Algorithms for the assessment of peak purity in liquid chromatography with photodiode-array detection. Part II

Recent Advances in Standardization of Herbal Drugs

Background:

Herbal drugs play a significant role to maintain the human healthiness and to treat the ailments since the dawn of civilization. Moreover, these plants have provided many lead compounds that culminated in modern medicine. A single herb is regarded as mini-combinatorial library of phytoconstituents hence the quality control of herbal drugs in an herbal formulation is not an easy task because a number of factors impact their pharmacological efficiency and consistent therapeutic effects. Hence, to provide consistent beneficial therapeutic effects, standardized herbal products of consistent quality and purity are required.

Methods:

This review is based on publications obtained by a selective search in PubMed using the keywords “Standardized herbal products”, “fingerprinting”, “authentication”, “chemometric, hyphenated techniques”, “quality control of herbal drugs”, “identification”.

Results:

In the era of modernization, chromatographic techniques coupled with sophisticated spectroscopic analytical methods are used in estimating the authenticity, identity and characteristic of herbal products. Further, with the advancement of computer technology, chemometrics methods have become a leading tool with an unsupervised pattern recognition technique for handling multivariate data without prior knowledge about the studied samples and mines more beneficial and valuable information about the chemical entities from the raw data.

Conclusion:

Standardization of HDs chromatographic fingerprint is not always a perfect way to present all compounds. To assess the quality of medicinal plants, new ways are regularly being explored such as combination chemical fingerprint with biological methods, biofingerprint and metabolic fingerprint quality metrology, pharmacodynamics and export system of medicinal plants have been researched in some groups but still a significant amount of work is required to achieve a perfect system for quality evaluation of herbal drugs. Further, novel chemometric techniques have been unfolded that mines more beneficial and valuable information about the chemical entities from the raw data. So this review emphasis mainly on hyphenated techniques associated with chemometric method used in herbal drugs for identifying more valuable information and various methods for providing data, among which most commonly used techniques are chemometric resolution method and Principal Component Analysis (PCA) method.

Enhanced target factor analysis

Target testing or target factor analysis, TFA, is a well-established soft analysis method. TFA answers the question whether an independent target test vector measured at the same wavelengths as the collection of spectra in a data matrix can be excluded as the spectrum of one of the components in the system under investigation. Essentially, TFA cannot positively prove that a particular test spectrum is the true spectrum of one of the components, it can, only reject a spectrum. However, TFA will not reject, or in other words TFA will accept, many spectra which cannot be component spectra. Enhanced Target Factor Analysis, ETFA addresses the above problem. Compared with traditional TFA, ETFA results in a significantly narrower range of positive results, i.e. the chance of a false positive test result is dramatically reduced. ETFA is based on feasibility testing as described in Refs. [16-19]. The method has been tested and validated with computer generated and real data sets.

Chemometrics: A new scenario in herbal drug standardization

Chromatography and spectroscopy techniques are the most commonly used methods in standardization of herbal medicines but the herbal system is not easy to analyze because of their complexity of chemical composition. Many cutting-edge analytical technologies have been introduced to evaluate the quality of medicinal plants and significant amount of measurement data has been produced. Chemometric techniques provide a good opportunity for mining more useful chemical information from the original data. Then, the application of chemometrics in the field of medicinal plants is spontaneous and necessary. Comprehensive methods and hyphenated techniques associated with chemometrics used for extracting useful information and supplying various methods of data processing are now more and more widely used in medicinal plants, among which chemometrics resolution methods and principal component analysis (PCA) are most commonly used techniques. This review focuses on the recent various important analytical techniques, important chemometrics tools and interpretation of results by PCA, and applications of chemometrics in quality evaluation of medicinal plants in the authenticity, efficacy and consistency.

Resolving co-eluting chromatographic patterns by means of dissimilarity analysis in iterative target transformation factor analysis

The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively).

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%).

Soft-modeling based spectrofluorimetric study of simultaneous equilibria

A two-way soft resolution method will fail when applied to a simultaneous equilibria system due to rank deficiency in its concentration profiles. Increasing the dimensionality of measurements from two-way to three-way data can be used to overcome this problem. Simultaneous dissociation of two weak acids is considered as a model for simultaneous equilibria. Three-way data obtained from excitation-emission spectrofluorimetric monitoring of a pH-metric titration is analyzed using a proper combination of well-known soft-modeling methods. Multivariate curve resolution-alternating least squares is used for calculating the excitation and emission spectral profiles of involved species and rank annihilation factor analysis for obtaining the contribution of each species in measured excitation-emission matrices at different pHs. The results of simulated and real simultaneous acids dissociation equilibria showed that the proposed combined method performs well even in situation when the equilibrium constants are close to each other. The applicability of method for study of an acidic dissociation is also shown.

Characterization of essential oil components of Iranian geranium oil using gas chromatography–mass spectrometry combined with chemometric resolution techniques

The essential oil components of geranium oil cultivated in center of Iran were identified and determined using gas chromatography-mass spectrometry data combined with the chemometric resolution techniques. A total of 61 components accounting for 91.51% were identified using similarity searches between the mass spectra and MS database. This number was extended to 85 components using chemometric techniques. Various chemometric methods such as morphological scores, simplified Borgen method (SBM) and fixed size moving window evolving factor analysis (FSMWEFA) were used for determining the number of components, pure variables, zero concentration and selective regions. Then the overlapping peak clusters were resolved into pure chromatograms and pure mass spectra using heuristic evolving latent projections (HELP) method. A characteristic feature of the Iranian geranium oil is the absence of 10-epi-gamma-eudesmol in its constituents compared with the oil from northern and southern parts of India. The results of this work show that combination of hyphenated chromatographic methods and resolution techniques provide a complementary method for accurate analysis of essential oils.

Application of multivariate curve resolution methods to on-flow LC-NMR

The application of evolving window factor analysis (EFA), subwindow factor analysis (SFA), iterative target transformation factor analysis (ITTFA), alternating least squares (ALS), Gentle, automatic window factor analysis (AUTOWFA) and constrained key variable regression (CKVR) to resolve on-flow LC-NMR data of eight compounds into individual concentration and spectral profiles is described. CKVR has been reviewed critically and modifications are suggested to obtain improved results. A comparison is made between three single variable selection methods namely, orthogonal projection approach (OPA), simple-to-use interactive self-modelling mixture analysis approach (SIMPLISMA) and simplified Borgen method (SBM). It is demonstrated that LC-NMR data can be resolved if NMR peak cluster information is utilised.

Two novel procedures for automatic resolution of two-way data from coupled chromatography

In order to deal with the problem of simultaneous qualitative and quantitative analysis of the complicated samples in analytical chemistry, two novel procedures for automatic resolution of two-way data from coupled chromatography were developed in this work. The first can be used to determine automatically the number of components involved in a certain peak cluster. The second method is an iterative resolution procedure named the stepwise key spectrum selection which has also been developed to resolve automatically the spectra and chromatographic profiles of every component in the peak cluster investigated. The results obtained from simulated and real data show that the proposed methods perform fairly well.

Automated system for the on-line monitoring of powder blending processes using near-infrared spectroscopy. Part II. Qualitative approaches to blend evaluation

Near Infrared (NIR) spectroscopy is seen as a very powerful tool in a variety of applications involving powder characterisation. Here we deal with a typical pharmaceutical application of powder blend monitoring. A D-optimal experimental design is used to cover the 85-115% range of the target formulation which is comprised of the active component at 3.5% w/w, Microcrystalline Cellulose (Avicel PH102) at 62%, Dibasic Calcium Phosphate Anhydrous at 31.5%, Sodium Starch Glycolate (Explotab) at 2%, and 1% Magnesium Stearate. A miniature Flobin blender has been modified to enable the use of a fibre optic probe for on-line NIR spectral data collection. The experiments were successful in detecting spectral changes which eventually converged to constant variance. While the NIR spectrum of a powdered sample is rich in information which is representative of both the physical and chemical characteristics of the sample, it is at times difficult to select the appropriate mathematical treatments in order to extract the desired information. This article investigates several possible pre-treatments (including detrending (DT), standard normal variates (SNV), second derivatives, and the combination of SNV and DT) together with several ways in establishing blend homogeneity, which includes the running block standard deviation, dissimilarity calculations and principal components analysis (PCA). The focus of this work is to investigate qualitative tools of analysis for blend homogeneity determinations, while future work will focus on quantitative data interpretation.