Hierarchical Cluster Analysis Dendrogram Research Articles

FT-IR Characterization and Phylogenetic Analysis of Patchouli Oil from West Aceh and Aceh Jaya using PCA and HCA

This study aims to characterize and compare the chemical composition of patchouli oil from various regions in Aceh, Indonesia, using Fourier-transform Infrared Spectroscopy (FT-IR) combined with Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Patchouli oil samples from five distinct locations within West Aceh and Aceh Jaya Regencies were analyzed, focusing on the 1800-500 cm⁻¹ range. The FT-IR spectra revealed significant chemical markers, with notable absorption bands corresponding to aliphatic C-H, C=O, and C=C stretching vibrations. Multivariate analyses showed distinct clustering patterns, with four samples (POSM, POSP, POT, and POW) exhibiting high chemical similarity and clustering together, while one sample (POP) formed a separate cluster, indicating a lower degree of similarity. PCA analysis of the original spectra in the 1800-500 cm⁻¹ range yielded a cumulative PC1 and PC2 value of 89%. The HCA dendrogram highlighted a significant relative distance of 10 between the POP sample and the other four samples, indicating minimal relatedness, with the closest relatedness observed between POSM and POSP, having a relative distance of 1.1. This study demonstrates the effectiveness of FT-IR spectroscopy combined with PCA and HCA in distinguishing chemical profiles of patchouli oil from different regions, providing insights into geographical and varietal influences on oil composition. The findings are crucial for quality control and standardization in the patchouli oil industry, with further molecular studies recommended to corroborate and enhance understanding of genetic diversity among patchouli varieties in Aceh.

Electronic nose based on Pd- and Pt-incorporated ZnO nanowires: a case study

Effect of incorporation of sensitizers namely palladium (Pd) and platinum (Pt) on the gas-sensing behaviour of zinc oxide (ZnO) nanowires has been studied. The specificity achieved is further studied and demonstrated for its efficacy towards the simultaneous detection of multiple gases employing the developed sensors in an electronic nose configuration. Incorporation of salt solutions containing the desired sensitizer concentration in the starting reaction mixture of hydrothermal growth has been effectively used to achieve heterostructure ZnO nanowires. Pd and Pt gets incorporated as PdO and metallic Pt, in the host matrix resulting in the formation of random heterojunctions namely p–n junction and Schottky junctions. Consequently, an increase in the work function as studied using Kelvin probe studies is observed. Utilizing statistical implements namely principal component analysis (PCA) and hierarchical cluster analysis (HCA) the discrimination of three gases namely H2, H2S and NO2 has been successfully accomplished. 3D PCA discriminates the three gases successfully with first three components exhibiting a percentage of variance of 42.32, 33.26 and 24.20%, respectively. A reasonable discrimination of H2, H2S and NO2, grouped into three clusters as evident from HCA dendrograms, was achieved using utilizing Ward’s method and Euclidian distance metric approach.

Colorimetric sensor array for versatile detection and discrimination of model analytes with environmental relevance

In the current work, a rapid, simple, low-cost, and sensitive smartphone-based colorimetric sensor array coupled with pattern-recognition methods was proposed for the determination and differentiation of some organic and inorganic bases (i.e., OH−, CO32−, PO43−, NH3, ClO−, diethanolamine, triethanolamine) as model compounds. The sensing system has been designed based on color-sensitive dyes (Fuchsine, Giemsa, Thionine, and CoCl2) which were used as sensor elements. The color changes of a sensor array were observed by the naked eye. The color patterns were recorded using digital imaging in a three-dimensional (red, green, and blue) space and quantitatively analyzed with color calibration techniques. Distinctive colorimetric patterns for target bases via linear discriminant analysis (LDA) and hierarchical clustering analysis (HCA) were observed. The results indicated that the analytes related to each class (at the different concentration levels in the range of 0.001–1.0 mol L−1) were clustered together in the canonical discriminant plot and HCA dendrogram with high sensitivity and an overall precision of 85%. Furthermore, the first function factor of LDA correlated with the concentration of each target analyte in a correlation coefficient (R2) range of 0.864–0.996. These described procedures based on the colorimetric sensor array technique could be a promising candidate for practical applications in package technology and facile detection of pollutants.

High performance thin layer chromatography fingerprint on cyano-bonded stationary phase of selected Stachys and Betonica species and their antioxidant activity with chemometric calculations

The methanolic extracts of selected Stachys and Betonica species were analyzed by high performance thin layer chromatography (HPTLC) method using cyano bonded stationary phase and mobile phase consisted of methanol and water 45:55 (v/v) with Natural product reagent derivatization. The HPTLC method was validated (linearity, precision, accuracy, robustness, LOD, LOQ) and the validation results confirm the usefulness of the method used. The obtained images of fingerprint chromatograms presented as numerical data (using the TLC analyzer program) were next processed using Excel and SpecAlign softwares. The phytochemical similarity between analyzed plant extracts was evaluated using the similarity index (Pearson correlation coefficient) and the results were presented as hierarchical cluster analysis dendrograms and as graph for principal component analysis (PCA). The total phenolic content (TPC) by Folin-Ciocalteu method and the antioxidant activity with Ferric Ion Reducing Antioxidant Potential (FRAP) technique were determined for studied extracts using gallic acid as standards in both methods.

Gold nanoclusters-based fluorescence sensor array for herbicides qualitative and quantitative analysis

Herbicides have been extensively used around the world, which poses a potential hazard to humans and wildlife. Accurate detection of herbicides is crucial for the environment and human health. Herein, a simple and sensitive fluorescence sensor array was constructed for discrimination and identification of herbicides. Fluorescent gold nanoclusters modified with 11-mercaptoundecanoic acid or reduced glutathione were prepared, respectively. Metal ions quenched the fluorescence of nanoclusters through coordination and leading to the aggregation of gold nanoclusters. The addition of auxin herbicides (2,4-dichlorophenoxyacetic acid, 2-methyl-4-chlorophenoxyacetic acid, decamba, picloram, quinclorac) restored the fluorescence of nanoclusters with different degrees. The mechanism study showed auxin herbicides can bind with metal ions and re-disperse the gold nanoclusters from the aggregation state. The "on-off-on" fluorescent sensor array was constructed basic on above detection mechanism. Combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods, auxin herbicides are well separated on 2D/3D PCA score plots and HCA dendrogram in the range of 40–500 μm. In addition, the fluorescence sensor array performed successful in detecting real samples and blind samples. The developed sensor system shows a promising in practical detection of herbicides.

Particle Shedding from Cotton and Cotton-Polyester Fabrics in the Dry State and in Washes.

The influence of 3, 10 and 50 washing cycles on the properties of cotton fabric and cotton-polyester blend in plain weave, was investigated in this study. In addition to the analysis of tensile properties in weft and warp directions and thickness, the number of particles produced in the dry state was also measured after 3, 10 and 50 washes. After washing, the entire effluent was analysed by determining the total suspended solids (TSS), the total solids (TS), the pH value and the conductivity. To determine the similarity of the observed wash cycles and properties of all processed samples, hierarchical cluster analysis (HCA) was performed. The fabric changes indicated by total wear in the warp direction after 50 washing cycles compared to unwashed ones amounting to 41.2% for cotton and 30.9% for cotton-polyester blend, may be attributed to the synergy of washing factors and raw material composition. Cotton fabric produced significantly more particles than cotton-polyester fabric in the dry state after the examined washing cycles in all size categories. A smaller number of released particles are in the larger size category >25 μm. The obtained TSS values confirm the degree of loading of the effluent with particulate matter from the analysed fabrics, since the detergent consists of water-soluble components. The HCA dendrograms confirmed that the release of particles during the first washing cycles is mainly determined by the structural properties of fabrics, while in the subsequent cycles the synergistic effect of chemical, mechanical and thermal effects in the interaction with the material prevailed.

Pollen morphology of Doronicum L. (Asteraceae: Senecioneae) from Turkey and its taxonomical implications

The main purpose of this study is the palynological characterization of Doronicum L. taxa that occur naturally in Turkey. In this context, the pollen morphology of 11 species and one subspecies of Doronicum was researched in detail by light microscopy and scanning electron microscopy (SEM). Furthermore, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were carried out to identify closely related species and palynological characters that could potentially be used to distinguish the studied taxa. Pollen grains were found to be isopolar monad and tricolporate. Pollen were prolate-spheroidal in shape, with a circular amb. SEM showed the exine ornamentation was echinate-perforate. Differences in the bases and ends of the spines were observed; therefore, pollen grains were categorized based on spine morphology. Three components of the studied palynological characteristics were determined with PCA. The PCA results explained 78.79% of the variation, and polar diameter, equatorial diameter, diameter in polar view, exine thickness, intine thickness, pore length, colpus width, and spine length were found to be the most valuable palynological characteristics for discrimination of the Doronicum taxa. The HCA dendrogram also revealed three main clusters. This study is helpful for understanding the relationships among the taxa of Doronicum and provides insight for future taxonomic research.

Ancient Olmec tar trade revealed by combined biomarker and chemometric analysis

Resources such as tar, basalt, kaolin clay, and hematite are available in distinct areas near the ancient Olmec region of the southern Gulf Coast of Mexico. This uneven distribution of raw materials has led scholars to suggest that Olmec leaders controlled the sources of raw materials and regional trade, from which they derived economic and political power. The purpose of this study is to improve understanding of Olmec tar trade, which served as binder, sealant, and decoration. Our novel approach combines molecular archaeology based on the geochemistry of biomarkers (biodegradation-resistant molecular fossils) with chemometrics (multivariate statistics) to identify genetic relationships among crude oils, seep oils, modern applications, and archaeological artifacts excavated from ten Early and Middle Formative sites (1800-400 BCE) sites. Seven source-related biomarker ratios were calculated from peak heights on terpane and sterane mass chromatograms. We employed hierarchical cluster analysis dendrograms, which yield a simple view of genetic families of samples where cluster distance measures the degree of similarity. Our results illustrate patterns of Olmec commodity exchange and intra-regional interaction and the possibility of elite control of some tar procurement and distribution.

A visible colorimetric sensor array based on chemo-responsive dyes and chemometric algorithms for real-time potato quality monitoring systems

The colorimetric sensor array real-time monitoring system with multivariate analysis was established for discrimination of potato varieties with different types and degrees of corruption. The characteristic volatile compounds of fresh, dry rot and soft rot potatoes was identified by Gas Chromatography-Mass Spectrometry and the 3 × 4 array was fabricated to capture the characteristics volatile compounds. The sensor array system produced a visible color difference map upon its exposure to volatile compounds of potato. Discrimination of potatoes with the same types or different degrees of corruption was subsequently achieved using principal component analysis and hierarchical clustering analysis dendrogram. The k-nearest neighbor algorithm for potato classification provided the best results with 100 % discrimination on both the calibration and prediction sets. The linear discriminant analysis model achieved a 99.76 % calibration set and a 99.31 % prediction set for potato grading. An online warning device based on array was devised to realize unmanned monitoring for potato quality.

Near infrared spectroscopy (NIRS) coupled with chemometric methods to identify and estimate taxonomic relationships of flies with forensic potential (Diptera: Calliphoridae and Sarcophagidae)

Infrared spectroscopy has been gaining prominence in entomology, such as for solving taxonomic problems, sexing adult specimens, determining the age of immature specimens, detecting drugs of abuse in fly larvae, and can be an important technique in Forensic Entomology. In order to help identify the species of Calliphoridae and Sarcophagidae families, the present study aimed to evaluate the use of near infrared spectroscopy (NIRS) coupled with chemometric methods for separating fly specimens into taxonomic categories and understanding the taxonomic relationship between them. Spectra collected from nine species of flies were subjected to unsupervised principal component analysis (PCA) and hierarchical cluster analysis (HCA), in which we sought to visualize the relationship between the samples (segregation of genera and families) with subsequent identification. In PCA, the best model was achieved using five principal components (PCs), which explained 99.16% of total variance of the original data set. The first principal component (PC1) and the fourth principal component (PC4) provided the best segregation, the latter being more important in the segregation of the species Chrysomya albiceps, Lucilia eximia, and Ravinia belforti from the others. In the HCA dendrogram, there was a clear separation between the specimens by family (Calliphoridae and Sarcophagidae) and genera (Chrysomya, Lucilia, Oxysarcodexia, Peckia and Ravinia). This study shows that NIRS is efficient to identify flies’ taxonomic properties, such as family and genera, providing quick evidence for the tested species identity.

Noble metal sensitized SnO2/RGO nanohybrids as chemiresistive E-nose for H2, H2S and NO2 detection

Hydrothermally synthesised noble metal (Pt, Pd) doped SnO2/RGO nanohybrids has been demonstrated first time for chemiresistive e-nose application towards the detection of H2, H2S and NO2 below their threshold limit values. More specifically, the multivariate statistical tools namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Hierarchical Cluster Analysis (HCA) have been employed effectively to achieve fruitful discrimination of H2, H2S, and NO2. The two-dimensional PCA studies reveals the discrimination between the three test-gases under observation with the variance of 59.16% and 40.84% for first and second principal components, respectively. Importantly, LDA has been demonstrated to be useful to classify the data with 76.39% accuracy. HCA dendrograms depicted into three different clusters indicates a significant discrimination of gases with 95.55% classification confidence. The adsorption studies along with the validity of the kinematical models indicated that SnO2/RGO and SnO2/RGO: Pt follows the pseudo second order equations for H2S and H2 adsorption respectively. On the contrary, SnO2/RGO: Pd obeys Ritchie equation for NO2 adsorption. In addition, the work function values for SnO2/RGO and SnO2/RGO: Pd were found decrease and increase by 153 and 135 meV, respectively upon exposure to H2S and NO2 gases. This clearly indicates the n-type nature of the synthesised nanohybrids.

Discrimination of basmati rice adulteration using colorimetric sensor array system

This study involved the fabrication of colorimetric sensor array system for rapid discrimination of adulterants in premium grade basmati rice. Purposely, low-grade white rice was used to adulterate the pure basmati rice at 5%, 10%, 15%, 25%, 50%, and 75% weight ratios. Sensor array system was used to capture the odors of prepared samples, resulting in color difference map based on chemical environment. The principal component analysis (PCA), hierarchical cluster analysis (HCA), and k-nearest neighbors (kNN) algorithm were subsequently used to identify the similarity between authentic and adulterated samples. A decent discrimination between authentic and adulterated rice samples was observed in the scatter plot of PCA and HCA dendrogram. The multilayered kNN models were able to effectively discriminate the prepared rice samples. The study concludes that fabricated sensor array system may be used as an effective tool for rapid discrimination of authentic and adulterated samples. • Colorimetric sensor array was fabricated for discrimination of rice adulteration. • Volatile compounds of basmati rice and adulterant were identified using GC-MS. • PCA and HCA were able to discriminate different levels of adulteration. • Optimal discrimination results were achieved with multilayered kNN algorithm. • Fabricated system promotes the development and application of sensor arrays.

Know your enemy: Application of ATR-FTIR spectroscopy to invasive species control.

Extreme weather and globalisation leave our climate vulnerable to invasion by alien species, which have negative impacts on the economy, biodiversity, and ecosystem services. Rapid and accurate identification is key to the control of invasive alien species. However, visually similar species hinder conservation efforts, for example hybrids within the Japanese Knotweed complex.We applied the novel method of ATR-FTIR spectroscopy combined with chemometrics (mathematics applied to chemical data) to historic herbarium samples, taking 1580 spectra in total. Samples included five species from within the interbreeding Japanese Knotweed complex (including three varieties of Japanese Knotweed), six hybrids and five species from the wider Polygonaceae family. Spectral data from herbarium specimens were analysed with several chemometric techniques: support vector machines (SVM) for differentiation between plant types, supported by ploidy levels; principal component analysis loadings and spectral biomarkers to explore differences between the highly invasive Reynoutria japonica var. japonica and its non-invasive counterpart Reynoutria japonica var. compacta; hierarchical cluster analysis (HCA) to investigate the relationship between plants within the Polygonaceae family, of the Fallopia, Reynoutria, Rumex and Fagopyrum genera.ATR-FTIR spectroscopy coupled with SVM successfully differentiated between plant type, leaf surface and geographical location, even in herbarium samples of varying age. Differences between Reynoutria japonica var. japonica and Reynoutria japonica var. compacta included the presence of two polysaccharides, glucomannan and xyloglucan, at higher concentrations in Reynoutria japonica var. japonica than Reynoutria japonica var. compacta. HCA analysis indicated that potential genetic linkages are sometimes masked by environmental factors; an effect that can either be reduced or encouraged by altering the input parameters. Entering the absorbance values for key wavenumbers, previously highlighted by principal component analysis loadings, favours linkages in the resultant HCA dendrogram corresponding to expected genetic relationships, whilst environmental associations are encouraged using the spectral fingerprint region.The ability to distinguish between closely related interbreeding species and hybrids, based on their spectral signature, raises the possibility of using this approach for determining the origin of Japanese knotweed infestations in legal cases where the clonal nature of plants currently makes this difficult and for the targeted control of species and hybrids. These techniques also provide a new method for supporting biogeographical studies.

Discrimination of rice varieties using smartphone-based colorimetric sensor arrays and gas chromatography techniques

A smartphone-based colorimetric sensor array system was established for discrimination of rice varieties having different geographical origins. Purposely, aroma profiling of nine rice varieties was performed using solid-phase microextraction gas chromatography-mass spectrometry. Alcohols, aldehydes, alkanes, ketones, heterocyclic compounds, and organic acids represent the abundant compounds. Colorimetric sensor array system produced a characteristic color difference map upon its exposure to volatile compounds of rice. Discrimination of rice varieties was subsequently achieved using principal component analysis, hierarchical clustering analysis, and k-nearest neighbors. Rice varieties from same geographical source were clustered together in the scatter plot of principal component analysis and hierarchical clustering analysis dendrogram. The k-nearest neighbors algorithm delivered optimal results with discrimination rate of 100% for both calibration and prediction sets using sensor array system. The smartphone-based colorimetric sensor array system and gas chromatography technique were able to effectively differentiate rice varieties with the advantage of being simple, rapid, and low-cost.

Deciphering allelic variability and population structure in buckwheat: An analogy between the efficiency of ISSR and SSR markers.

Food and nutritional security continue to be the issues of concern in developing countries like ours. Exploring the reservoir of high potential unexplored genetic resources could address the world’s food and nutritional insecurity. The availability of diverse data and the population structure of any crop germplasm is a valuable genetic resource for discovering genes that can help achieve food and nutritional stability. We used seven ISSR and seven SSR markers to investigate diversity among 63 buckwheat genotypes, including landraces from India's northwestern Himalayas. Various parameters such as percent polymorphism, PIC, resolving power, and marker index was used to evaluate the inequitable efficacy of these markers. We foundthat both marker systems are effective in detecting polymorphism in buckwheat germplasm. Seven ISSRs produced 55 polymorphic bands, while seven SSRs produced 32bands. When compared to ISSRs, SSRs had a greater average PIC value (0.43) than that of (0.36). ISSRs, on the other hand, had a resolving power of (4.38) compared to (1.42) for SSRs. The hierarchical cluster analysis dendrogram divided genotypes into three major clusters. We found that both marker systems were equally accurate in grouping buckwheat genotypes according to their geographical origins. Using 7 ISSR and 7 SSR markers, the model-based STRUCTURE analysis established a population with two sub-populations that correspond to species-based groupings. Within the population, there was a high level of genetic diversity. These results have consequences for both buckwheat breeding and conservation efforts.

Lanthanide Metal-Organic Framework-Based Fluorescent Sensor Arrays to Discriminate and Quantify Ingredients of Natural Medicine.

The discrimination and quantification of the ingredients from natural medicines are a challenging issue due to their complicated and various structures. Metal-organic frameworks (MOFs) have shown great promise in sensing applications. Here, we report a fluorescent sensor array for rapid identification of some natural compounds using a sensor array composed of four kinds of lanthanide (Eu3+ and Tb3+) fluorescent MOFs (Ln-MOFs), which have diversified fluorescent responses to 26 active/toxic compounds including 12 saponins, 7 flavonoids, 3 stilbenes, and 4 anthraquinones. The fluorescence of the Ln-MOFs after reaction with the compounds was summarized as datasets and processed by principle component analysis (PCA) and hierarchical cluster analysis (HCA) methods. The corresponding responses of the 4 types of compounds are well separated on 2D/3D PCA score plots and HCA dendrograms. We have also tested typical blind samples by concentration-dependent PCA, and an accuracy of 100% was obtained. In addition, the response mechanisms of the Ln-MOFs to the compounds were also studied. Compared with traditional methods using liquid chromatography-mass spectrometry, the developed fluorescent sensor array provides a more efficient and economic strategy to discriminate various active/toxic ingredients in natural medicine.

Comparative metabolic study of Citrus sinensis leaves cultivars based on GC–MS and their cytotoxic activity

Citrus sinensis L. Osbeck, (family Rutaceae), known as sweet orange, is a fruit-bearing shrub that widely cultivated all over the world due to its nutritive value, nutraceutical attributes and economic importance. In the present study, a comparative metabolic profile study of the essential oils of the leaves of nine cultivars of Citrus sinensis cultivated in Egypt was carried out based on GC–MS analysis coupled to chemometrics. A total of 47 compounds were identified, where monoterpenes hydrocarbons (61.39 %–78.26 %) represented the main recognized class in the essential oils. Sabinene (8.25 %–28.81 %), 2-carene (11.25 %–16.72 %) and cis-β-ocimene (10.22 %–13.93 %) were the major components identified in different cultivars. Chemometric analyses comprising Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were experienced for their ability to discriminate between closely related cultivars. Both PCA score plot and HCA dendrogram could successfully segregate different cultivars based on their metabolic profiles. The MTT assay was used to evaluate the cytotoxicity of essential oils of Citrus sinensis leaves cultivars on different cell lines; MCF-7, HepG-2, HeLa, were all essential oils showed moderate cytotoxic activity. The MCF-7 cell lines were the most resistant cells with IC50 range from 243 to 343 μg/mL, while HeLa cells were the most sensitive cell lines IC50 range from 203 to 283 μg/mL. This may be attributed to the synergistic effects of different chemical components.

Pattern recognition analysis of gas chromatographic and infrared spectroscopic fingerprints of crude oil for source identification

In this study, a chemometric strategy was developed for analysis of gas chromatographic (GC) and infrared spectroscopic (FT-IR) fingerprints of nine crude oil samples from the main oil wells of Iran to classify them and to find their origins. In this regard, a fractionation method based on saturated, aromatic, resin, and asphaltene (SARA) test was used. Then, these fractions were analyzed by GC-FID and GC–MS. Also, nine crude oil samples were analyzed by FT-IR.The obtained GC fingerprints were aligned using correlation optimized warping (COW) and auto-scaled, and then analyzed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Evaluation of PCA scores plot (explaining 89.35% of variance for two PCs) and HCA dendrogram showed that aromatic fractions belong to three classes. The clustering results of aliphatic and resin fractions also showed the presence of three clusters but with different samples due to the difference in their composition. The results of unsupervised classification were then used as a starting point for partial least squares-discriminant analysis (PLS-DA) and counter propagation-artificial neural network (CP-ANN).On the other side, FTIR fingerprints of crude oil samples were also clustered using PCA and HCA. Evaluation of PCA scores plot (explaining 95.15% of variance for two PCs) and HCA dendrogram showed that samples belong to three clusters. These initial results were used as a starting point for PLS-DA and CP-ANN. Accuracy of PLS-DA was 0.890 for calibration and 0.818 for test sets. Also, CP-ANN accuracy values were 1.000 and 0.818 for train and test sets, respectively.

Molecular characteristics of the soluble components from three low-rank coals based on the analyses using GC/MS and GC/Q-TOF MS

Three low-rank coals were subjected to a thermal dissolution (TD) process sequentially using cyclohexane (CYH) and methanol (MeOH) solvent to obtain six TD extracts. The six TD extracts were analyzed by Fourier transform infrared spectroscopy (FTIR), gas chromatograph/mass spectrometry (GC/MS) and gas chromotography/quadrupole time-of-flight MS (GC/Q-TOF MS) to understand the compositional features of three low-rank coals. Hierarchical clustering analysis (HCA) and principle component analysis (PCA) were applied to show the relationships among 10 classes in the six TD extracts. Compared with GC/MS, a routine analytical method, more species with relative high molecular mass and more oxygen-containing organic compounds (OCOCs) were determined using GC/Q-TOF MS. In addition, HCA dendrogram and heatmap reveal the difference among 10 classes in the TD extracts. Meanwhile, the distributional and compositional features of six TD extracts were also exhibited in the score plot and loading plot of PCA.

Flavonoids rather than alkaloids as the diagnostic constituents to distinguish Sophorae Flavescentis Radix from Sophorae Tonkinensis Radix et Rhizoma: an HPLC fingerprint study

Sophorae Flavescentis Radix (Sophora flavescens Ait., SFR) and Sophorae Tonkinensis Radix et Rhizoma (S. tonkinensis Gapnep., STR) are two commonly used traditional Chinese medicines from Sophora (Leguminosae) plants, which are believed to possess similar bioactive components with entirely different clinical applications. In order to find out the characteristic chemical constituents potentially leading to the unique medicinal properties claimed for each of the two closely related TCMs, an HPLC fingerprint method was developed for analyses of the alkaloid and flavonoid constituents of SFR and STR, respectively, which were further evaluated and compared through similarity calculation and hierarchical clustering analysis (HCA). The results from the present study showed that the alkaloid fingerprints of the two herbs were similar, with many components co-existing in both drugs and various batches of samples from different species being mixed together in the HCA dendrogram. However, their flavonoid constituents were totally different with specific fingerprints being yielded for each herb, and further HCA analysis showed that the tested samples could almost be clearly divided into two groups based on their origins of species. The results from the present study indicated that the flavonoid constituents could serve as the differentially diagnostic constituents of SFR and STR and might potentially attributed to their distinct therapeutic effects.