Search Results (382)

Efficient and accurate identification of spectra from large databases remains a critical challenge in spectroscopic analysis. Previous coarse-to-fine frameworks, typically combining Principal Component Analysis (PCA)-based preprocessing and k-d tree search, have shown that structured search can reduce computational cost without sacrificing accuracy. Building on this foundation, we propose an enhanced algorithm that integrates an improved preprocessing and a novel limited axis check (LAC) method. The preprocessing stage applies running average filtering, downsampling, and threshold-based noise-cutting, followed by PCA to construct a compact, noise-suppressed spectral representation. In the search stage, the proposed LAC algorithm replaces conventional tree-based structures by performing an axis-wise limited-range search and voting strategy to efficiently locate the candidate spectrum closest to the query within the reduced PCA domain. A subsequent refined search determines the closest spectrum by computing distances to the shortlisted candidates. Experimental results demonstrate that the proposed approach attains accuracy equivalent to that of the full search while markedly reducing computational complexity. These results confirm that the integration of enhanced preprocessing and LAC substantially accelerates the spectral search process. Full article

The rapid emergence of novel psychoactive substances (NPSs) after 2020 has created one of the most dynamic analytical challenges in modern forensic science. Hundreds of new synthetic cannabinoids, synthetic cathinones, synthetic opioids, hallucinogens, and dissociatives, appearing as hybrid or structurally modified analogues of conventional drugs, have entered the illicit market, frequently found in complex polydrug mixtures. This review summarizes recent advances in gas chromatography-mass spectrometry (GC-MS) for their detection, structural elucidation, and differentiation between 2020 and 2025 based on the ScienceDirect and Google Scholar databases. Due to its reproducible electron-ionization spectra, established reference libraries, and robustness toward complex matrices, GC-MS remains the primary tool for the separation and identification of emerging NPS. The current literature highlights significant improvements in extraction and pre-concentration procedures, derivatization strategies for thermally unstable analogues, and chromatographic optimization that enable discrimination between positional and stereoisomers. This review covers a wide range of matrices, including powders, herbal materials, vaping liquids, and infused papers, as well as biological specimens such as blood, urine, and hair. Chemometric interpretation of GC-MS data now supports automated classification and prediction of fragmentation pathways, while coupling with complementary spectroscopic techniques strengthens compound confirmation. The review emphasizes how continuous innovation in GC-MS methodology has paralleled the rapid evolution of the NPS landscape, ensuring its enduring role as a reliable, adaptable, and cost-effective platform for monitoring emerging psychoactive substances in seized materials. Full article

Background: The Ames test, a biological assay employing various strains of Salmonella typhimurium, serves as a cornerstone in genetic toxicology for evaluating the mutagenic and potentially carcinogenic properties of chemical compounds. However, experimental testing is resource-intensive and time-consuming for screening the vast chemical space of existing and novel drug candidates in pharmaceutical development. Methods: To address this limitation, we have developed the Ames Mutagenicity Predictor web application, which predicts mutagenic activity in the Ames test for given structural formulas across a comprehensive panel of different bacterial strains. The application utilizes advanced structure–activity relationship (SAR) models generated by PASS (Prediction of Activity Spectra for Substances) v2024 software. The training set comprised 3250 compounds with experimentally determined mutagenicity across 69 different strains, compiled from peer-reviewed literature and established databases, and 4285 non-mutagenic compounds from the WWAD as negative examples. Results: Leave-one-out cross-validation (LOOCV) of the 69 strain-specific models yielded an average Invariant Accuracy of Prediction (IAP) of about 0.944, and for the unspecified mutagenicity, a value of 0.962 was obtained. Conclusions: These validated models have been integrated into a freely accessible web application Ames Mutagenicity Predictor that enables users to input compound structures through multiple formats: a built-in chemical editor, SMILES notation, or compound name search. The application generates comprehensive reports detailing the predicted probability of positive Ames test results for each individual strain, providing researchers with detailed mutagenicity profiles. Full article

Health wines are alcoholic beverages produced by infusing traditional liquors or rice wines with natural, medicinal, and food-safe ingredients. However, to accelerate efficacy, some manufacturers illegally adulterate health wines with phosphodiesterase type 5 (PDE-5) inhibitors, which may cause severe adverse effects. This study developed a method based on ultra-high-performance liquid chromatography–time-of-flight mass spectrometry (UPLC–TOF/MS) for the rapid screening and identification of 68 PDE-5 inhibitors illegally added to health wines. After optimizing the sample preparation procedure, chromatographic conditions, mass spectrometric parameters, and primary and secondary mass spectra of the 68 PDE-5 inhibitors were acquired as reference standards. Retention times and mass spectral data were imported into the Personal Compound Database and Library, establishing a high-resolution screening database with matched drug names, molecular formulas, and accurate molecular weights. A quantitative method was validated using 11 commonly adulterated compounds, including sildenafil. The response was highly linear (r ≥ 0.9988; 0.8–400 μg/L) with low detection limits (0.2–1.0 μg/L). The average spiked recoveries were 71.2–104.1%, with relative standard deviations of ≤10.1%. Among 59 commercial health wine samples, three batches tested positive for PDE-5 inhibitors (detection rate: 5.1%). The proposed method can assist market surveillance even when reference standards are unavailable for all compounds. Full article

Unsymmetrical dimethylhydrazine (UDMH) was previously used as a rocket propellant in launch vehicles. During the operation and accidents of launch vehicles, hundreds of tons of UDMH were released. While these launch vehicles are gradually being phased out, UDMH continues to be used in space technology and other industries. When released into the environment, UDMH forms numerous transformation products. Several dozen have been reliably identified, and hundreds are believed to exist, many of which are highly toxic and quite persistent in the environment. Gas chromatography–mass spectrometry (GC-MS) is one of the primary methods for identifying these compounds. Library searches using mass spectra and retention indices are often used. However, UDMH transformation products are highly specific—they are organic compounds, typically aromatic heterocycles, with unusually high nitrogen content. Such compounds are poorly represented in GC-MS databases, while existing data are often of poor quality and were obtained back in the 1980s. A database of such compounds was presented, containing information on retention indices for non-polar (5%-phenylpolydimethylsiloxane) and polar (polyethylene glycol) stationary phases, as well as electron ionization mass spectra (70 eV) for 104 nitrogen-containing compounds: derivatives of triazoles, pyrazoles, imidazoles, pyridines, diazines, and triazines, as well as amides and other compounds. Many of the compounds presented in the database are proven UDMH transformation products, while many of the other compounds are probable. Derivatives of triazoles and triazines are also used as pesticides, and our database can be useful in detecting their derivatives. The database is free and available online. Full article

High-resolution Fourier transform infrared (FTIR) spectra of methyl fluoride (CH₃F) were recorded in the mid- and far-infrared regions using the Bruker IFS 125HR spectrometers at GSMA (Reims, France) and at the SOLEIL synchrotron facility (Saint-Aubin, France). The measurements cover both the pure rotational transitions of the ground state (10–100 cm⁻¹) and the vibrational triad region (1950–2450 cm⁻¹), which includes the 2${\nu }_3$, ${\nu }_3+{\nu }_6$, and 2${\nu }_6$ bands. Spectra were recorded under various pressure conditions to optimize line visibility, with a high resolution. Line assignments were performed using predictions from the tensorial effective Hamiltonian implemented in the MIRS package, together with a newly developed automated assignment tool, SpectraMatcher, which facilitates line matching and discrimination of CH₃F transitions from overlapping CO₂ features. More than 5000 transitions (up to $J=52$ in the ground state and up to $J=45$ in the triad and $K=19$) were assigned and included in a global fit. The sixth-order tensorial effective Hamiltonian model yielded excellent agreement with experiment, with root mean square (RMS) deviations better than 7 × 10⁻⁴ cm⁻¹ across all regions. This paper presents the first continuous rovibrational study of CH₃F over both the triad and far-infrared ground state regions. The improved accuracy from previous studies stems from the improved set of effective Hamiltonian parameters which will also form a good basis from future applications in atmospheric modelling and spectroscopic databases. Full article

Ductility, as a fundamental mechanical property, allows structures to undergo inelastic deformations and dissipate seismic energy while maintaining their load-carrying capacity without substantial strength degradation. Thus, the estimation of structural ductility demand has consistently constituted an essential topic of research interest in earthquake engineering. In this study, an iterative procedure for estimating the ductility demand of elastoplastic single-degree-of-freedom (SDOF) systems through dissipated energy is introduced. The proposed procedure helps the determination of ductility demand by use of only elastic response spectra. It initially estimates the hysteretic energy as a proportion of the total input energy. Then, ductility demand is estimated with the help of a developed equation by performing regression analyses based on the nonlinear time history analyses results of elastoplastic single-degree-of-freedom (SDOF) systems with a certain strength. Time history analyses were carried out by using an extensive earthquake ground motion database, which includes a total of 268 far-field records, two horizontal components from 134 recording stations located on firm soil sites. Full article

This article introduces the main characteristics of PyMossFit, a software for Mössbauer spectra fitting. It is explained how each aspect of the code works. Based on the Lmfit Python package, it is a robust data fitting tool. Designed to run through Jupyter Notebook in the Google Colab cloud, it also allows one to work via multiple devices and operating systems. In addition, it allows the fitting procedure to be performed collaboratively among researchers. The software performs the folding of raw data with a discrete Fourier transform. Data smoothing is available with the use of a Savitzky–Golay algorithm. Moreover, a K-nearest neighbor algorithm enables users to determine the present phases by matching the correlations of hyperfine parameters from a local database. Full article

Surface organic coatings (SOCs) composed of drying oils, resins, and bitumen were commonly applied to small Renaissance bronze sculptures to enhance their visual and physical properties, producing dark, lustrous surfaces that were both esthetic and protective. Yet, the identification of these coatings remains challenging due to aging, conservation interventions, and the damage caused by physical sampling. This study presents a reproducible, non-destructive protocol for characterizing SOCs on metal substrates using external reflection Fourier transform infrared spectroscopy (ER-FTIR) and fiber optic reflectance spectroscopy (FORS). Twenty-seven reference coating mock-ups of linseed oil, walnut oil, mastic resin, pine resin, and bitumen were stoved onto bronze coupons and artificially aged. Spectra were analyzed across the visible/near-infrared (VIS-NIR) (~400–1000 nm), short-wave-infrared (SWIR) (~1000–2500 nm), and mid-infrared (MIR) (~2.5–25 µm) ranges, with key diagnostic features identified for each component and blend, including primary absorptions, combination bands, and overtones. ER-FTIR proved highly effective in detecting oil–resin mixtures and later wax coatings through characteristic bands in the MIR, while FORS, enhanced by first-derivative processing, successfully differentiated triterpenoid and diterpenoid resins and identified multi-component SOCs in the SWIR region. The reference spectral database generated in this study is intended to serve as a comparative tool for future non-invasive analysis of organic coatings on metal surfaces and to demonstrate that ER-FTIR and FORS, used in tandem, offer a practical and scalable framework for the non-destructive identification of SOCs. Full article

Based on low-resolution OSIRIS/GTC optical spectra, we assign spectral classes to 38 poorly studied ultracool/brown dwarf candidates from the 2MASS database. For almost all of the targets, this is the first optical spectral classification. For the dwarfs showing H$\alpha$ emission, we calculate the ratio of H$\alpha$ to bolometric luminosity, which is the most common characteristic of magnetic activity in cool stars. For the others, we give 3$\sigma$ upper limits. We also include estimates of the effective temperatures and log g and distances from Gaia based on a comparison with models. For one of our targets—LSR J2105+2514, previously classified as a dwarf carbon star—we confirm this classification and report H$\alpha$ and H$\beta$ line emission in the spectrum for the first time. Dwarf carbon stars (dC) are low-mass main sequence stars that have undergone mass-transfer binary evolution. The Balmer line emission from these objects most likely indicates coronal activity of the dwarf, which in turn may be due to either intrinsic magnetic activity or spin-up from accretion or tidal locking. Full article

Molecular networking (MN) is a novel mass spectrometry data analysis method that has advanced significantly in recent years and has rapidly emerged as a popular technique. By visualizing the connections between structurally similar compounds in mass spectra, MN greatly enhances the efficiency with which harmful substances and bioactive ingredients in cosmetics are screened. In this review, we summarize the principles and main categories of MN technology and systematically synthesize its progress in cosmetic testing applications based on 83 recent studies (2020 to 2025). These applications include screening banned additives, analyzing complex matrix components, and identifying efficacy-related ingredients. We highlight MN’s successful application in detecting prohibited substances, such as synthetic dyes and adulterants, with limits of detection (LOD) as low as 0.1–1 ng/g, even in complex matrices, such as emulsions and colored products. MN-guided isolation has enabled the structural elucidation of over 40 known and novel compounds in the analysis of natural ingredients. We also discuss current challenges, such as limitations in instrument sensitivity, matrix effects, and the lack of cosmetic-specific component databases. Additionally, we outline future prospects for expanding MN’s application scope in cosmetic testing and developing it toward computer-aided intelligence. This review aims to provide valuable references for promoting innovation in cosmetic testing methods and strengthening quality control in the industry. Full article

The distribution of iron oxide (FeO) across the lunar surface is a key parameter for reconstructing the Moon’s geological evolution and evaluating its in situ resource potential for future exploration. This study applies a spectral-based approach to estimate FeO concentrations using remote sensing reflectance data combined with a Random Forest (RF) regression model. The model was trained on a dataset comprising 89 lunar samples from the Reflectance Experiment Laboratory (RELAB) database, supplemented with compositional data from Apollo samples available via the Lunar Sample Compendium and reflectance spectra from the Clementine mission. Spectral data spanning the visible to shortwave infrared range (415–2780 nm) were analysed, with diagnostic absorption features centred around 950 nm, typically associated with Fe²⁺. Model validation was conducted against FeO estimates from independent nearside locations not included in the training set, as reported by an external remote sensing study. The trained model was also applied to produce a new global FeO abundance map, demonstrating strong spatial consistency with recent high-resolution reference datasets. These results confirm the model’s predictive accuracy and support the use of legacy multispectral data for large-scale lunar geochemical mapping. This work highlights the potential of combining machine learning techniques, such as Random Forest, with remote sensing data to enhance lunar surface composition analysis, supporting the planning of future exploration and resource utilisation missions. Full article

The reliability of the EDS (Electric Drive System) in electric vehicles is crucial to overall vehicle performance. This study addresses the challenge of acquiring high-fidelity internal load data in the early development phase due to the absence of prototypes, overcoming the limitations of traditional road tests, which are costly, time-consuming, and unable to measure gear meshing forces. A method based on a VPG (Virtual Proving Ground) is proposed to acquire internal loads of a dual-motor EDS, analyze the impact of typical virtual fatigue durability road conditions on critical components, and generate load spectra for test bench experiments. Through point cloud data-based road modeling and rigid-flexible coupled simulation, dynamic loads are accurately extracted, with pseudo-damage contributions from eight intensified road conditions quantified using pseudo-damage calculations, and equivalent sinusoidal load spectra generated using the rainflow counting method and linear cumulative damage theory. Compared to the limitations of existing VPG methods that rely on simplified models, this study enhances the accuracy of internal load extraction, providing technical support for EDS durability testing. Building on existing research, it focuses on high-fidelity acquisition of EDS loads and load spectrum generation, improving applicability and addressing deficiencies in simulation accuracy. This study represents a novel application of VPG technology in electric drive system development, resolving the issue of insufficient early-stage load spectra. It provides data support for durability optimization and bench testing, with future validation planned using real vehicle data. Full article

The main objective of this research consists in finding a rapid method for cheese lipidomics based on NMR data. This study plays an important role in differentiation and characterization of cheese samples in accordance with fat composition, especially in the case of fat substitution with exogenous animal or vegetal fat. Our findings play an important role in relation to religious requirements regarding non-allowed foods (pork fat, for example, in some cultures) and in the correct characterization of foods according to their lipidic profile. The approach consists in establishing a fingerprint region (0.86–0.93 ppm from ¹H-NMR spectra) and then creating a database of the results obtained. The evaluation of the long-chain saturated fatty acids and the saturated short-chain fatty acids (C4 to C8) was established with a newly developed set of equations that make the computation possible even when mixtures of fats from different sources are present. This was accomplished by developing a new method for quantification of the fatty acid composition of different types of cheese, based on ¹H-NMR spectroscopy. Principal component analysis (PCA) was applied to 40 cheese samples with varying degrees (0%, 5%, 12%, or 15%) of milk fat substitution (pork fat, vegetable fat, hydrogenated oils) and different clotting agents (calcium chloride or citric acid). The best sample discrimination was achieved using fatty acid profiles estimated from ¹H-NMR data (using a total of six variables), explaining 89.7% of the total variance. Clear separation was observed between samples containing only milk fat and those with added fats. These results demonstrate that the integration of ¹H-NMR spectroscopy with principal component analysis (PCA) provides a reliable approach for discriminating cheese samples according to their fat composition. Full article

Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in Staphylococcus aureus were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. Methods: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. Results: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. Conclusions: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. Full article