Analysis Of Chromatographic Data Research Articles

Establishment of an HPLC Fingerprint and Cluster Analysis for Miao Ethnic Medicine Osbeckia opipara

: Osbeckia opipara, a traditional Miao medicine, is commonly used by the renowned national-level Chinese Traditional Medicine practitioner Zhengshi Wu for the treatment of diarrhea due to its strong antioxidative, anti-inflammatory, and antidiarrheal effects. This study aimed to establish a high-performance liquid chromatography (HPLC) fingerprint for O. opipara to provide new evidence and technical means for the scientific evaluation and effective quality control of O. opipara. The procedure involved isolation with a Nano ChromCore C18 column (250 mm × 4.6 mm, 5 μm), using a gradient elution of 0.1% formic acid in water and 0.1% formic acid in acetonitrile as the mobile phase, with a flow rate of 1.0 mL/min, a column temperature of 30°C, an injection volume of 10 μL, and detection at a wavelength of 254 nm. Under these chromatographic conditions, fingerprint analysis was conducted on 11 batches of O. opipara collected from different origins. The National Pharmacopoeia Committee developed the 'Chromatographic Fingerprint Similarity Evaluation System' (2004A version) for automated comparison, similarity computation, and analysis of chromatographic data. The results revealed 13 common peaks across the 11 batches of O. opipara samples, with a similarity to the automatically generated reference spectrum exceeding 0.9. SPSS 26.0 software was used to conduct cluster analysis on the peak areas of the 13 common peaks. The observations indicated that the reference spectrum generated from the 11 batches could serve as the standard fingerprint profile for O. opipara, providing sufficient characteristic information extraction.

Exploring the chemogeographical variation of a commercially important medicinal tree (Prunus africana (Hook.f.) Kalkman) using a metabolomics approach

The stem-bark of Prunus africana (Hook.f.) Kalkman (African cherry) is traditionally used for the treatment of stomach ache, chest pain, malaria, fever, diabetes and high blood pressure. A large number of constituents have been isolated from the stem-bark including β-sitosterol and ursolic acid, which were reported to have anti-inflammatory activity. The aim of the study was to establish chemotypic variation among Prunus africana samples collected from three African countries, Cameroon, Democratic Republic of Congo (DRC) and Zimbabwe. Ninety-five stem-bark samples were collected from a total of nineteen populations, 11 in Cameroon (n = 55), 4 in Democratic Republic of Congo (n = 20) and 4 in Zimbabwe (n = 20). The samples were extracted with ethyl acetate, dichloromethane and methanol (1 g in 10 mL, 1 g in 10 mL and 2 mg in 5 µL, respectively). Chemometric analysis of chromatographic data obtained from high-performance thin layer chromatography (HPTLC), ultra performance liquid chromatography coupled with mass spectrometry (UPLC–MS), one-dimensional gas chromatography-time-of-flight-mass spectrometry (1D GC–ToF–MS) and spectroscopic data obtained from proton nuclear magnetic resonance (1H NMR) were done using MetaboAnalyst 4.0 software to explore chemotypic variation. A sample from each country was analysed by two-dimensional gas chromatography-time-of-flight-mass spectrometry (2D GC–ToF–MS) to resolve compounds that co-eluted in the 1D GC analysis. Quantification of selected compounds (β-sitosterol and ursolic acid) were done using a validated ultra performance liquid chromatography-photodiode-array detection (UPLC–PDA) method. The chemical profiles obtained from each of the four techniques were very similar for the samples from each country. The HPTLC profiles for Zimbabwe samples were distinctly different from the other two countries with a unique band at Rf value of 0.80. Multivariate analysis of the HPTLC data (using rTLC software), UPLC–MS, 1D GC–ToF–MS and 1H–NMR data revealed three clusters that were country specific. Chemometric analysis through the construction of PCA and a PLS–DA scores plots was performed, followed by the construction of variable important in projection plots, whereby marker compounds contributing significantly to the separation of the three clusters were selected and tentatively identified. Quantitative analysis revealed that samples from Zimbabwe contained higher levels of β-sitosterol (29.2 – 119 µg/g dry weight (DW) of the extract) compared to DRC and Cameroon, while samples from Cameroon were found to contain the highest levels of ursolic acid (1.80 – 141 µg/g DW of the extract). HPTLC, UPLC–MS, 1D and 2D GC–ToF–MS and 1H–NMR are valid tools for quality control of P. africana plant materials after revealing three clusters of samples collected from the three countries. Cameroon samples had high content of ursolic acid, while Zimbabwe samples showed high levels of β-sitosterol. The number of compounds detected across the three countries by 1D GC–ToF–MS was less compared to 2D GC–ToF–MS, which enhanced separation and resolution.

Extracts with antifungal activity against Botrytis cinerea from potato industry waste

Potato tuber (Solanum tuberosum L.) is the fourth most important agricultural product after wheat, rice, and maize. Fresh potatoes or processed potato products generate large amount of by-products, such as peel, leaves, and stems. In peel and leaf potato, organic acids, amino acids, phenolic acids, flavonoids, iridoids, oxylipins, glycoalkaloids among other compounds have been identified. In this work, potato leaves were used to extract compounds with antifungal activity against the phytophatogenic fungus Botrytis cinerea. Solvents with different polarities were used to extract bioactive compounds from potato leaves. The purification of bioactive compounds from leaf extracts from S. tuberosum was performed in various chromatographic steps, obtaining a bioactive fraction with at least two compounds. Based on the spectroscopic and thin-layer chromatographic data analysis, it can be suggested that this active fraction would have a terpenoid and an aromatic compound. This fraction inhibited the mycelial growth of B. cinerea with an ED50 of 4.3 mg L−1. The unpurified extracts showed only pro-oxidant activity; however, the bioactive fraction did not show pro-oxidant or antioxidant effects. The results of this work suggest that potato by products can be used in the production of biofungicides to control to B. cinerea.

Physicochemical Quality of Oyster Mushroom for Functional Food

This article presents studies on the physicochemical quality of oyster mushrooms grown within a housing and controlled by the Internet of Things (IoT). The goals of this study were to assess (1) the impact of indoor air quality on the growth and quality of mushrooms and (2) the antioxidant content of oyster mushrooms. In this study, the air temperature and humidity of oyster mushroom house per unit time was recorded and controlled automatically by an IoT system. Additionally, their physicochemical and microbiological quality were evaluated using physico-analytical instruments, and the potency of their ergothioneine (EGT) content was investigated using the HPLC method. The temperature of the air inside was between 29 and 35 °C, and the relative humidity was between 60% and 90%. The average texture of mushroom is soft. The average length, width, and height of the fresh mushroom were 41.5 mm, 60.0 mm, and 29.5 mm, respectively. The microbiological test confirmed that there was no salmonella infection in the collected mushrooms. The button-stage mushrooms have less total fungus than the bloom-stage mushrooms. The extraction standard method employs an EGT content of 0.674 mg/g as determined by chromatography data analysis. The oyster mushrooms can be consumed as a healthy meal, and the study of EGT showed also very prospective as one of immunotherapeutic food. Keywords: Ergothioneine, Fibre-reinforced plastic house, Internet of think, Oyster mushrooms

Open-Source Chromatographic Data Analysis for Reaction Optimization and Screening.

Automation and digitalization solutions in the field of small molecule synthesis face new challenges for chemical reaction analysis, especially in the field of high-performance liquid chromatography (HPLC). Chromatographic data remains locked in vendors' hardware and software components, limiting their potential in automated workflows and data science applications. In this work, we present an open-source Python project called MOCCA for the analysis of HPLC-DAD (photodiode array detector) raw data. MOCCA provides a comprehensive set of data analysis features, including an automated peak deconvolution routine of known signals, even if overlapped with signals of unexpected impurities or side products. We highlight the broad applicability of MOCCA in four studies: (i) a simulation study to validate MOCCA's data analysis features; (ii) a reaction kinetics study on a Knoevenagel condensation reaction demonstrating MOCCA's peak deconvolution feature; (iii) a closed-loop optimization study for the alkylation of 2-pyridone without human control during data analysis; (iv) a well plate screening of categorical reaction parameters for a novel palladium-catalyzed cyanation of aryl halides employing O-protected cyanohydrins. By publishing MOCCA as a Python package with this work, we envision an open-source community project for chromatographic data analysis with the potential of further advancing its scope and capabilities.

Development of a fuzzy expert system for power transformer diagnostics

In this paper, an expert information system for assessing the technical condition of a power transformer is developed. The system will work on the basis of the fuzzy logic device, and provide operational information about the state of the power transformer. The paper uses fuzzy inference algorithms. The R programming language is used to write a program that uses fuzzy logic. We analyzed the data of chromatographic analysis of gases dissolved in oil, as well as the data of thermal imaging images, identifying the most heated points in power transformers. A database of fuzzy logic rules has been formed. Several examples of defuzzification of the results obtained by the center of gravity method are given. As a result of the program, a three-dimensional graph was obtained that characterizes the surface of the fuzzy output. The developed software package allows you to detect defects in working electrical equipment at an early stage of their development, which not only prevents a sudden shutdown of production as a result of an accident, but also significantly reduces the cost of repairing equipment and increases its service life

Geographical discrimination of propolis using dynamic time warping kernel principal components analysis

The chemometric analysis of chromatographic data is commonly used for discriminating bee propolis based on their geographical origin. Traditional machine learning methods for this purpose include principal component analysis and hierarchical clustering. When viewed as a time-series, the key discriminatory features in chromatographic data are the peaks, which should have similar location patterns for the propolis of the same origin. However, the peaks between same-origin samples are not always exactly aligned. Without proper alignment, samples from the same origin may be perceived by the clustering method to be very different. In this paper, we propose a novel dynamic time warping kernel principal component analysis (DTW-KPCA) method for the chemometric discrimination of propolis. The proposed method uses a Gaussian dynamic time warping kernel to measure the similarity between chromatographic time-series which incorporates time-series alignment. Results show a better clustering of propolis samples compared to the currently used methods, as measured by the silhouette coefficient. Hence, the proposed method enables a more reliable clustering as to the origin of propolis samples.

Applying Log-Normal Peak Fitting to Parallel Reaction Monitoring Data Analysis.

Chromatographic separation is often an important part of mass-spectrometry-based proteomic analysis. It reduces the complexity of the initial samples before they are introduced to mass-spectrometric detection and chromatographic characteristics (such as retention time) add analytical features to the analyte. The acquisition and analysis of chromatographic data are thus of great importance, and specialized software is used for the extraction of quantitative information in an efficient and optimized manner. However, occasionally, automatic peak picking and correct peak boundary setting is challenged by, for instance, aberration of peak shape, peak truncation, and peak tailing, and a manual review of a large number of peaks is frequently required. To support this part of the analysis, we present here a software tool, Peakfit, that fits acquired chromatographic data to the log-normal peak equation and reports the calculated peak parameters. The program is written in R and can easily be integrated into Skyline, a popular software packages that is frequently used for proteomic parallel reaction monitoring applications. The program is capable of processing large data sets (>10 000 peaks) and detecting sporadic outliers in peak boundary selection performed, for instance, in Skyline. In an example data set, available via ProteomeXchange with identifier PXD026875, we demonstrated the capability of the program to characterize chromatographic peaks and showed an example of its ability to objectively and reproducibly detect and solve problematic peak-picking situations.

Chemometric assisted determination of 16 PAHs in water samples by ultrasonic assisted emulsification microextraction followed by fast high-performance liquid chromatography with diode array detector

Combination of fast high-performance liquid chromatography–diode array detection (HPLC–DAD) technique and ultrasonic assisted emulsification micro-extraction (USAEME) as extraction method are used for simultaneous determination of sixteen polycyclic aromatic hydrocarbons (PAHs) in river samples. The optimum conditions of USAEME procedure are selected according to the literature. Due to presence of different chromatographic issues and the complexity of water matrices, multivariate curve resolution-alternating least squares (MCR-ALS) is a good resolution technique for simultaneous quantification of PAHs in standard and real samples. On this matter, region-based and whole chromatographic data analysis of 16 PAHs are done and the results are compared. Although the results of both methods were acceptable, region-based analysis had improvement regarding to the whole data analysis. Recoveries ranging from 81.80% to 105.86% and relative standard deviations (RSDs) less than 10% for all PAHs verified the precision and accuracy of this method. Also, the relative error of calibration curves (RE, %) were less than 14.69%. Also, analytical figures of merit using multivariate approach were used for whole and segment-based strategies. In this regard, the values of multivariate figures of merit in region-based analysis had better results compared to pseudo-univariate techniques and total analysis. These results confirmed that using second order calibration algorithms in USAEME–HPLC–DAD data is a powerful method for fast, cost effective and simple determination of PAHs in highly polluted samples.

Chemometric analysis of chromatographic data in stability investigation of cephalosporins

The specific aim of this investigation was to study the kinetics of the degradation of cefazolin, cefaclor, cefuroxime axetil, and cefepime in aqueous solution, in the presence (or absence) of various redox agents (iodine solution, potassium permanganate, hydrogen peroxide, sodium thiosulfate, and ascorbic acid) as a function of temperature. Various factors, such as concentration of the analyzed compounds and redox agents, storage time, and temperature, were analyzed. The degradation process of chosen antibiotics was observed chromatographically and fitted to the kinetic models, obtaining model parameters (k, t0.1, t0.5). Principal component analysis (PCA), parallel factor analysis (PARAFAC), and hierarchical cluster analysis (HCA) methods were carried out to interpret the dependencies between these factors on the drug stability.

An effective quantitative fingerprint method for evaluating the quality consistency of Desmodium styracifolium.

An HPLC method for combined quantification of five major constituents (vicenin-1, schaftoside, isoschaftoside, vicenin-3 and isovitexin) of Desmodium styracifolium was developed to evaluate the quality consistency of this medicinal herb. The chromatographic separation was accomplished on an Agilent 5 TC-C18 column (4.6 × 250 mm, 5 μm) with gradient elution using acetonitrile and 0.1 % aqueous formic acid (v/v). The column temperature was 30 °C and the detection wavelength was 272 nm. The chromatographic data were analyzed with a novel systematic quantitative fingerprint method (SQFM). The established quantitative fingerprint method was successfully applied to simultaneously determine the levels of the major constituents, and 39 common peaks were found. The samples collected in Guangdong province had a good quality consistency, in accordance with the traditional opinion that Guangdong province is the best region for cultivation of D. styracifolium. The fingerprint-antioxidant efficacy relationship of the different samples was investigated by examining the correlation between the common peaks and the antioxidant effect. Twenty-two common peaks were positively correlated with the antioxidant effect, while the others showed a negative correlation. This quantitative fingerprint method could be considered as a suitable approach to evaluate the quality of traditional Chinese medicines and herbal preparations. The SQFM was reliable and efficient for analysis of the chromatographic data.

Molecular interaction of heterogeneous catalyst in catalytic cracking process of vegetable oils: chromatographic and biofuel performance investigation

Catalytic cracking processes (CC) of non-edible vegetable oils (VOs) are considered efficient pathway to produce alternative energy sources to replace the fossil fuel. Castor oil (CO) and jatropha oil (JO) are the appropriate resources for biofuels because of their chemical structures and their hydrocarbon chains which are similar to fuel. This investigation was conducted to synthesize, characterize and evaluate acidic catalyst in the CC of castor and JO. The interaction between the oils and the catalyst was studied based on the brand of the produces biofuels. The effect of the concentration of the catalyst on the reaction product was studied including: viscosity, kinematic viscosity, pour point and cloud point. The results revealed that the prepared catalyst can catalyze the cracking reaction of the VOs and produce several grades of biofuels. The study displayed that the physical and fuel characteristics of the obtained biofuels are located within the ASTM specification limits. Also, the chemical nature of the cracked oil has a substantial impact on the composition of the achieved biofuel. The mechanisms of the CC processes of CO and JO were proposed. The physical and fuel properties of the achieved biofuels were discussed using their chromatographic analysis data.

A preliminary study on the chemical characteristics of Kei apple (Dovyalis caffra), an undervalued South African fruit

Although a profusion of indigenous fruits occur in South Africa, knowledge of their uses lies mainly with the local people. This study was aimed at filling the void in scientific literature by investigating the nutritional value and characteristics of a lesser known wild fruit, the Kei apple (Dovyalis caffra). The colour was assessed, in addition to the chemical characteristics (total soluble phenolics, protein, volatiles, antioxidants) of the fruits. The results indicate that the fruit is a rich source of amino acids with good antioxidant properties. Although, the protein content was found to be lower than those of commercial fruits, it was similar to values reported for the wild plum. Chemometric analysis of chromatographic data on secondary metabolites produced by the fruit was used to determine if the collection of Kei apple trees established at the Agricultural Research Council's experimental farm in Mbombela is representative of the wild population of South Africa.

Characterization and classification of medicinal plants according to their antioxidant profile estimated by thin layer chromatography assisted by chemometric expertise

ABSTRACTThis study focuses on the characterization and classification of 42 medicinal plants extracts according to their antioxidant activity. Principal component analysis (PCA), cluster analysis (CA) and the combination of PCA with linear discriminant analysis (PCA-LDA) were used as multivariate exploratory techniques for chromatographic data analysis. For the separation of the compounds a mobile phase containing ethyl acetate: toluene: formic acid: water (30:1.5:4:3 v/v/v/v) and different HPTLC plates (Silica gel 60 and HPTLC Silica gel 60 F254) were used. The chromatographic plates were evaluated using the images obtained after spraying the plates with 2-aminoethyldiphenylborate solution (NTS, 0.2% in ethanol) and also after their reaction with 2, 2-diphenyl-1-picrylhydrazyl solution (DPPH•) (0.2% in ethanol). The score projection on the plane defined by first two components (PC1 and PC2) revealed two large groups of the investigated samples depicted according to their antioxidant capacity. A better classification of samples according to their antioxidant capacity was obtained using the CA and PCA-LDA methodology in all cases. The excellent results obtained in this study concerning the classification of medicinal plants according to their antioxidant capacity using the PCA-LDA methodology applied to TLC chromatograms might lead to a new paradigm in the field of medicinal plant holistic evaluation.

Untargeted analysis of chromatographic data for green and fermented rooibos: Problem with size effect removal

While analyzing chromatographic data, it is necessary to preprocess it properly before exploration and/or supervised modeling. To make chromatographic signals comparable, it is crucial to remove the scaling effect, caused by differences in overall sample concentrations. One of the efficient methods of signal scaling is Probabilistic Quotient Normalization (PQN) [1]. However, it can be applied only to data for which the majority of features do not vary systematically among the studied classes of signals. When studying the influence of the traditional “fermentation” (oxidation) process on the concentration of 56 individual peaks detected in rooibos plant material, this assumption is not fulfilled. In this case, the only possible solution is the analysis of pairwise log-ratios, which are not influenced by the scaling constant. To estimate significant features, i.e., peaks differentiating the studied classes of samples (green and fermented rooibos plant material), we propose the application of rPLR (robust pair-wise log-ratios) as proposed by Walach et al. [2]. It allows for fast computation and identification of the significant features in terms of original variables (peaks) which is problematic, while working with the unfolded pair-wise log ratios. As demonstrated, it can be applied to designed data sets and in the case of contaminated data, it allows proper conclusions.

Automatic time-shift alignment method for chromatographic data analysis

Time shift among samples remains a significant challenge in data analysis, such as quality control of natural plant extracts and metabolic profiling analysis, because this phenomenon may lead to invalid conclusions. In this work, we propose a new time shift alignment method, namely, automatic time-shift alignment (ATSA), for complicated chromatographic data analysis. This technique comprised the following alignment stages: (1) automatic baseline correction and peak detection stage for providing useful chromatographic information; (2) preliminary alignment stage through adaptive segment partition to correct alignment for the entire chromatogram; and (3) precise alignment stage based on test chromatographic peak information to accurately align time shift. In ATSA, the chromatographic peak information of both reference and test samples can be completely employed for time-shift alignment to determine segment boundaries and avoid loss of information. ATSA was used to analyze a complicated chromatographic dataset. The obtained correlation coefficients among samples and data analysis efficiency indicated that the influences of time shift can be considerably reduced by ATSA; thus accurate conclusion could be obtained.

Reference Standards Based on the Grosheimin and Cynaropicrin

The aim of this work is the development of effective method for producing the reference standards of Grosheimin and Cynaropicrin for quality control of domestic herbal medicines, as well as project development concerning normative documents on reference standards of Grosheimin and Cynaropicrin, an introduction of reference standards to the Pharmacopoeia of Kazakhstan. This article discusses the method of allocation and purification of reference standards of Grosheimin and Cynaropicrin which are sesquiterpene lactones of guaiane type were obtained from the ethyl acetate extract of <em>Chartolepis intermedia</em> Boiss., using centrifugal distribution chromatography and high performance liquid chromatography, allows to obtain the qualitative target products. The effectiveness of developed technology has reduced the labor contribution in 2.0 times and as a consequence the prime cost of end products come down 3 times. Using of Grosheimin and Cynaropicrin as own<br />reference standard of the Republic of Kazakhstan for monitoring of staged quality control of pharmaceutical production: access control, intermediate control and output control, it means the finished dosage form. There are some chromatographic analysis data of the samples. Such methods as IR and NMR- spectophotometries were applied for determination of molecular structure. Pharmacopoeial methods were used to determine the color, taste, odor and solubility of reference standards in various solvents. Projects for temporary Analytical Normative Documents on reference standards of Grosheimin and Cynaropicrin were developed in accordance with requirement of State Pharmacopeia of the Republic of Kazakhstan. On the basis of obtained results the database for State Pharmacopoeia of the Republic of Kazakhstan will be formed, as well as control over production of new pharmaceuticals from <em>Chartolepis intermedia</em> Boiss. and <em>Saussurea salsa</em> (Pall.) Spreng.

A simple multi-scale Gaussian smoothing-based strategy for automatic chromatographic peak extraction

Peak detection is a critical step in chromatographic data analysis. In the present work, we developed a multi-scale Gaussian smoothing-based strategy for accurate peak extraction. The strategy consisted of three stages: background drift correction, peak detection, and peak filtration. Background drift correction was implemented using a moving window strategy. The new peak detection method is a variant of the system used by the well-known MassSpecWavelet, i.e., chromatographic peaks are found at local maximum values under various smoothing window scales. Therefore, peaks can be detected through the ridge lines of maximum values under these window scales, and signals that are monotonously increased/decreased around the peak position could be treated as part of the peak. Instrumental noise was estimated after peak elimination, and a peak filtration strategy was performed to remove peaks with signal-to-noise ratios smaller than 3. The performance of our method was evaluated using two complex datasets. These datasets include essential oil samples for quality control obtained from gas chromatography and tobacco plant samples for metabolic profiling analysis obtained from gas chromatography coupled with mass spectrometry. Results confirmed the reasonability of the developed method.

Simple automatic strategy for background drift correction in chromatographic data analysis

Chromatographic background drift correction, which influences peak detection and time shift alignment results, is a critical stage in chromatographic data analysis. In this study, an automatic background drift correction methodology was developed. Local minimum values in a chromatogram were initially detected and organized as a new baseline vector. Iterative optimization was then employed to recognize outliers, which belong to the chromatographic peaks, in this vector, and update the outliers in the baseline until convergence. The optimized baseline vector was finally expanded into the original chromatogram, and linear interpolation was employed to estimate background drift in the chromatogram. The principle underlying the proposed method was confirmed using a complex gas chromatographic dataset. Finally, the proposed approach was applied to eliminate background drift in liquid chromatography quadrupole time-of-flight samples used in the metabolic study of Escherichia coli samples. The proposed method was comparable with three classical techniques: morphological weighted penalized least squares, moving window minimum value strategy and background drift correction by orthogonal subspace projection. The proposed method allows almost automatic implementation of background drift correction, which is convenient for practical use.

Multivariate look at the TLC retention: A concise review

ABSTRACTThe multivariate analysis of retention data in thin-layer chromatography (TLC) allows the chromatographer to visualize the multidimensional retention data and explore hidden properties and dependences. The most often used techniques are hierarchical cluster analysis and principal component analysis; however, some other techniques are also reported. This review presents an introduction and focuses on the application of multivariate TLC retention data in retention modeling, lipophilicity determination, QSAR/QSRR (as supporting tool), solvent selection, and comparison of TLC with other separation methods. It seems that multivariate analysis of chromatographic data can bring significant improvement to discussion of any chromatographic study.