Analytical Methods最新文献

Our environment is being polluted by numerous sources of heavy metals, which are discharged by various industries, and hence, monitoring of heavy metals in environment and other fields is very essential. Herein, an amine-functionalized metal organic framework (NH₂-MIL-101(Fe)) was prepared via a hydrothermal method. DNA sequences with enzyme and substrate strands were annealed to form a Y-shaped DNAzyme and used as the recognition element. Subsequently, we developed a fluorescence/colorimetric dual channel for the sensitive detection of Pb²⁺, Hg²⁺ and Cu²⁺ using NH₂-MIL-101(Fe) and Y-shaped DNAzyme with the ability to be magnetically separated. A good linear response and detection limit were observed by fluorescence method like 0.21 nM for Pb²⁺, 0.23 nM for Hg²⁺ and 0.37 nM for Cu²⁺ and by colorimetric method such as 0.43 nM for Pb²⁺, 0.57 nM for Hg²⁺ and 0.99 nM for Cu²⁺. Therefore, we employed the conventional method for the dual-mode detection of Pb²⁺, Hg²⁺ and Cu²⁺ ions and obtained high specificity, high accuracy and satisfactory results in real sample analyses.

Zearalenone (ZEN) is a secondary metabolite produced by various Fusarium species. Its widespread contamination has raised deep concern globally. Also, ZEN is reproductively toxic, hepatotoxic and carcinogenic. Rapid detection methods for ZEN mainly suffer from the aggregation-caused quenching (ACQ) effect of fluorescent materials. Therefore, there is an urgent need to develop new methods for detecting ZEN. On this basis, we synthesised AIE polymer microspheres (AIEPMs) using aggregation-induced emission (AIE) dyes. We coupled these microspheres with antibodies to prepare immunoprobes to establish immunochromatographic test strips with a colorimetric/fluorescent dual mode. Under optimal parameters, the visual limit of detection (vLOD) for the colorimetric mode was 0.625 ng mL^-1. In comparison, the quantitative limit of detection (qLOD) for the fluorescent mode was as low as 0.039 ng mL^-1. The average recoveries ranged from 97.60% to 102.46%, and the coefficients of variation (CV) ranged from 2.10% to 10.70%. These data demonstrate the excellent reproducibility and reliability of the established method. The researchers have successfully applied the method for accurate sample detection. The method demonstrated the great potential of AIEPMs-ICA as a colorimetric/fluorescent dual-mode test strip for the rapid detection of ZEN.

The release of endocrine-disrupting chemicals (EDCs) can harm humans and wildlife. It is therefore important to monitor bisphenol A (BPA) consumption, an endocrine disruptor commonly found in water from plastic products, and detect BPA at low concentrations for accurate health risk assessments. We present a method for estimating BPA levels in plastic-bottled products that is highly sensitive, precise, and effective. BPA analysis was performed using advanced liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS), with multiple reaction monitoring (MRM) on a state-of-the-art Orbitrap mass spectrometry system using negative ionization techniques. To assess the quality of Indian brands of water, we used LC-MS to obtain balanced hydrophilic–lipophilic extracts. Brand water samples showed efficiently separated BPA in 2.35 minutes, with other sources typically taking between 5 and 8 minutes. BPA concentrations, measured within a range of 10 ng mL⁻¹ to 1 pg mL⁻¹ with a lower detection limit (LOD) of 0.037 ng mL⁻¹, were characterized by dynamic linear ranges and corresponding linear equations for each compound. We also evaluated the reproducibility and sensitivity of the detection of BPA in different water samples, including mineral, river, and tap water, with low levels of BPA found in Indian river water (below 4.54 ng mL⁻¹). Thus, this study explored alternatives to solid phase extraction (SPE) for screening BPA analogs in water samples, and real samples from an Indian supermarket revealed BPA in plastic bottles at concentrations comparable to those described in Europe, the United States, Korea, Japan, and China.

Heavy metal pollution, particularly from copper ions (Cu²⁺), poses a significant threat to both the ecological environment and human health. However, traditional copper ion analysis techniques are often hindered by the need for expensive equipment, labor-intensive sample preparation and skilled operation, which limits their effectiveness for field and real-time applications. In this work, we report a novel near-infrared aptamer sensor (NIRApt) that originates from the binding reaction between the DNA aptamer Apt_Cu and the fluorescent small molecule crystal violet (CV), enabling rapid detection of Cu²⁺ through the competitive effect of Cu²⁺ with Apt_Cu. This sensor shows a significant enhancement in NIR fluorescence after aptamer binding. NIRApt exhibits superior performance, requiring only three core components to achieve a fast response time and operational simplicity in less than a minute. The sensor shows high sensitivity with a detection limit as low as 61 nM, making it suitable for the detection of trace amounts of Cu²⁺ in diverse samples. The efficacy of NIRApt has been validated through successful applications in real water samples, highlighting its promising potential for environmental monitoring.

Creatine monohydrate is an important sports nutrition supplement that enhances energy and promotes muscle growth. Recent concerns about the quality and authenticity of creatine monohydrate have highlighted the urgent need for rapid and cost-effective assessment methods. This study presents a new approach for assessing the quality of creatine monohydrate using spectroscopy combined with machine learning. Spectral data of creatine monohydrate samples from 15 brands are acquired using portable near-infrared (NIR) spectroscopy and benchtop hyperspectral imaging (HSI). Machine learning methods are employed to extract high-level features from the spectral data and model the relationship between the data and creatine concentrations. The root mean square error (RMSE) for models based on NIR data ranges from 0.258 to 0.291, whereas those derived from HSI data vary between 0.468 and 0.576. To improve the accuracy and reliability of spectral data analysis, multi-model fusion and data fusion strategies are used to integrate the outputs of different models and data from different sources, respectively. By combining NIR-HSI data fusion with multi-model fusion, the lowest RMSE for creatine quantification is reduced to 0.18. These results demonstrate that spectroscopic techniques coupled with machine learning can provide a rapid and cost-effective solution for assessing the quality and authenticity of creatine monohydrate.

Early diagnosis of ovarian cancer remains challenging due to the absence of effective screening tests. The success of treatment and 5 year survival rates are significantly reliant on identifying the disease at a non-advanced stage, which highlights the urgent need for novel early detection and diagnostic approaches. Blood-based spectroscopic techniques, combined with chemometrics, have the potential to be used as tools for screening and diagnostic purposes in this context. In this study, we utilised attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse blood plasma samples from benign (n = 15) and ovarian cancer (n = 15) cases. We conducted multivariate discrimination models to compare the results in terms of sensitivity, specificity, and diagnostic accuracy when using either plasmatic protein precipitates or whole plasma to distinguish between benign and ovarian cancer. Notably, diagnostic accuracy values of 96% (sensitivity and specificity of 96%) and 92% (sensitivity and specificity of 88% and 96%, respectively) were achieved for the protein precipitates and whole plasma datasets respectively using genetic algorithms with linear and quadratic discriminant analysis. Furthermore, this methodology demonstrated its capability to categorise samples within the ovarian cancer class, distinguishing between early stage (FIGO I) and advanced stage (FIGO II–III), with excellent accuracy exceeding 97% for protein precipitate dataset. These findings highlight the utilisation of a specific class of biomolecules in a proteomic-like approach based on infrared spectroscopy and chemometrics for detecting ovarian cancer using blood plasma samples.

This study presents a novel method for direct amplification of multiple microRNAs (miRNAs) from serum samples using Sensitive and Multiplexed One-Step RT-qPCR (SMOS-qPCR). The technique eliminates the need for separate miRNA extraction and purification steps, offering a streamlined approach for non-invasive early disease diagnosis. We optimized reaction conditions, including serum treatment methods and PCR system volumes, to enhance interference resistance and detection sensitivity. The optimized serum direct SMOS-qPCR demonstrated a detection limit as low as 6 × 10³ copies per μL for single-target miRNA, with excellent amplification efficiency (R² > 0.99). In multiplex detection, the method successfully quantified four miRNAs simultaneously, maintaining high sensitivity and reproducibility. Analysis of 20 clinical serum samples further validated the method's applicability for large-scale screening. Overall, this rapid, cost-effective, and user-friendly approach represents a significant advancement in miRNA detection technology, potentially facilitating earlier and more accessible disease diagnosis.

Flow-batch analyzers demand meticulous volume control for successful application in quantitative determinations, with the incorporation of high-efficiency pumps and valves recommended for their construction. However, routine recalibrations are frequently needed to uphold the accuracy of manipulated volumes, highlighting the value of exploring new alternatives for volume control and measurement, with less sophisticated apparatus. In this work a compact Flow-Batch (FB) analyzer with piezoelectric micropumps was developed to perform standard addition calibration, incorporating image-based detection to perform volume control. The instrument was evaluated by quantitatively determining Cr(VI), NO₂⁻, and Fe(II) in water samples, combining RGB-based colorimetry with established spectrophotometric methods. The results demonstrate that a comprehensive analysis with five standard additions can be completed in approximately 10 minutes, maintaining a suitable linear correlation (R² > 0.99) and precision (0.4 ≤ RSD ≤ 12.1%). Recoveries between 90% and 105% for analyte levels below Brazilian regulatory limits underscore the accuracy of the proposed approach. The study confirms that digital image monitoring provides an elegant alternative for controlling solution volumes in FB systems, eliminating the need for more robust pumps with precisely controlled flow rates.

Nicotine is a harmful sympathomimetic drug associated with serious health issues. Herein, a novel amide-based bistren-type cage, BiP-Am, has been developed for the selective fluorescence-based sensing of nicotine in human urine samples and cigarettes. The corresponding detection limit features a value of 0.4 nM, among the best reported in the literature. Selectivity experiments demonstrate that BiP-Am can efficiently detect nicotine in the presence of multiple interfering analytes such as sodium, potassium, urea and uric acid. A plausible mechanism has been proposed herein, revealing that nicotine is showing an inner-filter effect quenching the BiP-Am fluorescence emission. Our strategy poses a facile and versatile method for nicotine detection in portable applications.

The extensive use of antibiotics poses significant public health concerns, including the increase in drug-resistant bacteria and environmental pollution, underscoring the urgent need for rapid, sensitive, and specific antibiotic detection methods. Most current reviews on antibiotic detection primarily focus on categorizing antibiotics based on their types or the classification of sensors used, such as electrochemical, optical, or colorimetric sensors. In contrast, this review proposes a novel and systematic theoretical framework for the detection of antibiotics using sensors using seven popular molecular recognition elements-antibodies, aptamers, microorganisms, cells, peptides, molecularly imprinted polymers (MIPs), metal–organic frameworks (MOFs) and direct recognition modalities and briefly discusses the mechanism of molecular recognition elements and antibiotic recognition. Additionally, it explores biosensors developed using these elements, offering a detailed analysis of their strengths and limitations in terms of sensitivity, specificity, and practicality. The review concludes by addressing current challenges and future directions, providing a comprehensive perspective essential for enhancing food safety and protecting public health.