David W. Morton

Analysis of phenolics in wine by high performance thin-layer chromatography with gradient elution and high resolution plate imaging

Health benefits of wine, especially with red wine, have been linked to the presence of a wide range of phenolic antioxidants. Thus, the aim of this study was to develop a simple, high performance thin layer chromatographic (HPTLC) method combined with high resolution digital plate images to visually compare multiple wine samples simultaneously on a single chromatographic plate and to quantify levels of gallic acid, caffeic acid, resveratrol and rutin, as representatives of the four different classes of phenolics found in wines. We also wanted to investigate the contribution of the investigated phenolic compounds to the total polyphenolic content (TPC) and total antioxidant capacity (TAC) of the wine samples. The average concentrations of caffeic acid, gallic acid, resveratrol, and rutin in the red wines were 2.15, 30.17, 0.59 and 2.47 mg/L respectively with their concentration below limit of quantification in the white wine samples. The highest concentration of resveratrol and rutin is found in the Cabernet and Shiraz wine samples. The amounts of gallic acid are correlated with TPC (r=0.58). Italian wines have the highest correlation between TPC and TAC (r=0.99) although they do not contain detectable amounts of resveratrol, they contain significant amount of rutin. Therefore, antioxidant properties might be associated with the presence of flavanols in these wines.

Rapid evaluation and comparison of natural products and antioxidant activity in calendula, feverfew, and German chamomile extracts.

The present study describes a simple high performance thin layer chromatographic (HPTLC) method for the simultaneous quantification of apigenin, chamazulene, bisabolol and the use of DPPH free radical as a post-chromatographic derivatization agent to compare the free radical scavenging activities of these components in leaf and flower head extracts from feverfew, German chamomile and marigold from the Asteraceae family. Feverfew (Tanacetum parthenium) leaves have been traditionally used in the treatment of migraine with parthenolide being the main bioactive compound. However, due to similar flowers, feverfew is sometimes mistaken for the German chamomile (Matricaria recutita). Bisabolol and chamazulene are the main components in chamomile essential oil. Marigold (Calendula officinalis) was included in the study for comparison, as it belongs to the same family. Parthenolide was found to be present in all leaf extracts but was not detected in calendula flower extract. Chamazulene and bisabolol were found to be present in higher concentrations in chamomile and Calendula flowers. Apigenin was detected and quantified only in chamomile extracts (highest concentration in flower head extracts). Antioxidant activity in sample extracts was compared by superimposing the chromatograms obtained after post-chromatographic derivatization with DPPH and post-chromatographic derivatization with anisaldehyde. It was found that extracts from chamomile flower heads and leaves have the most prominent antioxidant activity, with bisabolol and chamazulene being the most effective antioxidants.

Cosmeceuticals Derived from Bioactive Substances Found in Marine Algae

Among marine organisms, marine algae are rich sources of structurally diverse bioactive compounds with various biological activities. Algae are widespread everywhere on earth where there is light to carry out photosynthesis and have the potential to produce a number of valuable compounds like Polyunsaturated Fatty Acids (PUFAs), pigments, and antioxidants for pharmaceuticals, biomedical and nutraceutical products. This review considers recent progress in the application of bioactive substances derived from marine algae as potential cosmeceuticals. The biological activities of terpenoids, carotenoids, tocopherol, phenolic compounds, polysaccharides (fucoidan, carrageenans, alginates and agar), unsaturated fatty acids and mycosporin-like amino acids are reported in order to evaluate their potential use in future cosmetic skin care products.

High performance thin layer chromatography (HPTLC) and high performance liquid chromatography (HPLC) for the qualitative and quantitative analysis of Calendula officinalis—Advantages and limitations

Chromatography techniques such as HPTLC and HPLC are commonly used to produce a chemical fingerprint of a plant to allow identification and quantify the main constituents within the plant. The aims of this study were to compare HPTLC and HPLC, for qualitative and quantitative analysis of the major constituents of Calendula officinalis and to investigate the effect of different extraction techniques on the C. officinalis extract composition from different parts of the plant. The results found HPTLC to be effective for qualitative analysis, however, HPLC was found to be more accurate for quantitative analysis. A combination of the two methods may be useful in a quality control setting as it would allow rapid qualitative analysis of herbal material while maintaining accurate quantification of extract composition.

The Use of Fourier Transform Infrared (FTIR) Spectroscopy and ArtificialNeural Networks (ANNs) to Assess Wine Quality

The aim of this study was to develop a simple method to assess wine quality from its Fourier Transform Infrared Spectroscopy (FTIR) spectrum with minimal or no sample preparation. FTIR spectral data of selected wine samples, grape variety, wine barrel type, wine type and production year were correlated with total phenolic content, total and volatile acidity and alcohol content using Artificial Neural Networks (ANNs). A total of 20 (2 whites and 18 reds) different wines used in this study came from three different states across Australia; New South Wales, Victoria and South Australia. FTIR spectroscopy proved to be a promising technique that provides a rapid and accurate method in the quality assessment of wine. A plot of the values predicted by the validated ANN models showed excellent correlation with the experimentally measured values for acetic acid concentration, alcohol content, total phenols, and total acidity (r=0.898- 0.942).

Development and validation of a simple high performance thin layer chromatography method combined with direct 1,1-diphenyl-2-picrylhydrazyl assay to quantify free radical scavenging activity in wine

The aim of this study was to: (a) develop a simple, high performance thin layer chromatographic (HPTLC) method combined with direct 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay to rapidly assess and compare free radical scavenging activity or anti-oxidant activity for major classes of polyphenolics present in wines; and (b) to investigate relationship between free radical scavenging activity to the total polyphenolic content (TPC) and total antioxidant capacity (TAC) in the wine samples. The most potent free radical scavengers that we tested for in the wine samples were found to be resveratrol (polyphenolic non-flavonoid) and rutin (flavonoid), while polyphenolic acids (caffeic acid and gallic acid) although present in all wine samples were found to be less potent free radical scavengers. Therefore, the total antioxidant capacity was mostly affected by the presence of resveratrol and rutin, while total polyphenolic content was mostly influenced by the presence of the less potent free radical scavengers gallic and caffeic acids.

Assessment of antioxidant activity in Victorian marine algal extracts using high performance thin-layer chromatography and multivariate analysis

The aim of this study was to develop and validate a rapid and simple high performance thin layer chromatographic (HPTLC) method to screen for antioxidant activity in algal samples. 16 algal species were collected from local Victorian beaches. Fucoxanthin, one of the most abundant marine carotenoids was quantified directly from the HPTLC plates before derivatization, while derivatization either with 2,2-diphenyl-1-picrylhydrazyl (DPPH) or ferric chloride (FeCl3) was used to analyze antioxidants in marine algae, based on their ability to scavenge non biological stable free radical (DPPH) or to chelate iron ions. Principal component analysis of obtained HPTLC fingerprints has classified algae species into 5 groups according to their chemical/antioxidant profiles. The investigated brown algae samples were found to be rich in non-and moderate-polar compounds and phenolic compounds with antioxidant activity. Most of the phenolic iron chelators also have shown free radical scavenging activity. Strong positive and significant correlations between total phenolic content and DPPH radical scavenging activity showed that, phenolic compounds, including flavonoids are the main contributors of antioxidant activity in these species. The results suggest that certain brown algae possess significantly higher antioxidant potential when compared to red or green algae and could be considered for future applications in medicine, dietary supplements, cosmetics or food industries. Cystophora monilifera extract was found to have the highest antioxidant concentration, followed by Zonaria angustata, Cystophora pectinate, Codium fragile, and Cystophora pectinata. Fucoxanthin was found mainly in the brown algae species. The proposed methods provide an edge in terms of screening for antioxidants and quantification of antioxidant constituents in complex mixtures. The current application also demonstrates flexibility and versatility of a standard HPTLC system in the drug discovery. Proposed methods could be used for the bioassay-guided isolation of unknown natural antioxidants and subsequent identification if combined with spectroscopic identification.

The Use of UV-Visible Reflectance Spectroscopy as an Objective Tool to Evaluate Pearl Quality

Assessing the quality of pearls involves the use of various tools and methods, which are mainly visual and often quite subjective. Pearls are normally classified by origin and are then graded by luster, nacre thickness, surface quality, size, color and shape. The aim of this study was to investigate the capacity of Artificial Neural Networks (ANNs) to classify and estimate the quality of 27 different pearls from their UV-Visible spectra. Due to the opaque nature of pearls, spectroscopy measurements were performed using the Diffuse Reflectance UV-Visible spectroscopy technique. The spectra were acquired at two different locations on each pearl sample in order to assess surface homogeneity. The spectral data (inputs) were smoothed to reduce the noise, fed into ANNs and correlated to the pearl’s quality/grading criteria (outputs). The developed ANNs were successful in predicting pearl type, mollusk growing species, possible luster and color enhancing, donor condition/type, recipient/host color, donor color, pearl luster, pearl color, origin. The results of this study shows that the developed UV-Vis spectroscopy-ANN method could be used as a more objective method of assessing pearl quality (grading) and may become a valuable tool for the pearl grading industry.

Chemometric characterization of wines according to their HPTLC fingerprints

The objective of this study was to determine which major grape varieties are present in a given wine using both high-performance thin-layer chromatography (HPTLC) fingerprinting and multivariate analysis. For this purpose, 40 mono- and multi-varietal commercial wine samples from four vintages between 2003 and 2012 were collected and analyzed for their polyphenolic composition using HPTLC peak profiles. Polyphenolic compounds such as gallic acid, caffeic acid, resveratrol and rutin (each belonging to one of the four common classes of wine polyphenolic antioxidants) were identified. Unsupervised chemometric method, principal component analysis was used to analyze variance in HPTLC patterns as a function of wine grape variety. An artificial neutral network, as the efficient supervised chemometric tool, was used to develop a predictive model for classification of wine samples and discrimination between them.

Molecular Structural Characteristics Important in Drug-HSA Binding

A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular structure.