The system of universal retention indices and aspects of its application in gas chromatography

Summary The universal retention index has been developed on the basis of a critical analysis of the forms of chromatographic retention data presentation. Methods are presented for the determination of the universal retention index. The advantages of the universal retention index system in the evaluation of the selectivity and classification of stationary phases and in the determination of the composition of binary and polynary stationary phases are discussed. The thermodynamic aspect of the suggested system of chromatographic retention data presentation is also considered.     

Recent developments in chromatographic fingerprints from herbal products: set-up and data analysis

The use of chromatographic fingerprints from herbal products where the whole chromatographic profile is applied to evaluate the quality of the investigated product. In this paper, recent developments in the set-up and data analysis of chromatographic fingerprints for herbal products are discussed. First different set-ups for fingerprint development are reviewed. Prior to fingerprint development, a suitable sample preparation, e.g. extraction, should be considered. In a second instance, this review focuses on the data analysis with regards to the different applications of fingerprints. Usually, chemometric data pretreatment is necessary. This is discussed first, followed by a short overview of the data handling techniques used in the two main application areas of herbal fingerprints, i.e. quality assurance and classification or calibration. The quality assurance, which involves the identification and quality control of the herbal products, is reviewed, followed by the use of fingerprints in classification or modelling. The different application areas are illustrated and discussed with several case studies.     

Data Preprocessing for Chromatographic Fingerprint of Herbal Medicine with Chemometric Approaches

Abstract

Recently, the fingerprinting approach using chromatography has become one of the most potent tools for quality assessment of herbal medicine. Due to the complexity of the chromatographic fingerprint and the irreproducibility of chromatographic instruments and experimental conditions, several chemometric approaches such as variance analysis, peak alignment, correlation analysis, and pattern recognition were employed to deal with the chromatographic fingerprint in this work. To facilitate the data preprocessing, a software named Computer Aided Similarity Evaluation (CASE) was also developed. All programs of chemometric algorithms for CASE were coded in MATLAB5.3 based on Windows. Data loading, removing, cutting, smoothing, compressing, background and retention time shift correction, normalization, peak identification and matching, variation determination of common peaks/regions, similarity comparison, sample classification, and other data processes associated with the chromatographic fingerprint were investigated in this software. The case study of high pressure liquid chromatographic HPLC fingerprints of 50 Rhizoma chuanxiong samples from different sources demonstrated that the chemometric approaches investigated in this work were reliable and user friendly for data preprocessing of chromatographic fingerprints of herbal medicines for quality assessment.     

Minimal number of chromatographic test parameters for the characterisation of reversed-phase liquid chromatographic stationary phases

This paper focuses on the classification or differentiation of RP-HPLC columns based on measured chromatographic properties. A chemometric study has been conducted on a published data set consisting of 85 RP-HPLC columns and on a data set consisting of 47 self-tested columns. Principal component analysis enables determination of the number of parameters necessary for a rational differentiation. The results show that reducing the number of parameters for such differentiation still allows classification of the columns just as a higher number did. It is shown that three test parameters produce a classification similar to that obtained with five parameters.     

Comprehensive evaluation of chemical stability of Xuebijing injection based on multiwavelength chromatographic fingerprints and multivariate chemometric techniques

A strategy for monitoring and analyzing the chemical stability of Xuebijing injection (XBJ) by multiwavelength chromatographic fingerprints and multivariate classification techniques is presented in this paper. Multiwavelength chromatographic fingerprints were constructed using chromatographic data obtained at four wavelengths (260, 280, 320, and 400?nm). The raw chromatography data were preprocessed by noise reduction, baseline correction, data normalization, and interval correlation optimized shifting (icoshift). Using this method, fingerprints of 166 samples of XBJ subjected to different forced degradation conditions (irradiation, high temperature, and a range of pH values) were properly represented. Forty-one chemical components were identified using the iPeak program. In addition, the identified peak area profiling of chemical components were used for multivariate classification analysis. Principal component analysis (PCA) and Ward’s method were used to classify different XBJ degradation samples. The PCA score plot showed that XBJ degradation samples were clustered into four groups, and the results are confirmed by Ward’s method. Ten key chemical markers under different degradation conditions were found and identified by counterpropagation artificial neural networks (CP-ANN), statistical t-tests, and UPLC-Q-TOF-MS. The results suggest that the proposed strategy could be successfully applied to the comprehensive analysis of complex chemical systems.     

Hair analysis for methamphetamine, ketamine, morphine and codeine by cation-selective exhaustive injection and sweeping micellar electrokinetic chromatography

This article presents the state of the art of qualitative analysis in the framework of the chromatographic analysis. After establishing the differences between two main classes of qualitative analysis (analyte identification and sample classification/qualification) the particularities of instrumental qualitative analysis are commented on. Qualitative chromatographic analysis for sample classification/qualification through the so-called chromatographic fingerprint (for complex samples) or the volatiles profile (through the direct coupling headspace-mass spectrometry using the chromatograph as interface) is discussed. Next, more technical exposition of the qualitative chromatographic information is presented supported by a variety of representative examples.     

Semi-automated alignment and quantification of peaks using parallel factor analysis for comprehensive two-dimensional liquid chromatography–diode array detector data sets

Parallel factor analysis was used to quantify the relative concentrations of peaks within four-way comprehensive two dimensional liquid chromatography–diode array detector data sets. Since parallel factor analysis requires that the retention times of peaks between each injection are reproducible, a semi-automated alignment method was developed that utilizes the spectra of the compounds to independently align the peaks without the need for a reference injection. Peak alignment is achieved by shifting the optimized chromatographic component profiles from a three-way parallel factor analysis model applied to each injection. To ensure accurate shifting, components are matched up based on their spectral signature and the position of the peak in both chromatographic dimensions. The degree of shift, for each peak, is determined by calculating the distance between the median data point of the respective dimension (in either the second or first chromatographic dimension) and the maximum data point of the peak furthest from the median. All peaks that were matched to this peak are then aligned to this common retention data point. Target analyte recoveries for four simulated data sets were within 2% of 100% recovery in all cases. Two different experimental data sets were also evaluated. Precision of quantification of two spectrally similar and partially coeluting peaks present in urine was as good as or better than 4%. Good results were also obtained for a challenging analysis of phenytoin in waste water effluent, where the results of the semi-automated alignment method agreed with the reference LC–LC MS/MS method within the precision of the methods.     

Quality evaluation of fingerprints of herbal medicine with chromatographic data

In this study, local least squares (LLS) and principal component analysis (PCA) were applied to deal with the disturbances in a data set of chromatographic fingerprints after necessary data transformations. It has been demonstrated that PCA with standard normal variate (SNV) transformation of data led to meaningful classification of 33 different Erigeron breviscapus herbal samples. The result was also corroborated by variance squares discriminant method. The quality of herbal objects was further evaluated, and the causes of this fact have been explained from a chemical point of view. At the same time, it implied an idea for qualitative evaluation of the herbal objects with a common class pattern of chromatographic fingerprints.     

Classification of weathered petroleum oils by multi-way analysis of gas chromatography-mass spectrometry data using PARAFAC2 parallel factor analysis

The application of multi-way parallel factor analysis (PARAFAC2) is described for the classification of different kinds of petroleum oils using GC-MS. Oils were subjected to controlled weathering for 2, 7 and 15 days and PARAFAC2 was applied to the three-way GC-MS data set (MSxGCxsample). The classification patterns visualized in scores plots and it was shown that fitting multi-way PARAFAC2 model to the natural three-way structure of GC-MS data can lead to the successful classification of weathered oils. The shift of chromatographic peaks was tackled using the specific structure of the PARAFAC2 model. A new preprocessing of spectra followed by a novel use of analysis of variance (ANOVA)-least significant difference (LSD) variable selection method were proposed as a supervised pattern recognition tool to improve classification among the highly similar diesel oils. This lead to the identification of diagnostic compounds in the studied diesel oil samples.     

GC-MS STUDY ON DIURETICS IN URINE Ⅰ.BETECTION METHOD USING METHYLATION

A reliable and sensitive method is developed for the detection of commondiuretics in human urine. diuretics along with probenecid are methylated and subjectedto GC-MS analysis.Both chromatographic and mass spectrometric data are obtained inselected ion monitoring and full scanning modes.The average detection limit is 0.1-1.0ag/mi urine.This method is suitable to large-scale and routine screening orconfirmation of diuretics in doping control.     

Alternative moving window factor analysis for comparison analysis between complex chromatographic data

In this investigation, a novel chemometric method is developed for the analysis of five possible relationships of components or spectral features between two correlative but different hyphenated chromatographic systems. It is very helpful for comparison study of components present in different complex systems in both chemistry and systems biology. The proposed method, named alternative moving window factor analysis (AMWFA), could be utilized to determine the number of common components between different samples and then to identify their corresponding spectra half-automatically. AMWFA can alternatively be employed to mind for the selective information hiding in anyone of the two compared data X and Y, and to self-verify the resolution results by changing the extracted target matrices in analysis. From the results of comparison of simulated hyphenated chromatographic data, volatile chemical components in drug pair rhizoma ligustici chuanxiong-radix paeoniae rubra (RLC-RPR) and its single herbal medicines, and analysis of Angelica oral solution and its plasma sample after oral intake to rabbit, powerful ability of the proposed method is shown.     

Quantitative determination of indapamide in pharmaceuticals and urine by high-performance liquid chromatography with amperometric detection.

A high-performance liquid chromatographic method with amperometric detection for the determination of the diuretic indapamide using a muBondapak C18 column is developed. The mobile phase consists of an acetonitrile-water mixture (45:55, 5 mM) in KH2PO4-K2HPO4 (pH 4.0). The compound is monitored at +1200 mV with an amperometric detector equipped with a glassy carbon working electrode. A liquid-liquid or solid-liquid extraction is performed prior to chromatographic analysis to avoid the interferences found in urine matrix. Percentages of recovery are 88.3 +/- 5.6 and 82.9 +/- 7.8 for liquid-liquid and solid-liquid extraction, respectively. The developed method has a linear concentration range from 25 to 315 ng/mL with a reproducibility in terms of relative standard deviation of 4% for a concentration level of 0.5 microgram/mL and a quantitation limit of 1 ng/mL. The method is applied to the determination of indapamide in tablets and urine obtained from hypertensive patients after the ingestion of Tertensif (indapamide 2.5 mg).     

Classification of high-speed gas chromatography-mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection

A useful methodology is introduced for the analysis of data obtained via gas chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum at each retention time interval in which a mass spectrum was collected. Principal component analysis (PCA) with preprocessing by both piecewise retention time alignment and analysis of variance (ANOVA) feature selection is applied to all mass channels collected. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS separation into a row vector. All of the sample row vectors are incorporated into a matrix where each row is a sample vector. This matrix is piecewise aligned and reduced by ANOVA feature selection. Application of the preprocessing steps (retention time alignment and feature selection) to all mass channels collected during the chromatographic separation allows considerably more selective chemical information to be incorporated in the PCA classification, and is the primary novelty of the report. This methodology is objective and requires no knowledge of the specific analytes of interest, as in selective ion monitoring (SIM), and does not restrict the mass spectral data used, as in both SIM and total ion current (TIC) methods. Significantly, the methodology allows for the classification of data with low resolution in the chromatographic dimension because of the added selectivity from the complete mass spectral dimension. This allows for the successful classification of data over significantly decreased chromatographic separation times, since high-speed separations can be employed. The methodology is demonstrated through the analysis of a set of four differing gasoline samples that serve as model complex samples. For comparison, the gasoline samples are analyzed by GC-MS over both 10-min and 10-s separation times. The successfully classified 10-min GC-MS TIC data served as the benchmark analysis to compare to the 10-s data. When only alignment and feature selection was applied to the 10-s gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 10-s gasoline separations when the methodology was applied with all the m/z information. With ANOVA feature selection, chromatographic regions with Fisher ratios greater than 1500 were retained in a new matrix and subjected to PCA yielding successful classification for the 10-s separations.     

Use of genetic algorithms with multivariate regression for determination of gelatine in historic papers based on FT-IR and NIR spectral data

Quantitative non-destructive analysis of individual constituents of historic rag paper is crucial for its effective preservation. In this work, we examine the potentials of mid- and near-infrared spectroscopy, however, in order to fully utilise the selectivity inherent to spectroscopic multivariate measurements, genetic algorithms were used to select spectral data derived from information-rich FT-IR or UV-vis-NIR measurements to build multivariate calibration models based on partial least squares regression, relating spectra to gelatine content in paper. A selective but laborious chromatographic method for the quantification of hydroxyproline (HYP) has been developed to provide the reference data on gelatine content. We used 9-fluorenylmethyl chloroformate (FMOC) to derivatise HYP, which was subsequently determined using reverse-phase liquid chromatographic separation and fluorimetric detection. In this process, the sample is consumed, which is why the method can only be used as a reference method.The sampling flexibility afforded by small-size field-portable spectroscopic instrumentation combined with chemometric data analysis, represents an attractive addition to existing analytical techniques for cultural heritage materials.     

Ion chromatographic method development for monitoring of seawater quality used in over-the-counter pharmaceutical industry

A methodology is developed for the analysis of inorganic anions (fluoride, chloride, bromide, sulphate) in seawater used for over-the-counter (OTC) nasal spray production. The eluent flow rate and concentration of eluent competing ions are optimised by using an artificial neural network resolution model in combination with normalised resolution product criterion function. The developed artificial neural network resolution model shows good predictive ability R2 > or = 0.9973. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing a validation procedure and a number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 > or = 0.9993, recovery = 99.77-100.65%, repeatability RSD < or = 1.85%. This result proves that the proposed method can be used for routine quality assurance analysis in OTC pharmaceutical industry.     

Direct coupling of a gas-liquid separator to an ion mobility spectrometer for the classification of different white wines using chemometrics tools

The potential of a vanguard technique as is the ion mobility spectrometry with ultraviolet ionization (UV-IMS) coupled to a continuous flow system (CFS) have been demonstrated in this work using a gas phase separator (GPS). This vanguard system (CFS-GPS-UV-IMS) has been used for the analysis of different types of white wines to obtain a characteristic profile for each type of wine and their posterior classification using different chemometric tools. Precision of the method was 3.1% expressed as relative standard deviation. A deep chemometric study was carried out for the classification of the four types of wines selected. The best classification performance was obtained by first reducing the data dimensionality by principal component analysis (PCA) followed by linear discriminant analysis (LDA) and finally using a k-nearest neighbour (kNN) classifier. The classification rate in an independent validation set was 92.0% classification rate value with confidence interval [89.0%, 95.0%] at 95% confidence level.The same white wines analyzed using CFS-GPS-UV-IMS were analyzed using gas chromatography with a flame detector (GC-FID) as conventional technique. The chromatographic method used for the determination of superior alcohols in wine samples shown in the Regulation CEE 1238/1992 was selected to carry out the analysis of the same samples set and later the classification using appropriate chemometrics tools. In this case, strategies PCA-LDA and kNN classifier were also used for the correct classification of the wine samples. This combination showed similar results to the ones obtained with the proposed method.     

Chemical Fingerprint Analysis for Quality Control of Herba Ephedrae Based on HPLC-DAD Combined with Chemometrics Methods

A method based on high performance liquid chromatography with photodiode array detector (HPLC-DAD) was developed for chemical fingerprinting analysis of Herba Ephedrae. The index of chromatographic fingerprint's information content was utilized to optimize the fingerprint detection conditions, which reduced the time of analysis and increased the veracity of analysis greatly. Then, the similarity analysis of fingerprints was used in quality consistency evaluation of Herba Ephedrae samples. Moreover, hierarchical clustering analysis (HCA) was applied to classify the samples according to their sources and varieties. In addition, the overlapped chromatographic peaks were resolved with the help of heuristic evolving latent projection (HELP) method in order to gain the better quantitative evaluation. The results indicated that the samples could be successfully grouped in accordance with their varieties and sources. Furthermore, five marker constituents were firstly screened out to be the main chemical markers, which importantly contribute to the classification of Herba Ephedrae samples. This investigation shows that the developed methodology can be generalized to the research of quality control of herbal medicines.     

Quantitative high-performance liquid chromatography of nucleosides in biological materials

A rigorous, comprehensive, and reliable reversed-phase high-performance liquid chromatographic (HPLC) method has been developed for the analysis of ribonucleosides in urine (psi, m1A, m1I, m2G, A, m2(2)G). An initial isolation of ribonucleosides with an affinity gel containing an immobilized phenylboronic acid was used to improve selectivity and sensitivity. Response for all nucleosides was linear from 0.1 to 50 nmoles injected and good quantitation was obtained for 25 microliter or less of sample placed on the HPLC column. Excellent precision of analysis for urinary nucleosides was achieved on matrix dependent and independent samples, and the high resolution of the reversed-phase column allowed the complete separation of 9 nucleosides from other unidentified UV absorbing components at the 1-ng level. Supporting experimental data are presented on precision, recovery, chromatographic methods, minimum detection limit, retention time, relative molar response, sample clean-up, stability of nucleosides, boronate gel capacity, and application to analysis of urine from patients with leukemia and breast cancer. This method is now being used routinely for the determination of the concentration and ratios of nucleosides in urine from patients with different types of cancer and in chemotherapy response studies.