Quality markers of Dajianzhong decoction based on multicomponent qualitative and quantitative analysis combined with network pharmacology and chemometric analysis

INTRODUCTION

Dajianzhong decoction (DJZD), a classic famous prescription, has a long history of medicinal application. Modern studies have demonstrated its clinical utility in the treatment of postoperative ileus (POI). But none of the current quality evaluation methods for this compound is associated with efficacy.

OBJECTIVES

This study aimed to identify the quality markers (Q-Markers) connected to the treatment of POI in DJZD.

METHODOLOGY

Ultra-performance liquid chromatography quadrupole Exactive Orbitrap mass spectrometry (UPLC-Q-Exactive Orbitrap-MS) was used to identify the main constituents in DJZD. Based on the qualitative results obtained by fingerprinting, chemical pattern recognition (CPR) was used to analyse the key components affecting the quality and finally to establish the network of the active ingredients in DJZD with POI.

RESULTS

A total of 64 chemical components were detected. After fingerprint analysis, 13 common peaks were identified. The fingerprint similarity of 15 batches of samples ranged from 0.860 to 1.000. CPR analysis was able to categorically classify 15 batches of DJZD into two groups. And gingerenone A, methyl-6-gingerdiol, 6-gingerol, and hydroxy-β-sanshool contributed to their grouping. Twelve common components interact with the therapeutic targets for treating POI. In addition, the mechanism of this prescription for treating POI may be related to the jurisdiction of the neurological system, the immunological system, and the inflammatory response.

CONCLUSIONS

This integrated approach can accurately assess and forecast the quality of DJZD, presume the Q-Markers of DJZD for POI, and lay the foundation for studying the theoretical underpinnings and exploring the mechanism of DJZD in the treatment of POI.

GC-MS Fingerprinting Combined with Chemical Pattern-Recognition Analysis Reveals Novel Chemical Markers of the Medicinal Seahorse

Seahorse is a valuable marine-animal drug widely used in traditional Chinese medicine (TCM), and which was first documented in the "Ben Cao Jing Ji Zhu" during the Liang Dynasty. Hippocampus kelloggi (HK) is the most common seahorse species in the medicinal material market and is one of the genuine sources of medicinal seahorse documented in the Chinese pharmacopeia. It is mainly cultivated in the Shandong, Fujian, and Guangxi Provinces in China. However, pseudo-HK, represented by Hippocampus ingens (HI) due to its similar appearance and traits, is often found in the market, compromising the safety and efficacy of clinical use. Currently, there is a lack of reliable methods for identifying these species based on their chemical composition. In this study, we employed, for the first time, a strategy combining gas chromatography-mass spectrometry (GC-MS) fingerprints and chemical patterns in order to identify HK and HI; it is also the first metabolomic study to date of HI as to chemical components. The obtained results revealed remarkable similarities in the chemical fingerprints, while significant differences were also observed. By employing hierarchical cluster analysis (HCA) and principal component analysis (PCA), based on the relative contents of their characteristic peaks, all 34 samples were successfully differentiated according to their species of origin, with samples from the same species forming distinct clusters. Moreover, nonadecanoic acid and behenic acid were exclusively detected in HK samples, further distinguishing them from HI samples. Additionally, the relative contents of lauric acid, tetradecanoic acid, pentadecanoic acid, n-hexadecanoic acid, palmitoleic acid, margaric acid, oleic acid, fenozan acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) exhibited significant differences between HK and HI (p < 0.0001), as determined by an unpaired t-test. Orthogonal partial least squares discriminant analysis (OPLS-DA) identified seven components (DHA, EPA, n-hexadecanoic acid, tetradecanoic acid, palmitoleic acid, octadecanoic acid, and margaric acid) with high discriminatory value (VIP value > 1). Thus, nonadecanoic acid, behenic acid, and these seven compounds can be utilized as chemical markers for distinguishing HK from HI. In conclusion, our study successfully developed a combined strategy of GC-MS fingerprinting and chemical pattern recognition for the identification of HK and HI, and we also discovered chemical markers that can directly differentiate between the two species. This study can provide a foundation for the authentication of Hippocampus and holds significant importance for the conservation of wild seahorse resources.

Classification and Authentication of Lonicerae Japonicae Flos and Lonicerae Flos by Using 1H-NMR Spectroscopy and Chemical Pattern Recognition Analysis

Lonicerae japonicae flos and Lonicerae flos are increasingly widely used in food and traditional medicine products around the world. Due to their high demand and similar appearance, they are often used in a confused or adulterated way; therefore, a rapid and comprehensive analytical method is highly required. In this case, the comparative analysis of a total of 100 samples with different species, growth modes, and processing methods was carried out by nuclear magnetic resonance (1H-NMR) spectroscopy and chemical pattern recognition analysis. The obtained 1H-NMR spectrums were employed by principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), orthogonal partial least-squares discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA). Specifically, after the dimensionality reduction of data, linear discriminant analysis (LDA) exhibited good classification abilities for the species, growth modes, and processing methods. It is worth noting that the sample prediction accuracy from the testing set and the cross-validation predictions of the LDA models were higher than 95.65% and 98.1%, respectively. In addition, the results showed that macranthoidin A, macranthoidin B, and dipsacoside B could be considered as the main differential components of Lonicerae japonicae flos and Lonicerae Flos, while secoxyloganin, secologanoside, and sweroside could be responsible for distinguishing cultivated and wild Lonicerae japonicae Flos. Accordingly, 1H-NMR spectroscopy combined with chemical pattern recognition gives a comprehensive overview and provides new insight into the quality control and evaluation of Lonicerae japonicae flos.

Discrimination of Radix Astragali from Different Growth Patterns, Origins, Species, and Growth Years by an H1-NMR Spectrogram of Polysaccharide Analysis Combined with Chemical Pattern Recognition and Determination of Its Polysaccharide Content and Immunological Activity

The fraud phenomenon is currently widespread in the traditional Chinese medicine Radix Astragali (RA) market, especially where high-quality RA is substituted with low-quality RA. In this case, focused on polysaccharides from RA, the classification models were established for discrimination of RA from different growth patterns, origins, species, and growth years. 1H Nuclear Magnetic Resonance (H1-NMR) was used to establish the spectroscopy of polysaccharides from RA, which were used to distinguish RA via chemical pattern recognition methods. Specifically, orthogonal partial least squares discriminant analysis (OPLS-DA) and linear discriminant analysis (LDA) were used to successfully establish the classification models for RA from different growth patterns, origins, species, and growth years. The satisfactory parameters and high accuracy of internal and external verification of each model exhibited the reliable and good prediction ability of the developed models. In addition, the polysaccharide content and immunological activity were also tested, which was evaluated by the phagocytic activity of RAW 264.7. And the result showed that growth patterns and origins significantly affected the quality of RA. However, there was no significant difference in the aspects of origins and growth years. Accordingly, the developed strategy combined with chemical information, biological activity, and multivariate statistical method can provide new insight for the quality evaluation of traditional Chinese medicine.

Application of HPLC Fingerprint Combined with Chemical Pattern Recognition and Multi-Component Determination in Quality Evaluation of Echinacea purpurea (L.) Moench

Echinacea purpurea (EP) is a common medicinal material for extracting anti-RSV components. However, up to now, there has been no effective and simple method to comprehensively reflect the quality of EP. In our current study, the quality of Echinacea purpurea (L.) Moench samples from six different cultivation locations in China was evaluated by establishing a high-performance liquid chromatography (HPLC) fingerprint, combining chemical pattern recognition and multi-component determination. In this study, the chemical fingerprints of 15 common peaks were obtained using the similarity evaluation system of the chromatographic fingerprints of traditional Chinese medicine (2012A Edition). Among the 15 components, three phenolic acids (caftaric acid, chlorogenic acid and cichoric acid) were identified and determined. The similarity of fingerprints of 16 batches of Echinacea purpurea (L.) Moench samples ranged from 0.905 to 0.998. The similarity between fingerprints of five batches of commercially available Echinacea pupurea (L.) Moench and the standard fingerprint "R" ranged from 0.980 to 0.997, which proved the successful establishment of the fingerprint. PCA and HCA were performed with the relative peak areas of 15 common peaks (peak 3 as the reference peak) as variables. Anhui and Shaanxi can be successfully distinguished from the other four cultivation areas. In addition, the index components of caftaric acid, chlorogenic acid and cichoric acid were in the range of 1.77-8.60 mg/g, 0.02-0.20 mg/g and 2.27-15.87 mg/g. The results of multi-component index content determination show that the contents of the Shandong cultivation area were higher, followed by Gansu, Henan and Hebei, and the lowest were Anhui and Shaanxi. The results are consistent with PCA and HCA, which proved that the quality of Echinacea purpurea (L.) Moench from different origins was different. HPLC fingerprint combined with chemical pattern recognition and multi-component content determination was a reliable, comprehensive and prospective method for evaluating the quality of Echinacea purpurea (L.) Moench. This method provides a scientific basis for the quality control and evaluation of Echinacea purpurea (L.) Moench.

Quality evaluation of the classical prescription, Danggui Jianzhong Decoction, using ultra-high-performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology

Danggui Jianzhong Decoction is a classical prescription that has been widely used for thousands of years. However, the quality of this formula is difficult to control owing to its complex chemical component system. In this study, a simple and efficient method comprising ultra-high-performance liquid chromatography fingerprint, chemical pattern recognition, and network pharmacology was established to evaluate the quality of this decoction. A total of 20 common peaks were obtained by fingerprint analysis and 19 chemicals were identified. The fingerprint similarity of 15 batch samples ranged from 0.963-0.991. Chemical pattern recognition analysis could clearly classify 15 batches of Danggui Jianzhong Decoction into 3 groups. Further, 7 chemical markers were screened out. A herbs-active components-targets-disease network was constructed and enrichment analyses were performed, which indicated that these 19 chemical components are the medicinal substances of Danggui Jianzhong Decoction. Further, the mechanism employed by this formula to treat primary dysmenorrhea may be related to the regulation of inflammatory response. In conclusion, this combination approach enables accurate evaluation and prediction of the quality of Danggui Jianzhong Decoction, and lays the foundation for studies on the material basis and exploration of the mechanism of Danggui Jianzhong Decoction in the treatment of primary dysmenorrhea. This article is protected by copyright. All rights reserved.

Based on GC-MS and Chemical Pattern Recognition Analysis

Ligustici Rhizoma et Radix (LReR) is the dried rhizomes and roots of Ligusticum sinese Oliv. (LS) or Ligusticum jeholense Nakai et Kitag. (LJ). However, in the market, LS and LJ are frequently confused with each other. Since the volatile oils are both the main active components and quality control indicators of LReR, a strategy combining gas chromatography-mass spectrometry (GC-MS) and chemical pattern recognition (CPR) was used to compare the volatile components of LJ and LS. Total ion chromatography (TIC) revealed that phthalides (i.e., neocnidilide) and phenylpropanoids (i.e., myristicin) could be thought of as the most critical components in the volatile oils of LJ and LS, respectively. In addition, the chemical components of the volatile oils in LJ and LS were successfully distinguished by hierarchical cluster analysis (HCA) and principal component analysis (PCA). Moreover, two quality markers, including myristicin and neocnidilide, with a very high discriminative value for the classification of LJ and LS, were found by orthogonal partial least squares discriminant analysis (OPLS-DA). The relative contents of myristicin and neocnidilide were 10.86 ± 6.18% and 26.43 ± 19.63% for LJ, and 47.43 ± 12.66% and 2.87 ± 2.31% for LS. In conclusion, this research has developed an effective approach to discriminating LJ and LS based on volatile oils by combining GC-MS with chemical pattern recognition analysis.

[Chemical pattern recognition of Atractylodes chinensis from different producing areas and establishment of quantitative analysis of multi-components by single marker (QAMS) method for four components]

This study established the fingerprint and combined it with chemical pattern recognition to evaluate the quality of Atractylodes chinensis samples from different producing areas and then employed the quantitative analysis of multi-components by single marker(QAMS) method to verify the feasibility and applicability of the established method in the quality evaluation of A. chinensis. The fingerprints of A. chinensis samples were constructed via high performance liquid chromatography(HPLC) to evaluate the inter-batch consistency. With the quality control component atractylodin as the internal reference, the relative correction factors(RCFs) were established for atractylenolide Ⅰ, atractylenolide Ⅲ, and β-eudesmol and the content of the four components was calculated. The external standard method was used to verify the accuracy of QAMS method. The quality of A. chinensis was further evaluated by similarity analysis, clustering analysis, and principal component analysis. The fingerprints of 13 batches of samples were calibrated with 21 common peaks, and 4 common peaks were identified with the similarities all above 0.9. The RCFs established with atractylodin as the internal reference represented good reproducibility under different experimental conditions. Specifically, the RCFs of atractylenolide Ⅰ, atractylenolide Ⅲ, and β-eudesmol in A. chinensis were 2.091, 4.253, and 6.010, respectively. QAMS and ESM showed no significant difference in the results, indicating that the QAMS method established in this study was stable and reliable. Thus, HPLC fingerprint combined with QAMS can be used for the quality evaluation of A. chinensis, providing a basis for comprehensive and rapid quality evaluation of A. chinensis.

Chemical Pattern Recognition for Quality Analysis of Lonicerae Japonicae Flos and Lonicerae Flos Based on Ultra-High Performance Liquid Chromatography and Anti-SARS-CoV2 Main Protease Activity

Lonicerae japonicae flos (L. japonicae flos, Lonicera japonica Thunb.) is one of the most commonly prescribed botanical drugs in the treatment or prevention of corona virus disease 2019. However, L. japonicae flos is often confused or adulterated with Lonicerae flos (L. flos, Lonicera macrantha (D.Don) Spreng., Shanyinhua in Chinese). The anti-SARS-CoV2 activity and related differentiation method of L. japonicae flos and L. flos have not been documented. In this study, we established a chemical pattern recognition model for quality analysis of L. japonicae flos and L. flos based on ultra-high performance liquid chromatography (UHPLC) and anti-SARS-CoV2 activity. Firstly, chemical data of 59 batches of L. japonicae flos and L. flos were obtained by UHPLC, and partial least squares-discriminant analysis was applied to extract the components that lead to classification. Next, anti-SARS-CoV2 activity was measured and bioactive components were acquired by spectrum-effect relationship analysis. Finally, characteristic components were explored by overlapping feature extracted components and bioactive components. Accordingly, eleven characteristic components were successfully selected, identified, quantified and could be recommended as quality control marker. In addition, chemical pattern recognition model based on these eleven components was established to effectively discriminate L. japonicae flos and L. flos. In sum, the demonstrated strategy provided effective and highly feasible tool for quality assessment of natural products, and offer reference for the quality standard setting.

[Identification of Cordyceps cicadae and Tolypocladium dujiaolongae based on ITS sequences and chemical pattern recognition method]

Based on ITS sequences, the molecular identification of Cordyceps cicadae and Tolypocladium dujiaolongae was carried out, and high-performance liquid chromatography(HPLC) fingerprint combined with chemical pattern recognition method was established to differentiate C. cicadae from its adulterant T. dujiaolongae. The genomic DNA from 10 batches of C. cicadae and five batches of T. dujiaolongae was extracted, and ITS sequences were amplified by PCR and sequenced. The stable differential sites of these two species were compared and the phylogenetic tree was constructed via MEGA 7.0. HPLC was used to establish the fingerprints of C. cicadae and T. dujiaolongae, and similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and partial least squares discriminant analysis(PLS-DA) were applied to investigate the chemical pattern recognition. The result showed that the sources of these two species were different, and there were 115 stable differential sites in ITS sequences of C. cicadae and T. dujiao-longae. The phylogenetic tree could distinguish them effectively. HPLC fingerprints of 18 batches of C. cicadae and 5 batches of T. dujiaolongae were established. The results of CA, PCA, and PLS-DA were consistent, which could distinguish them well, indicating that there were great differences in chemical components between C. cicadae and T. dujiaolongae. The results of PLS-DA showed that six components such as uridine, guanosine, adenosine, and N~6-(2-hydroxyethyl) adenosine were the main differential markers of the two species. ITS sequences and HPLC fingerprint combined with the chemical pattern recognition method can serve as the identification and differentiation methods for C. cicadae and T. dujiaolongae.

Application of UHPLC Fingerprints Combined with Chemical Pattern Recognition Analysis in the Differentiation of Six Rhodiola Species

Rhodiola, especially Rhodiola crenulate and Rhodiola rosea, is an increasingly widely used traditional medicine or dietary supplement in Asian and western countries. Because of the phytochemical diversity and difference of therapeutic efficacy among Rhodiola species, it is crucial to accurately identify them. In this study, a simple and efficient method of the classification of Rhodiola crenulate, Rhodiola rosea, and their confusable species (Rhodiola serrata, Rhodiola yunnanensis, Rhodiola kirilowii and Rhodiola fastigiate) was established by UHPLC fingerprints combined with chemical pattern recognition analysis. The results showed that similarity analysis and principal component analysis (PCA) could not achieve accurate classification among the six Rhodiola species. Linear discriminant analysis (LDA) combined with stepwise feature selection exhibited effective discrimination. Seven characteristic peaks that are responsible for accurate classification were selected, and their distinguishing ability was successfully verified by partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), respectively. Finally, the components of these seven characteristic peaks were identified as 1-(2-Hydroxy-2-methylbutanoate) β-D-glucopyranose, 4-O-glucosyl-p-coumaric acid, salidroside, epigallocatechin, 1,2,3,4,6-pentagalloyglucose, epigallocatechin gallate, and (+)-isolarisiresinol-4′-O-β-D-glucopyranoside or (+)-isolarisiresinol-4-O-β-D-glucopyranoside, respectively. The results obtained in our study provided useful information for authenticity identification and classification of Rhodiola species.

[Comparison between peel and pulp of Aurantii Fructus Immaturus by UPLC fingerprint and multicomponent quantitative analysis]

Twenty batches of Aurantii Fructus Immaturus(AFI) were collected, with their peel and pulp taken as research objects. Ultra-high performance liquid chromatography(UPLC) fingerprints of peel and pulp of AFI were established with 17 common peaks in peel and 10 in pulp. Six kinds of flavonoids were identified, i.e., narirutin, naringin, rhoifolin, hesperidin, neohesperidin and nobiletin. The Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine was employed for similarity analysis, which showed that the chromatographic peaks of peel and pulp were basically similar to their respective reference fingerprints, with all similarities greater than 0.90. The similarity between peel and pulp of the same batch of AFI ranged from 0.850 to 0.983. Cluster analysis(CA), principal component analysis(PCA), and orthogonal partial least squares discriminant analysis(OPLS-DA) were conducted on the common peaks of peel and pulp of AFI with SPSS 17.0 and SIMCA 14.1. Combined with the reference fingerprints, these analyses revealed 12 differential components regarding peel and pulp. Further, the content of the 6 flavonoids and synephrine was determined. The proposed method integrating UPLC fingerprint and multicomponent quantitative analysis is applicable to the quality evaluation of AFI. The results provide a certain basis for the scientific connotation about the appearance characteristic of AFI.

[Comparative study on HPLC fingerprints between crude and processed Ligustri Lucidi Fructus]

To establish high performance liquid chromatography(HPLC) fingerprints for crude and processed Ligustri Lucidi Fructus,and to evaluate their quality through the similarity calculation and chemical pattern recognition. The separation was performed with Syncronis C_(18) column(4.6 mm × 250 mm, 5 μm), with acetonitrile(A) and 0.1% phosphoric acid solution(B) as the mobile phase for gradient elution, and a detection wavelength of 280 nm. HPLC was used to detect 22 batches of crude and processed Ligustri Lucidi Fructus,and the Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine(2012 Edition) was used to evaluate the similarity among 22 batches. The research on pattern recognition was conducted with cluster analysis(CA), principal component analysis(PCA), and partial least squares discriminate analysis(PLS-DA). HPLC fingerprints of crude and processed Ligustri Lucidi Fructus were established, with similarity ranging from 0.9 to 1.0. The crude and processed Ligustri Lucidi Fructus can be obviously distinguished by using CA, PCA and PLS-DA. According to the results of PLS-DA,11 constituents including hydroxytyrosol, tyrosol, specnuezhenide and oleuropein were the main marker components leading to the difference. The established fingerprint method is stable and reliable, and can provide method basis for quality control of crude and processed Ligustri Lucidi Fructus. Chemical pattern recognition is proved to be helpful in comprehensive quality control and evaluation of Ligustri Lucidi Fructus before and after the process.

High-performance liquid chromatography-based fingerprint analysis with chemical pattern recognition for evaluation of Mahonia bealei (Fort.) Carr

A simple and efficient high-performance liquid chromatography method combined with chemical pattern recognition was established for quality evaluation of Mahonia bealei (Fort.) Carr. A common pattern of 30 characteristic peaks was applied for similarity analysis, hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis in the 37 batches of M. bealei (Fort.) Carr. to discriminate wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes. The results showed that partial least squares discriminant analysis was the most effective method for discrimination. Eight characteristics peaks with higher variable importance in projection values were selected for pattern recognition model. A permutation test and 26 batches of testing set samples were performed to validate the model that was successfully established. All of the training and testing set samples were correctly classified into three clusters (wild M. bealei (Fort.) Carr., cultivated M. bealei (Fort.) Carr., and its substitutes) based on the selected chemical markers. Moreover, 26 batches of unknown samples were used to predict the accuracy of the established model with a discrimination accuracy of 100%. The obtained results indicated that the method showed great potential application for accurate evaluation and prediction of the quality of M. bealei (Fort.) Carr.

[UPLC characteristic fingerprint and chemical pattern recognition of Angong Niuhuang Pills]

To establish the UPLC fingerprint of Zhongyi Angong Niuhuang Pills, in order to evaluate its quality by chemical pattern recognition. The method was developed on a column of Poroshell 120 EC-C_(18), with methanol-0.1% formic acid solution as the mobile phase for gradient elution at a flow rate of 0.4 mL·min~(-1). The column temperature was 30 ℃,and the detective wavelength was 254 nm. The similarity of 24 batches of Angong Niuhuang Pills was compared by using Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System(2004 A). Hydrophobic cluster analysis,principal components analysis and partial least squares discriminant analysis were conducted by using SIMCA 13.0 software to investigate different components among these products. The UPLC characteristic fingerprint was established in this study. And 17 common peaks were identified by standard reference and UPLC-MS. The similarity of 24 batches samples were above 0.980,which can be classified into three categories for pattern recognition. Baicalin,berberine,jatrorrhizine,wogonin and wogonoside were identified as the main markers that cause differences of various batches. The method is simple,rapid,accurate and reproducible,and can provide scientific basis for improving the quality standard of Zhongyi Angong Niuhuang Pills.

Using Supercritical Fluid Chromatography with Chemical Pattern Recognition

Mahonia bealei (Fort.) Carr. (M. bealei) plays an important role in the treatment of many diseases. In the present study, a comprehensive method combining supercritical fluid chromatography (SFC) fingerprints and chemical pattern recognition (CPR) for quality evaluation of M. bealei was developed. Similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) were applied to classify and evaluate the samples of wild M. bealei, cultivated M. bealei and its substitutes according to the peak area of 11 components but an accurate classification could not be achieved. PLS-DA was then adopted to select the characteristic variables based on variable importance in projection (VIP) values that responsible for accurate classification. Six characteristics peaks with higher VIP values (≥1) were selected for building the CPR model. Based on the six variables, three types of samples were accurately classified into three related clusters. The model was further validated by a testing set samples and predication set samples. The results indicated the model was successfully established and predictive ability was also verified satisfactory. The established model demonstrated that the developed SFC coupled with PLS-DA method showed a great potential application for quality assessment of M. bealei.

Comparative Investigation for Rotten Xylem (kuqin) and Strip Types (tiaoqin) of Scutellaria baicalensis Georgi Based on Fingerprinting and Chemical Pattern Recognition

Scutellaria baicalensis Georgi (SBG) is not just as a traditional herbal medicine but also a popular functional food in China and other Asian countries. A sensitive simple strategy was developed for the first time to analyze SBG from eight different geographical sources using high-performance liquid chromatography (HPLC) coupled with multivariate chemometric methods. Two unsupervised pattern recognition models, hierarchical cluster analysis (HCA) and principal components analysis (PCA), and a supervised pattern recognition model, partial least squares discriminant analysis (PLS-DA), were used to analyze the chemical compositions and physical traits of SBG. The important chemical markers baicalin, baicalein, and wogonoside were analyzed quantitatively and with PLS-DA. These methods distinguished rotten xylem (kuqin) and strip types (tiaoqin) of SBG and found that the thickness of the slice had a significant impact on the classification of SBG. Two classes of strip types were identified: one as the uncut pharmaceutical, which was sectioned with a thickness >3 mm; the other as a thin-sectioned strip type, with a thickness of <2 mm. This fingerprinting technique coupled to a chemometric analysis was used for the simultaneous quantitation of three components (chemical markers) of SBG, and greatly simplified the complicated identification of the multiple components of this plant relative to traditional methods. The strategy can clearly distinguish between kuqin and tiaoqin of SBG, and suggests that the thickness of the slice can be used as the basis for evaluation of SBG. These data provide a theoretical basis and scientific evidence for the development and utilization of SBG.

Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint⁻Chemical Pattern Recognition

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.

Identification of Salvia species using high-performance liquid chromatography combined with chemical pattern recognition analysis

Salvia miltiorrhiza, also known as Danshen, is a widely used traditional Chinese medicine for the treatment of cardiovascular diseases and hematological abnormalities. The root and rhizome of Salvia przewalskii and Salvia yunnanensis have been found as substitutes for Salvia miltiorrhiza in the market. In this study, the chemical information of 14 major compounds in Salvia miltiorrhiza and its substitutes were determined using a high-performance liquid chromatography method. Stepwise discriminant analysis was adopted to select the characteristic variables. Partial least squares discriminant and hierarchical cluster analysis were performed to classify Salvia miltiorrhiza and its substitutes. The results showed that all of the samples were correctly classified both in partial least squares discriminant analysis and hierarchical cluster analysis based on the four compounds (caffeic acid, rosmarinic acid, salvianolic acid B, and salvianolic acid A). This method can not only distinguish Salvia miltiorrhiza and its substitutes, but also classify Salvia przewalskii and Salvia yunnanensis. The method can be applied for the quality assessment of Salvia miltiorrhiza and identification of unknown samples.

Distributed classifier based on genetically engineered bacterial cell cultures

We describe a conceptual design of a distributed classifier formed by a population of genetically engineered microbial cells. The central idea is to create a complex classifier from a population of weak or simple classifiers. We create a master population of cells with randomized synthetic biosensor circuits that have a broad range of sensitivities toward chemical signals of interest that form the input vectors subject to classification. The randomized sensitivities are achieved by constructing a library of synthetic gene circuits with randomized control sequences (e.g., ribosome-binding sites) in the front element. The training procedure consists in reshaping of the master population in such a way that it collectively responds to the “positive” patterns of input signals by producing above-threshold output (e.g., fluorescent signal), and below-threshold output in case of the “negative” patterns. The population reshaping is achieved by presenting sequential examples and pruning the population using either graded selection/counterselection or by fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of experimental implementation of such system computationally using a realistic model of the synthetic sensing gene circuits.