Spectral Identification Research Articles

The Short-Wave Infrared (SWIR) Spectral Exploration Identification and Indicative Significance of the Yixingzhai Gold Deposit, Shanxi Province

The Yixingzhai gold deposit is the largest gold deposit in Shanxi Province and develops three types of mineralization: porphyry, quartz vein, and breccia. Spectral characteristic parameters of muscovite are studied by short-wave infrared (SWIR) spectral, and the exploration significance is discussed. The Al-OH wavelength of muscovite associated with porphyry mineralization gradually becomes shorter from the periphery (>2206 nm) to the center (2201–2205 nm), and the crystallinity (>2.6) gradually increases. In quartz vein mineralization, the wavelength gradually increases from the periphery (<2203 nm) to the center (2210–2211 nm), while the crystallinity does not change significantly and in a small value (<1.5). The wavelength variation range of breccia mineralization is 2198~2214 nm and is concentrated in 2201~2204 nm near the center, while the overall crystallinity is lesser than 5.5 and concentrated around 1–2.2 near the center. The wavelength and crystallinity of muscovite are mainly affected by Tschermak substitution and temperature. When the contents of Si, Fe, and Mg are low and AlVI is high, the wavelength tends toward the short-wave (SW) direction, while the opposite tends toward the long-wave (LW) direction. The high crystallinity (4.1–8.4) of muscovite can be used as an indicator of porphyry gold mineralization and also provides an important indicator to explore similar types of gold deposits.

Artificial intelligence with wrapper multioutput regression to identify chemical contents of fresh and dried manure samples using NIR spectroscopy.

Analyzing manure nutrients such as total ammonium nitrogen (NH+4), dry matter (DM), calcium oxide (CaO), total nitrogen (-N), phosphorus pentoxide (P2O5), magnesium oxide (MgO), and potassium oxide (K2O) helps in fulfilling crop nutritional needs while improving the profitability and a lower risk of pollutants. This study used two Near Infra Red (NIR) spectral datasets of fresh and dried manure. The freshly prepared NH4Cl, CaO, Ca(OH)2, P2O5, MgO, and K2O samples were used for spectral signature peak identification and calibration. The pre-processing methods, Savitzky-Golay (SG), Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV), were applied to correct the spectra and PCA to reduce the dimension of these pre-processed spectra. Several regressors and classifiers with wrapper multioutput regression algorithms (WMRA) and stacking generalization models were applied to identify the chemical contents. In the regression analysis, the WMRA achieved the highest coefficient of determination (R2) of 0.983 and RPD of 7.68 for fresh manure, while R2 of 0.949 and RPD of 4.42 for dried manure. In classification analysis, k-nearest neighbors (KNN) achieved 100.0% highest accuracy, while 98.1% 5-fold average accuracy. The overall findings of this study indicate that NIR, in conjunction with WMRA and KNN, has a high potential for quick detection of nutrients in manure.

Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.

Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using machine learning and deep learning. Usually, model predictions do not contain any indication of the possible error (uncertainty) or only one criterion is used for this purpose. The spread of predictions of several models included in the ensemble, and the molecular similarity of the considered molecule and the most "similar" molecule from the training set, are values that allow us to estimate the uncertainty. The Euclidean distance between vectors, calculated based on real-valued molecular descriptors, can be used for the assessment of molecular similarity. Another factor indicating uncertainty is the molecule's belonging to one of the clusters (data set clustering). Together, all three factors can be used as features for the uncertainty assessment model. Classification models that predict whether a prediction belongs to the worst 15% were obtained. The area under the receiver operating curve value is in the range of 0.73-0.82 for the considered tasks: the prediction of retention indices in gas chromatography, retention times in liquid chromatography, and collision cross-sections in ion mobility spectroscopy.

Machine learning based workflow for (micro)plastic spectral reconstruction and classification

With the advancement of artificial intelligence, it is foreseeable that computer-assisted identification of microplastics (MPs) will become increasingly widespread. Therefore, exploring a machine learning-based workflow to facilitate the identification of MPs is both meaningful and practically significant. However, interferences present in MPs spectra often compromise identification accuracy, making the improvement of spectral quality a critical prerequisite for precise identification. This study developed a fully machine learning-based workflow that combines spectral reconstruction and identification of MPs. To enhance the quality of MPs spectra, two reconstruction models named autoencoders (AE) and V-like convolutional neural networks (VCNN) were employed. Then, four classification models including decision tree, random forest, linear support vector machines (LSVM) and 1D convolutional neural networks were developed to accurately identify MPs. In terms of reconstruction, VCNN outperformed AE with a higher R2 value of 0.965, while both models outperformed conventional widely used Savitzky-Golay algorithm. For classification, LSVM exhibited the best performance with an overall accuracy of 91.35% on the original dataset and 98.00% on the VCNN-reconstructed dataset. When applied to real environmental datasets, a slight decrease in performance was observed, but a maximum top-1 accuracy of 71.43% and top-3 accuracy of >90% was still practically significant, indicating that the combined workflow has great potential for spectral reconstruction and identification of MPs.

Phenological and Spectral Unmixing-Based Identification of Corn Cultivars in Tuban Regency, Indonesia: An Analysis of Sentinel-2 and MODIS Data

Abstract In Indonesia, monitoring and mapping corn growth are crucial to promote the implementation of the Government Corn Reserve (CJP) as an alternative for improving national corn management. East Java is the highest corn producer in Indonesia, with Tuban Regency leading the production. According to data from the Central Bureau of Statistics (BPS) in 2024, corn production reached 788,477 tons by 2023. The advancement of technology in the food sector has led to the production of various corn cultivars, such as NK-6172, NK-212, and NK-7328, each with different traits and yields. Therefore, information on corn cultivars is important for predicting the harvest area of corn. Until now, the monitoring of the area and growth of corn in Indonesia has been conventional, requiring considerable time and high costs. Remote sensing technology can be utilized to monitor corn growth and distribution. Linear Spectral Unmixing can identify the distribution of corn areas for each cultivar, where phenology data are used for the date selection of Sentinel-2 satellite images. The results obtained from this study include the corn field area, corn growth phenology, endmember fraction of corn cultivars, and the area of dominant corn cultivars in the research area. The percentages of the dominant cultivars obtained were as follows: NK-6172 at 80.97%, NK-7328 at 15.55%, and NK-212 at 3.48%.

Enhancing Confidence in Microplastic Spectral Identification via Conformal Prediction.

Microplastics are an emerging pollutant of concern, with environmental observations recorded across the world. Identifying the type of microplastic is challenging due to spectral similarities among the most common polymers, necessitating methods that can confidently distinguish plastic identities. In practice, a researcher chooses the reference vibrational spectrum that is most like the unknown spectrum, where the likeness between the two spectra is expressed numerically as the hit quality index (HQI). Despite the widespread use of HQI thresholds in the literature, acceptance of a spectral label often lacks any associated confidence. To address this gap, we apply a machine-learning framework called conformal prediction to output a set of possible labels that contain the true identity of the unknown spectrum with a user-defined probability (e.g., 90%). Microplastic reference libraries of environmentally aged and pristine polymeric materials, as well as unknown environmental plastic spectra, were employed to illustrate the benefits of this approach when used with two similarity metrics to compute HQI. We present an adaptable workflow using our open-access code to ensure spectral matching confidence for the microplastic community, reducing manual inspection of spectral matches and enhancing the robustness of quantification in the field.

Effects of noise intensity on early warning indicators of thermoacoustic instability: An experimental investigation on a lean-premixed combustion system

In this work, we experimentally investigate the noise-induced dynamics of a lean premixed combustion system operating on natural gas–air mixtures that exhibit thermoacoustic instability via a subcritical Hopf bifurcation. The investigation is done before the bistable region with equivalence ratio (ϕ) as the control parameter. We analyze the acoustic pressure oscillations (p′) in the combustor and fluctuations in the heat release rate (q′) from the laminar quasi-flat flame at increasing levels of white noise. We show the effects of noise intensity on the reliability of various types of early warning indicators (EWIs) to predict the onset of the impending thermoacoustic oscillations. We investigate the indicators based on statistical measures (variance, skewness, and kurtosis), autocorrelation and spectral properties (coherence factor), system identification (growth/decay rates of p′), multi-fractality (Hurst exponent), and time series complexity (permutation entropy and Jensen–Shannon complexity). The coherence factor, variance, and decay rates of p′ always increases as the system approaches thermoacoustic instability, indicating their robustness as an EWI under most noise levels. An increase in kurtosis cannot be employed as an EWI. Implementing autocorrelation, skewness, Hurst exponent, permutation entropy and Jensen–Shannon complexity as effective EWIs has limitations: they can be estimated accurately only from pressure oscillations (p′) data and work only above a particular threshold value of noise intensity. Our results have direct implication on early prediction and control of thermoacoustic instability in practical gas turbine combustors.Novelty and significance statementDeveloping effective early warning indicators (EWIs) to anticipate the onset of thermoacoustic instability is crucial for preventing potential damage and ensuring the reliable operation of lean premixed gas turbine combustion systems. In such systems, inherent noise dynamics undergo variations with changing operating conditions and combustor designs. Specifically, noise intensity increases as the system becomes more turbulent. In this study, we demonstrate that the inherent noise dynamics in a lean premixed combustion system play a crucial role in influencing the trends observed in early warning indicators of thermoacoustic instability. We address several key questions, including (a) whether comparative reliability assessments of different classes of EWIs exist, (b) the effect of variations in noise properties on the efficacy of EWIs, and (c) which EWIs are most reliable considering that noise characteristics depend on the system and may even change with operating conditions within a specific system. This information is critical for engine designers/users, facilitating the development of robust and effective monitoring systems for gas turbine combustion systems.

An intelligent spectral identification approach for the simultaneous detection of endocrine-disrupting chemicals in aquatic environments

With the rapid progression of industrialization, the application and release of endocrine disruptors (EDCs), including bisphenol A (BPA), octylphenol and nonylphenol have significantly increased, presenting substantial health hazards. Conventional analytical techniques, such as high-performance liquid chromatography and gas chromatography-mass spectrometry, are highly sophisticated but suffer from complex procedures and high costs. To overcome these limitations, this study introduces an innovative spectral methodology for the simultaneous detection of multiple aquatic multicomponent EDCs. By leveraging chemical machine vision, specifically with convolutional neural network (CNN) models, we employed a long-path holographic spectrometer for rapid, cost-effective identification of BPA, 4-tert-octylphenol, and 4-nonylphenol in aqueous samples. The CNN, refined with the ResNet-50 architecture, demonstrated superior predictive performance, achieving detection limits as low as 3.34, 3.71 and 4.36 μg/L, respectively. The sensitivity and quantification capability of our approach were confirmed through the analysis of spectral image Euclidean distances, while its universality and resistance properties were validated by assessments of environmental samples. This technology offers significantly advantages over conventional techniques in terms of efficiency and cost, offering a novel solution for EDC monitoring in aquatic environments. The implications of this research extend beyond improved detection speed and cost reduction, presenting new methodologies for analyzing complex chemical systems and contributing to environmental protection and public health.

A New Graph Autoencoder-Based Multi-level Kernel Subspace Fusion Framework for Single-cell Type Identification.

The advent of single-cell RNA sequencing (scRNA-seq) technology offers the opportunity to conduct biological research at the cellular level. Single-cell type identification based on unsupervised clustering is one of the fundamental tasks of scRNA-seq data analysis. Although many single-cell clustering methods have been developed recently, few can fully exploit the deep potential relationships between cells, resulting in suboptimal clustering. In this paper, we propose scGAMF, a graph autoencoder-based multi-level kernel subspace fusion framework for scRNA-seq data analysis. Based on multiple top feature sets, scGAMF unifies deep feature embedding and kernel space analysis into a single framework to learn an accurate clustering affinity matrix. First, we construct multiple top feature sets to avoid the high variability caused by single feature set learning. Second, scGAMF uses a graph autoencoder (GAEs) to extract deep information embedded in the data, and learn embeddings including gene expression patterns and cell-cell relationships. Third, to fully explore the deep potential relationships between cells, we design a multi-level kernel space fusion strategy. This strategy uses a kernel expression model with adaptive similarity preservation to learn a self-expression matrix shared by all embedding spaces of a given feature set, and a consensus affinity matrix across multiple top feature sets. Finally, the consensus affinity matrix is used for spectral clustering, visualization, and identification of gene markers. Extensive validation on real datasets shows that scGAMF achieves higher clustering accuracy than many popular single-cell analysis methods.

A bio scientific study of the new gold complex as an anti-breast cancer agent, as well as its preparation , Molar Conductivity and Spectral identification of some other metal complexes

Synthesis of Heterocyclic Organic Ligand involved the preparation of non-homogeneous ring ligand derived from ortho-aminobenzylamine. This was achieved by the azo compound prepared in the first step. The second step included the reaction of the azo compound with 4-(benzyloxy)benzaldehyde .ligand was characterized using various spectroscopic and diagnostic techniques, including mass spectrometry, proton nuclear magnetic resonance, Fourier-transform infrared spectroscopy , and ultraviolet-visible spectroscopy . In addition, precise analysis of C, H, N elements and melting point measurements were conducted. the molar ratio (1:2) (M:L) was found for the ligand (BPDIM ) to prepare metal complexes for cobalt, and copper (divalent) while maintaining a molar ratio of (1:1) for gold complexes (trivalent). While supporting the values of accurate analysis of elements (C.H.N) and the results of flame atomic absorption spectroscopy, the proposed molecular formulas for all studied complexes were investigated. In addition, infrared spectroscopy, Electronic spectra of metal complexes, molar conductance.The new complexes showed variations in conductance depending on the metal ion and oxidation state. This study aimed to conduct an in vitro cytotoxicity comparative study of BPDIM and its Au(III) complex on human breast cancer cells (MCF-7) and other normal cells. The Au(III) complex was found to be highly selective in targeting cancer cells without affecting normal healthy cells, compared to the ligand. Thus, this complex can be considered as a new drug for treating breast cancer cells (MCF-7), and an attempt in the future to study its effect on other types of cancer.

Quantitative analysis of acetamiprid and thiacloprid in green tea using enhanced SERS and neural networks

Neonicotinoids (NEOs) are widely used in agricultural cultivation, and the presence of residual NEOs poses a serious threat to consumer health. Therefore, trace detection of NEOs is particularly important. In this study, a response surface experimental design was employed to develop AgNP with high enhancement factor and surface height anisotropy, effectively enhancing the Raman signals of acetamiprid (ACE) and thiacloprid (THI) through densely populated hotspots on the surface. Gaussian simulation calculations were used to assign peaks in the ACE and THI spectra. Subsequently, convolutional neural network, backpropagation neural network, and AlexNet neural network were employed to identify the ACE, THI, and mixed component spectra, successfully distinguishing the spectra of the three compounds and achieving qualitative analysis of unknown spectra. Furthermore, genetic algorithm-partial least squares was used to model the spectral data, achieving a correlation coefficient of 0.97. The method was applied for the detection of spiked tea samples, achieving a precision RSD of 4.84 %. Therefore, this sensor combined with intelligent algorithms can be used for spectral identification and trace detection of mixed pesticide samples with structurally similar compounds.

Spectral Characteristics and Identification of Degraded Alpine Meadow in Qinghai–Tibetan Plateau Based on Hyperspectral Data

Grassland degradation poses a significant challenge to achieving the Sustainable Development Goals (SDGs) on the Qinghai–Tibetan Plateau (QTP). Effective monitoring of grassland degradation is essential for ecological restoration. Hyperspectral technology offers efficient and accurate identification of degradation. However, the influence of observation time, data analysis methods and classification techniques on the accuracy of identifying alpine grasslands remains unclear. In this study, the spectral reflectance of degraded alpine meadow, alpine meadow, alpine shrub and Tibetan barley was measured from May to September 2023 using a ground spectrometer in the northeastern QTP. First-order derivatives (FDR) and continuum removal were applied to the spectra, and characteristic parameters and vegetation indices were calculated. Support vector machine (SVM), random forest (RF), artificial neural network (ANN) and decision tree (DT) were then used to compare the classification accuracy between different months, transformation methods and characteristic parameters. The results showed that the spectral reflectance peaked in July, with significant differences in the near infrared (NIR) bands between alpine meadow and degraded alpine meadow. Alpine shrub and Tibetan barley showed greater differences in reflectance compared to other vegetation types, especially in the NIR bands. Data transformations improved reflectance and absorption characteristics in the NIR and visible bands. Indices such as DVI, RVI and NDGI effectively differentiated vegetation types. Optimal accuracy for the identification of degraded alpine meadow in July was achieved using FDR transformations and ANN or SVM for classification. This study provides methodological insights for monitoring grassland degradation on the QTP.

LTFM: Long-tail few-shot module with loose coupling strategy for mineral spectral identification

In recent years, deep learning methods have exhibited superior performance in mineral identification when especially compared with conventional machine learning methods such as Support Vector Machine (SVM) and Partial Least Squares (PLS). Nevertheless, almost all of these deep learning methods pay more attention to improving and designing network structures, while neglecting the phenomenon of long-tail distribution in spectral data due to the inconsistency of ore distribution and the scarcity of several natural minerals. To alleviate the interference of majority categories on minority categories, we propose Long-Tail Few-shot Module (LTFM) which is inspired by rethinking the fashionable decoupling strategy that conducts primary representation learning and further classifier retrained on mineral spectral data. In particular, LTFM serves as a multi-expert mode, where these experts are respectively specialized in improving feature representation learning, mitigating the long-tail effect, and alleviating the interference of few shots. Additionally, the loose coupling learning strategy is introduced to facilitate primary representation learning and the subsequent additional experts to inherit this knowledge. Experiments on two publicly available spectral datasets show that the proposed LTFM significantly outperforms existing methods. In the end, extensive ablation studies are conducted to investigate the effectiveness, correctness, and robustness of our proposal.

ELSA-PSYCHE: An Improved Method for Protecting Pure Shift Spectra from Artifacts.

Numerous 1H-1H J couplings contribute to complex multiplets in 1H nuclear magnetic resonance spectroscopy, leading to an ambiguous spectral assignment, particularly for strongly coupled protons. Although the PSYCHE approach has proven to be effective in simplifying complex spectra by collapsing J couplings, the PSYCHE pure shift spectrum of strongly coupled protons always suffers from sideband artifacts and baseline oscillations, which impede spectral identification. Herein, we introduce a novel universal technique designed to separate artifacts from the desired absorption-mode pure shift signals. This new study will significantly benefit the development of molecular structure elucidations and composition analysis in the fields of chemistry, biochemistry, and metabonomics.

Fully relativistic energies, transition properties, and lifetimes of lithium-like germanium

Abstract Employing two fully relativistic methods, the multi-reference configuration Dirac–Hartree–Fock (MCDHF) method and the relativistic many-body perturbation theory (RMBPT) method, we report energies and lifetime values for the lowest 35 energy levels of the (1s2)nl configurations (where the principal quantum number n = 2–6 and the angular quantum number l = 0, …, n–1) of lithium-like germanium (Ge XXX), as well as complete data on the transition wavelengths, radiative rates, absorption oscillator strengths, and line strengths between the levels. Both the allowed (E1) and forbidden (magnetic dipole M1, magnetic quadrupole M2, and electric quadrupole E2) ones are reported. The results from the two methods are consistent with each other and align well with previous accurate experimental and theoretical findings. We assess the overall accuracies of present RMBPT results to be likely the most precise ones to date. The present fully relativistic results should be helpful for soft x-ray laser research, spectral line identification, plasma modeling and diagnosing. The datasets presented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00113.00135.

Application of handheld near infrared spectrometer in quality control of traditional Chinese medicine: Rapid screening and quantitative analysis of Lonicerae Japonicae Flos adulteration

Traditional Chinese medicine (TCM) prescription, with its intricate formulations and nuanced compositions, is a cornerstone of holistic health practices. However, the expansion of the TCM market has led to a surge in herb adulteration, which significantly undermines the quality and safety of these medicinal products. A case in point is Lonicerae Japonicae Flos (LJF), a widely utilized herb for treating colds, which has been adulterated by the cheaper Lonicerae Flos (LF), thereby affecting its therapeutic effectiveness. Therefore, a method utilizing handheld NIR spectroscopy combined with chemometrics has been developed to provide a portable, real-time solution for the rapid and accurate detection and quantification of adulterants in TCM. By collecting NIR spectra from LJF, LF and adulterated samples (AS), we’ve established a spectral database enabling deep insights into the correlation between spectral features and sample compositions. Resultantly, a classification model with a 99.58 % cross-validation accuracy, reaching 100 % for test set, effectively identified adulterants. And further spectral similarity analysis and classification identification of samples with different adulteration ratios were carried out. The cross-validation accuracy under the optimal model reached 98.38 %, and the test set accuracy was 99.20 %. In addition, the study extends to quantifying different levels of adulteration, employing 20 standard adulterated samples across a 0–100 % adulteration gradient. Via data preprocessing, feature extraction, and regression techniques, the full concentration prediction models were developed, later refined by segmenting samples based on high and low adulteration ratios. Under the SGFD_CARS_PLS (Savitzky-Golay smoothing with the first derivative_competitive adaptive reweighted sampling_partial least squares) model, exceptional performance was achieved, with a R2p of 0.983, RMSEp of 3.402, and RPDp of 7.757 for the homemade adulterated prediction set. In conclusion, the application of this technology not only improves the efficiency and accuracy of screening, but also has the advantages of low cost, easy operation and rapid results compared with traditional chemical analysis methods. It effectively protects the safety of drugs for consumers, maintains the integrity of the TCM market, and provides a strong technical support for the on-site rapid detection of TCM.

Biomass flame spectroscopy technique to identify wood species through spectral emission during combustion processes

The energy emitted by a flame and its spectra provide critical information about fuels, such as energy type, temperature, and molecular properties. While traditionally used to analyze combustion, we introduce a spectral-time-based index, optical calorific power (OCP), to calculate the total energy released per mass unit (J/Kg), relating to the calorific value of wood-based fuels. Using novel optical variables and machine learning, we identified different wood species during the combustion of the wood samples. Homogeneous samples of four wood species (sapwood and heartwood) were ignited in a temperature-controlled furnace. Spectra were measured using a calibrated spectrophotometer in the 450–900 nm range with a 100ms integration time. Continuous and discontinuous spectral patterns were observed in all samples, used to calculate spectral-time-based optical variables, and separated using the AirPLS algorithm. Discontinuous spectra correlated with Na and K emissions (589.4 nm and 766.5 nm, respectively). Continuous spectra were analyzed to determine optical variables such as flame temperature (K), total continuous radiation (TCR, μW/cm2), and total continuous energy (TCE, μJ). Five supervised classification models, XGBoost, LR, SVM, LDA, and RF, were trained using normalized physical parameters and optical variables with stratified cross-validation. The proposed optical variables yielded an identification accuracy of 93%, precision of 95%, and recall of 93% during combustion using the XGBoost method. These promising results demonstrate the potential of our method for accurately identifying wood species, while offering a cost-effective and novel alternative to NIR-SWIR reflectance spectral identification techniques.

High-resolution quadrupole improves spectral purity and reduces interference from non-target ions in isobaric multiplexed quantitative proteomics

BackgroundMass spectrometry (MS)-based proteomics is a powerful tool for identifying and quantifying proteins. However, chimeric spectra caused by the fragmentation of multiple precursors within the same isolation window impair the accuracy of peptide identification and isobaric mass tag-based quantification. While there have been advances in computational deconvolution of chimeric spectra and methods to further separate the peptides by ion mobility or through MSn, the use of narrower isolation windows to decrease the fraction of chimeric species remains to be fully explored. ResultsWe present results obtained on a SCIEX TripleTOF instrument where the quadrupole was optimized and tuned for precursor isolation at 0.1 Da (FWHH). Using a three-proteome model (trypsin digest of protein lysates from yeast, human and E. coli) and 8-plex iTRAQ labeling to document the interference effect, we investigated the impact of co-fragmentation on spectral purity, identification accuracy and quantification accuracy. The narrow quadrupole isolation window significantly improved the spectral purity and reduced the interference of non-target precursors on quantification accuracy. The high-resolution isolation strategy also reduced the number of false identifications caused by chimeric spectra. While these improvements came at the cost of sensitivity loss, combining high-resolution isolation with other advanced techniques, including ion mobility, may result in improved accuracy in identification and quantification. SignificanceCompared to standard-resolution quadrupole isolation (0.7 Da), high-resolution quadrupole isolation (0.1 Da) significantly improved the spectral purity and quantification accuracy while reducing the number of potential false identifications caused by chimeric spectra, thus showing excellent potential for further development to analyze clinical proteomics samples, for which high accuracy is essential.

Identification of HOC•HC(O)H, HOCH2C•O, and HOCH2CH2O• Intermediates in the Reaction of H + Glycolaldehyde in Solid Para-Hydrogen and Its Implication to the Interstellar Formation of Complex Sugars.

Glycolaldehyde [HOCH2C(O)H, GA], the primitive sugar-like molecule detected in the interstellar medium (ISM), is a potential precursor for the synthesis of complex sugars. Despite its importance, the mechanism governing the formation of these higher-order sugars from GA under interstellar circumstances remains elusive. Radical intermediates HOCH2CH2O• (1), HOCH2C•HOH (2), HOCH2C•O (3), HOC•HC(O)H (4), and O•CH2C(O)H (5) derived from GA could be potential precursors for the formation of glyceraldehyde (aldose sugar), dihydroxyacetone (ketose sugar), and ethylene glycol (sugar alcohol) in dark regions of ISM. However, the spectral identification of these intermediates and their roles were little investigated. We conducted reactions involving H atoms and the Cis-cis conformer of GA (Cc-GA) in solid p-H2 at 3.2 K and identified IR spectra of radicals Cc-HOCH2C•O (3) and Cc-HOC•HC(O)H (4) produced from H abstraction as well as closed-shell HOCHCO (6) produced via consecutive H abstraction of GA. In addition, Cc-HOCH2CH2O• (1) and C•H2OH + H2CO (7) were produced through the H addition and the H-induced fragmentation channels, respectively. In darkness, when only H-tunneling reactions occurred, the formation of (3) was major and that of (1) was minor. In contrast, during IR irradiation to produce H atoms with higher energy, the formation of (4) and C•H2OH + H2CO (7) became important. We also successfully converted most Cc-GA to the second-lowest-energy conformer Trans-trans-GA (Tt-GA) by prolonged IR irradiation at 2827 nm to investigate H + Tt-GA; Tt-HOCH2C•O (3'), Tt-HOC•HC(O)H (4'), HOCHCO (6), Tt-HOCH2CH2O• (1'), and C•H2OH + H2CO (7) were observed. We discuss possible routes for the formation of higher-order sugars or related compounds involving (7), (1), (3), and (4), but neither (2), which was proposed previously, nor (5) plays a significant role in H + GA. Such previously unreported rich chemistry in the reaction of H + GA, with four channels of three distinct types, indicates the multiple roles that GA might play in astronomical chemistry.

Strawberry Volatile Organic Compounds for Targeted Metabolomics: The AMDIS Strawberry User Library from Korean Germplasm

Strawberry aroma, crucial for determining quality, involves complex volatile compounds which are challenging to identify. This study explores strawberry aroma analysis using Gas Chromatography-Mass Spectrometry (GC-MS) and the Automated Mass Spectral Deconvolution and Identification System (AMDIS). Central to our research is the creation of a bespoke strawberry Volatile Organic Compounds (VOCs) user library using AMDIS, specifically for analyzing strawberry aromas. The library contains VOCs from 61 strawberry cultivars, integrating information on 104 VOCs, including mass spectra, retention index, chemical class, CAS number, formula, odor threshold, and odor description. This custom library significantly outperformed a commercial library by reducing potential false hits by 200, decreasing the size of report files by over 96%, and, most importantly, shortening AMDIS analysis processing time from 31 s to 9 s, representing an approximate 71% reduction. Further, the study demonstrates the library’s practical application by contrasting the aroma profiles of strawberries harvested in winter and spring. This comparison revealed significant VOC variations depending on seasonal temperature changes, offering insights into environmental influences on strawberry aroma. In conclusion, this research marks a significant advance in strawberry aroma quality analysis. The strawberry VOC library developed in this study is expected to greatly aid targeted metabolomics and flavor research in strawberry breeding.