Complex Mixture Of Compounds Research Articles

Chemical Composition, Antioxidant, Antibacterial, and Hemolytic Properties of Ylang-Ylang (Cananga odorata) Essential Oil: Potential Therapeutic Applications in Dermatology

Background/Objectives: This study investigates the chemical composition, antioxidant, antibacterial, and hemolytic properties of ylang-ylang (Cananga odorata) essential oil, with a focus on its potential therapeutic applications for dermatological diseases and the importance of transforming such bioactive properties into a stable, safe, and effective formulation. Methods/Rsults: Essential oils were extracted from flowers harvested in northern Grande Comore using hydro distillation at three different distillation times to examine the impact on yield and quality. Gas chromatographic analysis identified a complex mixture of compounds, including linalool, geranyl acetate, and benzyl benzoate. Antioxidant activity was assessed using DPPH, FRAP, TAC, and beta-carotene bleaching inhibition assays, revealing significant radical scavenging capabilities, with DPPH IC50 varying between 1.57 and 3.5 mg/mL. Antibacterial activity was tested against Escherichia coli, Staphylococcus aureus, Bacillus subtilis, and Pseudomonas aeruginosa, showing promising inhibition zones and minimum inhibitory concentrations. Hemolytic tests indicated varying degrees of red blood cell damage, emphasizing the need for careful concentration management in therapeutic applications. Molecular docking studies highlighted potential therapeutic targets for dermatological conditions, identifying high binding affinities for specific compounds against proteins involved in acne, eczema, and psoriasis. Conclusions: This comprehensive analysis underscores the potential of ylang-ylang essential oil (YEOs) as a natural alternative for antimicrobial treatments and dermatological applications, with its success dependent on optimized extraction methods and precise formulation to reduce cytotoxic effects. A formulation approach is crucial to ensure controlled release, improve bioavailability, and minimize skin irritation.

Development of RP-HPLC methods for the analysis of melatonin alone and in combination with sleep-enhancing dietary supplements

Melatonin, a hormone produced by the pineal gland, is widely recognized for its role in regulating sleep-wake cycles. With the increasing prevalence of sleep disorders, melatonin supplements have gained popularity as a natural remedy. Additionally, various sleep-enhancing dietary supplements combine melatonin with other active ingredients to enhance efficacy. Reliable and accurate analytical methods are essential for ensuring the quality, safety, and efficacy of these products. Reverse-phase high-performance liquid chromatography (RP-HPLC) is a powerful analytical technique widely used for the separation, identification, and quantification of compounds in complex mixtures. This article explores the development and validation of RP-HPLC methods for the analysis of melatonin, both as a single ingredient and in combination with other components in sleep-enhancing dietary supplements.

Plasticity in inhibitory networks improves pattern separation in early olfactory processing.

By combining computational modeling, machine learning, and analysis of calcium imaging data, we demonstrate that associative and non-associative plasticity in the honeybee antennal lobe (AL) - first relay of the insect olfactory system - work together to enhance the contrast between rewarded and unrewarded odors. Training the AL's inhibitory network within specific odor environments enables the suppression of neural responses to common odor components, while amplifying responses to distinctive ones. This study sheds light on the olfactory system's ability to adapt and efficiently learn new odor-reward associations across varying environments, and it proposes innovative, energy-efficient principles applicable to artificial intelligence.

Comprehensive identification of key quality markers of Buzhong Yiqi Oral Liquid based on multi-dimensional characteristic networks

Traditional Chinese Medicine (TCM) formulations often contain a complex mixture of compounds, making it challenging to evaluate their quality. Buzhong Yiqi Oral Liquid (BYOL), a well-known TCM formulation, has been used to treat gastrointestinal diseases and fatigue for a long time. However, the effective substances for its therapeutic effects remain unclear. This study aimed to establish a novel quality control approach for BYOL using a multidimensional feature network to identify quality markers (Q-markers). The multidimensional characteristic network was constructed by a “Spider-Web” model, including the compatibility of ingredients, traceability of compounds from raw materials to the final product, effectiveness, measurability, and stability. Multivariate statistical analysis was applied to quantify the individual dimensions of the feature network, and candidate compounds were identified based on regression regions within the network. Using the multidimensional characteristic network, 12 components were identified as key Q-markers for BYOL, consisting of calycosin-7-O-β-D-glucoside, formononetin, astragaloside IV, liquiritin, quercetin, hesperidin, glycyrrhizic acid, isorhamnetin, liquiritin apioside, hesperidin, luteolin, and nobiletin. The identified Q-markers offered valuable insights for future research on medicinal substances and quality control of BYOL.

Synergistic Effect of β-Cryptoxanthin and Epigallocatechin Gallate on Obesity Reduction.

Chronic obesity is an alarmingly growing global public health concern, posing substantial challenges for the prevention of chronic diseases, including hyperinsulinemia, type 2 diabetes, hyperlipidemia, hypertension, and coronary artery disease, and there is an urgent need for early mitigation strategies. We previously reported the obesity-reducing effects of green tea and β-cryptoxanthin intake. However, since tea has a complex mixture of compounds, it remained unclear which component contributed the most to this effect. Using high-performance liquid chromatography, we analyzed the components of tea in this study to determine if consumption of any combination of these compounds with β-cryptoxanthin had an obesity-reducing effect. Consuming epigallocatechin gallate (EGCG), a component of green tea, and β-cryptoxanthin for 4 weeks led to a decrease in body weight. Moreover, the weight and size of the white adipose tissues were significantly reduced, and blood biochemistry test results were comparable to normal values, with particular improvement in liver function. This indicated that intake of EGCG and β-cryptoxanthin reduces obesity in both subcutaneous and visceral fat. These findings suggest that simultaneous intake of EGCG and β-cryptoxanthin not only reduces obesity but also has a systemic beneficial effect on the body's normal physiological function.

Carbon fibers from bitumen-derived asphaltenes: Strategies for optimizing melt spinnability and improving mechanical properties

The demand for low-cost carbon fibers with exceptional mechanical properties continues to grow across various industries. Bitumen-derived asphaltenes have emerged as a promising alternative to polyacrylonitrile (PAN), yet asphaltenes are a complex mixture of compounds, and the fundamental understanding of which asphaltenes are melt spinnable is not clearly understood. In this study, we utilized three distinct types of asphaltenes, each exhibiting notably distinct thermal softening temperatures (Ts) and aromaticity. The optimal melt processing conditions of asphaltenes were determined by dynamic rheology tests. Moreover, we introduced a novel strategy to facilitate the melt spinning of non-spinnable samples by preparing 100 % asphaltene blends with a low Ts asphaltene at different weight fractions. By this means, we achieved continuous melt spinning of precursor fibers. After careful selection of the stabilization and carbonization conditions, uniform carbon fibers were obtained from asphaltenes, which had the highest tensile strength (∼570 MPa) and modulus (∼60 GPa) values, comparable to isotropic pitch-based carbon fibers. Further post-processing of the as-spun fiber resulted in a significant increase in tensile properties, ∼1.1 GPa for strength and ∼91 GPa for modulus. Overall, this study identified spinnable asphaltene precursor characteristics and fiber post-processing methods for achieving low-cost, high-quality carbon fibers from asphaltenes.

NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Identification of new endocrine disruptive transthyretin ligands in polluted waters using pull-down assay coupled to non-target mass spectrometry

Surface and treated wastewater are contaminated with highly complex mixtures of micropollutants, which may cause numerous adverse effects, often mediated by endocrine disruption. However, there is limited knowledge regarding some important modes of action, such as interference with thyroid hormone (TH) regulation, and the compounds driving these effects. This study describes an effective approach for the identification of compounds with the potential to bind to transthyretin (TTR; protein distributing TH to target tissues), based on their specific separation in a pull-down assay followed by non-target analysis (NTA). The method was optimized with known TTR ligands and applied to complex water samples. The specific separation of TTR ligands provided a substantial reduction of chromatographic features from the original samples. The applied NTA workflow resulted in the identification of 34 structures. Twelve compounds with available standards were quantified in the original extracts and their TH-displacement potency was confirmed. Eleven compounds were discovered as TTR binders for the first time and linear alkylbenzene sulfonates (LAS) were highlighted as contaminants of concern. Pull-down assay combined with NTA proved to be a well-functioning approach for the identification of unknown bioactive compounds in complex mixtures with great application potential across various biological targets and environmental compartments.

Quantitative genetic analysis of attractiveness of yeast products to Drosophila.

An attractive perfume is a complex mixture of compounds, some of which may be unpleasant on their own. This is also true for the volatile combinations from yeast fermentation products in vineyards and orchards when assessed by Drosophila. Here, we used crosses between a yeast strain with an attractive fermentation profile and another strain with a repulsive one and tested fly responses using a T-maze. QTL analysis reveals allelic variation in four yeast genes, namely PTC6, SAT4, YFL040W, and ARI1, that modulated expression levels of volatile compounds [assessed by gas chromatography-mass spectrometry (GC-MS)] and in different combinations, generated various levels of attractiveness. The parent strain that is more attractive to Drosophila has repulsive alleles at two of the loci, while the least attractive parent has attractive alleles. Behavioral assays using artificial mixtures mimicking the composition of odors from fermentation validated the results of GC-MS and QTL mapping, thereby directly connecting genetic variation in yeast to attractiveness in flies. This study can be used as a basis for dissecting the combination of olfactory receptors that mediate the attractiveness/repulsion of flies to yeast volatiles and may also serve as a model for testing the attractiveness of pest species such as Drosophila suzukii to their host fruit.

Extraction and characterization of essential oils from synthesized food spice (Lippia multiflora, ginger & turmeric)

Spices are used globally, and are of great importance in indigenous culinary and traditional medicine applications, they are the product of dried fruits, seed root, bark or vegetative substances. This study synthesized food spice from Lippia multiflora, ginger and turmeric. Some anti-nutritional factors and the chemical composition of the extracted essential oils were evaluated. The chemical composition of the extracted essential oils revealed the presence of over ten (10) compounds, with 2-Pyrrolidinone, 1 methyl (35.21 %), Cyclo-hexane-carboxylic acid (24.00 %), Diethyl Phthalate (15.46 %) and 2-Pyrimidinamine,4,6-dimethyl (5.60 %) being the predominant compounds in the oil. The phytochemical screening of the food spice indicated the respective phytate, oxalate and tannin values for the food spice as; 0.71 mg/100 g, 1.2 mg/100 g and 0.51 mg/100 g respectively. These results are in agreement with previous studies where essential oils were shown to contain complex mixtures of compounds with varying chemical compositions.

Toward a More Comprehensive Approach for Dissolved Organic Matter Chemical Characterization Using an Orbitrap Fusion Tribrid Mass Spectrometer Coupled with Ion and Liquid Chromatography Techniques.

Dissolved organic matter (DOM) represents one of the largest active organic carbon pools in the global carbon cycle. Although extensively studied, only <10% of DOM has been chemically characterized into individual dissolved compounds due to its molecular complexity. This study introduced a more comprehensive DOM characterization method by coupling both ion chromatography (IC) and liquid chromatography (LC) with high mass accuracy and resolution mass spectrometry. We presented a new on-the-fly mass calibration of the Orbitrap technique by utilizing the "lock mass" function in the Orbitrap Fusion Tribrid mass spectrometer (OT-FTMS), which assures high mass accuracy at every scan by a postcolumn introduction of internal labeled standards. With both IC and LC, tested unlabeled standards of amino acids, small peptides, and organic acids were consistently below 1.0 ppm mass error, giving the OT-FTMS the potential of reaching mass accuracy of the Fourier-transform ion cyclotron resonance mass spectrometer. In addition to mass accuracy, a pooled quality control sample (QC) was used to increase reproducibility by applying systematic error removal using random forest (SERRF). Using an untargeted mass spectrometry approach, estuarine DOM samples were analyzed by OT-FTMS coupled to IC in negative mode and LC in positive mode detection to cover a wide range of highly cationic to highly anionic molecules. As a proof of concept, we focused on elucidating the structures of three distinct DOM compound classes with varied acidities and basicities. In UPLC-OT-FTMS, a total of 915 compounds were detected. We putatively elucidated 44 small peptides and 33 deaminated peptides of these compounds. With IC-OT-FTMS, a total of 1432 compounds were detected. We putatively elucidated 20 peptides, 268 deaminated peptides, and 188 organic acids. Except for five compounds, all putatively elucidated compounds were uniquely detected in their corresponding chromatography technique. These results highlight the need for combining these two techniques to provide a more comprehensive method for DOM characterization. Application of the combined IC and LC techniques is not limited to DOM chemical characterization. It can analyze other complex compound mixtures, such as metabolites, and anthropogenic pollutants, such as pesticides and endocrine-disrupting chemicals, in environmental and biological samples.

Identification of Ligands for Ion Channels: TRPM2.

Transient receptor potential melastatin 2 (TRPM2) is a calcium-permeable, nonselective cation channel with a widespread distribution throughout the body. It is involved in many pathological and physiological processes, making it a potential therapeutic target for various diseases, including Alzheimer's disease, Parkinson's disease, and cancers. New analytical techniques are beneficial for gaining a deeper understanding of its involvement in disease pathogenesis and for advancing the drug discovery for TRPM2-related diseases. In this work, we present the application of collision-induced affinity selection mass spectrometry (CIAS-MS) for the direct identification of ligands binding to TRPM2. CIAS-MS circumvents the need for high mass detection typically associated with mass spectrometry of large membrane proteins. Instead, it focuses on the detection of small molecules dissociated from the ligand-protein-detergent complexes. This affinity selection approach consolidates all affinity selection steps within the mass spectrometer, resulting in a streamlined process. We showed the direct identification of a known TRPM2 ligand dissociated from the protein-ligand complex. We demonstrated that CIAS-MS can identify binding ligands from complex mixtures of compounds and screened a compound library against TRPM2. We investigated the impact of voltage increments and ligand concentrations on the dissociation behavior of the binding ligand, revealing a dose-dependent relationship.

Chemometrically assisted differential pulse voltammetry for simultaneous and interference-free quantification of gallic and caffeic acids.

This article explores the application of chemometric tools including multivariate curve resolution with alternating least squares for the simultaneous determination of gallic and caffeic acids on the surface of a glassy carbon electrode without additional modification. Gallic and caffeic acids are primary polyphenols, the most abundant in red wines produced in Argentina, and are often used as quality markers for them. These polyphenols significantly contribute to the organoleptic properties of wines from this origin, but their electrochemical signals overlap significantly, making simultaneous quantification challenging without additional experiments such as electrode modification or alternative analytical techniques beyond differential pulse voltammetry. This study successfully quantified these compounds in complex mixtures by generating second-order data from differential pulse voltammetry experiments conducted at various potential steps and subsequently applying multivariate curve resolution with alternating least squares. The use of constraints during optimization prevented rotational ambiguities common in this modeling, leading to unique results in validation samples. The limits of detection (LOD) found for gallic and caffeic acids were 1.6 and 7.6 mg L-1, which are in excellent agreement with the expected concentrations of these compounds in red wines. The concentration ranges analyzed showed a linear dependency (between the LOD and 300 mg L-1) with the signals estimated by the model for both analytes. Advantages such as simplicity, low cost, and high speed, as well as not requiring electrode modification, combined with excellent results obtained for real samples, make it a promising alternative for polyphenol analysis in the wine industry.

Influence of Glow Discharge Plasma Treatment on Cashew Apple Juice’s Aroma Profile and Volatile Compounds

Cashew apple juice has a distinctive fruity aroma but contains an undesired balsamic/chemical note caused by the high concentration of styrene and other aromatic hydrocarbons. Cold plasma technology can induce chemical changes to fruit juices’ volatile compounds, improving the aroma of fruit juices. This study is aimed at evaluating the chemical effects of cold plasma on the volatile compounds and aroma of cashew apple juice, which is characterized by a complex mixture of compounds. Glow discharge plasma was applied to cashew apple juice, varying the plasma flow rate (10 to 30 mL/min) and processing time (10 to 20 min) at a constant voltage (80 kV). Plasma treatment induced several changes in the juice’s volatile compound composition, with a significant decrease in fatty acids and fatty acid esters (92%) and an increase in aldehydes (50%), alcohols (86%), and short-chain esters (21%). The primary reaction observed during plasma treatment was the internal scission of fatty acid and fatty acid esters, which formed short-chain esters and aldehydes. Further hydrogenation of aldehydes produced alcohols. The chemical changes induced by plasma treatment intensified cashew apple juice’s aroma by 28% while maintaining its aroma profile.

Convenient high resolution mass spectrometry characterization of aromatic sulfur-containing petroleum components following by preliminary S-alkylation with aliphatic alcohols

This paper describes rather suitable and variable preliminary derivatization strategy that may precede the molecular level characterization of sulfur-containing compounds of a particularly aromatic nature by high-resolution MALDI and ESI mass spectrometry. We demonstrated for the first time that free aliphatic alcohols (primary 1-alkanols C3–C20) in the presence of triflic acid provide easy S-alkylation of not only saturated sulfides but also most typical aromatic sulfur-containing compounds (benzothiophene, dibenzothiophene and their homologues) widely distributed and frequently analyzed in oil. The reaction proceeds quantitatively at rather mild conditions and gives rise to corresponding S-alkyl sulfonium salts the cation moieties of which can be detected using MALDI and ESI mass spectrometry with excellent signal/noise (S/N) ratios; the response ratios for target ions being quite close for both methods. Collision-induced dissociation (CID) of S-alkylsulfonium cations proceeds only by the elimination of entire S-alkyl group yielding protonated molecule of the analyte. This process can be useful for a reliable determination of target aromatic heterocyclic compounds in complex mixtures. The applicability of the method is illustrated by the analysis of deasphalted medium petroleum sample. The proposed derivatization principle is considered to be highly applicable as an alternative approach to routine characterization and sensitive determination of most typical sulfur-containing compounds and particularly of aromatic S-heterocycles in crude oils by soft-ionization mass spectrometry methods.

Machine Learning Analysis of Raman Spectra To Quantify the Organic Constituents in Complex Organic-Mineral Mixtures.

Important decisions in local agricultural policy and practice often hinge on the soil's chemical composition. Raman spectroscopy offers a rapid noninvasive means to quantify the constituents of complex organic systems. But the application of Raman spectroscopy to soils presents a multifaceted challenge due to organic/mineral compositional complexity and spectral interference arising from overwhelming fluorescence. The present work compares methodologies with the capacity to help overcome common obstacles that arise in the analysis of soils. We created conditions representative of these challenges by combining varying proportions of six amino acids commonly found in soils with fluorescent bentonite clay and coarse mineral components. Referring to an extensive data set of Raman spectra, we compare the performance of the convolutional neural network (CNN) and partial least-squares regression (PLSR) multivariate models for amino acid composition. Strategies employing volume-averaged spectral sampling and data preprocessing algorithms improve the predictive power of these models. Our average test R2 for PLSR models exceeds 0.89 and approaches 0.98, depending on the complexity of the matrix, whereas CNN yields an R2 range from 0.91 to 0.97, demonstrating that classic PLSR and CNN perform comparably, except in cases where the signal-to-noise ratio of the organic component is very low, whereupon CNN models outperform. Artificially isolating two of the most prevalent obstacles in evaluating the Raman spectra of soils, we have characterized the effect of each obstacle on the performance of machine learning models in the absence of other complexities. These results highlight important considerations and modeling strategies necessary to improve the Raman analysis of organic compounds in complex mixtures in the presence of mineral spectral components and significant fluorescence.

Bothrops atrox venom: Biochemical properties and cellular phenotypes of three highly toxic classes of toxins

Snake venoms have a complex mixture of compounds that are conserved across species and act synergistically, triggering severe local and systemic effects. Identification of the toxin classes that are most damaging to cell homeostasis would be a powerful approach to focus on the main activities that underpin envenomation. Here, we focus on the venom of Bothrops atrox, snake responsible for most of the accidents in Amazon region of South America. We identified the key cytotoxic toxin fractions from B. atrox venom and mapped their biochemical properties, protein composition and cell damage. Five fractions were obtained by mass exclusion chromatography and contained either a single class of enzymatic activity (i.e., L-amino acid oxidases or Hyaluronidases) or different activities co-distributed in two or more protein fractions (e.g., Metalloproteinases, Serine Proteases, or Phospholipases A2). Only three protein fractions reduced cell viability of primary human cells. Strikingly, such activity is accompanied by disruption of cell attachment to substratum and to neighbouring cells. Such strong perturbation of morphological cell features indicates likely defects in tissue integrity in vivo. Mass spectrometry identified the main classes of toxins that contribute to these phenotypes. We provide here a strategy for the selection of key cytotoxic proteins for targeted investigation of their mechanism of action and potential synergism during snakebite envenomation. Our data highlights putative toxins (or combinations of) that may be the focus of future therapeutic interference.

Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol−1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

Differentiation of the Aziridine Functionality from Related Functional Groups in Protonated Analytes by Using Selective Ion-Molecule Reactions Followed by Collision-Activated Dissociation in a Linear Quadrupole Ion Trap Mass Spectrometer.

Aziridines are commonly used as reagents for the synthesis of drug substances although they are potentially mutagenic and genotoxic. Therefore, their unambiguous detection is critically important. Unfortunately, tandem mass spectrometry (MS2) based on collision-activated dissociation (CAD), a powerful method used for the identification of many unknown compounds in complex mixtures, does not provide diagnostic fragmentation patterns for ionized aziridines. Therefore, a different mass spectrometry approach based on MS3 experiments is presented here for the identification of the aziridine functionalities. This approach is based on selective gas-phase ion-molecule reactions of protonated analytes with tris(dimethylamino)borane (TDMAB) followed by diagnostic CAD reactions in a modified linear quadrupole ion trap (LQIT) mass spectrometer. TDMAB reacts with protonated aziridines by forming adduct ions that have lost a dimethylamine (DMA) molecule ([M + H + TDMAB - HN(CH3)2]+). CAD on these product ions generated diagnostic fragment ions with m/z-values 25- and 43-units lower than those of the ion-molecule reaction product ions. None of the ion-molecule reaction product ions formed from other, structurally related, protonated analytes produced related fragment ions. Quantum chemical calculations were employed to explore the mechanisms of the observed reactions.

Comparative Methods to Evaluate the Antioxidant Capacity of Propolis: An Attempt to Explain the Differences.

Propolis is a natural product produced by bees that contains a complex mixture of compounds, including phenolic compounds and flavonoids. These compounds contribute to its biological activities, such as antioxidant capacity. This study analysed the pollen profile, total phenolic content (TPC), antioxidant properties, and phenolic compound profile of four propolis samples from Portugal. The total phenolic compounds in the samples were determined by six different techniques: four different Folin-Ciocalteu (F-C) methods, spectrophotometry (SPECT), and voltammetry (SWV). Of the six methods, SPECT allowed the highest quantification, while SWV achieved the lowest. The mean TPC values for these methods were 422 ± 98 and 47 ± 11 mg GAE/g sample, respectively. Antioxidant capacity was determined by four different methods: DPPH, FRAP, original ferrocyanide (OFec), and modified ferrocyanide (MFec). The MFec method gave the highest antioxidant capacity for all samples, followed by the DPPH method. The study also investigated the correlation between TPC and antioxidant capacity with the presence of hydroxybenzoic acid (HBA), hydroxycinnamic acid (HCA), and flavonoids (FLAV) in propolis samples. The results showed that the concentrations of specific compounds in propolis samples can significantly impact their antioxidant capacity and TPC quantification. Analysis of the profile of phenolic compounds by the UHPLC-DAD-ESI-MS technique identified chrysin, caffeic acid isoprenyl ester, pinocembrin, galangin, pinobanksin-3-O-acetate, and caffeic acid phenyl ester as the major compounds in the four propolis samples. In conclusion, this study shows the importance of the choice of method for determining TPC and antioxidant activity in samples and the contribution of HBA and HCA content to their quantification.