Spectral Database Research Articles

Generative Simplex Mapping: Non-Linear Endmember Extraction and Spectral Unmixing for Hyperspectral Imagery

We introduce a new model for non-linear endmember extraction and spectral unmixing of hyperspectral imagery called Generative Simplex Mapping (GSM). The model represents endmember mixing using a latent space of points sampled within a (n−1)-simplex corresponding to n unique sources. Barycentric coordinates within this simplex are naturally interpreted as relative endmember abundances satisfying both the abundance sum-to-one and abundance non-negativity constraints. Points in this latent space are mapped to reflectance spectra via a flexible function combining linear and non-linear mixing. Due to the probabilistic formulation of the GSM, spectral variability is also estimated by a precision parameter describing the distribution of observed spectra. Model parameters are determined using a generalized expectation-maximization algorithm, which guarantees non-negativity for extracted endmembers. We first compare the GSM against three varieties of non-negative matrix factorization (NMF) on a synthetic data set of linearly mixed spectra from the USGS spectral database. Here, the GSM performed favorably for both endmember accuracy and abundance estimation with all non-linear contributions driven to zero by the fitting procedure. In a second experiment, we apply the GTM to model non-linear mixing in real hyperspectral imagery captured over a pond in North Texas. The model accurately identified spectral signatures corresponding to near-shore algae, water, and rhodamine tracer dye introduced into the pond to simulate water contamination by a localized source. Abundance maps generated using the GSM accurately track the evolution of the dye plume as it mixes into the surrounding water.

The Identity Algorithm: How the Most Popular Electron Ionization Mass Spectral Library Search Engine Actually Works.

The Identity algorithm implemented in the MS Search (NIST) software is widely used for library searches of gas chromatography/mass spectrometry data against electron ionization mass spectral databases. It has been available to researchers since 1993, with the release of MS Search 1.5a. Despite its extensive use, the operational details of the algorithm have remained ambiguous. Attempts to replicate the algorithm have been unsuccessful because, as found in this research, the description in the manual is neither fully complete nor accurate. The main novelty of this work is the development of a unique approach for deconstructing the Identity algorithm. It is purely based on analyzing library search results obtained from several groups of synthetic mass spectra, each tailored to isolate and examine specific components of the algorithm. This approach facilitated a comprehensive understanding of the Identity algorithm and led to the development of a custom implementation that fully replicates the results obtained from the original MS Search software. The custom implementation of the Identity algorithm is now available in the mssearchr R package, enhancing accessibility for researchers.

Determination of persistent and mobile organic compounds in the river–groundwater interface of the Besòs river delta, Spain, using a wide extraction approach

Climate change impacts the Mediterranean region, transforming it from region with a semi-arid climate to a region with an arid climate. Under this situation, while groundwater is an essential hydric resource, its existence is in danger due to anthropogenic pressures. Persistent mobile organic compounds (PMOCs) have recently been recognised as an emerging problem; however, PMOCs in groundwater need to be better characterised. Here, we present a new analytical method to characterise the profile of PMOCs in groundwater based on two parallel solid-phase extraction (SPE), using weak anion exchange and weak cation exchange. Extracts were analysed by ultraperformance liquid chromatography (UPLC) using mix-mode chromatography for those compounds analysed under negative ionisation conditions and hydrophilic interaction liquid chromatography (HILIC) under positive conditions coupled to high-resolution mass-spectrometry (HRMS) using a Q-Exactive Orbitrap™ analyser. For the suspect screening of PMOCs in groundwater, the acquisition mode was in full scan (FS) by “independent scan of all ion fragmentation”. For the tentative identification, different online databases such as Environmental and Food Safety (EFS) HRAM Compound database, PFAS NIST database, ChemSpider for chemical structural information, MzCloud as a mass spectral database, and an in-house list with 1280 PMOC structures have been used. The performance of the method was assessed with 29 representative PMOCs which were selected based on the previous literature. The recovery rates have been between 63 and 110 % for 90 % of the target compounds and method limits of quantification (MLQ) between 0.3 and 10.5 ng/L.The optimised approach was applied to assess PMOCs in the Besòs River aquifer, NE Spain, showing 148 tentatively identified structures at confidence levels 1–3. Among them, 66 suspects were tentatively identified at level 3, 54 at level 2, and 28 confirmed at level 1. Most of these compounds were polar and highly polar compounds which are difficult to retain with other extraction approaches. Major detected compounds were pharmaceuticals and personal care products (46), followed by perfluoroalkyl and polyfluoroalkyl substances (PFAS) (32), industrial additives (27), and pesticides (23), among other groups. Some compounds, such as ultrashort chain PFAS and fluorinated betaines, were detected for the first time in groundwaters in Spain.

Rapid selection of milk mid-infrared spectra for creating a world representative spectral database of dairy cow population

The advantage of employing mid-infrared spectrometry for milk analysis in breeding lies in its ability to quickly generate millions of records. However, these records may be biased if the calibration process does not account for their spectral variability when constructing the predictive model. So, this study introduces a novel method for developing a World Representative Spectral Database (WRSD) to reduce the risks of spectral extrapolation when predicting dairy traits in new samples. Utilizing a 2-phase selection procedure that is both efficient and minimizes memory usage, we first generate a decomposition matrix via Principal Component Analysis (PCA) on a data set of 2,324,443 records. The next phase iterates spectral selection based on a location index from PCA scores, calculating spectra occurrence frequency for refined barycenter estimations. The chosen spectra's barycenter closely aligns with the entire data set, proving the efficacy of using just 3 principal components (PCs). Applied to 4 varied data sets, totaling over 21 million records, we select 583,440 spectra to represent spectral diversity, with selection rates between 2.00% and 14.88%. This selection illustrates the spectral variability across different dairy populations and data providers. Demonstrated through a hypothetical calibration set of 71 samples, the WRSD's utility for algorithm developers becomes apparent. This calibration set covers between 91.42 to 98.50% of the WRSD variability, except for the Irish data set (3.50%), indicating a need for additional samples to accurately represent Irish variability and minimize spectral extrapolation. This study offers valuable insights into the representativeness of training sets for capturing spectral variability within targeted dairy populations. While the current WRSD does not fully encompass global milk spectral diversity, its development underscores the importance of gathering more data and standardizing spectral information across spectrometer brands. Ultimately, the WRSD proves crucial not just for trait prediction but also for identifying abnormal milk samples, also marking a significant relevance in dairy science technology.

When LAMOST meets Gaia DR3. Exploring the metallicity of open clusters

Open clusters (OCs) are excellent probes as their age and abundance can be tightly constrained, allowing us to explore the distribution of metallicity and composition across the disk of the Milky Way. By conducting a comprehensive analysis of the metallicity of OCs, we can obtain valuable information about the history of their chemical enrichment. Moreover, by observing stars in different regions of the Milky Way, we can identify significant spatial structures in their chemical composition and abundance. This enables us to understand stellar birth radii through chemical tagging. Nevertheless, it remains challenging to infer the original positions of OCs using current data alone. The aim of this study is to investigate the distribution of metallicity in the solar neighborhood using a large dataset from Gaia DR3 combined with LAMOST spectra. With accurate ages and metallicity measurements, we can determine birth radii for the stars and attempt to understand their migration pattern. We chose a total of 1131 OCs within 3 Kpc of the Sun from the Gaia DR3 and LAMOST DR8 low-resolution spectral database (R=1800). We used an artificial neural network to correct the LAMOST data by incorporating high-resolution spectral data from GALAH DR3 (R=28000). The average metallicity of the OCs was determined based on the reliable Fe/H values for their members. We then examined the distribution of metallicity across different regions within the Galaxy and inferred birth radii of the OCs from their age and metallicity. The correction method presented here can partially eliminate the systematic offset for LAMOST data. We discuss the metallicity trend as a function of Galactocentric distance and the guiding radii. We also compare these observational results with those from chemo-dynamic simulations. Values derived from observational metallicity data are slightly lower than predicted values when the uncertainties are not considered. However, the metallicity gradients are consistent with previous calculations. Finally, we investigated the birthplace of OCs and find hints that the majority of OCs near the Sun have migrated from the outer Galactic disk.

Net emission coefficient of magnesium oxide crystal arc plasma with different Mg particle concentrations

The aim of the present study was to construct a molecular spectral database of magnesium oxide crystal arc plasma, thereby allowing for the net emission coefficient of arc to be calculated at different temperatures, pressures and Mg ratios. First, Rydberg-Klein-Rees inversion method was adopted to reconstruct the potential-energy curves of diatomic molecules. Subsequently, the Schrödinger equation was solved to determine the molecular wave function. This solution combined with the electrical transition moment function facilitates the calculation of radiative transition probabilities between different energy levels and the development of this database. Second, calculation models of the bound-free and free-free radiative absorption cross-section were established and use to calculate the continuous radiation of atoms. Finally, the radiative capability of different particles was calculated separately, followed by calculation of the net emission coefficient of plasma through addition The experimental results reveal that the net emission coefficient of air plasma under atmospheric pressure is consistent with existing research in both its magnitude and trend. Given that Mg can be ionized at lower temperatures, the magnesium oxide crystal arc plasma exhibited a pronounced radiative capacity at these temperatures. Additionally, a larger optical transmission distance corresponded to a reduced net emission coefficient of the plasma.

Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection and accurate diagnosis. This review examines recent advancements in hyperspectral imaging (HSI), integrated with computer-aided diagnostic (CAD) techniques, to enhance HNC detection and diagnosis. Methods: A systematic review of seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), and linear discriminant analysis (LDA). These are applicable to the hyperspectral imaging of HNC tissues. Results: The meta-analysis findings indicate that LDA surpasses other algorithms, achieving an accuracy of 92%, sensitivity of 91%, and specificity of 93%. CNNs exhibit moderate performance, with an accuracy of 82%, sensitivity of 77%, and specificity of 86%. SVMs demonstrate the lowest performance, with an accuracy of 76% and sensitivity of 48%, but maintain a high specificity level at 89%. Additionally, in vivo studies demonstrate superior performance when compared to ex vivo studies, reporting higher accuracy (81%), sensitivity (83%), and specificity (79%). Conclusion: Despite these promising findings, challenges persist, such as HSI's sensitivity to external conditions, the need for high-resolution and high-speed imaging, and the lack of comprehensive spectral databases. Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI's clinical utility in HNC diagnostics. This review underscores the transformative potential of HSI and CAD techniques in revolutionizing HNC diagnostics, facilitating more accurate and earlier detection, and improving patient outcomes.

Gravimetric and SIM-Headspace GC-MS for Residual Organic Solvents Detection in Halal and Wholesomeness Food Analysis

The demand for halal foods and beverages is increasing globally. While most halal analysis focuses on porcine, this study focuses on assessing residual organic solvents to ensure their halal compliance and wholesomeness, following several Malaysian standards and guidelines. A significant challenge in this study was the volatility of the residual solvents during the preparation of standards and quality control. To address this issue, a gravimetric technique was employed and effectively minimized the difference between theoretical (1,000 ppm) and actual (710 – 892 ppm) concentrations of the residual organic standard stock solution, except for acetone (588 ppm). The aim of this study was to establish a validated, reliable, and accurate method using SIM-headspace GC-MS to identify and quantify residual organic solvents for halal and wholesomeness analysis. Confirmation of each residual organic solvent was achieved by comparing the obtained spectra with the NIST 11 spectral database, containing 70,832 compounds, with similarity ranging from 80.9% to 96.6%, except for acetonitrile at 52.2%. The validation parameters were carried out according to ISO 17025:2017, the Center for Drug Evaluation and Research, and the European Guidelines. The parameters included recovery ranging from 95.65% to 95.68%, precision from 10.08% to 19.65% RSD, linearity between 0.996 to 0.999, limit of detection from 0.01 to 0.08 ppm, and limit of quantification from 0.02 to 0.24 ppm. Uncertainty considerations were limited to recovery, precision, and linearity, as other uncertainties were negligible based on the bottom-up approach using in-house validation data. This combination of gravimetric and SIM-headspace GC-MS techniques has provided valuable insights for discussions and collaborations among halal authorities worldwide to establish a consensus analytical methodology for halal and wholesomeness assessment.

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.

Determinations of transition probabilities and oscillator strengths of some levels in Eu I

Through combining experimental branching fractions (BFs) with radiative lifetimes, transition probabilities and oscillator strengths of Eu I for 39 lines from 16 excited levels were derived for the first time. The energies range from 27852.90 to 41443.70 cm-1. The BFs were determined in this work using the Fourier transform spectra available from National Solar Observatory database, and the lifetimes were obtained from literature. Furthermore, BFs for 22 lines, previously reported in the literature, were also determined for comparisons. The new results reported in this work will enrich spectral database and are expected to be widely used in many fields.

Deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy employed for rapid and accurate identification of bismuth brass

BackgroundOwing to its excellent machinability and less toxicity, bismuth brass has been widely used in manufacturing various industrial products. Thus, it is of significance to perform rapid and accurate identification of bismuth brass to reveal the alloying properties. However, the analytical lines of various elements in bismuth brass alloy products based on conventional laser-induced breakdown spectroscopy (LIBS) are usually weak. Moreover, the analytical lines of various elements are often overlaped, seriously interfering with the identification of bismuth brass alloys. To address these challenges, developing an advanced strategy enabling to achieve ultra-high accuracy identification of bismuth brass alloys is highly desirable. ResultsThis work proposed a novel method for rapidly and accurately identifying bismuth brass samples using deep learning assisted femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS). With the help of fs-LA-SIBS, a spectral database containing high quality LIBS spectra on element components were constructed. Then, one-dimensional convolutional neural network (CNN) was introduced to distinguish five species of bismuth brass alloy. Amazingly, the optimal CNN model can provide an identification accuracy of 100 % for specie identification. To figure out the spectral features, we proposed a novel approach named “segmented fs-LA-SIBS wavelength”. The identification contribution from various wavelength intervals were extracted by optimal CNN model. It clearly showed that, the differences of spectra feature in the wavelength interval from 336.05 to 364.66 nm can produce the largest identification contribution for an identification accuracy of 100 %. More importantly, the feature differences in the four elements such as Ni, Cu, Sn, and Zn, were verified to mostly contribute to identification accuracy of 100 %. SignificanceTo the best of our knowledge, it is the first study on one-dimensional CNN configuration assisted with fs-LA-SIBS successfully employed for performing identification of bismuth brass. Compared with conventional machine learning methods, CNN has shown significant more superiority. To reveal the tiny spectra differences, the classification contribution from spectra features were accurately defined by our proposed “segmented fs-LA-SIBS wavelength” method. It can be expected that, CNN assisted with fs-LA-SIBS has great promising for identifying the differences from various element components in metallurgical field.

Beyond Artists' Colors: A Spectral Reference Database for the Identification of β-Naphthol and Triarylcarbonium Colorants by MeVSIMS.

β-Naphthol and triarylcarbonium colorants were often used by modern and contemporary artists in materials such as inks and paints. Their poor stability and ability to fade upon exposure to light make their identification in artworks crucial for planning exhibitions and preventive conservation. Secondary ion mass spectrometry with MeV primary ions (MeV SIMS) is necessary when analyzing synthetic organic colorants (SOCs) with similar molecular structures due to its advantages in high sensitivity and soft ionization, which causes a low fragmentation of organic molecules. In this work, we applied MeV SIMS with 5 MeV Si4+ to identify selected β-naphthol and triarylcarbonium colorants from the 19th/20th century Materials Collection kept at the Academy of Fine Arts Vienna. The collection contains SOCs from renowned companies, such as I.G. Farben and I.C.I., and serves as a unique source of reference materials in the analysis of artworks. Previous research on these colorants with X-ray fluorescence analysis (XRF), micro-Raman, and Fourier transform infrared (FTIR) spectroscopies failed in the case of colorant mixtures. Similar spectral features of the SOCs within one chemical class and their low concentrations in mixtures did not lead to their identification using these techniques. MeV SIMS detected molecular ions or protonated molecules in the positive-ion mode. In the negative-ion mode, the functional groups (NO2 - and SO3 -) of β-naphthol lakes/pigments and heteropolyacid species (WO3 - and MoO3 -) characteristic of triarylcarbonium toners were determined. The results demonstrate that MeV SIMS is highly effective for identifying β-naphthol and triarylcarbonium colorants in mixtures and distinguishing between pigments, toners, lakes, and dyes.

Phytochemical analysis and GC-MS based bioactive compounds determination of 60 days Nigerian Vigna Radiata aqueous root extract

In the present study, the sturdy root of Nigerian Vigna radiata (L.) commonly called mung bean was investigated for the phytochemical content. This was necessitated as a result of limited information observed on the phytochemical content of fully matured Nigerian Vigna radiata root. 60 days old Vigna radiata (L.) plant, a newly introduced crop in Nigeria, was harvested from the farm of National Biotechnology and Research Development Agency, Abagana Centre, Nigeria. The roots were neatly separated from the plant, rinsed well with distilled water, air dried and grounded into flour. The 60 days old Nigerian Vigna radiata root flour sample (NVrR) subjected to preliminary phytochemical assay revealed the presence of 12 bioactive compounds with a remarkable high percentage concentration of 26.780% recorded for flavonoids content. Tannins also recorded appreciable value of 8.927% while values < 5% were noted for the remaining compounds. Further confirmation of the actual bioactive compounds present in 60 days old NVrR through GC-MS studies, generated 30 observable peaks with 28 bioactive compounds identified through spectrum matching with MassHunter\Library\NIST14.L spectral database. The major component, eluted at RT 23.565 (peak area 33.38%) revealed a bioactive compound which has been reported as an active ingredient in the production of detergents and biodiesel. This discovery represents a groundbreaking innovation in the utilization of NVrR for the production of briquettes, offering a cost-effective alternative energy source. Isolation of the identified compounds may prove the NVrR an important raw material for industrial productions.

Contribution of machine learning for subspecies identification from Mycobacterium abscessus with MALDI-TOF MS in solid and liquid media.

Mycobacterium abscessus (MABS) displays differential subspecies susceptibility to macrolides. Thus, identifying MABS's subspecies (M. abscessus, M. bolletii and M. massiliense) is a clinical necessity for guiding treatment decisions. We aimed to assess the potential of Machine Learning (ML)-based classifiers coupled to Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) MS to identify MABS subspecies. Two spectral databases were created by using 40 confirmed MABS strains. Spectra were obtained by using MALDI-TOF MS from strains cultivated on solid (Columbia Blood Agar, CBA) or liquid (MGIT®) media for 1 to 13 days. Each database was divided into a dataset for ML-based pipeline development and a dataset to assess the performance. An in-house programme was developed to identify discriminant peaks specific to each subspecies. The peak-based approach successfully distinguished M. massiliense from the other subspecies for strains grown on CBA. The ML approach achieved 100% accuracy for subspecies identification on CBA, falling to 77.5% on MGIT®. This study validates the usefulness of ML, in particular the Random Forest algorithm, to discriminate MABS subspecies by MALDI-TOF MS. However, identification in MGIT®, a medium largely used in mycobacteriology laboratories, is not yet reliable and should be a development priority.

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.

Diagnostic screening of private well water using gas chromatography with high resolution mass spectrometry to support well water management

In the US, private well users are responsible for their own water quality testing, but local health officials are often uncertain what tests to recommend, particularly for regulated organic chemical contaminants. This study evaluated the utility of suspect and non-target screening (NTS) high resolution mass spectrometry (HRMS) as a tool to identify a wide range of organic chemicals of emerging concern in private well water and to inform well water management decisions. Qualitative NTS, which detects chemicals without providing concentrations, was used to analyze 25 private well water samples from Wake County, North Carolina. Using the NIST 20 mass spectral database (M1), NTS tentatively identified 106 unique organic chemicals across the 25 samples and an average of 11 organic chemicals per sample. At least one USEPA ToxCast chemical was identified in each private well water sample. Private well water users were interviewed prior to and after their sample's NTS results were reported back; four county groundwater managers were interviewed after aggregated results for all 25 water samples were reported back. All but one well user participant chose to participate in the reporting-back post-interview. The 24 private well users found NTS results useful and valued the contextualization of their results using NTS results for other well users and a local municipal water sample. Most private well users (67%) were surprised by their well water results, especially regarding the number of tentatively identified organic chemicals detected. All the groundwater managers believed the NTS results were useful and could help improve their testing recommendations to private well users. Even with qualitative limitations, NTS results can be an effective and valuable tool to inform the public and governance stakeholders in decisions around groundwater quality management.

A bidirectional domain separation adversarial network based transfer learning method for near-infrared spectra

Traditional calibration transfer (CT) methods usually fail to adapt the source domain model to the target domain because of changes associated with the instrument, detection environment, sample composition, or sample type. Most deep transfer learning (DTL) methods have shown reliable capability in extracting domain-invariant representations. However, factors unique to the spectral domain limit their development and application, including difficulty in obtaining labeled samples, numerous spectral perturbation factors, and the scarcity of spectral databases. To address these challenges, we here introduce the Bidirectional Domain-Separating Adversarial Network (BiDSAN), based on a deep adversarial network transfer architecture, for learning and adapting to distribution/domain differences between sample spectra. BiDSAN incorporates Wasserstein divergence (w-div) and dynamic adversarial factor into the domain critic (discriminator) to accelerate the extraction of spectral shared components. Additionally, the use of the spectral signal distortion ratio as a loss function and the introduction of a bidirectional mechanism further enhanced the training accuracy and speed of spectral models. Calibration analyses were tested on pharmaceutical and two sets of wood datasets, corresponding to three common domain/distribution difference scenarios discussed in the study. Compared to existing CT and DTL methods, the proposed BiDSAN method demonstrated superior spectral signal calibration capability and stability, achieving unsupervised domain adaptation for similar samples and, for the first time, obtaining ideal prediction results in cross-species spectral domain adaptation. Our method represents significant potential for using DTL approaches in spectral domain adaptation.

Interlaboratory and cross-platform accessibility of time-of-flight wood identification database

The mass spectral database of tree species built by US Fish and Wildlife Service has thousands of entries and has been a valuable resource to combat illegal logging and international trade. The database was and continues to be constructed using a particular ambient ionization time-of-flight mass spectrometry (TOF-MS) platform in the agency branch in Ashland, OR, with which queries of unknown wood samples are investigated exclusively. Laboratories that operate different MS instruments also have an interest in using the database if they can produce valid matches to known samples compatible with the database. Four species were selected for inter-laboratory comparison using Orbitrap MS instruments and the equivalent TOF-MS platform with direct analysis in real time ionization of institution-sourced wood samples. Identities of the known samples were confirmed by examination of their microscopic wood anatomy. Orbitrap analysis was able to identify each species as confidently as the TOF instruments, often with less variation in spectra but not necessarily greater mass accuracy or better-matched signal abundance to the control database. The Orbitrap program also had to be doubled to two scanned mass ranges appended for greater peak intensity, before spectra could be correctly matched to the database, but the program was successful.

Larvicidal potency of four Egyptian herbs on Culex pipiens larvae: Phytochemical composition and molecular networking for most potent extracts

Utilizing synthetic insecticides to control mosquito populations has many adverse side effects as they can cause environmental pollution and insecticide resistance. Therefore, the search for eco-friendly and effective mosquito control agents has led to the exploration of plant extracts with larvicidal efficacy. In this study, we aimed to explore the larvicidal activity of four Egyptian plant extracts: Rosmarinus officinalis, Melissa officinalis, Cichorium intybus, and Beta vulgaris var. cicla against Culex pipiens and to identify the chemical constituents present in the active extracts. The methanolic extracts of the four plants were prepared and tested against the third instar larvae of C. pipiens using a standard method. The lethal concentrations (LC50 and LC90) were calculated using probit analysis. The active extracts were subjected to ultra-performance liquid chromatography-mass spectroscopy (UPLC-MS/MS) analysis and the data were processed using the global natural products social molecular networking (GNPS) platform to generate molecular networks and identify the compounds based on spectral similarity and database matching. The methanolic extracts of R. officinalis L. and M. officinalis L. had the highest larvicidal activity, where LC50 values were 9.795 and 26.505 μg/mL, respectively. Their exposure caused a high mortality rate and disrupted the biochemical and physiological parameters (total carbohydrates, total lipids, total protein, acetylcholinesterase (AChE), and glutathione S-transferase (GST) in the body of C. pipiens when compared with Cichorium intybus (Chicory) and Beta vulgaris var. cicla (Chard). The UPLC-MS/MS analysis with the aid of the GNPS platform, revealed the presence of 23 and 41 metabolites from R. officinalis L. and M. officinalis L., respectively. The identified metabolites may act as larvicidal agents by interfering with the physiological or biochemical processes of the mosquito larvae. Overall, the findings suggest that the methanolic extracts of R. officinalis and M. officinalis are potential sources of natural larvicides for mosquito control.

Sapwood and nikhra heartwood volatiles of two Combretaceae species traditionally used in Sudanese cosmetology and therapy

The smoke from Combretum hartmannianum and Terminalia laxiflora sapwood and fermented heartwood (nikhra) are used in body smoke baths and homemade skincare products in Sudan, which are believed to have various health benefits. In this study, the volatiles of sapwood and nikhra were collected using two extraction techniques: a Mono-TrapTM sampling technique (at low temperature) and a smoke trapping system (at high temperature). After GC-MS analysis, the chromatogram of each sample revealed numerous peaks. The detected peaks were identified using the DIST08 mass spectral library database, resulting in known and unknown compounds. The results showed that 1,2,4,5-tetrachloro-3,6-dimethoxybenzene was the major compound in the Mono-Trap and smoke extracts from nikhra samples of C. hartmannianum and T. laxiflora. In contrast, the dominant compounds in the Mono-Trap extracts of C. hartmannianum and T. laxiflora sapwoods were acetonyldimethylcarbinol and cadina-1(10),4-diene, respectively. The largest peaks in the chromatograms of the sapwood smoke extracts of both species corresponded to unknown compounds. Some of the detected compounds are known for their aromatic and pharmaceutical properties. Identifying these aromatic compounds in nikhra and not in sapwood might prove the traditional Sudanese beliefs, and it could potentially improve the manufacturing of health products.