Talanta最新文献

Gas-chromatography hyphenated with low-resolution mass spectrometry is a very flexible tool for the cost-effective identification and quantification of volatile compounds in complex matrices. In some analytical fields, criteria for the agreement between retention time and mass spectra of the analyte in calibrators and samples are defined based on the general understanding of the performance of these parameters. However, since this harmonisation is not based on experimental performance observed for specific GC-MS conditions and analyte it leads to false identifications. This research proposes a novel and robust tool for defining statistically sound criteria for the identification of compounds by GC-MS and LC-MS using experimental data. The Monte Carlo Method (MCM) simulation of the correlated abundance of characteristic ions of analyte mass spectrum allows simulating the abundance ratio difference of the analyte in a calibrator and sample used for statistically sound identifications. The Cholesky decomposition of the covariance matrix of ion abundances for MCM simulations allows the reliable use of many ion abundance ratios in identifications. The developed methodology was implemented in a user-friendly Excel spreadsheet and applied to the identification of tear gas agents in tear gas sprays. Criteria defined by SANTE for identifying pesticide residues in foodstuffs were compared with the developed tool. The cross-validation of computational and SANTE tools allowed concluding that the statistical control of retention time and mass spectra performs according to the defined confidence level. On the other hand, the SANTE criteria can produce up to 92 % false identifications for being too strict considering signal dispersion.

Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting.

This study introduces a novel approach for the sensitive and accurate detection of small molecule metabolites, employing metal-phenolic network (MPN) functionalized AuNPs as both adsorbent and matrix to enhance laser desorption/ionization mass spectrometry (LDI-MS) performance. The MPN comprising tannic acid (TA) and transition metal ions (Fe³⁺, Co²⁺, Ni²⁺, Cu²⁺, or Zn²⁺) was coated on the surface of AuNPs, forming metal-TA network-coated AuNPs (M-TA@AuNPs). The M-TA@AuNPs provided a tunable surface for specific interactions with analytes, displaying distinct enrichment efficacies for different amino acids, especially for Cu-TA@AuNPs exhibiting the highest affinity for histidine (His). Under the optimized condition, the proposed method enabled ultrasensitive detection of His, with good linearity (R² = 0.9627) in the low-concentration range (50 nM-1 μM) and a limit of detection (LOD) as low as 0.9 nM. Furthermore, the method was successfully applied to detect His from human urine samples, showcasing its practical applications in clinical diagnostics, particularly in the realm of amino acid-based targeted metabolomics.

Immunosensors based on electrical impedance spectroscopy allow for label-free, real-time detection of biologically relevant molecules and pathogens, without requiring electro-active materials. Here, we investigate the influence of bare gold nanoparticles (AuNPs), synthesized via laser ablation in solution, on the performance of an impedimetric immunosensor for detecting severe acute respiratory syndrome coronavirus (SARS-CoV-2). Graphene acetic acid (GAA) was used in the active layer for immobilizing anti-SARS-CoV-2 antibodies, owing to its high density of carboxylic groups. Immunosensors incorporating AuNPs exhibited superior performance compared to those relying solely on GAA, achieving a limit of detection (LoD) of 3 x 10^-20 g/mL to detect the Spike Receptor Binding Domain (RBD) protein of SARS-CoV-2 and of 2 PFU/mL for inactivated virus. Moreover, these immunosensors presented high selectivity against the H1N1 influenza virus. We anticipate that this platform will be versatile and applicable in the early diagnosis of various diseases and viral infections, thereby facilitating Point-of-Care testing.

Background: Analyte-triggered semiconductor quantum dots (QDs) modulation in the presence of non-consistently responsive fluorescent species represents a challenging analytical issue in concrete multi-way data handling. QDs with heterogeneous sizes and/or uneven distribution of functional moieties on their surfaces exhibit significant fluctuations in the fluorescent response components, known as chemical rank, across different excitation/emission modes. This phenomenon may lead to a substantial deviation from the proportionality prescribed by Beer-Lambert law. Nonetheless, even in the presence of such deviation, a multi-way model may be successfully selected after determining a proper chemical rank in a QDs system.

Results: We show that in a valid PARAllel FACtor (PARAFAC) model under properly determined chemical rank, meaningfully resolved pure spectral profiles can be reached for each fluorescent responsive constituent in the original excitation-emission fluorescence matrix (EEFM) measurements. This was thoroughly illustrated by applying PARAFAC trilinear decomposition of a three-way data array of two distinct datasets acquired from semiconductor QDs sensing systems with low-rank trilinear assumption. The first dataset, presented here for the first time, comprises EEFM measurements of the ligand-driven quenching of thiomalic acid (TMA)-capped AgInS₂ (AIS) QDs by vomitoxin. The second dataset, employed for illustrative purposes, comprises EEFM measurements of the quenching, via cation bridging, of glutathione (GSH)-capped CdTe QDs by Pb(II). The results of this study enabled the determination of vomitoxin at a ppb level in real samples of fish feeds, showcasing the efficacy of the PARAFAC model in resolving spectral signatures (loadings) and pure concentration profiles (scores).

Significance: PARAFAC under a properly examined chemical rank can be easily adapted for retrieval the underlying Beer-Lambert law of the original EEFM measurements with a low-rank trilinear structure through the chemically meaningful information either when (i) no deviation of Beer-Lambert law was observed as deeply discussed in connection with the dataset acquired from vomitoxin-driven molecular sensing through TMA-capped AIS QDs, or when (ii) substantial deviations of the Beer-Lambert law are evident, as discussed in connection with the dataset collected from sensing ionic species through Pb(II) bridging of GSH-capped CdTe QDs.

A comprehensive understanding of chemical composition of cultural heritage materials usually requires several complementary analytical techniques. Given the fragility and value of artworks, minimizing or avoiding sampling and performing in situ analysis under ambient light is an important goal. This article outlines a novel prototype designed to merge LIBS, laser-induced fluorescence spectroscopy (LIF), Raman spectroscopy using a single pulsed laser, and reflectance spectroscopy in a multi-spectroscopic characterization system for cultural heritage analysis (SYSPECTRAL). The aim is to analyze cultural heritage materials in their original place, obtaining both elemental and molecular information at such same point that is not always insured with several separated experimental settings. The SYSPECTRAL system focuses on compactness, mobility, and ease of operation. Software designed for the prototype controls multi-spectroscopic measurements, allows for image capture, precise localization, and data acquisition. Reflectance spectra examined the material and colors at the surface, and the LIBS-LIF-Raman package examines the stratigraphic structure of a multi-layered painted sample.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) shows great promise in clinical application for its high specificity, high sensitivity and wide linear range for the determination of small molecules. However, its application in clinical laboratory is hampered by matrix effect of clinical samples which could greatly affect quantification accuracy and the difficulty to be automated for the traditional sample preparation procedures. Thus, new techniques which could achieve selective enrichment to minimize matrix effect and automatic sample preparation of mass spectrometry are needed. We developed an immunologic mass spectrometry (iMS) method to overcome matrix effect and its clinical application was demonstrated for automatic analysis of testosterone (T), progesterone (P) and estradiol (E2) in human serum simultaneously. Firstly, three monoclonal antibodies were coupled to magnetic beads for selective enrichment of target hormones from serum. The immunomagnetic beads were separated, washed and eluted automatically for LC-MS/MS analysis. Analytical performance of the iMS method was validated and compared with traditional LC-MS/MS and chemiluminescence immunoassay (CLIA). Hormone levels were measured for 160 pregnancy women at different gestational weeks. Results showed that target hormones could be selectively captured with absolute recoveries of 93.9%-110.8 %. Relative responses for high, medium and low concentrations of the hormones between serum and methanol solution were 98.0%-109.7 %, 92.2%-105.3 % and 91.7%-96.0 % for T, P and E2, respectively. Calibration curves prepared in methanol solution, BSA solution and blank serum showed good consistency for the iMS method. The automated iMS method could overcome matrix effect of LC-MS/MS and cross-reaction of CLIA. Matrix effect of the iMS method was negligible as high specificity of target hormone enrichment before LC-MS/MS analysis. Matrix-matched calibration standards were no longer necessary for accurate quantification, which was of great benefit for the clinical application of mass spetrometry.

For industrial production and disease diagnosis, real-time detection of low concentrations of NH₃ is crucial, necessitating a gas-sensitive sensor compatible with integrated processes and exhibiting excellent performance. Herein, we employed wet etching and rapid in-situ polymerization on silicon nanowire substrates to grow polyaniline fibers, thereby fabricating NH₃ gas sensors with p-p heterojunction and three-dimensional network structures. Characterization and gas sensing performance testing were conducted. The results demonstrate the outstanding NH₃ detection capabilities of the sensor, providing stable responses down to concentrations as low as 1 ppb, which indicates its LOD is one to two orders of magnitude lower than current similar products. It also exhibits verified selectivity and long-term reliability. The excellent sensing performance is attributed to the high surface area from the silicon nanowire structure and efficient synergy of p-p heterojunction. Additionally, the influence of doping types of the substrates and annealing process were explored. This work serves as a reference for the design of silicon-based gas sensors with high sensitivity, low detection limits, and extended operational lifetimes, suitable for deployment in commercial integrated monitoring systems.

Soil pollution is predominantly attributed to the presence of heavy metal elements and organic compounds; However, current detection methodologies are restricted to the identification of only one of these two sources at a time. A novel analytical approach, known as nanoliter spray enhanced microwave plasma ionization mass spectrometry (Nano-Spray-EMPI-MS), has been developed to facilitate the simultaneous detection of both heavy metals and organic pollutants in soil samples. This technique is characterized by its requirement for minimal sample volumes, thereby allowing for efficient and rapid analysis. The research concentrated on the simultaneous analysis of five heavy metals (Pb, Zn, Cu, Cr, and Ni) and three major phthalates (PAEs), specifically DEHP, DBP, and DMP. The detection and quantification limits for the heavy metals were established to be between 0.16-0.57 and 0.53-1.88 μg L^-1, respectively, while the limits for the PAEs ranged from 0.02 to 0.05 and 0.07-0.16 μg L^-1. Validation of the method's efficacy in soil detection demonstrated recovery rates of 90.9 %-105.7 % for heavy metals and 89.4 %-97.2 % for PAEs. The application of this method analyzing soil samples collected from an area adjacent to a lead-acid battery industrial park in China revealed varying levels of contamination by both heavy metals and PAEs. Notably, Lead contamination was found to be the most pronounced, with a peak concentration of 862.5 mg kg^-1 and a correspondingly high pollution index. These findings are significant for evaluating local ecological risks, pinpointing sources of pollution, and formulating effective pollution management strategies in the region.

Nanoplastics present a significant hazard to both the environment and human health. However, the development of rapid and sensitive analysis techniques for nanoplastics is limited by their small size, lack of specificity, and low concentrations. In this study, a surface-enhanced Raman scattering (SERS) chessboard substrate was introduced as a multi-channel platform for the pre-concentration and detection of nanoplastics, achieved by polydomain aggregating silver nanoparticles (PASN) on a hydrophilic and a punched hydrophobic PVDF combined filter membrane. Through a straightforward suction filtration process, nanoplastics were captured by the PASN gap in a single step for subsequent SERS detection, while excess moisture was promptly eliminated from the filter membrane. The PASN-based SERS chessboard substrate, benefiting from the enhanced electromagnetic (EM) field, effectively discriminated polystyrene (PS) nanoplastics ranging in size from 30 nm to 1000 nm. Furthermore, this substrate demonstrated favorable repeatability (RSD of 8.6 %), high sensitivity with a detection limit of 0.001 mg/mL for 100 nm of PS nanoplastics, and broad linear detection ranges spanning from 0.001 to 0.5 mg/mL (R² = 0.9916). Additionally, the SERS chessboard substrate enabled quantitative analysis of nanoplastics spiked in tap and lake water samples. Notably, the entire pre-concentration and detection procedure required only 3 μL of sample and could be completed within 1 min. With the accessibility of portable detection instruments and the ability to prepare substrates on demand, the PASN-based SERS chessboard substrate is anticipated to facilitate the establishment of a comprehensive global nanoplastics map.