Targeted metabolomics study of fatty-acid metabolism in lean metabolic-associated fatty liver disease patients.

BACKGROUND: The annual incidence of metabolic-associated fatty liver disease (MAFLD) in China has been increasing and is often overlooked owing to its insidious characteristics. Approximately 50% of the patients have a normal weight or are not obese. They are said to have lean-type MAFLD, and few studies of such patients are available. Because MAFLD is associated with abnormal lipid metabolism, lipid-targeted metabolomics was used in this study to provide experimental evidence for early diagnosis and pathogenesis. AIM: To investigate the serum fatty-acid metabolic characteristics in lean-type MAFLD patients using targeted serum metabolomic technology. METHODS: Between January and June 2022, serum samples were collected from MAFLD patients and healthy individuals who were treated at Shanghai Putuo District Central Hospital for serum metabolomics analysis. Principal component analysis and orthogonal partial least squares-discriminant analysis models were developed, and univariate analysis was used to screen for biomarkers of lean-type MAFLD and analyze metabolic pathways. UPLC-Q-Orbitrap/MS content determination was used to determine serum palmitic acid (PA), oleic acid (OA), linoleic acid (LA), and arachidonic acid (AA) levels in lean-type MAFLD patients. RESULTS: Urea nitrogen and uric acid levels were higher in lean-type MAFLD patients than in healthy individuals (P < 0.05). Alanine transaminase and cholinesterase levels were higher in lean-type MAFLD patients than in healthy individuals (P < 0.01). The expression of high-density lipoprotein and apolipoprotein A-1 were lower in lean-type MAFLD patients than in healthy individuals (P < 0.05) and the expression of triglycerides and fasting blood glucose were increased (P < 0.01). A total of 65 biomarkers that affected the synthesis and metabolism of fatty acids were found with P < 0.05 and variable importance in projection > 1". The levels of PA, OA, LA, and AA were significantly increased compared with healthy individuals. CONCLUSION: The metabolic profiles of lean-type MAFLD patients and healthy participants differed significantly, yielding 65 identified biomarkers. PA, OA, LA, and AA exhibited the most significant changes, offering valuable clinical guidance for prevention and treatment of lean-type MAFLD.

Characterization of key aroma compounds in a novel Chinese rice wine Xijiao Huojiu during its biological-ageing-like process by untargeted metabolomics.

Xijiao Huojiu (Xijiao), an ancient Chinese rice wine (ACRW), is produced using traditional methods, which involve biological-ageing-like process and result in distinctive sensory profiles. However, its aroma composition is still unclear. In this study, the aroma characteristics of three samples with varying ageing times were examined. Xijiao_SCT, with a short cellar time, exhibited a strong fruity and floral aroma and a less grain-like aroma. Conversely, Xijiao_LCT, which had a long cellar time, had a deep cocoa- and caramel-like aroma. A total of 27 key odorants that greatly influenced the aroma characteristics of Xijiao were identified. Comparative studies were used to identify 12 key odorants that distinguish Xijiao from modern Chinese rice wine (MCRW) and grape wines (GW). Additionally, 13 dominant latent ageing markers differentiated Xijiao_SCT from Xijiao_LCT. Our results suggested that ACRW and MCRW have overlapping but distinct volatile metabolomic profiles, highlighting the characteristics of ACRW during ageing process.

Development of a determination method for quality control markers utilizing metabolic profiling and its application on processed Zingiber officinale Roscoe rhizome.

This study established an Orthogonal Partial Least Squares (OPLS) model combining 1H-NMR and GC-MS data to identify characteristic metabolites in complex extracts. Both in metabolomics studies, and natural product chemistry, the reliable identification of marker metabolites usually requires laborious isolation and purification steps, which remains a bottleneck in many studies. Both ginger (GR) and processed ginger (PGR) are listed in the Japanese pharmacopeia. The plant of origin, the rhizome of Zingiber officinale Roscoe, is differently processed for these crude drugs. Notably, the quality of crude drugs is affected by genetic and environmental factors, making it difficult to maintain a certain quality standard. Therefore, characteristic markers for the quality control of GR and PGR are required. Metabolomic analysis using 1H-NMR was able to discriminate between GR and PGR, but there were unidentified signals that were difficult to distinguish based on NMR data alone. Therefore, we combined 1H-NMR and GC-MS analytical data to identify them by OPLS. As a result, αr-curcumene was found to be a useful marker for these identifications. This new approach enabled rapid identification of characteristic marker compounds and reduced the labor involved in the isolation process.

Rapid discrimination of Alismatis Rhizoma and quantitative analysis of triterpenoids based on near-infrared spectroscopy.

This study developed a rapid, accurate, objective and economic method to identify and evaluate the quality of Alismatis Rhizoma (AR) commodities. Traditionally, the identification of plant species and geographical origins of AR commodities mainly relied on experienced staff. However, the subjectivity and inaccuracy of human identification negatively impacted the trade of AR. Besides, liquid chromatographic methods such as ultra-high-performance liquid chromatography (UPLC) and high-performance liquid chromatography (HPLC), the major approach for the determination of triterpenoid contents in AR was time-consuming, expensive, and highly demanded in manoeuvre specialists. In this study, the combination of near-infrared (NIR) spectroscopy and chemometrics as the method was developed and utilised to address the two common issues of identifying the quality of AR commodities. Through the discriminant analysis (DA), the raw NIR spectroscopy data on 119 batches samples from two species and four origins in China were processed to the best pre-processed data. Subsequently, orthogonal partial least squares-discriminant analysis (OPLS-DA) and random forest (RF) as the major chemometrics were used to analyse the best pre-processed data. The accuracy rates by OPLS-DA and RF were respectively 100% and 97.2% for the two species of AR, and respectively100% and 94.4% for the four origins of AR. Meanwhile, a quantitative correction model was established to rapidly and economically predict the seven triterpenoid contents of AR through combining the partial least squares (PLS) method and NIR spectroscopy, and taking the triterpenoid contents measured by UPLC as the reference value, and carry out spectral pre-processing methods and band selection. The final quantitative model correlation coefficients of the seven triterpenoid contents of AR ranged from 0.9000 to 0.9999, indicating that prediction ability of this model had good stability and applicability.

Combining multivariate statistical analysis to characterize changes in amino acids and volatiles during growth of Lou onion pseudostems.

BACKGROUND: The main edible part of the Lou onion is the pseudostem, which is highly valued for its distinctive flavour. However, harvesting decisions for the pseudostem are often based on size and market price, with little consideration given to flavour. By clarifying the growth of flavour in pseudostems, farmers and consumers may benefit from evidence-based insights that help optimize harvesting time and maximize flavour quality. RESULTS: This study employed amino acid analysis and gas chromatography-ion migration spectroscopy (GC-IMS) to elucidate the compounds of the pseudostem across different growth phases, and 17 amino acids and 61 volatile substances. Subsequently, analysis revealed that 18 compounds, including arginine (Arg), aspartic acid (Asp), glutamic acid (Glu), valine (Val), (E)-2-nonenal, decanal, 2,4-nonadienal, 2-octenal, (Z)-4-decenal, 2,4-decadienal benzeneacetaldehyde, linalool, eugenol, (Z)-6-nonen-1-ol, methyl anthranilate, 2-acetylpyridine, 3-sec-butyl-2-methoxypyrazine, and 2,6-dichlorophenol, were the key compounds in determining the flavour characteristics of the pseudostems, as assessed by taste activity value and relative odour activity value calculations. In addition, correlation analysis, focusing on five amino acids and 38 volatile compounds with variable importance for predictive components scores of >1, identified anisaldehyde, eugenol, (Z)-6-nonen-1-ol, 2,4-decadienal, 3-sec-butyl-2-methoxypyrazine, Arg, Asp, and Val as the key differentiators and contributors to the pseudostems flavour profile. CONCLUSION: During the rapid growth of Lou onions just before the emergence of flower stems, the pseudostem exhibited the most prominent flavour, making this stage most suitable for harvesting compared to the regreening growth stage and the rapid growth period of the aerial bulbs. © 2024 Society of Chemical Industry.

Analysis of characteristic aromas of buckwheat with different germplasm using gas chromatography-mass spectrometry combined with chemometrics and multivariate statistical analysis.

In this study, the volatile components in 40 samples of Tartary buckwheat and common buckwheat from 6 major producing areas in China were analyzed. A total of 77 volatile substances were identified, among which aldehydes and hydrocarbons were the main volatile components. Odor activity value analysis revealed 26 aromatic compounds, with aldehydes making a significant contribution to the aroma of buckwheat. Seven key compounds that could be used to distinguish Tartary buckwheat from common buckwheat were identified. The orthogonal partial least squares-discriminant analysis was effectively used to classify Tartary buckwheat and common buckwheat from different producing areas. This study provides valuable information for evaluating buckwheat quality, breeding high-quality varieties, and enhancing rational resource development.

New insights into the characteristic flavor components of traditional sour beers such as Lambic and Flanders Red Ale beers.

In recent years, the demand for beers with a variety of flavors has increased considerably owing to the diversification of consumer preferences. Sour beer is characterized by a sour taste unlike normal beer flavor, and previous studies on sour beer have been primarily focused on addressing issues, such as inconsistent product quality and long production time, and on the associated microorganisms. Scientific knowledge regarding the characteristic flavor of sour beer and flavor components is limited. Therefore, in this study, we aimed to clarify the characteristic sensory attributes of sour beer and the component profiles that explain these attributes. Component analysis was performed on 10 traditional sour beers (eight Flanders Red Ales and two Lambics), using untargeted gas chromatography-mass spectrometry with liquid-liquid extraction, liquid chromatography-mass spectrometry targeting amines and anionic compounds. Further, sensory evaluation was conducted by well-trained panelists via quantitative descriptive analysis. Orthogonal partial least squares regression analysis was also conducted to investigate candidate flavor components. Thus, 261 components were identified and our methods could explain the flavor attributes of the examined samples. Comprehensive component profiling data also showed that differences in fermentation method, barrel aging duration, and blending ratio affected beer flavor. Further, Lambics were found to be characterized by citrus and phenolic aroma, while Flanders Red Ales were characterized by solvent-like aroma, sourness complexity, full bodied, graininess, astringency, and bitterness. These findings may serve as a basis for addressing issues related to sour beer production and may facilitate process design for obtaining targeted sour beer flavors.

Characterization of Flavor Profile of Sauced Pork from Different Regions of China Based on E-Nose, E-Tongue and Gas Chromatography-Ion Mobility Spectroscopy.

This study aimed to investigate the volatile flavor compounds and tastes of six kinds of sauced pork from the southwest and eastern coastal areas of China using gas chromatography-ion mobility spectroscopy (GC-IMS) combined with an electronic nose (E-nose) and electronic tongue (E-tongue). The results showed that the combined use of the E-nose and E-tongue could effectively identify different kinds of sauced pork. A total of 52 volatile flavor compounds were identified, with aldehydes being the main flavor compounds in sauced pork. The relative odor activity value (ROAV) showed that seven key volatile compounds, including 2-methylbutanal, 2-ethyl-3, 5-dimethylpyrazine, 3-octanone, ethyl 3-methylbutanoate, dimethyl disulfide, 2,3-butanedione, and heptane, contributed the most to the flavor of sauced pork (ROAV ≥1). Multivariate data analysis showed that 13 volatile compounds with the variable importance in projection (VIP) values > 1 could be used as flavor markers to distinguish six kinds of sauced pork. Pearson correlation analysis revealed a significant link between the E-nose sensor and alcohols, aldehydes, terpenes, esters, and hetero-cycle compounds. The results of the current study provide insights into the volatile flavor compounds and tastes of sauced pork. Additionally, intelligent sensory technologies can be a promising tool for discriminating different types of sauced pork.

Spectrum-effect relationship between ultra-performance liquid chromatography fingerprints and melanogenic effect of Vernonia anthelmintica effective part.

The fingerprint of Vernonia anthelmintica effective part (VAEP) from 15 different producing areas was established, followed by cluster analysis and principal component analysis. The relationship between the fingerprint and the melanogenesis-promoting activity of VAEP was then analyzed using the grey correlation degree and the orthogonal partial least square method. The characteristic peaks reflecting the pharmacodynamic effect of VAEP were identified as vernodalin, 3,5-O-dicaffeoyl quinic acid (3,5-diCQA), and butin. Based on the distribution characteristics of these components in plants from different habitats and the verification of results from the spectrum-effect relationship, vernodalin and 3,5-diCQA can be used as characteristic components for quality control and pharmacodynamic assessment of V. anthelmintica products. This research establishes a theoretical foundation for planting areas and provides a scientific evaluation of the melanogenesis-promoting effect of V. anthelmintica.

HPLC Fingerprint Analysis of Cibotii rhizoma from Different Regions and Identification of Common Peaks by LC-MS.

To establish the fingerprint of Cibotii rhizoma using high-performance liquid chromatography (HPLC) and evaluate the quality of Cibotii rhizoma from different regions using chemometrics to identify the potential quality markers, thirteen batches of Cibotii rhizoma samples were analyzed. the similarity evaluation system of TCM chromatographic fingerprint similarity evaluation was used to confirm common peaks. The SPSS 27 software was used for hierarchical cluster analysis (HCA), and SIMCA 14.1 software was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, a batch of Cibotii rhizoma was selected for LC-MS analysis and speculated on 15 common components. HPLC fingerprint were established, 15 common peaks were matched, two chromatographic peaks were identified using standard substances (protocatechuic acid and protocatechuic aldehyde), and 13 common components were inferred through liquid chromatograph-mass spectrometer (LC-MS). The 13 batches of the samples showed good similarities (>0.910). The results of HCA, PCA and OPLS-DA showed that 13 batches of samples were divided into three groups, and different markers were selected. The method is simple, rapid and reproducible, and can provide a reference for the overall quality evaluation of Cibotii rhizoma.

Untargeted metabolomics and machine learning unveil quality and authenticity interactions in grated Parmigiano Reggiano PDO cheese.

The chemical composition of Parmigiano Reggiano (PR) hard cheese can be significantly affected by different factors across the dairy supply chain, including ripening, altimetric zone, and rind inclusion levels in grated hard cheeses. The present study proposes an untargeted metabolomics approach combined with machine learning chemometrics to evaluate the combined effect of these three critical parameters. Specifically, ripening was found to exert a pivotal role in defining the signature of PR cheeses, with amino acids and lipid derivatives that exhibited their role as key discriminant compounds. In parallel, a random forest classifier was used to predict the rind inclusion levels (> 18%) in grated cheeses and to authenticate the specific effect of altimetry dairy production, achieving a high prediction ability in both model performances (i.e., ∼60% and > 90%, respectively). Overall, these results open a novel perspective to identifying quality and authenticity markers metabolites in cheese.

Plasma metabolomics identifies S-adenosylmethionine as a biomarker and potential therapeutic target for vascular aging in older adult males.

Brachial-ankle pulse wave velocity (baPWV) is a noninvasive index of vascular aging. However, the metabolic profile underlying vascular aging has not yet been fully elucidated. The current study aimed to identify circulating markers of vascular aging as assessed by baPWV and to elucidate its mechanism from a metabolomic perspective in older adults. A total of 60 and 61 Chinese male participants aged ≥80 years were recruited to the metabolome and validation cohorts, respectively. The baPWV of participants was measured using an automatic waveform analyzer. Plasma metabolic profile was investigated using ultra-performance liquid chromatography coupled with triple quadrupole linear ion trap tandem mass spectrometry. Orthogonal partial least squares (OPLS) regression modeling established the association between metabolic profile and baPWV to determine important metabolites predictive of vascular aging. Additionally, an enzyme-linked immunosorbent assay was employed to validate the metabolites in plasma and culture media of vascular smooth muscle cells in vitro. OPLS modeling identified 14 and 22 metabolites inversely and positively associated with baPWV, respectively. These 36 biomarkers were significantly enriched in seven metabolite sets, especially in cysteine and methionine metabolism (p <0.05). Notably, among metabolites involved in cysteine and methionine metabolism, S-adenosylmethionine (SAM) level was inversely related to baPWV, with a significant correlation coefficient in the OPLS model (p <0.05). Furthermore, the relationship between SAM and vascular aging was reconfirmed in an independent cohort and at the cellular level in vitro. SAM was independently associated with baPWV after adjustments for clinical covariates (ß = -0.448, p <0.001) in the validation cohort. In summary, plasma metabolomics identified an inverse correlation between SAM and baPWV in older males. SAM has the potential to be a novel biomarker and therapeutic target for vascular aging.

Classification and characterization on sorghums based on HS-GC-IMS combined with OPLS-DA and GA-PLS.

Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) detected 206 and 186 samples of fresh and stored sorghums respectively with three major types in Baijiu industry. The fingerprints showed the differences of volatile compounds among fresh sorghum types by qualitative analysis and artificial recognition. Organic waxy sorghums had more contents of nonanal and 2-ethyl-1-hexanol but fewer ketones. The contents of acetoin in non-glutinous sorghums and organic non-glutinous sorghums were high. On the other hand, genetic algorithm-partial least squares (GA-PLS) selected 19 and 32 characteristic volatile compounds in fresh and stored sorghums. After centering and auto scaling to unit variance, the classification models with three major types of organic waxy sorghum, non-glutinous sorghum and organic non-glutinous sorghum were established based on orthogonal partial least squares-discriminant analysis (OPLS-DA). The goodness-of-fit (R2Y) and the goodness-of-prediction in cross-validation (Q2) in the model of fresh sorghum types all exceeded 0.9, in stored were over 0.8, the correct classification rates of external prediction were 95 % and 100 %, which revealed good performance and prediction. On this basis, the correct classification rates reached 87 % in organic waxy sorghums adulterated over 10 % ratio. GC-IMS combined with chemometrics is applicable in practical production for rapid identification of sorghum types and adulterations.

Quality evaluation of Lonicerae Japonicae Flos from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition.

INTRODUCTION: Lonicerae Japonicae Flos (LJF) is widely used in food and traditional Chinese medicine. To meet demand, Lonicera japonica Thunb. is widely cultivated in many provinces of China. However, reported studies on the quality evaluation of LJF only used a single or a few active components as indicators, which could not fully reflect the quality of LJF. OBJECTIVES: In the present study, we aimed to develop a methodology for comprehensively evaluating the quality of LJF from different origins based on high-performance liquid chromatography (HPLC) fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition. MATERIALS AND METHODS: The HPLC method was developed for fingerprint analysis and was used to determine the contents of 19 components of LJF. To distinguish between samples and identify differential components, similarity analysis, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis were performed. RESULTS: The HPLC fingerprint was established. Using the developed method, the contents of 19 components recognized in the fingerprint analysis were determined. Samples from different origins could be effectively distinguished. CONCLUSIONS: HPLC fingerprinting and multicomponent quantitative analysis combined with chemical pattern recognition is an efficient method for evaluating LJF.

Identification of Dalbergia odorifera and Its Counterfeits by HS-GC-MS / 中国实验方剂学杂志

ObjectiveTo screen the differential markers by analyzing volatile components in Dalbergia odorifera and its counterfeits， in order to provide reference for authentication of D. odorifera. MethodThe volatile components in D. odorifera and its counterfeits were detected by headspace gas chromatography-mass spectrometry（HS-GC-MS）， and the GC conditions were heated by procedure（the initial temperature of the column was 50 ℃， the retention time was 1 min， and then the temperature was raised to 300 ℃ at 10 ℃ for 10 min）， the carrier gas was helium， and the flow rate was 1.0 mL·min-1， the split ratio was 10∶1， and the injection volume was 1 mL. The MS conditions used electron bombardment ionization（EI） with the scanning range of m/z 35-550. The compound species were identified by database matching， the relative content of each component was calculated by the peak area normalization method， and principal component analysis（PCA）， orthogonal partial least squares-discrimination analysis（OPLS-DA） and cluster analysis were performed on the detection results by SIMCA 14.1 software， and the differential components of D. odorifera and its counterfeits were screened out according to the variable importance in the projection（VIP） value>2 and P<0.05. ResultA total of 26， 17， 8， 22， 24 and 7 volatile components were identified from D. odorifera， D. bariensis， D. latifolia， D. benthamii， D. pinnata and D. cochinchinensis， respectively. Among them， there were 11 unique volatile components of D. odorifera， 6 unique volatile components of D. bariensis， 3 unique volatile components of D. latifolia， 6 unique volatile components of D. benthamii， 8 unique volatile components of D. pinnata， 4 unique volatile components of D. cochinchinensis. The PCA results showed that， except for D. latifolia and D. cochinchinensis， which could not be clearly distinguished， D. odorifera and other counterfeits could be distributed in a certain area， respectively. The OPLS-DA results showed that D. odorifera and its five counterfeits were clustered into one group each， indicating significant differences in volatile components between D. odorifera and its counterfeits. Finally， a total of 31 differential markers of volatile components between D. odoriferae and its counterfeits were screened. ConclusionHS-GC-MS combined with SIMCA 14.1 software can systematically elucidate the volatile differential components between D. odorifera and its counterfeits， which is suitable for rapid identification of them.

Study on consistency of traditional decoction and formula granules of Sangju Decoction based on fingerprint printing and chemical pattern recognition / 国际中医中药杂志

Objective:To compare the chemical composition of decoction and granules of Sangju Decoction; To provide a method for quality evaluation of Sangju Decoction.Methods:HPLC was used to establish fingerprints, and a comprehensive comparative study was conducted on the traditional decoction and formula granules of Sangju Decoction from four aspects: chemical composition type, fingerprint similarity, chemical pattern recognition analysis, and representative index component content.Results:The fingerprint similarity of the 10 batches of traditional decoction was >0.988. 35 peaks were identified and 12 peaks were identified as common peaks (neochlorogenic acid for peak 7, chlorogenic acid for peak 10, cryptochlorogenic acid for peak 11, 1,3-dicaffeoylquinic acid for peak 13, rutin for peak 17, lenoside A for peak 19, lignan for peak 20, isochlorogenic acid B for peak 24, ammonium glycyrrhizate for peak 25). The fingerprint similarity of the formulation pellets was >0.983, and 29 characteristic peaks were identified. Compared with the traditional decoction, some batches of the granules lacked peaks 14, 26, 27, 30, 32 and 34, and clustering analysis (CA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) could distinguish between the two. The contents of the 10 index components neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, 1,3-dicaffeoylquinic acid, forsythia ester glycoside A, grass glycosides, isochlorogenic acid B, 3,5-O-dicaffeoylquinic acid, forsythia glycosides, monkshood glycosides in the traditional soup were higher than that in the granules, and the contents of rutin and ammonium glycyrrhizate in the granules were higher than that in traditional decoction.Conclusions:The content and composition of traditional decoction and formula granules of Sangju Decoction are significantly different. The combination of fingerprinting and chemical pattern identification effectively can effectively evaluate the difference between traditional decoction and formula granules of Sangju Decoction, which can lay a foundation for the quality control and rational clinical application of formula granules of Sangju Decoction.

Application of HPLC multi-components quantification combined with chemometrics and entropy weight-TOPSIS method in comprehensive quality evaluation of Solanum nigrum / 中国药师

Objective To establish a method for simultaneous determination of 11 components of Solanum nigrum from different producing areas,and to evaluate the quality by chemometrics and entropy weight-technique for order preference by similarity to ideal solution(EW-TOPSIS).Methods The 17 batches of Solanum nigrum samples from 8 provinces were collected.The high performance liquid chromatography(HPLC)method was used to simultaneously determine the contents of medioresino,pinoresinol,quercetin,rutoside,solasonine,solamargine,khasianine,solasodine,desgalactotigonin,diosgenin and β-sitosterol,and the multi-components quantitative control mode of Solanum nigrum was established.The quality evaluation model of Solanum nigrum was established by using chemical recognition pattern and EW-TOPSIS method,and the overall quality was evaluated comprehensively.Results When the 11 components were in the 0.78-39.00,0.55-27.50,0.34-17.00,0.21-10.50,41.87-2 093.50,60.95-3 047.50,2.58-129.00,1.02-51.00,0.46-23.00,1.05-52.50 and 0.42-21.00 μg/mL(r＞0.999 0),their linear relationships were good.The average recovery was 96.81％-100.28％with the RSD＜2.0％(n=9).17 batches of samples clustered into 3 categories.Solamargine,solasonine,desgalactotigonin and medioresino may be the main potential markers affecting the quality of Solanum nigrum.The results of EW-TOPSIS method showed that,the quality evaluation closeness of 17 batches of Solanum nigrum were 0.433 6,0.416 8,0.624 2,0.500 8,0.479 1,0.636 1,0.568 3,0.250 0,0.190 9,0.222 1,0.170 7,0.720 0,0.698 3,0.744 7,0.717 9,0.720 9 and 0.718 3,respectively,indicating that the overall quality of Solanum nigrum from Liaoning,Jilin and Heilongjiang were better,followed by Jiangsu,Henan and Anhui.Conclusion The established HPLC method for simultaneous determination of 11 components in Solanum nigrum is convenient and accurate.Chemometrics and EW-TOPSIS method are objective and comprehensive,which can be used for the overall quality evaluation of Solanum nigrum.

Variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip / 中成药

AIM To investigate the variation rules of main secondary metabolites in Hedysari Radix before and after rubbing strip.METHODS UPLC-MS/MS was adopted in the content determination of formononetin,ononin,calycosin,calycosin-7-glucoside,medicarpin,genistein,luteolin,liquiritigenin,isoliquiritigenin,vanillic acid,ferulic acid,γ-aminobutyric acid,adenosine and betaine,after which cluster analysis,principal component analysis and orthogonal partial least squares discriminant analysis were used for chemical pattern recognition to explore differential components.RESULTS After rubbing strip,formononetin,calycosin,liquiritigenin and γ-aminobutynic acid demonstrated increased contents,along with decreased contents of ononin,calycosin-7-glucoside and vanillic acid.The samples with and without rubbing strip were clustered into two types,calycosin-7-glucoside,formononetin,γ-aminobutynic acid,vanillic acid,calycosin-7-glucoside and formononetin were differential components.CONCLUSION This experiment clarifies the differences of chemical constituents in Hedysari Radix before and after rubbing strip,which can provide a reference for the research on rubbing strip mechanism of other medicinal materials.

Analysis of the Differences in Volatile Organic Compounds in Different Rice Varieties Based on GC-IMS Technology Combined with Multivariate Statistical Modelling.

In order to investigate the flavour characteristics of aromatic, glutinous, and nonaromatic rice, gas chromatography-ion mobility spectrometry (GC-IMS) was used to analyse the differences in volatile organic compounds (VOCs) amongst different rice varieties. The results showed that 103 signal peaks were detected in these rice varieties, and 91 volatile flavour substances were identified. Amongst them, 28 aldehydes (28.89~31.17%), 24 alcohols (34.85~40.52%), 14 ketones (12.26~14.74%), 12 esters (2.30~4.15%), 5 acids (7.80~10.85%), 3 furans (0.30~0.68%), 3 terpenes (0.34~0.64%), and 2 species of ethers (0.80~1.78%) were detected. SIMCA14.1 was used to perform principal component analysis (PCA) and orthogonal partial least squares discriminant analysis, and some potential character markers (VIP > 1) were further screened out of the 91 flavour substances identified based on the variable important projections, including ethanol, 1-hexanol, hexanal, heptanal, nonanal, (E)-2-heptenal, octanal, trans-2-octenal, pentanal, acetone, 6-methyl-5-hepten-2-one, ethyl acetate, propyl acetate, acetic acid, and dimethyl sulphide. Based on the established fingerprint information, combined with principal component analysis and orthogonal partial least squares discriminant analysis, different rice varieties were also effectively classified, and the results of this study provide data references for the improvement in aromatic rice varieties.

Geographical classification of opium samples from Myanmar and Afghanistan by NMR profiling and chemometrics.

This study presents a new strategy to discriminate between opium samples obtained from different geographical regions. Nuclear magnetic resonance (NMR) profiling and chemometrics were applied to geographical classification of opium originating from Myanmar and Afghanistan, which are two major opium producing countries in the world. A total of 50 Myanmar and 46 Afghanistan authentic opium samples were analyzed by 1 H-NMR, and the chemical profiles were characterized. Different sample preparation procedures, data processing methods, and chemometrics were compared to obtain the best classification effect. It was found that drying and the addition of buffer solutions were unnecessary for classification purposes; thus, the gum opium samples were extracted directly with CD3 OD, which shortened sample preparation time. A full discrimination between the two geographical origins was achieved by 1 H-NMR profiling and orthogonal partial least squares discriminant analysis. All 30 opium samples were classified correctly by the developed orthogonal partial least squares discriminant analysis model. Compared with traditional chromatography and mass spectrometry profiling methods, the 1 H-NMR profiling method was faster (with instrument analysis time of less than 3 min) and reproducible. This study provides new insights into the applying of NMR profiling and chemometrics to rapid drug profiling analysis.