sfPESI probe with a table-top 3-axis robot, consecutive point analyses of various complex samples were performed. However, its applicability was limited to liquid and wet solid matrices. In this work, a touch sensor equipped with a lock-in amplifier was developed to enable the automated analysis of both wet and dry samples. The system was demonstrated across a range of matrices, including vegetables, meats, insecticides, and insect repellents, substantially expanding the applicability of automated sfPESI for rapid, surface-resolved chemical analysis of complex food and agricultural products.

Tadalafil (Cialis) is an FDA-approved oral drug used for benign prostatic hyperplasia, pulmonary arterial hypertension, and erectile dysfunction. A fast, environmentally friendly, and reliable UPLC-MS/MS method was established to quantify tadalafil (TDF) in human liver microsomes (HLMs) and assess its in vitro metabolic stability. Unlike previously reported TDF assays, which target plasma or other biological fluids, the present method uniquely couples HLM-based metabolic-stability assessment with in silico metabolic soft-spot, toxicity, and ADME prediction and dual greenness evaluation in a single workflow. The method is linear over 1 to 4000 ng mL^-1, provides detection within 1 min, and maintains high precision and repeatability regardless of the presence of HLMs. TDF and baricitinib (internal standard) were separated using an Eclipse Plus 1.8 µm C18 column (reversed-phase; 50 mm × 2.1 mm), with a mobile phase of 0.1% HCOOH in water (45%) and in acetonitrile (55%). The approach's accuracy and precision ranged from -4.35% to 7.33% (intra-day) and -5.02% to 12.0% (inter-day). TDF's in vitro half-life was 59.65 min, with a slow intrinsic clearance (Cl_int) of 13.59 mL min^-1 kg^-1. In silico results reveal that minor modifications at C28 of the 1,3-benzodioxole ring (95%) could enhance the safety and stability of the new derivatives relative to TDF.

Rapid and reliable determination of toxic dyes in complex aqueous environments remains challenging because matrix interference and complicated sample pretreatment often compromise analytical accuracy and practical applicability. Therefore, an integrated filtration-adsorption-SERS sensing platform coupled with one-dimensional convolutional neural network (1D-CNN) modeling was fabricated for rapid and quantitative detection of malachite green (MG) and methylene blue (MB) in complex water samples. Polypropylene filter cloth (PPFC) served as a porous fibrous support for sample filtration, while Ag nanoparticles (Ag NPs) were uniformly immobilized on the fiber surfaces via a seed-mediated in situ growth strategy to provide SERS-active sites. Activated carbon was incorporated into the PPFC/Ag filter configuration to provide adsorption of the target dyes during sample treatment. The resulting configuration integrates filtration, adsorption, and SERS detection within a common analytical workflow, while 1D-CNN modeling was employed for quantitative analysis of the acquired SERS spectra. Benefiting from the integrated filtration-adsorption configuration and porous fibrous architecture, the platform exhibited high signal uniformity and favorable analytical performance, with detection limits of 10^-9 M for both MG and MB. Adsorption kinetic analysis demonstrated that dye removal followed the pseudo-second-order model, with equilibrium removal efficiencies of 99.7% for MB and 98.6% for MG. To improve quantitative reliability in complex matrices, a 1D-CNN model was employed for SERS spectral analysis, yielding high accuracy with R² values of 0.992 and 0.982 for MB and MG, respectively, with low prediction errors. Furthermore, spiked Moyan Lake water samples showed recoveries of 98.14% and 96.37% for MB and MG, respectively, with RSD values of 4.53% and 4.91%, demonstrating satisfactory accuracy and precision in a real water matrix. By integrating these complementary functions within the same filtration configuration, the proposed platform establishes a continuous analytical workflow from sample filtration and adsorption to SERS detection and quantitative analysis. This integrated design simplifies the conventional multi-step procedure and provides a sensitive, reliable, and practical approach for rapid monitoring of trace organic dyes in complex water matrices.

Near-infrared spectroscopy provides a non-destructive and rapid route for soil analysis. However, conventional chemometric models based on nonlinear supervised learning remain limited for complex samples and multi-characteristics prediction. This paper proposes a chemometric framework based on spectral-characteristics fusion and minimization of representation mapping or prediction error. First, spectral-characteristics fusion integrated spectral data with single or multiple soil characteristics. Subsequently, quantitative models were constructed using a supervised spectral-to-compositional representation mapping model and a dynamic series forecasting model, which minimize the spectral-to-compositional representation mapping error and the prediction error, respectively. The dynamic series forecasting model was constructed based on dynamic sequential data analogous to time series data derived from spectral-characteristics fused data, with sliding windows covering all soil-characteristic positions. Experiments on organic samples from the LUCAS 2009 topsoil data showed that fused data with great continuity facilitated model construction, and the proposed framework achieved higher predictive accuracy than the selected conventional supervised learning baselines. This paper provides a near-infrared spectral-characteristics fusion and error-minimized prediction strategy for compositional analysis of complex samples, with potential applicability beyond soil analysis.

Ethanol, as a food additive, can effectively extend product shelf-life and improve food texture. However, its dosage requires stringent control, making ethanol quantification a critical quality assurance requirement in baked goods. Consequently, developing a rapid and simple method for detecting ethanol in baked food products is of great practical significance. This study fabricated a three-dimensional photonic crystal (PC) membrane by integrating plant membranes and a PC, utilizing an onion epidermal membrane as the natural substrate. PC arrays were imprinted onto the onion membrane at room temperature. The resulting onion-derived PC membrane biosensor demonstrated not only high sensitivity but also excellent selectivity towards ethanol. During detection, the spectral reflection peak of the onion-PC film shifted towards the red as the ethanol concentration increased, achieving signal stabilization within 5 min. Moreover, ethanol detection induced visually observable structural color changes (yellow-green to orange-red) on the membrane. The biosensor exhibited excellent practicability and retained stable performance after multiple reuse cycles. Overall, this onion-PC membrane demonstrates significant potential for the visual and rapid detection of ethanol in baked foods.

A novel fluorescent probe EFC, featuring a 4H-furo[3,2-b]pyrrole scaffold, was designed, synthesized, and investigated for its optical properties. EFC was identified as a novel hydrazine probe, which undergoes a nucleophilic substitution reaction with hydrazine. In the presence of hydrazine, EFC exhibits a color change from yellow to colorless under daylight, and a fluorescence change from yellow to blue under UV illumination. Additionally, EFC demonstrates acid-base tolerance, coupled with high selectivity and sensitivity for residual N₂H₄. Furthermore, paper strips loaded with EFC were fabricated and used with a smartphone to enable point-of-care visual detection of N₂H₄. The use of EFC for detecting N₂H₄ in real water samples demonstrated recovery rates spanning from 98.60% to 101.36%, with relative standard deviations between 1.72% and 1.74%, thereby affirming the method's accuracy and reliability. Furthermore, the probe exhibits equivalent efficacy in identifying residual hydrazine within soil samples and a silica support. Consequently, this research introduces an innovative fluorescent probe for hydrazine detection, distinguished by its cost-effectiveness, facile fabrication process, and simple operation.

Adiponectin (ADI) and leptin (LEP) have been proposed as two specific biomarkers of dysfunctional adipose tissue. We aim to establish a one-step time-resolved fluorescence immunochromatographic assay (TRFICA) for simultaneous quantitative detection of ADI and LEP and to evaluate its application value in patients with metabolic syndrome (MS). The TRFICA, using europium(III) (Eu³⁺) fluorescent microspheres and based on the double-antibody sandwich format, for the quantitative detection of ADI and LEP was individually optimized and established. Subsequently, a dual-target test strip based on TRFICA (TRFICAS) was assembled and evaluated using the clinical samples. The dual-target TRFIAS allows for quantitative detection of serum ADI and LEP within 15 min. Its sensitivity for detecting ADI was 0.01 µg mL^-1, and for LEP it was 0.07 ng mL^-1, with high specificity. The intra-batch coefficient of variation (CV) and the inter-batch CV were both <11.62%, and recovery ranged from 105.33% to 119.17%. Moreover, strong Pearson correlations were observed between the results measured by the dual-target TRFICAS assay and those obtained from commercial kits (Pearson r = 0.9183 and 0.9051, respectively). Applied to sera from healthy donors and MS patients, the assay clearly distinguished MS patients from the healthy donors. The dual-target TRFICAS has good sensitivity, specificity, precision, a simple procedure, rapid detection and long-term stability, and it is suitable for rapid and quantitative determination of serum ADI and LEP on-site.