Spectral Quality Research Articles

Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data

Context. Hot subdwarf stars are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. In the Hertzsprung-Russell Diagram they are located by the blue end of the Horizontal Branch. Most models agree on a quite probable common envelope binary evolution scenario in the Red Giant phase. However, the current binarity rate for these objects is yet unsolved, but key, question in this field. Aims. This study aims to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence techniques and data from the third Gaia data release (GDR3). The results will be compared with those obtained previously using Virtual Observatory techniques on coincident samples. Methods. The methods used for hot subdwarf binary classification include supervised and unsupervised machine learning techniques. Specifically, we have used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their colour-magnitude properties. Among these, 2815 objects have Gaia DR3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). In order to ensure spectral quality, previously to SOM and CNN classification, our 2815 BP/RP set were pre-analysed with two different approaches: the cosine similarity technique and the Uniform Manifold Approximation and Projection (UMAP) technique. Additional analysis onto a golden sample of 88 well-defined objects, is also presented. Results. The findings demonstrate a high agreement level (∼70–90%) with the classifications from the Virtual Observatory Sed Analyzer (VOSA) tool. This shows that the SVM, SOM, and CNN methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. Notably, SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry, and CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single–binary differences are especially observable on the infrared flux in our Gaia DR3 BP/BR spectra, at wavelengths larger than ∼700 nm. Conclusions. We find that all the methods used are in fairly good agreement and are particularly effective to discern between single and binary systems. The agreement is also consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods also appear effective for detecting peculiarities in the spectra. While promising, challenges in dealing with uncertain compositions highlight the need for caution, suggesting further research is needed to refine techniques and enhance automated classification reliability, particularly for large-scale surveys.

Evaluating the general V(λ), mismatch spectral quality factor index ƒ₁ and the uncertainty of photometers

The general term photometry refers to the measurement of light and its perceived brightness; it can be applied to various purposes, including lighting design, display calibration, and the setting of safety standards. A device used for measuring light is called a light meter or photometer. Ideally, such devices should have a spectral responsivity-that is, they should respond to light in a way which mimics the human eye's sensitivity to different wavelengths. The sensitivity of the human eye is described by the Commission Internationale de l'Éclairage, CIE, luminous efficiency function, V(λ), a plot of the sensitivity of the eye versus wavelength over the visible region, from violet, approximately 380 nm, through red, approximately 780 nm. In practical applications, it is usually preferable to detect light with silicon or selenium photodetectors and measure the electrical current generated. These photodetectors are sensitive to a wide range of wavelengths, including those outside of the visible spectrum: ultraviolet (UV) and infrared (IR). However, due to its nature, the spectral responsivity of silicon and selenium photodetectors is also different from that of the human eye. For example, silicon photodetectors are much more sensitive to infrared radiation, which is imperceptible to human vision. This in turn leads to erroneous light measurements when using the direct raw response of the detector. The spectral quality factor and , indicating the mismatch between the spectral responsivity and the CIE luminous efficiency function V(λ), is the most crucial characteristic of photometers. The general mismatch index , denoted as V(λ), delineates the disparity between a photometer's relative spectral responsivity and the spectral luminous efficiency function for photopic vision, V(λ). It's highly probable that photometers will adopt white light-emitting diode (LED) sources as calibration references going forward. This transition may necessitate refining the general V(λ) mismatch index's definition, possibly through alternative normalization methods for or by introducing a different assessment function for evaluating mismatches . In this research, spectral quality factor for the photometer NIS-2 has been determined with value 3.8%. This percentage value is classified the photometer NIS-2 as medium quality. The uncertainty was determined through a calculation performed using my Microsoft Excel program, and the final value obtained is 0.010.

Classification of Vaginal Cleanliness Grades through Surface‐Enhanced Raman Spectral Analysis via The Deep‐Learning Variational Autoencoder–Long Short‐Term Memory Model

In this study, it is aimed to establish a novel method based on a deep‐learning‐guided surface‐enhanced Raman spectroscopy (SERS) technique to achieve rapid and accurate classification of vaginal cleanliness levels. We proposed a variational autoencoder (VAE) approach to enhance spectral quality, coupled with a deep learning algorithm long short‐term memory (LSTM) neural network to analyze SERS spectra produced by vaginal secretions. The performance of various machine learning (ML) algorithms is assessed using multiple evaluation metrics. Finally, the reliability of the optimal model is tested using blind test data (N = 10/group for each cleanliness level). The data quality of the SERS fingerprints of four types of vaginal secretions is significantly improved after VAE decoding and reconstruction. The signal‐to‐noise ratio of the generated spectra increased from the original 2.58–11.13. Among all algorithms, the VAE–LSTM algorithm demonstrates the best prediction ability and time efficiency. Additionally, blind test datasets yielded an overall accuracy of 85%. In this study, it is concluded that the deep‐learning‐guided SERS technique holds significant potential in rapidly distinguishing between different levels of vaginal cleanliness through human vaginal secretion samples. This contributes to the efficient diagnosis of vaginal cleanliness levels in clinical settings.

Tropospheric NO2 retrieval algorithm for geostationary satellite instruments: applications to GEMS

Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO2) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar-orbiting satellites Global Ozone Monitoring Experiment-2 (GOME-2) and Tropospheric Monitoring Instrument (TROPOMI), but several improvements are implemented to account for specific features of geostationary satellites. The DLR GEMS NO2 retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO2 retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere–troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO2 retrieval and adapted to GEMS and (2) estimation of stratospheric NO2 columns from the Copernicus Atmosphere Monitoring Service (CAMS) Integrated Forecast System (IFS) cycle 48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO2 retrieval. While STREAM provides hourly estimates of stratospheric NO2, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO2 columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations. For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of the GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to the GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close to or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO2 vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO2 retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations throughout the day with high spatial resolution and the advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements. The retrieved DLR GEMS tropospheric NO2 columns show good capability for capturing hotspot signals at the scale of city clusters and describe spatial gradients from city centres to surrounding areas. Diurnal variations of tropospheric NO2 columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO2 columns against TROPOMI v2.4 and GEMS v2.0 operational products shows overall good agreement. The uncertainty of DLR GEMS tropospheric NO2 vertical columns varies based on observation scenarios. In regions with low pollution levels such as open-ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 50 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO2 columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO2 columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO2 peak.

ECCENTRIC: A fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR

Abstract A novel method for fast and high-resolution metabolic imaging, called ECcentric Circle ENcoding TRajectorIes for Compressed sensing (ECCENTRIC), has been developed at 7 Tesla MRI. ECCENTRIC is a non-Cartesian spatial-spectral encoding method designed to accelerate magnetic resonance spectroscopic imaging (MRSI) with high signal-to-noise at ultra-high field. The approach provides flexible and random sampling of the Fourier space without temporal interleaving to improve spatial response function and spectral quality. ECCENTRIC enables the implementation of spatial-spectral MRSI with reduced gradient amplitudes and slew-rates, thereby mitigating electrical, mechanical, and thermal stress of the scanner hardware. Moreover, it exhibits robustness against timing imperfections and eddy-current delay. Combined with a model-based low-rank reconstruction, this approach enables simultaneous imaging of up to 14 metabolites over the whole brain at 2–3 mm isotropic resolution in 4–10 min. MRSI ECCENTRIC was performed on four healthy volunteers, yielding high-resolution spatial mappings of neurochemical profiles within the human brain. This innovative tool introduces a novel approach to neuroscience, providing new insights into the exploration of brain activity and physiology.

VALIDATION OF 2HG-TARGETTED MR SPECTROSCOPY FOR IDENTIFYING IDH-MUTANT GLIOMAS

Abstract AIMS Diffuse gliomas, the most common adult primary brain tumour, are classified by their histological and molecular characteristics and genetic profile. Isocitrate dehydrogenase (IDH) mutant gliomas correlate with longer mean survival and progression-free survival time. In tumour cells, IDH mutations lead to metabolic changes and the production of oncometabolite 2-hydroxyglutarate (2HG). MR spectroscopy (MRS) can assess metabolic changes in disease states in-vivo and non-invasively. In this study, we demonstrate a validation pathway for two advanced MRS methods to be used as IDH-mutation biomarkers in clinic. METHOD Simulated, phantom, and healthy volunteer experiments were performed to optimise and validate three MRS sequences tuned for measuring 2HG. A PRESS sequence was used with TR/TE of 2500/97 ms aiming at the 2HG peaks around 2.25 ppm. An edited PRESS sequence, named MEGA-PRESS, tailored to 2HG 4.02 ppm peak was used with TR/TE of 3000/68 ms, 128 averages, and with editing pulses at 1.9 and 7.5 ppm. A long TE semi-LASER MRS sequence, with improved voxel localisation, was also used (TR/TE: 2500/110 ms). RESULTS Simulations were performed to find optimised sequence parameters and to develop basis sets for analysing the phantom and healthy volunteer acquisitions. Tumour-mimicking phantoms with variable concentrations of 2HG and cystathionine were created and validated with 800 MHz NMR spectroscopy. Phantoms were then used to assess the sensitivity and specificity of the MRS for 2HG and cystathionine. MEGA-PRESS proved Synthetic in-vivo 2HG spectra, combined from healthy volunteers and simulated or phantom-2HG spectra, were used to assess the spectral quality needed to reliably measure 2HG. CONCLUSION MEGA-PRESS proved to be the most robust MRS method and demonstrated accurate and precise measurement of phantom and synthetic 2HG with acceptable SNR. Clinical validation of the technique will need to be performed on LGG patient population, and the overall accuracy will be compared with patient IDH mutation status.

Analysis of mapping atomic models to coarse-grained resolution.

Low-resolution coarse-grained (CG) models provide significant computational and conceptual advantages for simulating soft materials. However, the properties of CG models depend quite sensitively upon the mapping, M, that maps each atomic configuration, r, to a CG configuration, R. In particular, M determines how the configurational information of the atomic model is partitioned between the mapped ensemble of CG configurations and the lost ensemble of atomic configurations that map to each R. In this work, we investigate how the mapping partitions the atomic configuration space into CG and intra-site components. We demonstrate that the corresponding coordinate transformation introduces a nontrivial Jacobian factor. This Jacobian factor defines a labeling entropy that corresponds to the uncertainty in the atoms that are associated with each CG site. Consequently, the labeling entropy effectively transfers configurational information from the lost ensemble into the mapped ensemble. Moreover, our analysis highlights the possibility of resonant mappings that separate the atomic potential into CG and intra-site contributions. We numerically illustrate these considerations with a Gaussian network model for the equilibrium fluctuations of actin. We demonstrate that the spectral quality, Q, provides a simple metric for identifying high quality representations for actin. Conversely, we find that neither maximizing nor minimizing the information content of the mapped ensemble results in high quality representations. However, if one accounts for the labeling uncertainty, Q(M) correlates quite well with the adjusted configurational information loss, Îmap(M), that results from the mapping.

Effect of Decreasing Aperture Diameter on Signal Transmission from the Scintillator to the Photomultiplier Tube Over a Wide Energy Range

In this paper, we study the effect of decreasing aperture diameter on signal transmission from the scintillator to the PMT. The apertures have been inserted directly between the scintillator and the photomultiplier. Three different aperture diameters have been used over a wide energy range. A newly developed fast digital spectrometer has been utilized for experimental measurement in the radial channel of the VR-1 research reactor. The detector signals with and without inserted apertures have been measured and evaluated. The aim of our research was to determine the effect of the apertures on the spectral quality, energy range and the evaluated physical quantities.

Interpretable Model-Driven Deep Network for Hyperspectral, Multispectral, and Panchromatic Image Fusion.

Simultaneously fusing hyperspectral (HS), multispectral (MS), and panchromatic (PAN) images brings a new paradigm to generate a high-resolution HS (HRHS) image. In this study, we propose an interpretable model-driven deep network for HS, MS, and PAN image fusion, called HMPNet. We first propose a new fusion model that utilizes a deep before describing the complicated relationship between the HRHS and PAN images owing to their large resolution difference. Consequently, the difficulty of traditional model-based approaches in designing suitable hand-crafted priors can be alleviated because this deep prior is learned from data. We further solve the optimization problem of this fusion model based on the proximal gradient descent (PGD) algorithm, achieved by a series of iterative steps. By unrolling these iterative steps into several network modules, we finally obtain the HMPNet. Therefore, all parameters besides the deep prior are learned in the deep network, simplifying the selection of optimal parameters in the fusion and achieving a favorable equilibrium between the spatial and spectral qualities. Meanwhile, all modules contained in the HMPNet have explainable physical meanings, which can improve its generalization capability. In the experiment, we exhibit the advantages of the HMPNet over other state-of-the-art methods from the aspects of visual comparison and quantitative analysis, where a series of simulated as well as real datasets are utilized for validation.

Optimising the acquisition conditions of high information quality low-field NMR signals based on a cutting-edge approach applying information theory and Taguchi's experimental designs – Virgin olive oil as an application example

BackgroundDeveloping a new spectrometric analytical method based on a fingerprinting approach requires optimization of the experimental stage, particularly with novel instruments like benchtop low-field NMR spectrometers. To ensure high-quality LF-NMR spectra before developing the multivariate model, an experimental design to optimize instrument conditions is essential. However, difficult-to-control factors may be critical for optimization. Taguchi methodology addresses these factors to obtain a system robust to variation. This study uses the Taguchi methodology to optimize instrument settings for acquiring high-quality 1H and 13C LF-NMR signals in a short time from virgin olive oil (VOO). ResultsTwo experimental trials (for 1H and 13C signals, respectively) were carried out and analysed to find an optimal and robust combination of instrument settings against changes in two difficult-to-control factors: ambient temperature and small deviations of the NMR tube volume (700 ± 50 μL). The responses to be optimised, run time and spectral information quality, were analysed separately and jointly, as some factors showed opposite behaviour in the effect on the responses. Multiple response analysis based on suitable desirability functions yielded a combination of factors resulting in desirability values above 0.8 for 1H LF-NMR signals and almost 1.0 for 13C LF-NMR signals.In addition, a novel approach to assess the information quality of an analytical signal was proposed, addressing a major challenge in analytical chemistry. By applying information theory and calculating information entropy, this approach demonstrated its potential for selecting the highest quality (i.e. most informative) analytical signals. SignificanceThe acquisition instrument conditions of LF-NMR were successfully optimised using Taguchi methodology to acquire highly informative 1H and 13C spectra in a minimum run time. The importance lies in the future development of non-targeted analytical applications for VOO quality control. In addition, the innovative use of information entropy to a priori assess the signal quality represents a significant advance and proposes a solution to a long-standing challenge in analytical chemistry.

A NIRS-based recognition of coal and rock using convolution-multiview broad learning system

Achieving high production in the top coal caving process from thick coal seams is crucial. Thus, the timely decision of when to stop caving poses an urgent challenge to impact the mining loss rate and cost recovery. To address this issue, an innovative recognition system has been developed using Near-Infrared Spectroscopy (NIRS) technology. It stands out for its on-site usability, it enables rapid data collection and local recognition at the longwall face. Furthermore, to overcome the limitations of existing methods in adapting to variations in spectral data quality during on-site collection and the lack of integration of spectral data across different feature processing stages, a coal-rock recognition method has been developed which can ignore the influence of acquisition factors(granularity, light source angle, and detection sensor angle). This method incorporates the features of convolution and multi-view into the BLS model, the designed model structure exhibits a remarkable recognition accuracy of 99.78 %. The model was deployed into the recognition system, and experimental tests were conducted on the working face. The results showed that the recognition system can effectively identify the entire coal-caving process and achieve a recognition accuracy of 92.3 %. This capability is crucial for determining the optimal point to stop roof caving.

Raman Spectroscopic Analysis of Steviol Glycosides: Spectral Database and Quality Control Algorithms

Besides all sharing an extraordinary high (i.e., up to ~450 times) sweetening power as compared to sucrose and while presenting strong similarities in their molecular structures, molecules belonging to the family of diterpene glycosides (i.e., the secondary metabolites of Stevia rebaudiana) differ in specific structural details that strongly impact on their levels of sweetness and bitter aftertaste. Given the nutritional and pharmacological benefits of steviol secondary metabolites as natural dietetic and anti-diabetic remedies, extraction and purification of steviol glycosides from plant material are nowadays widely spread among many countries. However, an unpleasant bitter aftertaste, which is linked to a genetic variation in human bitter taste receptors, hampers the full exploitation of such benefits and calls for a prompt improvement in organoleptic property control of stevia products. A deeper understanding of the molecular structure of different steviol glycosides and the consequent development of promptly measurable criteria for the organoleptic performance of their mixtures will support processing optimization and control of taste profiles within desired yields. The present research aimed at establishing Raman spectroscopic algorithms for quantitative characterizations of raw stevia-based sweetener products. First, a series of twelve high-purity diterpene glycosides were analyzed by high spectrally resolved Raman spectroscopy and their spectra analyzed in order to establish a complete Raman library of molecular structures. Then, quantitative spectroscopic parameters were built up and applied to characterize the organoleptic property of five different commercially available samples including the recently developed Rebaudioside M isoform. Raman spectroscopy was confirmed as a versatile analytical technique that could be used for quantitative quality control tasks on the production line and for prompt in situ characterizations of purchased products.

Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox.

The machine-learning tool MS2Tox can prioritize hazardous nontargeted molecular features in environmental waters, by predicting acute fish lethality of unknown molecules based on their MS2 spectra, prior to structural annotation. It has yet to be investigated how the extent of molecular coverage, MS2 spectra quality, and toxicity prediction confidence depend on sample complexity and MS2 data acquisition strategies. We compared two common nontargeted MS2 acquisition strategies with liquid chromatography high-resolution mass spectrometry for structural annotation accuracy by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference chemicals spiked to LC-MS water, groundwater, surface water, and wastewater. Data-dependent acquisition (DDA) resulted in higher rates (19-62%) of correct structural annotations among reference chemicals in all matrices except wastewaters, compared to data-independent acquisition (DIA, 19-50%). However, DIA resulted in higher MS2 detection rates (59-84% DIA, 37-82% DDA), leading to higher true positive rates for spectral library matching, 40-73% compared to 34-72%. DDA resulted in higher MS2Tox toxicity prediction accuracy than DIA, with root-mean-square errors of 0.62 and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a "CombinedConfidence" score to convey relative confidence in MS2Tox predictions and apply this approach to prioritize potentially ecotoxic nontargeted features in environmental waters.

Optimization of 1H-MRS methods for large-volume acquisition of low-concentration downfield resonances at 3 T and 7 T.

This goal of this study was to optimize spectrally selective 1H-MRS methods for large-volume acquisition of low-concentration metabolites with downfield resonances at 7 T and 3 T, with particular attention paid to detection of nicotinamide adenine dinucleotide (NAD+) and tryptophan. Spectrally selective excitation was used to avoid magnetization-transfer effects with water, and various sinc pulses were compared with a band-selective, uniform response, pure-phase (E-BURP) pulse. Localization using a single-slice selective pulse was compared with voxel-based localization that used three orthogonal refocusing pulses, and low bandwidth refocusing pulses were used to take advantage of the chemical shift displacement of water. A technique for water sideband removal was added, and a method of coil channel combination for large volumes was introduced. Proposed methods were compared qualitatively with previously reported techniques at 7 T. Sinc pulses resulted in reduced water signal excitation and improved spectral quality, with a symmetric, low bandwidth-time product pulse performing best. Single-slice localization allowed shorter TEs with large volumes, enhancing signal, whereas low-bandwidth slice-selective localization greatly reduced the observed water signal. Gradient cycling helped remove water sidebands, and frequency aligning and pruning individual channels narrowed spectral linewidths. High-quality brain spectra of NAD+ and tryptophan are shown in 4 subjects at 3 T. Improved spectral quality with higher downfield signal, shorter TE, lower nuisance signal, reduced artifacts, and narrower peaks was realized at 7 T. These methodological improvements allowed for previously unachievable detection of NAD+ and tryptophan in human brain at 3 T in under 5 min.

Using Variable Data-Independent Acquisition for Capillary Electrophoresis-Based Untargeted Metabolomics.

Capillary electrophoresis coupled with tandem mass spectrometry (CE-MS/MS) offers advantages in peak capacity and sensitivity for metabolic profiling owing to the electroosmotic flow-based separation. However, the utilization of data-independent MS/MS acquisition (DIA) is restricted due to the absence of an optimal procedure for analytical chemistry and its related informatics framework. We assessed the mass spectral quality using two DIA techniques, namely, all-ion fragmentation (AIF) and variable DIA (vDIA), to isolate 60-800 Da precursor ions with respect to annotation rates. Our findings indicate that vDIA, coupled with the updated MS-DIAL chromatogram deconvolution algorithm, yields higher spectral matching scores and annotation rates compared to AIF. Additionally, we evaluated a linear migration time (MT) correction method using internal standards to accurately align chromatographic peaks in a data set. Postcorrection, the data set exhibited less than 0.1 min MT drifts, a difference mostly equivalent to that of conventional reverse-phase liquid chromatography techniques. Moreover, we conducted MT prediction for metabolites recorded in mass spectral libraries and metabolite structure databases containing a total of 469,870 compounds, achieving an accuracy of less than 1.5 min root mean squares. Our platform provides a peak annotation platform utilizing MT information, accurate precursor m/z, and the MS/MS spectrum recommended by the metabolomics standards initiative. Applying this procedure, we investigated metabolic alterations in lipopolysaccharide (LPS)-induced macrophages, characterizing 170 metabolites. Furthermore, we assigned metabolite information to unannotated peaks using an in silico structure elucidation tool, MS-FINDER. The results were integrated into the nodes in the molecular spectrum network based on the MS/MS similarity score. Consequently, we identified significantly altered metabolites in the LPS-administration group, where glycinamide ribonucleotide, not present in any spectral libraries, was newly characterized. Additionally, we retrieved metabolites of false-negative hits during the initial spectral annotation procedure. Overall, our study underscores the potential of CE-MS/MS with DIA and computational mass spectrometry techniques for metabolic profiling.

An automatic method for accurate signal-to-noise ratio estimation and baseline correction of Raman spectra of environmental microplastics

In this study, we introduced a k-iterative double sliding-window (DSW^k) method for the estimation of spectral noise, signal-to-noise ratio (SNR), and baseline correction. The performance was evaluated using simulated spectra and compared against other commonly employed methods. Convergent evaluation determined that a k value of 20 strikes an optimal balance between convergence and computational intensity. The DSW^k method demonstrated outstanding performance across different spectral types (flat baseline, baseline with elevation, baseline with fluctuation, baseline with elevation and fluctuation) coupled with SNR values from 10 to 1000, achieving results that ranged from 1.01 to 1.08 times of the reference value in estimating spectral noise. It also showed that the estimated SNR values are 0.89 to 0.93 times of the reference value, demonstrating a 74.5 % − 131.7 % improvement over the conventional method in spectra with elevated and/or fluctuating baselines. Additionally, the DSW^k method proved effective in correcting baselines and identifying polymers in environmental samples of polyethylene (PE), polypropylene (PP), and polystyrene (PS), despite the limitation of reducing the peak height in spectra with low SNR. This method offers the potential to enhance the automatic and accurate evaluation of spectral quality and could assist in the development of guidelines for more rapid parameter adjustments in Raman measurements.

Crown ether dopant to reduce ion suppression and improve detection in the liquid microjunction surface sampling probe.

Sodium and potassium are required in agar media for the growth of some microorganisms (e.g., marine bacteria). However, alkali cations are a significant source of contamination for mass spectrometry causing ion suppression and adduct formation. Conventionally, salts can be removed before mass spectrometric analysis with appropriate and often lengthy sample preparation. The direct mass spectrometric sampling of bacterial colonies grown on agar media seeks to minimize or eliminate sample preparation to improve workflow. However, this may exacerbate ion suppression and contamination since these metal cations will degrade spectral quality and limit the rapid profiling of microbial metabolites. Different approaches are needed to sequester sodium and potassium ions to minimize unwanted background interferences. Herein, we use crown ethers (CEs) in combination with a liquid microjunction surface sampling probe (LMJ-SSP) to directly sample the surface of the bacterial colonies from two marine bacteria species (Pseudoalteromonas rubra DSM6842 and Pseudoalteromonas tunicata DSM 14096). CEs (e.g., 18-crown-6 or 15-crown-5) are added to the carrier solvent of the LMJ-SSP, the chemical noise is reduced, and spectra are easier to interpret. The liquid microjunction formed at the tip of LMJ-SSP was used to directly touch bacterial colonies on agar. The carrier solvent was either methanol (100%) or methanol: H2O (50:49.9%) with or without 0.01% CEs. Information-theoretic measures are employed to investigate qualitative changes between spectra before and after adding CEs. Our work demonstrates the capability of CEs to reduce background interferences within the direct profiling of bacterial colonies from agar plates. The data obtained from both P. rubra DSM6842 and P. tunicata DSM 14096 show that CEs can be used to mitigate the salty background and improve compound detection. Our approach can be implemented in natural product discovery using LMJ-SSP to allow fast and accurate detection of interesting/novel compounds.

Blue lighting combined with cold storage temperature can suppress gray mold in strawberry fruit

Cold storage is an optimal way of preventing quality deterioration in fruit crops, and this is exemplified by strawberries. However, Botrytis cinerea, the causal agent of gray mold, is cold-adaptive and can cause losses to cold-stored fruit. This study evaluated the regulatory effects of the spectral quality of light on the fungal growth in vitro, and the development of gray mold on postharvest strawberries at both ambient (21 ℃) and cold (4 ℃) temperatures. None of the light qualities tested (white, blue, green, or red) significantly affected colony growth of B. cinerea compared with the dark conditions in which they were tested at 21 ℃. In fungal cultures kept at 4 ℃, blue light significantly retarded colony expansion compared to other light conditions and darkness. Moreover, blue light and cold conditions can synergistically result in an increased number of septa, hyphal abnormality and intracellular vacuoles. An enhanced accumulation of reactive oxygen species (ROS) was also observed in fungal hypha exposed to blue light under cold conditions. Infection assay on strawberries demonstrated that blue light caused insignificant and significant suppressive effects at 21 ℃ and 4 ℃, respectively. In addition, blue-light caused significant weight loss in strawberries compared with all other treatments. Consequently, blue-light illumination under cold conditions may result in oxidative stress to the B. cinerea cells, thus preventing this cold-adaptive pathogen from causing unexpected deterioration in the stored fruit.

Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study.

Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD. We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase. A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models. The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (β = 1.6) and higher the tone of voice (β = 0.71), more clearly (β = 1.35), and more sharply they speak (β = 0.95), and their conversations are longer (β = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (β = -0.27) and lower the tone of voice (β = -0.21) and less clearly (β = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (β = -1.07) and less clearly they speak (β = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found. Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.

Combining Fast Pure Shift NMR and GEMSTONE-Based Selective TOCSY for Efficient NMR Analysis of Complex Systems.

As one of the commonly used intact detection techniques, liquid NMR spectroscopy offers unparalleled insights into the chemical environments, structures, and dynamics of molecules. However, it generally encounters the challenges of crowded or even overlapped spectra, especially when probing complex sample systems containing numerous components and complicated molecular structures. Herein, we exploit a general NMR protocol for efficient NMR analysis of complex systems by combining fast pure shift NMR and GEMSTONE-based selective TOCSY. First, this protocol enables ultrahigh-selective observation on the coupling networks that are totally correlated with targeted resonances or components, even where they are situated in severely overlapped spectral regions. Second, pure shift simplification is introduced to enhance the spectral resolution and further resolve the subspectra containing spectral congestion, thus facilitating the dissection of overlapped spectra. Additionally, sparse sampling accompanied by spectral reconstruction is adopted to significantly accelerate acquisition and improve spectral quality. The advantages of this protocol were demonstrated on different complex sample systems, including a challenging compound of estradiol, a mixture of sucrose and d-glucose, and natural grape juice, verifying its feasibility and power, and boosting the potential application landscapes in various chemical fields.