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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography–Mass Spectrometry Small Molecule Identification

Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this “molecular snapshot” is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗
DOE OSTI2024

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

domain adaptation↗
NASA NTRS2020

Automated Identification and Differentiation of Spectrally Similar Hydrothermal Minerals on Mars

Early telescopic observations corroborated hydration related absorptions on Mars in the infrared. Images from the Viking missions led to speculation of hydrothermal alteration and were followed by two missions which mapped the spatial variability of the ~ 3 μm hydration feature. Since then, the Compact Reconnaissance Imager for Mars (CRISM) has provided high spatial resolution (up to 18m) spectral identification of a suite of hydrothermal and diagenetic minerals which have illuminated a range of formation mechanisms. Presence/absence and spatial segregation or mixing of minerals like prehnite, epidote, chlorite amphiboles, and mixed-layer Fe/Mg smectite-chlorite provide valuable evidence for the geologic setting of deposits on Earth, and these phases are often used as temperature and aqueous chemistry indicators in terrestrial systems. Mapping the distribution of these phases will help to answer whether Mars had widespread conditions favorable for low-grade metamorphism and diagenesis, or only focused hydrothermal systems in areas of high heat flow. Further characterizing the chemistry and structure of these phases will then help to answer how most of the widespread Fe/Mg phyllosilicates formed, further defining early geochemical cycling and climate. A fully automated approach for accurate mapping of important hydrothermal mineral phases on Mars has been a challenge. Due to overlapping features in the M-OH region (~2.2-2.4 μm), the strongest absorption features of chlorite, prehnite, and epidote in the short-wave infrared are difficult to distinguish from one another and from the most commonly occurring hydrated silicates on Mars, Fe/Mg smectites. Weaker absorptions are present in both prehnite and epidote which help to distinguish them from chlorite and smectites, but their relative strength in the presence of noise and spatial mixing is often too low to confidently identify them without the noise suppression and feature enhancement methods described here. The spectral signatures of mixed-layer Fe/Mg smectite-chlorite and partially chloritized Fe/Mg smectites have not yet been adequately assessed. Here we evaluate the effectiveness of two empirical and statistical methods for identifying and differentiating these phases using CRISM data.

Rasmussen, B. P.↗
DOE OSTI2026

Spectral Similarity Masks Structural Diversity at Hydrophobic Water Interfaces

The air-water and graphene-water interfaces represent quintessential examples of the liquid-gas and liquid-solid boundaries, respectively. While the sum-frequency generation (SFG) spectra of these interfaces show similarities, a consensus on their signals and interpretations has yet to be reached. Leveraging deep learning, we computed first-principles SFG spectra for both systems, addressing experimental discrepancies. Here, our findings reveal that similarities in SFG signals do not translate into comparable interfacial microscopic properties. Instead, graphene-water and air-water interfaces exhibit fundamental differences in SFG-active thicknesses, hydrogen-bonding networks, and surface dynamics. These distinctions underscore roughness suppression and electronic interactions present at the solid-liquid interface but absent at the gas-liquid interface.

Wang, Yong [Princeton Univ., NJ (United States)] (↗
NASA NTRS1977

Spectral line similarity in the geomagnetic dipole field variations and length of day fluctuations

Power spectral density analysis using Burg's maximum entropy method (MEM) was applied to the geomagnetic dipole field and its rate of change for the years 1901-1969. Both spectra indicate relative maxima at 0.015 cycle/yr and its harmonics. These maxima correspond approximately to 66-, 33-, 22-, 17-, 13-, 11-, and 9-year spectral lines. The application of the same analysis techniques to the length of day (l.o.d.) fluctuations for the period 1865-1961 reveals similar spectral characteristics. The existence of the common spectral peaks with periods of 66 and 33 years in the l.o.d. fluctuations and the geomagnetic dipole field is clearly established. The existence of the higher harmonics is somewhat uncertain because of the line-splitting problem in the MEM spectral analysis. It is suggested that the spectral line similarity in the l.o.d. fluctuations and the dipole field variations is related to the motion within the earth's fluid core during the past 100 years.

Jin, R.-S.↗
NASA NTRS1977

Spectra of turbulent static pressure fluctuations in jet mixing layers

Spectral similarity laws are derived for the power spectra of turbulent static pressure fluctuations by application of dimensional analysis in the limit of large turbulent Reynolds number. The theory predicts that pressure spectra are generated by three distinct types of interaction in the velocity fields: a fourth order interaction between fluctuating velocities, an interaction between the first order mean shear and the third order velocity fluctuations, and an interaction between the second order mean shear rate and the second order fluctuating velocity. Measurements of one-dimensional power spectra of the turbulent static pressure fluctuations in the driven mixing layer of a subsonic, circular jet are presented, and the spectra are examined for evidence of spectral similarity. Spectral similarity is found for the low wavenumber range when the large scale flow on the centerline of the mixing layer is self-preserving. The data are also consistent with the existence of universal inertial subranges for the spectra of each interaction mode.

Jones, B. G.↗
DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗
DOE OSTI2021

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Extended infrared emission around IRAS 21282 + 5050

Multiaperture 3-4-micron spectra along with K- and L-band images of the compact planetary nebula IRAS 21282 + 5050 show a 5 arcsec - 20 arcsec diameter nebula with structure similar to many other planetary nebulae. The spectral observations and the L-band image show evidence for extended PAH emission out to a radius of 20 arcsec, while the K-band image shows a 5 arcsec diameter nebula. An observed linear increase of integrated brightness with aperture size at L band implies a 1/r exp 2 volume emissivity for a spherically symmetric model. The spectral similarity of the emission in the small and large apertures suggests fluorescent emission by the PAHs. If the observed emission is from PAHs which formed during the planetary nebulae stage of IRAs 21282 + 5050, then PAHs have been forming for not less than 3000 yr. If the PAH emission is from material produced during the earlier red giant phase, then the formation time frame was much longer. The morphological and spectral similarity of IRAS 21282 + 5050 to many other planetary nebulae suggests that this phenomenon may be widespread, and that planetary nebulae may be a significant source of interstellar PAHs.

Bregman, Jesse D.↗
NASA NTRS1992

Comparison of asteroid and meteorite spectra - Classification by principal component analysis

The spectra of 103 meteorites and 411 asteroids have been statistically compared via principal component analysis, in order to arrive at an overview of spectral similarities between and among asteroids and meteorites; the result is a map of statistically defined relative spectral similarities measured by distance. There is much more spectral variance in the meteorites than in the asteroids. The analysis indicates no direct meteorite analogs from the outer-belt B, D, F, P, and T type asteroids, indicating that only a limited portion of the inner asteroid belt is sampled by meteorites.

Britt, Daniel T.↗
NASA NTRS2011

Time-Frequency Analysis of Rocket Nozzle Wall Pressures During Start-up Transients

Surveys of the fluctuating wall pressure were conducted on a sub-scale, thrust- optimized parabolic nozzle in order to develop a physical intuition for its Fourier-azimuthal mode behavior during fixed and transient start-up conditions. These unsteady signatures are driven by shock wave turbulent boundary layer interactions which depend on the nozzle pressure ratio and nozzle geometry. The focus however, is on the degree of similarity between the spectral footprints of these modes obtained from transient start-ups as opposed to a sequence of fixed nozzle pressure ratio conditions. For the latter, statistically converged spectra are computed using conventional Fourier analyses techniques, whereas the former are investigated by way of time-frequency analysis. The findings suggest that at low nozzle pressure ratios -- where the flow resides in a Free Shock Separation state -- strong spectral similarities occur between fixed and transient conditions. Conversely, at higher nozzle pressure ratios -- where the flow resides in Restricted Shock Separation -- stark differences are observed between the fixed and transient conditions and depends greatly on the ramping rate of the transient period. And so, it appears that an understanding of the dynamics during transient start-up conditions cannot be furnished by a way of fixed flow analysis.

Baars, Woutijn J.↗
NASA NTRS1983

The Uranian satellites and Hyperion - New spectrophotometry and compositional implications

New reflectance spectra at 3.5 percent resolution have been obtained for Ariel, Titania, Oberon, and Hyperion in the 0.8 to 1.6-micron spectrum region. The new spectra show no absorptions other than the 1.5 micron water-ice feature (within the precision of the data), and demonstrate extension into the 0.8- to 1.6 micron region of the 1.5- to 2.5 micron spectral similarity ofo Ariel to Hyperion (Brown and Cruikshank, 1983). The new data confirm the presence of a dark, spectrally bland component on/in the water-ice surfaces of the Uranian satellites, which, with some reservations, has spectral similarities to the dark substance on the leading side of lapetus and the dark material on/in the surface of Hyperion, as well as other dark, spectrally neutral substances such as charcoal. Attempts were made to match the spectra of Ariel, Titania, and Oberon with additive reflectance mixes (aeral coverage) of fine-grained water frost and various dark components such as charcoal, lampblack, and charcoal-water-ice mixtures. The results were broad limits on the amounts of possible areal coverage of a charcoal-like spectral component on the surfaces of the Uranian satellites, but the data are not of sufficient precision to conclusively determine whether the dominant mode of contaminant dispersal is areal or voluminal. The effect of highly variegated albedos on the diameters derived by Brown, Cruikshank, and Morrison (1982) is found to be small.

Brown, R. H.↗
NASA NTRS1990

Optical constraints of kerogen from 0.15 to 40 microns: Comparison with meteoritic organics

Kerogens are dark, complex organic materials produced on the Earth primarily by geologic processing of biologic materials, but kerogens have chemical and spectral similarities to some classes of highly processed extraterrestrial organic materials. Kerogen-like solids were proposed as constitutents of the very dark reddish surfaces of some asteroids and are also spectrally similar to some carbonaceous organic residues and the Iapetus dark material. Kerogen can thus serve as a useful laboratory analog to very dark, spectrally red extraterrestrial materials; its optical constants can be used to investigate the effects of particle size, void space and mixing of bright and dark components in models of scattering by dark asteroidal, cometary, and satellite surfaces. Measurements of the optical constants of both Type 2 kerogen and of macromolecular organic residue from the Murchison carbonaceous chondrite via transmission and reflection measurements on thin films are reported. The real part of the refractive index, n, is determined by variable incidence-angle reflectance to be 1.60 + or - 0.05 from 0.4 to 2.0 micrometers wavelength. Work extending the measurement of n to longer wavelengths is in progress. The imaginary part of the refractive index, k, shows substantial structure from 0.15 to 40 micrometers. The values are accurate to + or - 20 percent in the UV and IR regions and to + or - 30 percent in the visible. The k values of organic residues were also measured from the Murchison meteorite. Comparison of the kerogen and Murchison data reveals that between 0.15 and 40 microns, Murchison has a similar structure but no bands as sharp as in kerogen, and that the k values for Murchison are significantly higher than those of kerogen.

Khare, Bishun N.↗