Chemical Composition and Antioxidant Activity of the Essential Oil of Cardiospermum grandiflorum Sw Harvested in Kokumbo in Ivory Coast

Aims: The objective of this work is to contribute to the valorization of medicinal and aromatic plants of the Ivorian flora. We propose to determine the chemical composition and to evaluate the antioxidant activity by spectrophotometry of the essential oil of Cardiospermum. grandiflorum Sw Study Design: Valorization of aromatic and medicinal plants. Methodology: The technical of steam distillation using a four-compartment stainless steel device was used to extract the essential oil from the plant matrix. The analysis of the essential oils was carried out on a GC chromatograph (7890A, Agilent Technologies) coupled to a mass spectrometer (5975C, Agilent Technologies). The identification of the compounds was carried out by comparison of the retention indices and mass spectra obtained with those from the National Institute of Standards and Technology (NIST) database and from the literature The antioxidant potential of the extracts was evaluated using the Blois method. Results: The essential oil obtained by steaming, with an aromatic odor and pale yellow color has a yield of (0.0045 ± 0.0002)%. Analysis of the chromatogram and mass spectra obtained by GC-MS identified 24 phytocompounds representing 99.45% of the total chemical composition. The chemical composition of EO consists mainly of hydrocarbon sesquiterpenes (97.72%).The major compound is γ-muurolene (46.06%) (A) followed by β-Caryophyllene (24.35%) (B) and γ-elemene (7.07 %). The essential oil extract of C. grandiflorum exhibits low antioxidant activity compared to vitamin C. The IC 50 value of vitamin C is 0.31 µg / mL while that of EO extract of C grandiflorum is 15.1 µg / mL Conclusion: In the present study, we are interested in the valuation of Cardiospermum grandiflorum an aromatic plant used in traditional Ivorian medicine. The yields of essential oil is low. (24) phytocompounds were identified there. The essential oil has less antioxidant activity than that of vitamin C,

COMPONENT COMPOSITION OF PHENOLIC COMPOUNDS AND TERPENOIDS OF THE ANGIOPROTECTIVE HERBAL COMPOSITION

The study of the component composition of phenolic and terpenoid compounds of the angioprotective herbal composition was carried out. Alcohol extraction of the angioprotective herbal composition was studied by MC-HPLC-UV method. The analysis used solutions of commercial samples of reference substances manufactured by Sigma-Aldrich (USA), Chem-Fages, Extrasynthese, Lione (France), Beijing (China). Seven phenolic compounds have been identified: quercetin, isoramnetin, rutin, isoquercitrin, narcissin, isoramnetin-3-O-glucoside, phenolcarboxylic acid 5-O-caffeylquinic. The total content of flavonoids is 11.64 mg/g, phenolcarboxylic acid – 2.30 mg/g. Among the isolated flavonoids rutin (3.35±0.06 mg/g), isoquercitrin (3.14±0.06 mg/g), narcissin (4.15±0.09 mg/g), phenolcarboxylic acid – 5-O-caffeylquinic (2.30±0.05 mg/g) prevail. The essential oil was obtained by hydrodistillation; analysis was performed by gas chromatography-mass spectrometry on an Agilent Technologies (6890N) instrument with a quadrupole mass spectrometer. The identified components were performed by comparing the linear retention indices and total mass spectra of the compounds with the data from the Nist 11 library and commercial samples. In the herbal composition of the essential oil, 21 components have been identified, the main of which are salicylic aldehyde (in the total amount is 58.30%), methyl salicylate (16.17%). The triterpene saponins of the angioprotective herbal composition are represented by calendulosides A and B, escin. The amount of triterpene saponins is 1.08±0.05%. The results of the quantitative analysis were processed statistically, the data are presented as the mean and ± standard deviation, SD.

GC/EI-MS method for the determination of phytosterols in vegetable oils

Sterols are a highly complex group of lipophilic compounds present in the unsaponifiable matter of virtually all living organisms. In this study, we developed a novel gas chromatography with mass spectrometry selected ion monitoring (GC/MS-SIM) method for the comprehensive analysis of sterols after saponification and silylation. A new referencing system was introduced by means of a series of saturated fatty acid pyrrolidides (FAPs) as internal standards. Linked with retention time locking (RTL), the resulting FAP retention indices (RIFAP) of the sterols could be determined with high precision. The GC/MS-SIM method was based on the parallel measurement of 17 SIM ions in four time windows. This set included eight molecular ions and seven diagnostic fragment ions of silylated sterols as well as two abundant ions of FAPs. Altogether, twenty molecular ions of C27- to C31-sterols with 0–3 double bonds were included in the final method. Screening of four common vegetable oils (sunflower oil, hemp oil, rapeseed oil, and corn oil) enabled the detection of 30 different sterols and triterpenes most of which could be identified. Graphical abstract

A Validation Model for Prediction of Kovats Retention Indices of Compounds Isolated from Origanum spp. and Thymus spp. Essential Oils

This work aimed to obtain a validated model for the prediction of retention times of compounds isolated from Origanum heracleoticum, Origanum vulgare, Thymus vulgaris, and Thymus serpyllum essential oils. In total 68 experimentally obtained retention times of compounds, which were separated and detected by GC-MS were further used to build the prediction models. The quantitative structure–retention relationship was employed to foresee the Kovats retention indices of compounds acquired by GC-MS analysis, using eight molecular descriptors selected by a genetic algorithm. The chosen descriptors were used as inputs for the four artificial neural networks, to construct a Kovats retention indices predictive quantitative structure–retention relationship model. The coefficients of determination in the training cycle were 0.830; 0.852; 0.922 and 0.815 (for compounds found in O. heracleoticum, O. vulgare, T. vulgaris and T. serpyllum essential oils, respectively), demonstrating that these models could be used for prediction of Kovats retention indices, due to low prediction error and high r2. Resumen. El objetivo de este trabajo es la obtención de modelos validados para la predicción del tiempo de retención de los compuestos aislados de aceites esenciales de Origanum heracleoticum, Origanum vulgare, Thymus vulgaris y Thymus serpyllum. Se han obtenido un total de 68 tiempos de retención de compuestos, separándose y detectándose por cromatografía de gases con detección por espectrometría de masas (GC-MS) con posterior desarrollo de modelos de predicción. La relación cuantitativa estructura-retención ha sido utilizada para predecir el índice de retención Kovats de los compuestos obtenidos por análisis de GC-MS, utilizando ocho descriptores moleculares seleccionados mediante algoritmo genético. Los descriptores seleccionados han sido utilizados como entrada para las cuatro redes neuronales artificiales y así elaborar los índices predictivos del modelo de relación cuantitativa estructura-retención. Los coeficientes de determinación en el ciclo de entrenamiento fueron de 0.830; 0.852; 0.922 y 0.815 (para los compuestos identificados en los aceites esenciales del O. heracleoticum, O. vulgare, T. vulgaris y T. serpyllum respectivamente) demostrando así que estos modelos son útiles en la predicción de los índices de retención de Kovats con un error de bajo predicción y alta r2.

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

Investigations on the Key Odorants Contributing to the Aroma of Children Soy Sauce by Molecular Sensory Science Approaches

To investigate the key odor-active compounds in children’s soy sauce (CSS), volatile components were extracted by means of solvent extraction coupled with solvent-assisted flavor evaporation (SE-SAFE) and solid-phase microextraction (SPME). Using gas chromatography-olfactometry (GC-O) and gas chromatography-mass spectrometry (GC-MS), we identified a total of 55 odor-active compounds in six CSSs by comparing the odor characteristics, MS data, and retention indices with those of authentic compounds. Applying aroma extract dilution analysis (AEDA), we measured flavor dilution (FD) factors in SE-SAFE isolates, ranging from 1 to 4096, and in SPME isolates, ranging from 1 to 800. Twenty-eight odorants with higher FD factors and GC-MS responses were quantitated using the internal standard curve method. According to their quantitated results and thresholds in water, their odor activity values (OAVs) were calculated. On the basis of the OAV results, 27 odorants with OAVs ≥ 1 were determined as key odorants in six CSSs. These had previously been reported as key odorants in general soy sauce (GSS), so it was concluded that the key odorants in CSS are the same as those in GSS.