Multi-wavelength HPLC fingerprints from complex substances: An exploratory chemometrics study of the Cassia seed example

Multi-wavelength fingerprints of Cassia seed, a traditional Chinese medicine (TCM), were collected by high-performance liquid chromatography (HPLC) at two wavelengths with the use of diode array detection. The two data sets of chromatograms were combined by the data fusion-based method. This data set of fingerprints was compared separately with the two data sets collected at each of the two wavelengths. It was demonstrated with the use of principal component analysis (PCA), that multi-wavelength fingerprints provided a much improved representation of the differences in the samples. Thereafter, the multi-wavelength fingerprint data set was submitted for classification to a suite of chemometrics methods viz. fuzzy clustering (FC), SIMCA and the rank ordering MCDM PROMETHEE and GAIA. Each method highlighted different properties of the data matrix according to the fingerprints from different types of Cassia seeds. In general, the PROMETHEE and GAIA MCDM methods provided the most comprehensive information for matching and discrimination of the fingerprints, and appeared to be best suited for quality assurance purposes for these and similar types of sample.     

Quality control and original discrimination of Ganoderma lucidum based on high-performance liquid chromatographic fingerprints and combined chemometrics methods

In this paper, the feasibility and advantages of employing high-performance liquid chromatographic (HPLC) fingerprints combined with chemometrics methods for quality control of the cultured fruiting bodies of Ganoderma lucidum were investigated and demonstrated for the first time. In order to compare the HPLC fingerprints chromatograms between G. lucidum from different origins, the similarities of all the 60 samples and relative peak areas of 19 characteristic compounds were firstly calculated respectively. Then different pattern recognition procedures, including hierarchical cluster analysis (HCA), principal component analysis (PCA), partial least squares-discrimination analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were applied to classify the G. lucidum samples according to their cultivated origins. Consistent results were obtained to show that G. lucidum samples could be successfully grouped in accordance with the province of origin. Furthermore, four marker constituents were screened out to be the most discriminant variables, which could be applied to accurate discrimination and quality control of G. lucidum by quantitative analysis. Finally, the chemical properties of those samples were also investigated to find out the differences of quality between them. Ranked in decreasing order, the quality of the G. lucidum can be arranged as Jinzhai/Huangshan, Shandong followed by Zhejiang samples. Our results revealed that the developed method has potential perspective for the original discrimination and quality control of G. lucidum.     

Multiple chromatographic fingerprinting and its application to the quality control of herbal medicines

Recently, chromatographic fingerprinting has become one of the most powerful approaches to quality control of herbal medicines. However, the performance of reported chromatographic fingerprinting constructed by single chromatogram sometimes turns out to be inadequate for complex herbal medicines, such as multi-herb botanical drug products. In this study, multiple chromatographic fingerprinting, which consists of more than one chromatographic fingerprint and represents the whole characteristics of chemical constitutions of the complex medicine, is proposed as a potential strategy in this complicated case. As a typical example, a binary chromatographic fingerprinting of “Danshen Dropping Pill” (DSDP), the best-sold traditional Chinese medicine in China, was developed. First, two HPLC fingerprints that, respectively, represent chemical characteristics of depsides and saponins of DSDP were developed, which were used to construct binary chromatographic fingerprints of DSDP. Moreover, the authentication and validation of the binary fingerprints were performed. Then, a data-level information fusion method was employed to capture the chemical information encoded in two chromatographic fingerprints. Based on the fusion results, the lot-to-lot consistency and frauds can be determined either using similarity measure or by chemometrics approach. The application of binary chromatographic fingerprinting to consistency assessment and frauds detection of DSDP clearly demonstrated that the proposed method was a powerful approach to quality control of complex herbal medicines.     

Volvalerenone A, a new type of mononorsesquiterpenoid with an unprecedented 3,12-oxo bridge from Valeriana officinalis

Volvalerenone A (1), a new type of mononorsesquiterpenoid with an unprecedented 5/6/6 tricyclic ring system, was isolated from the roots of Valeriana officinalis, the official species of valerian used in Europe. The structure of volvalerenone A was elucidated based on its spectroscopic and single-crystal X-ray crystallography data. The absolute configuration was assigned by the computational method. A possible biosynthetic pathway of volvalerenone A was also proposed. Preliminary biological studies showed that volvalerenone A had weak inhibitory activity on acetylcholine esterase (AChE), and no enhancing activity on nerve growth factor (NGF)-mediated neurite outgrowth in PC12 cells was observed at 50 μM.     

Analysis of Iranian rosemary essential oil: application of gas chromatography-mass spectrometry combined with chemometrics

This paper focuses on characterization of the components of Iranian rosemary essential oil using gas chromatography-mass spectrometry (GC-MS). Multivariate curve resolution (MCR) approach was used to overcome the problem of background, baseline offset and overlapping/embedded peaks in GC-MS. The analysis of GC-MS data revealed that sixty eight components exist in the rosemary essential oil. However, with the help of MCR this number was extended to ninety nine components with concentrations higher than 0.01%, which accounts for 98.23% of the total relative content of the rosemary essential oil. The most important constituents of the Iranian rosemary are 1,8-cineole (23.47%), α-pinene (21.74%), berbonone (7.57%), camphor (7.21%) and eucalyptol (4.49%).     

Combined microwave-assisted isolation and solid-phase purification procedures prior to the chromatographic determination of phenolic compounds in plant materials

A fast, sensitive and selective procedure employing a combination of microwave-assisted extraction (MAE) and solid phase extraction (SPE) was applied prior to liquid chromatographic identification and quantification of phenolic compounds in plant materials. MAE has been tested and optimized for the isolation of phenolic acids (gallic, protocatechuic, p-hydroxybenzoic, chlorogenic, vanilic, caffeic, syringic, p-coumaric, ferulic, sinapic, benzoic, m-coumaric, o-coumaric, rosmarinic, cinnamic acids) and 3,4-dihydroxybenzaldehyde, syringaldehyde, p-hydroxybenzaldehyde, and vanillin in various plants. The effects of experimental conditions on MAE efficiency, such as solvent composition, temperature, extraction time, have been studied. The extraction efficiencies were compared with those obtained by computer-controlled, two-step Soxhlet-like extractions. Plant extracts were purified and phenolic compounds were pre-concentrated using SPE on polymeric RP-105 SPE sorbent prior to HPLC analysis. Chromatographic separation was carried out on a Hypersil BDS C₁₈ column using a mobile phase consisted of 0.3% (v/v) acetic acid in water (solvent A) and methanol (solvent B) at flow rate 0.6 ml min⁻¹ and column temperature 30 °C with gradient elution.     

Evaluation of traditional Chinese herbal medicine: Chaihu (Bupleuri Radix) by both high-performance liquid chromatographic and high-performance thin-layer chromatographic fingerprint and chemometric analysis

Chaihu (Bupleuri Radix), roots of Bupleurum chinense and B. scorzonerifolium, is an authentic Chinese Materia Medica in the Chinese Pharmacopoeia. Some other species such as the roots of B. falcatum, B.bicaule and B. marginatum var. stenophyllum similar to Chaihu can also be occasionally found in local raw herb markets. The quality of 33 lots of authenticated Chaihu samples vs. 31 lots of commercial samples was evaluated by both high-performance liquid chromatography-evaporative light scattering detector (HPLC-ELSD) and high-performance thin-layer chromatography (HPTLC) analyses of its principal bioactive components (saikosaponins). The pre-treated data acquired from both HPLC fingerprints and HPTLC fluorescent images were processed by chemometrics for similarity and pattern recognition, including Artificial Neural Networks (ANNs), k-nearest neighbor (k-NN) and an expert’s panel. It was apparent that k-NN classifier exhibited good performance with sufficient flexibility for processing HPTLC fingerprint images which were otherwise not easily dealt with by other algorithms due to the shift of R_f values and varying hue/saturation of the band colours between different TLC plates. These two chromatographic fingerprint methods can be considered complementary measure of quality control. The roots of Chaihu from different species of the genus Bupleurum could readily be distinguished from each other so that commercial samples can easily be classified. Chaihu collected from several major herbal distribution centers was found to belong to B. chinense with great variation in the content of its major saikosaponins.     

Antifungal evaluation of cholic acid and its derivatives on Candida albicans by microcalorimetry and chemometrics

In the last few years, several fungus infections caused by multidrug-resistant pathogenic agents have got tremendous emergence and prevalence. Screening for novel antifungal agents is in great demand, but traditional microbiological techniques are far from sufficient to meet that requirement. In this study, a non-invasive and non-destructive microcalorimetric method was performed to investigate the antifungal activities of cholic acid (CA) and its derivatives, glycocholic acid (GCA) and taurocholic acid (TCA) on the multiplying and non-multiplying metabolism of Candida albicans. Then, the heat-flow power-time curves of C. albicans growth affected by different concentrations of CA, GCA and TCA were studied by similarity analysis (SA), the quantitative thermokinetic parameters from these curves were analyzed by multivariate analysis of variance (MANOVA) and principal component analysis (PCA). By comparing the values of two main parameters, P₂ (the heat-flow output power of the highest peak) and Q₂ (the heat output of the second exponential growth phase) of C. albicans, it could be found that CA had the strongest antifungal activity among the three steroid compounds, which might be used as a potential antifungal agent in the future. This study provided a useful method and idea of microcalorimetry with chemometrics to efficiently evaluate the antifungal activities of bile acid derivatives, giving some references for screening out new antifungal agents.However, it has to be stressed that all these experiments are carried out in vitro and they still require clinical validation.     

Coupling chemometrics and electrochemical-based sensor for detection of bacterial population

This work describes a home-made microelectrode array, based on reticulated vitreous carbon, which has been used to record the normal pulse voltammograms with the aim of obtaining the growth curves of Escherichia coli ATCC 13706 and Pseudomonas aeruginosa ATCC 27853 chosen as test microorganisms. The electrochemical signal data have been analysed with partial least squares (PLS) regression in order to highlight the useful analytical information and correlate with the data obtained from the aerobic plate counting. The obtained PLS models generally had a low root mean square error of cross-validation (RMSECV), a cross validated explained variance percentage near 90%.     

多波长薄层扫描分析非那西丁和咖啡因

将阵列感光的光纤光谱仪与二维电移平台结合,完成多波长薄层反射扫描定量,提供二阶张量数据输出.设计的仪器采取“弓”形二维移动扫描,探头与薄层板间距2mm,能在200ms积分时间内同时获取紫外—近红外2048个波长响应值,利用Kubelka-Munk函数积分值定量,提高了薄层扫描定量的线性度和准确度,检出范围覆盖纳克到毫克...     

Neomerane-type sesquiterpenoids from Valeriana officinalis var. latifolia

Valeneomerins A–D, two new neomerane-type sesquiterpenoids and two new nor-sesquiterpenoids, were isolated from the roots of Valeriana officinalis var. latifolia. Among them, valeneomerin A (1) was identified as a sesquiterpenoid with an unprecedented 15,3-lactone loop while valeneomerin C (3) and valeneomerin D (4) were determined to be nor-sesquiterpenoids with a new 15-reductive neomerane-type carbon skeleton. Their structures were elucidated by detailed spectroscopic analysis and Cu Kα X-ray crystallography. Compounds 1, 2, and 4 exhibited significant neuroprotective effects against H₂O₂ induced oxidative stress in SH-SY5Y cells with viability of 49.8, 40.9, and 44.6% each at 50 μM, respectively, in contrast to positive control (catechin, 50 μM) with viability of 32.6%.     

Solid phase microextraction/gas chromatography/mass spectrometry integrated with chemometrics for detection of Salmonella typhimurium contamination in a packaged fresh vegetable

A rapid method for detection of Salmonella typhimurium contamination in packaged alfalfa sprouts using solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) integrated with chemometrics was investigated. Alfalfa sprouts were inoculated with S. typhimurium, packed into commercial LDPE bags and stored at 10 + 2 °C for 0, 1, 2 and 3 days. Uninoculated sprouts were used as control samples. A SPME device was used to collect the volatiles from the headspace above the samples and the volatiles were identified using GC/MS. Chemometric techniques including linear discriminant analysis (LDA) and artificial neural network (ANN) were used as data processing tools. Numbers of Salmonella were followed using a colony counting method. From LDA, it was able to differentiate control samples from sprouts contaminated with S. typhimurium. The potential to predict the number of contaminated S. typhimurium from the SPME/GC/MS data was investigated using multilayer perceptron (MLP) neural network with back propagation training. The MLP comprised an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. The MLP neural network with a back propagation algorithm could predict number of S. typhimurium in unknown samples using the volatile fingerprints. Good prediction was found as measured by a regression coefficient (R² = 0.99) between actual and predicted data.     

Quantitative and fingerprinting analysis of Atractylodes rhizome based on gas chromatography with flame ionization detection combined with chemometrics

This study assessed the feasibility of gas chromatography with flame ionization detection fingerprinting combined with chemometrics for quality analysis of Atractylodes rhizome. We extracted essential oils from 20 Atractylodes lancea and Atractylodes koreana samples by hydrodistillation. The variation in extraction yields (1.33–4.06%) suggested that contents of the essential oils differed between species. The volatile components (atractylon, atractydin, and atractylenolide I, II, and III) were quantified by gas chromatography with flame ionization detection and confirmed by gas chromatography with mass spectrometry, and the results demonstrated that the number and content of volatile components differed between A. lancea and A. koreana. We then calculated the relative peak areas of common components and similarities of samples by comparing the chromatograms of A. lancea and A. koreana extracts. Also, we employed several chemometric techniques, including similarity analysis, hierarchical clustering analysis, principal component analysis, and partial least‐squares discriminate analysis, to analyze the samples. Results were consistent across analytical methods and showed that samples could be separated according to species. Five volatile components in the essential oils were quantified to further validate the results of the multivariate statistical analysis. The method is simple, stable, accurate, and reproducible. Our results provide a foundation for quality control analysis of A. lancea and A. koreana.     

Species differentiation and quality assessment of Radix Paeoniae Rubra (Chi-shao) by means of high-performance liquid chromatographic fingerprint

There are two species under the monograph of Radix Paeoniae Rubrae ("Chi-shao" in Chinese) in Chinese Pharmacopoeia 2005 edition-Paeonia lactiflora Pallas and Paeonia veitchii Lynch. Due to different species and growing conditions, there are significant chemical differences between the two species, which may result in the improper clinical usage under the same name. Chemical pattern expressed by high performance liquid chromatographic (HPLC) fingerprint analysis can play an important role in species differentiation and quality control of Radix Paeoniae Rubra. In the present work, HPLC fingerprints of two kinds of Radix Paeoniae Rubra have been established and analysed with chemometric methods including similarity evaluation and principal component analysis. Both of the fingerprint common patterns of the two species comprise 13 characteristic peaks, nine of which were common peaks of the two species. However, significant differences between the roots of P. veitchii and P. lactiflora exist not only in the content of certain constituents, especially phenolic acids but also in peak-to-peak ratios expressed by the fingerprint patterns. According to the recent pharmacological studies on polyphenolic constituents, root originating from P. veitchii may possess better efficacy and quality than that from P. lactiflora. Our research reveals that further pharmacological investigation is very necessary to determine whether the two species should be embodied under the same monograph in Chinese Pharmacopoeia.     

Optimization of dispersive liquid-liquid microextraction and improvement of detection limit of methyl tert-butyl ether in water with the aid of chemometrics

Dispersive liquid-liquid microextraction (DLLME) coupled with gas chromatography-mass spectrometry-selective ion monitoring (GC-MS-SIM) was applied to the determination of methyl tert-butyl ether (MTBE) in water samples. The effect of main parameters affecting the extraction efficiency was studied simultaneously. From selected parameters, volume of extraction solvent, volume of dispersive solvent, and salt concentration were optimized by means of experimental design. The statistical parameters of the derived model were R(2)=0.9987 and F=17.83. The optimal conditions were 42.0 μL for extraction solvent, 0.30 mL for disperser solvent and 5% (w/v) for sodium chloride. The calibration linear range was 0.001-370 μg L(-1). The improved detection limit with the aid of chemometrics was 0.3 ng L(-1). The relative standard deviation (RSD) with n=9 for 0.1 mg L(-1) MTBE in water with and without internal standard was 2.7% and 3.1%, respectively. Under the optimal conditions, the relative recoveries of spiked MTBE in different water samples were in the range of 100-105%.     

Exploratory analysis of chromatographic fingerprints to distinguish rhizoma Chuanxiong and rhizoma Ligustici

Identification and quality control of products of natural origin, used for preventive and therapeutical goals, is required by regulating authorities, as the World Health Organization. This study focuses on the identification and distinction of the rhizomes from two Chinese herbs, rhizoma Chuanxiong (from Ligusticum chuanxiong Hort.) and rhizoma Ligustici (from Ligusticum jeholense Nakai et Kitag), by chromatographic fingerprints. A second goal is using the fingerprints to assay ferulic acid, as its concentration provides an additional differentiation feature. Several extraction methods were tested, to obtain the highest number of peaks in the fingerprints. The best results were found using 76:19:5 (v/v/v) methanol/water/formic acid as solvent and extracting the pulverized material on a shaking bath for 15 min. Then fingerprint optimization was done. Most information about the herbs, i.e. the highest number of peaks, was observed on a Hypersil ODS column (250 mm × 4.6 mm ID, 5 μm), 1.0% acetic acid in the mobile phase and employing within 50 min linear gradient elution from 5:95 (v/v) to 95:5 (v/v) acetonitrile/water. The final fingerprints were able to distinguish rhizoma Chuanxiong and Ligustici, based on correlation coefficients combined with exploratory data analysis. The distinction was visualized using Principal Component Analysis, Projection Pursuit and Hierarchical Clustering Analysis techniques. Quantification of ferulic acid was possible in the fingerprints of both rhizomes. The time-different intermediate precisions of the fingerprints and of the ferulic acid quantification were shown to be acceptable.     

Protein fingerprinting of Staphylococcus species by capillary electrophoresis with on-capillary derivatization and laser-induced fluorescence detection

Capillary electrophoresis (CE) coupled with laser-induced fluorescence detection (LIF) has allowed to obtain protein fingerprints, which have demonstrated to be useful in microorganisms characterization. In this work, protein fingerprints of two species of Staphylococcus grown in different culture media and submitted to temperature and nitrosative stress were studied by CE-LIF. After the growth of the bacteria, protein extracts were obtained by cell lysis using sonication. The water-soluble fraction of these lysates was derivatized on-capillary with a fluorogenic dye, 3-(2-furoyl)quinoline-2-carboxaldehyde. The fluorescent products were analyzed by CE using phosphate buffer containing submicellar concentrations of sodium pentanesulfate and detected by LIF. Different protein fingerprints were obtained depending on the bacterial specie studied, indicating the usefulness of this method for the identification of different species of the same bacterial genus. It was also demonstrated that the CE protein fingerprints were dependent on the culture conditions, such as growth medium, or on stressing conditions, such as heat shock or nitrosative stress.     

Covalent selection of the thiol proteome on activated thiol sepharose: A robust tool for redox proteomics

Protein thiols contribute significantly to antioxidant defence and selective oxidation of cysteines is important in signal transduction even in sub-stress scenarios. However, cysteine is the second rarest residue in proteins and it can be difficult to target low-abundance thiol (-SH)-containing proteins in proteomic separations. Activated thiol sepharose (ATS) allows covalent selection of -SH-containing proteins which can then be recovered by reduction with mercaptoethanol or dithiothreitol. This is a robust method for enriching -SH-containing proteins. We have used ATS to estimate the percentage (by weight) of thiol-containing proteins in cell extracts from a range of biological sources: a bacterium, Escherichia coli; a fungus, Trichoderma harzianum; and a bivalve mollusc Mytilus edulis. -SH-containing proteins account for 2.52% (E. coli), 1.4% (T. harzianum) and 1.4% (M. edulis) of total protein. Exposure to pro-oxidants did not materially alter these values. On removal of low M_r thiols such as glutathione, the values for M. edulis did not significantly change but those for T. harzianum increased threefold. The two-dimensional electrophoresis profiles of ATS-selected proteins for each organism were compared in control and pro-oxidant-exposed preparations. This revealed that some proteins present in controls were absent in pro-oxidant-treated extracts which we attribute to thiol oxidation. ATS has significant potential in enrichment for -SH-containing proteins in redox proteomics.     

Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics

An efficient method for detecting malicious and accidental contamination of foods has been developed using a combined ¹H nuclear magnetic resonance (NMR) and chemometrics approach. The method has been demonstrated using a commercially available carbonated soft drink, as being capable of identifying atypical products and to identify contaminant resonances. Soft-independent modelling of class analogy (SIMCA) was used to compare ¹H NMR profiles of genuine products (obtained from the manufacturer) against retail products spiked in the laboratory with impurities. The benefits of using feature selection for extracting contaminant NMR frequencies were also assessed. Using example impurities (paraquat, p-cresol and glyphosate) NMR spectra were analysed using multivariate methods resulting in detection limits of approximately 0.075, 0.2, and 0.06 mM for p-cresol, paraquat and glyphosate, respectively. These detection limits are shown to be approximately 100-fold lower than the minimum lethal dose for paraquat. The methodology presented here is used to assess the composition of complex matrices for the presence of contaminating molecules without a priori knowledge of the nature of potential contaminants. The ability to detect if a sample does not fit into the expected profile without recourse to multiple targeted analyses is a valuable tool for incident detection and forensic applications.     

Mineral content and botanical origin of Spanish honeys

Eleven elements (Zn, P, B, Mn, Mg, Cu, Ca, Ba, Sr, Na and K) were determined by inductively plasma coupled spectrometry in 40 honey samples from different places of Spain and four different botanical origins: Eucalyptus (Eucalyptus sp.), Heather (Erica sp.), Orange-blossom (Citrus sinensis) and Rosemary (Rosmarinus officinalis). K, Ca and P show the higher levels with average concentrations ranged between 434.1-1935 mg kg⁻¹ for K; 42.59-341.0 mg kg⁻¹ for Ca and 51.17-154.3 mg kg⁻¹ for P. Levels of Cu (0.531-2.117 mg kg⁻¹), Ba (0.106-1.264 mg kg⁻¹) and Sr (0.257-1.462 mg kg⁻¹) are the lowest in all honey samples. Zn (1.332-7.825 mg kg⁻¹), Mn (0.133-9.471 mg kg⁻¹), Mg (13.26-74.38 mg kg⁻¹) and Na (11.69-218.5 mg kg⁻¹) concentrations were found strongly dependent on the kind of botanical origin.Results were submitted to pattern recognition procedures, unsupervised methods such as cluster and principal components analysis and supervised learning methods like linear discriminant analysis in order to evaluate the existence of data patterns and the possibility of differentiation of Spanish honeys from different botanical origins according to their mineral content. Cluster analysis shows four clusters corresponding to the four botanical origins of honey and PCA explained 71% of the variance with the first two PC variables. The best-grouped honeys were those from heather; eucalyptus honeys formed a more dispersed group and finally orange-blossom and rosemary honeys formed a less distinguishable group.