The use of chemometric modelling to determine chemical composition-antimicrobial activity relationships of essential oils used in respiratory tract infections.

The antimicrobial effects of essential oils are commonly cited within aromatherapeutic texts for use in respiratory tract infections. These essential oils are inhaled or applied to the skin to treat infections and manage symptoms associated with these conditions. A limited number of these essential oils have been scientifically studied to support these claims, specifically, against respiratory pathogens. This study reports on the minimum inhibitory concentration (MIC) of 49 commercial essential oils recommended for respiratory tract infections, and identifies putative biomarkers responsible for the determined antimicrobial effect following a biochemometric workflow. Essential oils were investigated against nine pathogens. Three essential oils, Amyris balsamifera (amyris), Coriandrum sativum (coriander) and Santalum austrocaledonicum (sandalwood) were identified as having greater activity (MIC value = 0.03-0.13 mg/ml) compared to the other essential oils investigated. The essential oil composition of all 49 oils were determined using Gas Chromatography coupled to Mass Spectroscopy (GC-MS) analysis and the GC-MS data analysed together with the antimicrobial data using chemometric tools. Eugenol was identified as the main biomarker responsible for antimicrobial activity in the majority of the essential oils. The ability of a chemometric model to accurately predict the active and inactive biomarkers of the investigated essential oils against pathogens of the respiratory tract was 80.33%.

Phytochemical Profiling and Quality Control of Terminalia sericea Burch. ex DC. Using HPTLC Metabolomics.

Terminalia sericea is used throughout Africa for the treatment of a variety of conditions and has been identified as a potential commercial plant. The study was aimed at establishing a high-performance thin layer chromatography (HPTLC) chemical fingerprint for T. sericea root bark as a reference for quality control and exploring chemical variation within the species using HPTLC metabo3lomics. Forty-two root bark samples were collected from ten populations in South Africa and extracted with dichloromethane: methanol (1:1). An HPTLC method was optimized to resolve the major compounds from other sample components. Dichloromethane: ethyl acetate: methanol: formic acid (90:10:30:1) was used as the developing solvent and the plates were visualized using 10% sulfuric acid in methanol as derivatizing agent. The concentrations of three major bioactive compounds, sericic acid, sericoside and resveratrol-3-O-ß-rutinoside, in the extracts were determined using a validated ultra-performance liquid chromatography-photodiode array (UPLC-PDA) detection method. The rTLC software (written in the R-programming language) was used to select the most informative retardation factor (Rf) ranges from the images of the analysed sample extracts. Further chemometric models, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), were constructed using the web-based high throughput metabolomic software. The rTLC chemometric models were compared with the models previously obtained from ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS). A characteristic fingerprint containing clear bands for the three bioactive compounds was established. All three bioactive compounds were present in all the samples, although their corresponding band intensities varied. The intensities correlated with the UPLC-PDA results, in that samples containing a high concentration of a particular compound, displayed a more intense band. Chemometric analysis using HCA revealed two chemotypes, and the subsequent construction of a loadings plot indicated that sericic acid and sericoside were responsible for the chemotypic variation; with sericoside concentrated in Chemotype 1, while sericic acid was more abundant in Chemotype 2. A characteristic chemical fingerprint with clearly distinguishable features was established for T. sericea root bark that can be used for species authentication, and to select samples with high concentrations of a particular marker compound(s). Different chemotypes, potentially differing in their therapeutic potency towards a particular target, could be distinguished. The models revealed the three analytes as biomarkers, corresponding to results reported for UPLC-MS profiling and thereby indicating that HPTLC is a suitable technique for the quality control of T. sericea root bark.

Chemotypic variation of non-volatile constituents of Artemisia afra (African wormwood) from South Africa.

Artemisia afra (African wormwood) is a popular medicinal plant of southern Africa and is an excellent candidate for commercialisation. This current study was aimed at exploring the phytochemistry and chemical variation of non-volatile compounds within wild populations of A. afra, and developing chromatographic quality control protocols for raw materials based on the identification of marker compounds. Chromatographic data, from samples representing 12 distinct populations, were obtained using liquid chromatography-mass spectrometry. An untargeted chemometric approach revealed three clusters. Marker compounds for each cluster, revealed through discriminant analysis, were isolated and identified using NMR spectroscopy, as acacetin (1) (Group 1), chrysoeriol (2) (Group 2), and 3,5-di-O-caffeoylquinic acid (3) and scopoletin (4) (Group 3). In addition, (3) and rutin (5), (both reported for the first time from A. afra), and (1), (2), (4) and 4-caffeoylquinic acid (6) were established as reliable markers for species identification, since they were abundant in most samples. Quantitative analysis using a validated method established (4) as the dominant compound in the samples (1080-19,600 µg/g dry weight (d.w.)), followed by (5) (49.5-2490 µg/g d.w.). A high performance thin layer chromatography (HPTLC) method was developed. The Rf values and colours of the bands corresponding to the marker compounds were recorded so that these compounds could be easily identified for quality control purposes. Multivariate analysis of the data using the rTLC online application confirmed the presence of different chemical groupings within the samples. It was deduced that quantitative, rather than qualitative differences, characterised the samples. Future research should focus on comparing the efficacy of the various chemical clusters in multi-target biological assays aligned to the traditional use of the plant.

Unravelling the Antibacterial Activity of Terminalia sericea Root Bark through a Metabolomic Approach.

Terminalia sericea Burch. ex. DC. (Combretaceae) is a popular remedy for the treatment of infectious diseases. It is widely prescribed by traditional healers and sold at informal markets and may be a good candidate for commercialisation. For this to be realised, a thorough phytochemical and bioactivity profile is required to identify constituents that may be associated with the antibacterial activity and hence the quality of raw materials and consumer products. The aim of this study was to explore the phytochemistry and identify the antibacterial constituents of T. sericea root bark, using a metabolomic approach. The chemical profiles and antibacterial activities of 42 root bark samples collected from three districts in the Limpopo Province, South Africa, were evaluated. Dichloromethane:methanol (1:1) extracts were analysed using ultraperformance liquid chromatography (UPLC)-mass spectrometry (MS), and chemometric models were constructed from the aligned data. The extracts were tested against Bacillus cereus (ATCC 11778), Staphylococcus epidermidis (ATCC 12223), Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 8739), Klebsiella pneumoniae (ATCC 13883), Pseudomonas aeruginosa (ATCC 27853), Shigella sonnei (ATCC 9292) and Salmonella typhimurium (ATCC 14028), using the minimum inhibition microdilution assay. Nine compounds; sericic acid, sericoside, resveratrol-3-O-ß-rutinoside, ellagic acid, flavogallonic acid dilactone, methyl-flavogallonate, quercetin-3-(2''-galloylrhamnoside), resveratrol-3-(6''-galloyl)-O-ß-d-glucopyranoside and arjunetin, were isolated from the root bark. All the compounds, with the exception of sericic acid, sericoside and resveratrol-3-O-ß-rutinoside, were isolated for the first time from the root bark of T. sericea. Chemometric analysis revealed clustering that was not population specific, and the presence of three groupings within the samples, characterised by sericic acid, sericoside and an unidentified compound (m/z 682/4.66 min), respectively. The crude extracts from different populations displayed varied antibacterial activities against S. typhimurium (minimum inhibitory concentrations (MICs) 0.25-1.0 mg/mL), but similar activity towards Bacillus cereus (1.0 mg/mL). Several compounds present in the root bark were highly active towards all or most of the pathogens tested, but this activity was not reflected by the chemical profiles of extracts prepared from the individual samples. Among the pure compounds tested, only flavogallonic acid dilactone and methyl-flavogallonate exhibited broad-spectrum activity. A biochemometric analysis indicated that there was no consistent association between the levels of phytochemicals and the activity of the active or non-active extracts. Although it was deduced that the major constituents of T. sericea root bark contributed to the chemotypic variation, further investigation of the interactions of compounds present in the root bark may provide antibacterial efficacies not evident when examining compounds singularly. The data reported herein will provide information that is fundamentally important for the development of quality control protocols.

Headspace analysis and characterisation of South African propolis volatile compounds using GCxGC-ToF-MS

ABSTRACT Propolis also known as "bee glue or bee resin" is a resinous mixture of bee saliva or bee wax and exudate from tree trunks and flowers, produced by honeybees. The composition of propolis varies depending on the vegetation the bees can access. It is therefore expected that propolis obtained from various localities may have different chemical profiles. In this study, the headspace volatiles of propolis (n = 39) collected from various locations in South Africa (Gauteng, Northern Cape and Western Cape Provinces) were explored for the first time using GCxGC-ToF-MS. Several GCxGC parameters were optimised including; incubation time, temperature and modulation period. Multivariate data analysis techniques (principal component and hierarchical cluster analyses) were applied on the GCxGC-ToF-MS data to investigate trends and clustering patterns within propolis samples. The results demonstrated that headspace volatiles of propolis varied between locations. The volatile profiles were dominated by monoterpenes such as α-pinene (1.2-46.5%), β-pinene (2.0-21.8%), dihydrosabinene (trace-17.8%), limonene (trace-11.6%), p-cymene (0.1-5.3%), 1,8-cineole (0.1-11.0%), 2,7-dimethyl-3-octen-5-yne (trace-11.7%), E-β-ocimene (trace-17.8%), octanal (trace-12.9%), styrene (trace-13.5%) and α-thujene (trace-11.0%). Principal component analysis revealed chemical variation within propolis from the various locations. The heatmap of the averages revealed dehydrosabinene, isopropentyltoluene, p-cymene, acetophenone and α-thujene as chemical markers for the Northern Cape propolis, while λ-terpinene, propanoic acid, furfural, 2-methoxy benzyl alcohol and hexanoic acid methylester were filtered out as markers for Gauteng propolis. The propolis samples originating from the Western Cape Province were dominated by prenal, cinnamaldehyde styrene, 1,8-cineole, decanal, prenyl acetate and butanoic acid. Using GCxGC-ToF-MS in combination with chemometrics, it was possible to profile headspace volatile constituents of propolis and further identify marker compounds that differentiate propolis from various provinces in South Africa.

Hyperspectral Imaging and Support Vector Machine: A Powerful Combination to Differentiate Black Cohosh (Actaea racemosa) from Other Cohosh Species.

Actaea racemosa (black cohosh) has a history of traditional use in the treatment of general gynecological problems. However, the plant is known to be vulnerable to adulteration with other cohosh species. This study evaluated the use of shortwave infrared hyperspectral imaging (SWIR-HSI) in tandem with chemometric data analysis as a fast alternative method for the discrimination of four cohosh species (Actaea racemosa, Actaea podocarpa, Actaea pachypoda, Actaea cimicifuga) and 36 commercial products labelled as black cohosh. The raw material and commercial products were analyzed using SWIR-HSI and ultra-high-performance liquid chromatography coupled to mass spectrometry (UHPLC-MS) followed by chemometric modeling. From SWIR-HSI data (920 - 2514 nm), the range containing the discriminating information of the four species was identified as 1204 - 1480 nm using Matlab software. After reduction of the data set range, partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) models with coefficients of determination (R2 ) of ≥ 0.8 were created. The novel SVM-DA model showed better predictions and was used to predict the commercial product content. Seven out of 36 commercial products were recognized by the SVM-DA model as being true black cohosh while 29 products indicated adulteration. Analysis of the UHPLC-MS data demonstrated that six commercial products could be authentic black cohosh. This was confirmed using the fragmentation patterns of three black cohosh markers (cimiracemoside C; 12-ß,21-dihydroxycimigenol-3-O-L-arabinoside; and 24-O-acetylhydroshengmanol-3-O-ß-D-xylopyranoside). SWIR-HSI in conjunction with chemometric tools (SVM-DA) could identify 80% adulteration of commercial products labelled as black cohosh.

Differentiation between two "fang ji" herbal medicines, Stephania tetrandra and the nephrotoxic Aristolochia fangchi, using hyperspectral imaging.

Stephania tetrandra ("hang fang ji") and Aristolochia fangchi ("guang fang ji") are two different plant species used in Traditional Chinese Medicine (TCM). Both are commonly referred to as "fang ji" and S. tetrandra is mistakenly substituted and adulterated with the nephrotoxic A. fangchi as they have several morphological similarities. A. fangchi contains aristolochic acid, a carcinogen that causes urothelial carcinoma as well as aristolochic acid nephropathy (AAN). In Belgium, 128 cases of AAN was reported while in China, a further 116 cases with end-stage renal disease were noted. Toxicity issues associated with species substitution and adulteration necessitate the development of reliable methods for the quality assessment of herbal medicines. Hyperspectral imaging in combination with partial least squares discriminant analysis (PLS-DA) is suggested as an effective method to distinguish between S. tetrandra and A. fangchi root powder. Hyperspectral images were obtained in the wavelength region of 920-2514nm. Reduction of the dimensionality of the data was done by selecting the discrimination information range (964-1774nm). A discrimination model with a coefficient of determination (R(2)) of 0.9 and a root mean square error of prediction (RMSEP) of 0.23 was created. The constructed model successfully identified A. fangchi and S. tetrandra samples inserted into the model as an external validation set. In addition, adulteration detection was investigated by preparing incremental adulteration mixtures of S. tetrandra with A. fangchi (10-90%). Hyperspectral imaging showed the ability to accurately predict adulteration as low as 10%. It is evident that hyperspectral imaging has tremendous potential in the development of visual quality control methods which may prevent cases of aristolochic acid nephropathy in the future.

Vibrational spectroscopy as a rapid quality control method for Melaleuca alternifolia cheel (tea tree oil).

INTRODUCTION: Tea tree oil (TTO) is an important commercial oil which has found application in the flavour, fragrance and cosmetic industries. The quality is determined by the relative concentration of its major constituents: 1,8-cineole, terpinen-4-ol, α-terpineol, α-terpinene, terpinolene, γ-terpinene and limonene. Gas chromatography coupled to mass spectrometry (GC-MS) is traditionally used for qualitative and quantitative analyses but is expensive and time consuming. OBJECTIVE: To evaluate the use of vibrational spectroscopy in tandem with chemometric data analysis as a fast and low-cost alternative method for the quality control of TTO. METHODS: Spectral data were acquired in both the mid-infrared (MIR) and near infrared (NIR) wavelength regions and reference data obtained using GC-MS with flame ionisation detection (FID). Principal component analysis (PCA) was used to investigate the data by observing clustering and identifying outliers. Partial least squares (PLS) multivariate calibration models were constructed for the quantification of the seven major constituents. RESULTS: High correlation coefficients (R(2) ) of ≥ 0.75 were obtained for the seven major compounds and 1,8-cineole showed the best correlation coefficients for both MIR and NIR data (R(2) = 0.97 and 0.95, respectively). Low values were obtained for the root mean square error of estimation (RMSEE) and root mean square error of prediction (RMSEP) values thereby confirming accuracy. CONCLUSION: The accurate prediction of the external dataset after introduction into the models confirmed that both MIR and NIR spectroscopy are valuable methods for quantification of the major compounds of TTO when compared with the reference data obtained using GC-MS. Copyright © 2013 John Wiley & Sons, Ltd.