Quality assessment and differentiation of Aucklandiae Radix and Vladimiriae Radix based on GC-MS fingerprint and chemometrics analysis: basis for clinical application

Yan, Xiaomin; Wang, Wenjun; Chen, Ziqiang; Xie, Yu; Li, Qijuan; Yu, Ziwei; Hu, Huiling; Wang, Zhanguo

doi:10.1007/s00216-019-02380-2

Cited by 29 publications

(6 citation statements)

References 46 publications

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“…e most similar samples are clustered preferentially [28]. In order to obtain the HCA dendrogram, the 11 components were set as variables, and Ward's method was selected as the cluster method and squared Euclidean distance as the interval measurement [33]. e results showed that citrus and grape were divided into five main groups, and the content of licorice is significantly different in the same area.…”

Section: Discussionmentioning

confidence: 99%

Quality Assessment of Licorice Based on Quantitative Analysis of Multicomponents by Single Marker Combined with HPLC Fingerprint

Zhou

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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Licorice is a commonly used traditional Chinese medicine and natural sweetening agent, rich in numerous bioactive compounds. Moreover, it is one of the oldest and most frequently employed folk medicines in both eastern and western countries. It is prescribed for the treatment of asthma, fever, and cough. However, with the increasing demand of licorice, its quality and safety become the important issue. The content in licorice varies significantly in materials from different geographical origins. In this study, a reasonable and feasible evaluation method for the quality assessment of licorice was developed based on the analysis of high-performance liquid chromatography (HPLC) fingerprint, combined with the quantitative analysis of multicomponents by single marker (QAMS) method. Glycyrrhizic acid was selected as the internal reference substance, and ten components were simultaneously determined based on relative correction factors. The contents of eleven components in 21 batches of licorice were determined by the QAMS and the ESM (external standard method); there was no significant difference by comparison of the quantitative results between the QAMS and the ESM method; the cosine value (Cir > 0.9999) confirmed the consistency of the two methods. According to the outcomes of 21 batches of licorice samples, the contents of the eleven components were used for further chemometric analysis. All of the samples of licorice from various geographical origins were divided into five categories based on hierarchical cluster analysis, which indicated the crucial influence of geographical origins on licorice. This study showed that QAMS combined with HPLC fingerprint and chemometrics methods could effectively control the quality of licorice. Hence, QAMS is a feasible and promising method for promoting the quality control standardization process of herbal medicines.

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Section: Discussionmentioning

confidence: 99%

Quality Assessment of Licorice Based on Quantitative Analysis of Multicomponents by Single Marker Combined with HPLC Fingerprint

Zhou

et al. 2021

Evidence-Based Complementary and Alternative Medicine

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“…The biplot curve also indicates that some variability of wavenumbers correlated with the sample type used is noticeable at 785 cm –1 , which is very identical to GBO, 603 cm –1 identical to PGBO. The characteristics of PO and CRM are indicated by several wavenumbers, such as 613, 615, 621, 623, 1436, 1442, 1444, and 1486 cm –1 . ,− …”

Section: Resultsmentioning

confidence: 99%

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

et al. 2023

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This study aims to test chemometrically partial least squares-discriminant analysis (PLS-DA) classification models to detect the adulteration of patchouli oil (PO) with gurjun balsam oil (GBO) by utilization of Fourier transform infrared spectroscopy. Unsupervised analysis was tested using the pattern recognition method using the principal component analysis model against the original spectrum at wavenumbers 4000–500 cm–1 and at the fingerprint area (1800–600 cm–1). Model testing was also carried out on the spectrum that had been pre-processed using the standard normal variate, second derivative Savitzky–Golay, and normalization approaches. Variable Y samples used were certified reference material (CRM), PO, GBO, and PO forged with GBO (PGBO) with a counterfeiting ratio of 0.5 (v/v) to 10% (v/v) with an interval of 0.5%. The same treatment was carried out on testing of the PLS-DA model. In pattern recognition tests, the best separation of the original spectrum was obtained at wavenumbers 1800–600 cm–1. The model was further tested on PLS-DA by making assumptions or codes for CRM, PO, GBO, and PGBO as +2, +1, 0, and −1, respectively. The results of the model analysis showed that even at the lowest counterfeiting ratio (0.5%), the presence of counterfeiting material was detected by the PLS-DA model. The RMSEC value is close to zero with a value of 0.22, and the R square is close to 1, which is 0.954. This very significant separation is clearly illustrated in the loading plot and bi-plot due to the contribution of chemical compounds in the GBO that undergo vibration at wavenumbers 603, 786, and 1386 cm–1. Validation of the PLS-DA model was carried out strongly using the PLS model, and it showed that the difference between the calibration concentration and the prediction was very low (average 0.45) with an accuracy percent above 99%. The efficacy of the model is further substantiated by the consistent and precise values of sensitivity and selectivity, obtained from both the training set and test set.

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“…It transforms the original set of variables to a new set of uncorrelated variables that are called PCs (Abegaz et al, 2019). PCA has been widely used for taxonomic discrimination, quality assessment or classification between plants from different geographic origins, and authenticity verification (for example, in Chinese herbal medicines) (Yan et al, 2020). Plotting the PCA scores highlighting the similarities and differences between groups.…”

Section: Discussionmentioning

confidence: 99%

“…Hierarchical cluster analysis is a statistical method used for finding relatively homogeneous clusters of objects based on measured characteristics. Samples with the most similarities are clustered preferentially (Yan et al, 2020). To obtain the HCA dendrogram, the 16 phenolic compounds were set as variables, Ward's method was selected as the cluster method, and squared Euclidean distance as the interval measurement (Yi et al, 2015;Zhao et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Simultaneous determination of phenolic metabolites in Chinese citrus and grape cultivars

Chen

Hong

Dao-fu

et al. 2020

PeerJ

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Background As the major bioactive compounds in citrus and grape, it is significant to use the contents of flavonoids and phenolic acids as quality evaluation criteria to provide a better view of classifying the quality and understanding the potential health benefits of each fruit variety. Methods A total of 15 varieties of citrus and 12 varieties of grapes were collected from Fujian, China. High-performance liquid chromatography method was used for the simultaneous determination of 17 phenolic compounds, including gallic acid, chlorogenic acid, caffeic acid, syringic acid, ρ-coumaric acid, ferulic acid, benzoic acid, salicylic acid, catechin, epicatechin, resveratrol, rutin, naringin, hesperidin, quercetin, nobiletin and tangeritin in the peels of citrus and grape cultivars. Further, the cultivars of citrus and grape were classified using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Results A thorough separation of the 17 compounds was achieved within 100 min. The tested method exhibited good linearity (the limits of detection and limits of quantification were in the range of 0.03–1.83 µg/mL and 0.09–5.55 µg/mL, respectively), precision (the relative standard deviations of repeatability were 1.02–1.97%), and recovery (92.2–102.82%) for all the compounds, which could be used for the simultaneous determination of phenolic compounds in citrus and grape. Hesperidin (12.93–26,160.98 µg/g DW) and salicylic acid (5.35–751.02 µg/g DW) were the main flavonoids and phenolic acids in 15 citrus varieties, respectively. Besides, the hesperidin (ND to 605.48 µg/g DW) and salicylic acid (ND to 1,461.79 µg/g DW) were found as the highest flavonoid and the most abundant phenolic acid in grapes, respectively. A total of 15 citrus and 12 grape samples were classified into two main groups by PCA and HCA with strong consistency.

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Quality assessment and differentiation of Aucklandiae Radix and Vladimiriae Radix based on GC-MS fingerprint and chemometrics analysis: basis for clinical application

Cited by 29 publications

References 46 publications

Quality Assessment of Licorice Based on Quantitative Analysis of Multicomponents by Single Marker Combined with HPLC Fingerprint

Quality Assessment of Licorice Based on Quantitative Analysis of Multicomponents by Single Marker Combined with HPLC Fingerprint

Partial Least Squares-Discriminant Analysis Classification for Patchouli Oil Adulteration Detection by Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

Simultaneous determination of phenolic metabolites in Chinese citrus and grape cultivars

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