Chemical Fingerprint Research Articles

Establishment of callus cultures and quantification of Caffeic acid using HPTLC from Desmodium gangeticum (L.)

Desmodium gangeticum (L.) belonging to family Fabaceae is an economically important medicinal plant which isutilised in Dashmoolarishta. Various bioactive compounds have been isolated from whole plant and roots, and one of them is an important phenolic compound – caffeic acid (CA). This phenolic acid and its derivatives have antioxidant, anti-inflammatory, anticarcinogenic and hepatocarcinoma, a highly aggressive and causing considerable mortality across the world. In the present study, leaf explants were placed on MS medium fortified with different concentration of cytokinin (BA/Kn) and auxin (IAA/NAA) for establishing callus cultures. MS medium fortified with BA (20 µM) and IAA (2 µM) was optimised for the same. Methanolic extracts of in vivo leaf sample (DG1) and in vitro sample (leaf derived callus) (DG2) were assessed for CA quantification using HPTLC. Thus, the chemical fingerprint that was obtained, confirmed that DG 2 of D. gangeticum exhibited the potency to synthesise more amount of CA (316 ± 7.5 µg/g DW) in comparison to DG1 which was 194 ± 2.3 µg/g DW.

Chemical fingerprinting and multicomponent quantitative analysis for quality control of Cinnamomum tamala collected from Western Himalaya by HPLC-DAD

Cinnamomum tamala, commonly known as “Indian bay leaf” or “Tejpat”, is an economically important plant widely used in medicine, food and cosmetic industries. Growing demand for its leaf and bark in the herbal trade and non-availability of quality materials lead to large-scale species admixture and adulteration in the global market. The present study aims at developing a validated HPLC-DAD (High-performance liquid chromatography coupled with diode array detection) method and multiple markers-based chemical fingerprints for quality evaluation of C. tamala leaf extracts. Five bioactive compounds, viz., coumarin, cinnamyl alcohol, cinnamic acid, cinnamaldehyde and cinnamyl acetate, were identified and quantified in 28 samples collected from the western Himalayan region of India. The chromatographic separation was achieved on Shimadzu Shimpak C18 column (dimension 250 × 4.6 mm, pore size 5 μm) with a gradient elution of mobile phase using acetonitrile and 0.1 percent phosphate buffer and the chromatograms were obtained at a wavelength of 265 nm. The method validation was done by analyzing the linearity, LOD, LOQ, precision, stability, repeatability and recovery rates of standard compounds for quantitative analysis. The values of coefficient of correlation (R2) were found to be close to 1 for linearity and similarity analysis; and standard deviation was less than 3 percent in case of precision, stability, repeatability and recovery rates. The content of target compounds such as coumarin, cinnamyl alcohol, cinnamic acid, cinnamaldehyde and cinnamyl acetate varied in the range of 0–1.09, 0–0.05, 0.07–0.51, 0.39–1.27 and 0–0.27 percent, respectively. In the chemical fingerprint of C. tamala leaves, a total of 13 peaks were assigned as common peaks. The results of the study indicated that the HPLC method now developed combining chemical fingerprint with quantification of analytes could serve as a useful tool for quality evaluation of herbal raw materials of C. tamala and a valuable reference for further study.

Integrated Multiplatform Analysis and Separation of Thirteen Flavonoids and Anthraquinones in Seven Medicinal Cassia Species.

Cassia (Family: Fabaceae) species are a large group of flowering plants rich in bioactive anthraquinone and flavonoids used in botanical supplements and nutraceuticals. A simple and reliable high-performance liquid chromatography-photodiode array (HPLC-PDA) method was developed and validated for separating and quantifying 13 anthraquinone and flavonoids. These compounds were further confirmed using an LC-based electrospray ionization mass spectrometry (ESI-MS/MS) method in the leaves and flowers of selected Cassia species. A simple and rapid HPTLC method was developed for chemical fingerprint analysis of all Cassia species. All 13 compounds were chromatographically separated on a Zorbax TC18 (4.6 × 250, 5 μm particle size) analytical column, and 0.1% formic acid and acetonitrile as elution solvents at a flow rate of 0.8 mL/min with detection at 259 nm. For HPTLC fingerprinting, the mobile phase compositions of toluene, ethyl acetate, and formic acid (5.5:4.2:0.6, v/v/v) were optimized to separate and identify all compounds using silica gel 60F254 aluminum plates. The validation data for the developed HPLC-PDA method for 13 compounds showed good linearity (r2 >0.99) with a sensitive LOD (0.082-1.969 μg/mL), LOQ (0.250-5.967 μg/mL), and excellent recoveries (85.22-100.32%). The quantification results were found to be precise and accurate (<5.0% and relative error), -0.77-0.44 with ESI-MS/MS confirmation in the Cassia samples. The novel HPTLC method was excellent separation for 13 compounds, with Rf values ranging between 0.12 and 0.61. The developed HPLC-PDA method was simple and precise and could separate and quantify anthraquinones and flavonoids along with confirmation, using a novel LC-based ESI-MS/MS. The HPTLC method was found to be simple and precise, with excellent separation capabilities for these compounds. This novel multiplatform approach successfully identified and quantified 13 compounds simultaneously using an integration of data strategy in seven medicinally important Cassia species' leaves and flowers.

Fungal removal of cyanotoxins in constructed wetlands: The forgotten degraders

Harmful cyanobacterial blooms have increased globally, releasing hazardous cyanotoxins that threaten the safety of water resources. Constructed wetlands (CWs) are a nature-based and low-cost solution to purify and remove cyanotoxins from water. However, bio-mechanistic understanding of the biotransformation processes expected to drive cyanotoxin removal in such systems is poor, and primarily focused on bacteria. Thus, the present study aimed at exploring the fungal contribution to microcystin-LR and cylindrospermopsin biodegradation in CWs. Based on CW mesocosms, two experimental approaches were taken: a) amplicon sequencing studies were conducted to investigate the involvement of the fungal community; and b) CW fungal isolates were tested for their microcystin-LR and cylindrospermopsin degradation capabilities. The data uncovered effects of seasonality (spring or summer), cyanotoxin exposure, vegetation (unplanted, Juncus effusus or Phragmites australis) and substratum (sand or gravel) on the fungal community structure. Additionally, the arbuscular mycorrhizal fungus Rhizophagus and the endophyte Myrmecridium showed positive correlations with cyanotoxin removal. Fungal isolates revealed microcystin-LR-removal potentials of approximately 25 % in in vitro biodegradation experiments, while the extracellular chemical fingerprint of the cultures suggested a potential intracellular metabolization. The results from this study may help us understand the fungal contribution to cyanotoxin removal, as well as their ecology in CWs.

The application of Raman spectroscopy for the diagnosis and monitoring of lung tumors.

Raman spectroscopy is an optical technique that uses inelastic light scattering in response to vibrating molecules to produce chemical fingerprints of tissues, cells, and biofluids. Raman spectroscopy strategies produce high levels of chemical specificity without requiring extensive sample preparation, allowing for the use of advanced optical tools such as microscopes, fiber optics, and lasers that operate in the visible and near-infrared spectral range, making them increasingly suitable for a wide range of medical diagnostic applications. Metal nanoparticles and nonlinear optical effects can improve Raman signals, and optimized fiber optic Raman probes can make real-time, in vivo, single-point observations. Furthermore, diagnostic speed and spatial accuracy can be improved through the multimodal integration of Raman measurements and other technologies. Recent studies have significantly contributed to the improvement of diagnostic speed and accuracy, making them suitable for clinical application. Lung cancer is a prevalent type of respiratory malignancy. However, the use of computed tomography for detection and screening frequently reveals numerous smaller lung nodules, which makes the diagnostic process more challenging from a clinical perspective. While the majority of small nodules detected are benign, there are currently no direct methods for identifying which nodules represent very early-stage lung cancer. Positron emission tomography and other auxiliary diagnostic methods for non-surgical biopsy samples from these small nodules yield low detection rates, which might result in significant expenses and the possibility of complications for patients. While certain subsets of patients can undergo curative treatment, other individuals have a less favorable prognosis and need alternative therapeutic interventions. With the emergence of new methods for treating cancer, such as immunotherapies, which can potentially extend patient survival and even lead to a complete cure in certain instances, it is crucial to determine the most suitable biomarkers and metrics for assessing the effectiveness of these novel compounds. This will ensure that significant treatment outcomes are accurately measured. This review provides a comprehensive overview of the prospects of Raman spectroscopy and its applications in the diagnosis and analysis of lung tumors.

From the grapevine to the glass: A wine metabolomics tale by FT-ICR-MS.

Wine is one of the most consumed beverages around the world. Its unique characteristics arise from numerous processes, from the selection of grapevine varieties and grapes, the effect of the terroir and geographical origin, through the biochemical process of fermentation by microorganisms, until its aging. All molecules found in wine define its chemical fingerprint and can be used to tell the story of its origin, production, authenticity and quality. Wine's chemical composition can be characterized using an untargeted metabolomics approach based on extreme resolution mass spectrometry. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is currently the most powerful analytical technique to analyse such complex sample, providing the most comprehensive analysis of the chemical fingerprint of wine.

Sensitive, Stable, and Recyclable ZnO/Ag Nanohybrid Substrates for Surface-Enhanced Raman Scattering Metrology.

Surface-enhanced Raman scattering is a practical, noninvasive spectroscopic technique that measures chemical fingerprints for varieties of molecules in multiple applications. However, synthesizing appropriate substrates for practical, long-term applications of this method has always been a challenging task. In the present study, we show that ZnO/Ag nanohybrid substrates may act as highly stable, sensitive, and recyclable substrates for surface-enhanced Raman scattering, as illustrated by the detection of methylene blue, selected as a test dye molecule with self-cleaning functionalities. Specifically, we demonstrate the detection enhancement factor of 3.7 × 107 along with exceptional long-term stability explained in terms of the localized surface plasmon resonance from the Ag nanocrystals embedded into the chemically inert ZnO nanoparticles, constituting the nanohybrid. Significantly, these substrates can be efficiently cleaned and regenerated while maintaining their high performance upon recycling. As a result, using these substrates, up to 10-12 M detection sensitivity has been demonstrated, enabling the accuracy required in modern environmental monitoring, bioassays, and analytical chemistry. Thus, ZnO nanoparticles with embedded Ag nanocrystals constitute a novel class of advanced nanohybrid substrates for use in multiple applications of surface-enhanced Raman scattering metrology.

Exploring anti-inflammatory and antioxidant-related quality markers of Artemisia absinthium L. based on spectrum-effect relationship.

Artemisia absinthium L. is a well-known medicinal, aromatic, and edible plant with important medicinal and economic properties and a long history of use in treating liver inflammation and other diseases; however, there has been insufficient progress in quality control. This study aimed to investigate the quality markers for the anti-inflammatory and antioxidant activities of A.absinthium based on spectrum-effect relationship analysis. Eighteen batches of A.absinthium from different origins were used. Chemical fingerprints were obtained by ultra-performance liquid chromatography (UPLC). The chemical compositions were identified by quadrupole-Orbitrap high-resolution mass spectrometry. Anti-inflammatory activity was assessed by inhibition of cyclooxygenase-2 and 15-lipoxygenase in vitro and inhibition of nitric oxide release in lipopolysaccharide-induced BV-2 cells. Antioxidant activity was assessed by DPPH and ABTS radical scavenging assays. The relationship between bioactivity and chemical fingerprints was then analyzed using chemometrics including gray relational analysis, bivariate correlation analysis, and orthogonal partial least squares analysis. Different batches of A.absinthium extracts possessed significant anti-inflammatory and antioxidant activities to varying degrees. Eighty compounds were identified from A.absinthium, and 12 main common peaks were obtained from the UPLC fingerprints. P3 (chlorogenic acid), P5 (isochlorogenic acid A), and P6 (isochlorogenic acid C) were screened as the most promising active compounds by correlation analysis and further validated for their remarkable anti-inflammatory effects. This is the first study to screen the quality markers of A.absinthium by establishing the spectrum-effect relationship, which can provide a reference for the development of quality standards and further research on A.absinthium.

UHPLC-HRMS/MS Chemical Fingerprinting of the Bioactive Partition from Cultivated Piper aduncum L.

Piper aduncum L. is widely distributed in tropical regions and the ethnobotanical uses of this species encompass medicinal applications for the treatment of respiratory, antimicrobial, and gynecological diseases. Chemical studies reveal a diverse array of secondary metabolites, including terpenes, flavonoids, and prenylated compounds. Extracts from P. aduncum have shown antibacterial, antifungal, and larvicidal activities. Our study explores the activity of extracts and partitions against Mycobacterium tuberculosis H37Rv, as well as the chemical diversity of the bioactive partition. This marks the first investigation of the bioactive partition of P. aduncum from agroecological cultivation. The ethyl acetate partition from the ethanolic leaf extract (PAEPL) was found to be the most active. PAEPL was subjected to column chromatography using Sephadex LH-20 and the obtained fractions were analyzed using UHPLC-HRMS/MS. The MS/MS data from the fractions were submitted to the online GNPS platform for the generation of the molecular network, which displayed 1714 nodes and 167 clusters. Compounds were identified via manual inspection and different libraries, allowing the annotation of 83 compounds, including flavonoids, benzoic acid derivatives, glycosides, free fatty acids, and glycerol-esterified fatty acids. This study provides the first chemical fingerprint of an antimycobacterial sample from P. aduncum cultivated in an agroecological system.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.

Distinguishing the intrinsic differences of Cynomorium songaricum from different origins using chemical fingerprinting combined with chemometric methods

Cynomorium songaricum is a commonly used medicinal and culinary plant renowned for its effects in tonifying kidney yang, as well as nourishing essence and blood. Consequently, it has garnered considerable attention within the fields of pharmacology and chemistry. However, given the distinct geographical distribution of C. songaricum resources, establishing an accurate analytical method to differentiate its origin becomes indispensable in ensuring its quality and efficacy. In this study, high-performance liquid chromatography (HPLC) was utilized to obtain comprehensive information on the chemical composition of C. songaricum samples from various regions and establish HPLC fingerprints. Through chemometrics analysis, essential chemical markers for classifying them were successfully identified. Additionally, the Kruskal–Wallis rank sum test was employed to evaluate differences in the content of intrinsic constituents of C. songaricum from different origins. The results demonstrated that C. songaricum samples from Neimenggu, Gansu, and Xinjiang could be accurately classified, highlighting the significance of chemical markers in assessing the quality of C. songaricum. This study provides a scientific basis for systematic quality control of Chinese herbal medicines, thereby contributing to advancements in the Chinese medicine industry.

Where do they come from?

Globular clusters (GCs), as old as our Galaxy, constantly lose their members to the field as they cross through the Milky Way (MW). These escaped GC stars (or escapees) are thought to contribute significantly to the MW halo. If a star left the host GC a long time ago, chemical finger prints (e.g., N enrichment) may reveal its origin. In this work we aim to establish dynamical connections between N-rich field stars recently identified by LAMOST and the existing MW GCs. By constructing the full action distribution in combination with metallicity, we found 29 potential GC progenitors for 15 N-rich field stars. In particular, some of them may be related to MW accretion events. On the other hand, if a star has recently left its host GC via tidal evaporation, it still maintains the kinematic properties of the cluster. Here we identify extra-tidal candidates based on their spatial locations, proper motions (PMs), and their positions on color-magnitude diagrams (CMDs). We successfully identified more than 1600 extra-tidal candidates in the vicinity of six Gaia-Enceladus (GE)-related GCs: NGC 1851, NGC 1904, NGC 6205, NGC 6341, NGC 6779, NGC 7089. The density map of the extra-tidal candidates is confirmed to be an efficient way to find extra-tidal structures. The two possible density peaks at opposite sides of the inner boundary is a good indicator for a long stellar stream. Among 95 extra-tidal candidates with spectroscopic radial velocities and metallicity, 54 of them are confirmed to be GC escaped stars as they share similar properties to host GCs. These extra-tidal candidates are ideal targets for follow-up spectroscopic observation as it greatly improves the scientific outcome. Once a statistically significant number of spectroscopic radial velocities and metallicities are available, the GC dynamical evolution (e.g., mass loss, rotation) can be carefully investigated.

Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few “known” types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a ‘dilute-and-shoot’ approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a “dilute and shoot” method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

A systematic approach for authentication of medicinal Patrinia species using an integration of morphological, chemical and molecular methods

Four common Patrinia species, including P. heterophylla, P. monandra, P. scabiosifolia and P. villosa, have been documented as herbal medicines with various clinical applications, such as anti-cancer, anti-diarrhea and sedative. However, the authentication of medicinal Patrinia species poses a problem, particularly with the processed herbal materials. This study aimed to systematically authenticate the four medicinal Patrinia species in the market using morphological and chemical characterization, as well as DNA markers. We found the species identity authenticated by traditional morphologies were in good agreement with both chemical and molecular results. The four species showed species-specific patterns in chromatographic profiles with distinct chemical markers. We also revealed the power of complete chloroplast genomes in species authentication. The sequences of targeted loci, namely atpB, petA, rpl2-rpl23 and psaI-ycf4, contained informative nucleotides for the species differentiation. Our results also facilitate authentication of medicinal Patrinia species using new DNA barcoding markers. To the best of our knowledge, this is the first report on the application of morphology, chemical fingerprinting, complete chloroplast genomes and species-specific Insertion-Deletions (InDels) in differentiating Patrinia species. This study reported on the power of a systematic, multidisciplinary approach in authenticating medicinal Patrinia species.

Antibacterial Mechanisms of Constituents from Galla chinensis Revealed by Experimental and Virtual Screening-Based Studies

Many traditional Chinese medicines (TCMs) have been confirmed to have antibacterial activities. However, very few substances have been found to be active against Gram-negative bacteria. This study aimed to identify antimicrobial activity substances against Gram-negative bacteria from fourteen TCMs. Fourteen TCMs with antibacterial potential were chosen for quantitative extraction and antibacterial activity assay, and the plant with the highest activity against Escherichia coli was selected to construct the component-target network. The following virtual screening and enzyme inhibition experiments were performed to analyse the antibacterial mechanisms of the compounds from Galla chinensis. The chemical constituents of Galla chinensis were identified by chemical fingerprinting. 1, 2, 3, 4, 6-Penta-O-galloyl-β-D-glucose (PGG) from Galla chinensis exhibited significant inhibition activity against adenylyl transferase (ATase) of E. coli and antibacterial activity against E. coli. Meanwhile, PGG was identified in the Galla chinensis ethanol extract as the abundant ingredient with a high content of 1.95% (w/w). PGG enriched in Galla chinensis is a promising natural antibiotic with the mode of action inhibiting ATase activity. To our knowledge, this is the first study attributing the antibacterial activity of PGG to its affinity with ATase.

PS2MS: A Deep Learning-Based Prediction System for Identifying New Psychoactive Substances Using Mass Spectrometry.

The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PS2MS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PS2MS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PS2MS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PS2MS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts.

An innovative method for predicting oxidation reaction rate constants by extracting vital information of organic contaminants (OCs) based on diverse molecular representations

The reaction rate constant (k) of oxidants with organic contaminants (OCs) is an important parameter to assess the efficiency of oxidants in removing contaminants. In this study, the degradation of OCs in three oxidation systems was evaluated. The modeling process applied three molecule representations (molecular descriptors (MD), quantum chemical descriptors (QCD) and MACCS fingerprints) and their variable integrations. Models based on integration molecule representations show significant performance improvements. Eventually, the optimal models for ozone, chlorine dioxide and hypochlorite were found to be (MD+QCD)-XGBoost (R2tra = 0.982, Q2tra = 0.715), (MD+QCD+MACCS)-XGBoost (R2tra = 0.982, Q2tra = 0.778), and (MD+QCD+MACCS)-CatBoost (R2tra = 0.856, Q2tra = 0.709) model, respectively. Here, we introduced a new perspective that differed from focusing on machine learning (ML) algorithm optimization. This perspective centered on the input variables (i.e., molecular representations) of models to improve model performance by capturing the key properties of OCs comprehensively. Furthermore, the key effects of pH, ionization potential, orbital energy, polarizability and electronegativity on the oxidation reaction in different oxidation systems were clarified. We hope that the mechanism explanation in this study can provide valuable insights for understanding the mechanism of various oxidation reactions of complex OCs.

Transfer learning-driven artificial intelligence model for glass transition temperature estimation of molecular glass formers mixtures

Predicting binary mixtures' glass transition temperature (Tg) is crucial in various fields, particularly for industrial materials affected by this property during production processes and in service-life. On the other hand, from the fundamental point of view, this predictive capability is relevant for understanding the chemical interactions between the two components and how this affects the Tg of the mixture. In this sense, some models provide different approaches for describing the Tg of the mixture. Among them, the Gordon-Taylor approach has been widely used since it only relies on the relationship between the Tg of the pure components, their weight fraction, and only one fitting parameter. Although simple, this approach still requires measurements of Tg of the pure components and at least some intermediated composition for the fitting procedure. In a previous work, our research has focused on neural networks methods for predicting Tg values directly from the chemical structure of monomers and molecules, but the scarcity of experimental data for binary mixtures limits the application of a similar approach. To address this problem, we propose to use in this work a transfer learning method that relays on the previous acquired knowledge of the chemical structure - Tg relationship, for the prediction of the Tg of the binary mixtures. Therefore, pure component characteristics are derived from chemical fingerprints originated in a pre-trained network, and enables a training process focused on their behavior within the mixtures. This approach successfully estimated K with very low deviations, even allowing for the exploration of the embedded chemical structure's relation to previously unknown mixtures.

Plastic Food Packaging from Five Countries Contains Endocrine- and Metabolism-Disrupting Chemicals.

Plastics are complex chemical mixtures of polymers and various intentionally and nonintentionally added substances. Despite the well-established links between certain plastic chemicals (bisphenols and phthalates) and adverse health effects, the composition and toxicity of real-world mixtures of plastic chemicals are not well understood. To assess both, we analyzed the chemicals from 36 plastic food contact articles from five countries using nontarget high-resolution mass spectrometry and reporter-gene assays for four nuclear receptors that represent key components of the endocrine and metabolic system. We found that chemicals activating the pregnane X receptor (PXR), peroxisome proliferator receptor γ (PPARγ), estrogen receptor α (ERα), and inhibiting the androgen receptor (AR) are prevalent in plastic packaging. We detected up to 9936 chemical features in a single product and found that each product had a rather unique chemical fingerprint. To tackle this chemical complexity, we used stepwise partial least-squares regressions and prioritized and tentatively identified the chemical features associated with receptor activity. Our findings demonstrate that most plastic food packaging contains endocrine- and metabolism-disrupting chemicals. Since samples with fewer chemical features induce less toxicity, chemical simplification is key to producing safer plastic packaging.