Quality evaluation of Lonicerae Japonicae Flos and Lonicerae Flos based on simultaneous determination of multiple bioactive constituents combined with multivariate statistical analysis

Cai, Zhichen; Wang, Yuming; Chen, Cuihua; Zou, Lisi; Chai, Chuan; Chen, Jiali; Tan, Mengxi; Liu, Xunhong

doi:10.1002/pca.2882

Cited by 25 publications

(12 citation statements)

References 31 publications

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“…flower buds vs. flowers). In this experiment, the chemical contents of the flower buds were greater than that of the flower products (Figure 5D), and some studies also reported that flower buds have better quality and higher medical value 36,37 …”

Section: Discussionmentioning

confidence: 56%

A comprehensive study of the aerial parts of Lonicera japonica Thunb. based on metabolite profiling coupled with PLS‐DA

Cai

Liao

Wang

et al. 2020

Phytochemical Analysis

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Introduction: Lonicera japonica Thunb. is an economically important species of honeysuckle belonging to the Caprifoliaceae family. All aerial parts of L. japonica (leaf, flower bud, flower, and caulis) are used as herbal remedies in traditional Chinese medicine. The application of plant metabolomics to the study of L. japonica provides the potential for identifying the phytochemical composition and useful chemical markers of the plant. Objective: To develop a strategy integrating metabolic profiling and partial least squares discriminant analysis (PLS-DA) to separate the aerial parts of L. japonica based on the occurrence of chemical markers. Methodology: The two-part strategy consisted of (1) ultra-fast liquid chromatography coupled with triple quadrupole-time of flight tandem mass spectrometry (UFLCtriple TOF-MS/MS), (2) PLS-DA, which was applied to distinguish between the different aerial parts and reveal their differential characteristic metabolites. Results: A total of 71 metabolites were identified from samples, and eight candidate compounds were identified (lonicerin, kaempferol-3-O-rutinoside, loganin, isochlorogenic acid B, isochlorogenic acid C, secologanic acid, luteoloside, astragalin) as optimal chemical markers based on variable importance in projection (VIP) and pvalue. The relative contents of eight candidate compounds were compared based on their peak intensities. Conclusion: This study established an efficient strategy for exploring metabolite profiling and defining chemical markers among the different aerial parts of L. japonica, and laid the foundation for elucidating the phytochemical differences in efficacy between Lonicerae Japonicae Flos (LJF) and Lonicerae Japonicae Caulis (LJC). Our findings also indicate that the leaves of L. japonica leaf could be used as an alternative medicinal resource for LJF and provide a reference for comprehensive exploitation and utilisation of L. japonica resources.

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Section: Discussionmentioning

confidence: 56%

A comprehensive study of the aerial parts of Lonicera japonica Thunb. based on metabolite profiling coupled with PLS‐DA

Cai

Liao

Wang

et al. 2020

Phytochemical Analysis

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“…Previous studies [ 16 , 17 ] reported that the positive ion modes presented higher sensitivity and intensity in examining components of amino acids and nucleosides than negative ion modes, so the ESI+ mode was adopted in the study. Most components obtained intensive product ions by optimization of the MS parameters; just adenine and hypoxanthine were too low to be detected.…”

Section: Resultsmentioning

confidence: 99%

“…Nucleosides have important physiological functions, such as adenosine can treat angina pectoris, myocardial infarction, cerebrovascular disorders, and other diseases [16]. Scholars have used the components of amino acids and nucleosides to evaluate the quality of medicines and edible medicinal plants [17][18][19], and the results showed that differences in the composition and contents of amino acids and nucleosides could be used as biomarkers to characterize the quality of medicines and food. Until now, few studies have focused on the components of amino acids and nucleosides in MF; the methodologies of the amino acid analyzer and PITC-HPLC methods mainly focus on quantification [20,21] and have some deficiencies in resolution and separation time.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Free Amino Acids and Nucleosides in Different Varieties of Mume Fructus Based on Simultaneous Determination and Multivariate Statistical Analyses

Rui

et al. 2020

International Journal of Analytical Chemistry

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Mume Fructus (MF) contains a variety of organic acids, free amino acids, and nucleoside components, and studies have not yet analyzed the relationship between the components of free amino acids and nucleosides with the varieties of MF. A rapid and sensitive method was established for simultaneous determination of 21 free amino acids and 9 nucleosides in MF by ultrafast liquid chromatography-mass spectrometry. The analysis was carried out on a Waters XBridge Amide column (100 mm × 2.1 mm, 3.5 μm) with elution by the mobile phase of 0.2% aqueous formic acid (A) and 0.2% formic acid acetonitrile (B) at a flow rate of 0.2 mL/min with 1 μL per injection. The column temperature was maintained at 30°C. The target compounds were analyzed by the positive ion multiple reaction monitoring (MRM) mode. The comprehensive evaluation of the samples was carried out by principal component analysis (PCA) and technique for order preference by similarity to an ideal solution (TOPSIS) analysis. Results showed the method could simultaneously determine 30 components in MF. The content of total analytes in six mainstream varieties was different, exhibited the order Nangao > Daqingmei > Zhaoshuimei > Yanmei > Shishengme > Baimei, and aspartic acid and adenosine were the most abundant amino acid and nucleoside. PCA and OPLS-DA could easily distinguish the samples, and 11 components could be chemical markers of sample classification. TOPSIS implied that the quality of Nangao and Daqingmei was superior to the other varieties. The results could provide a reliable basis for quality evaluation and utilisation of medicinal and edible MF.

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“…The results of elbow and knee joint injuries and other injuries caused by unreasonable exercise, poor mental state, and poor physical condition also showed that the detection accuracy of group A was hugely higher than that of group B, so the improved PCA algorithm had a more accurate joint injury detection effect. 17…”

Section: Discussionmentioning

confidence: 99%

The Improvement of Pca Algorithm and Its Application in the Prediction of Elbow Knee Joint Injury

Liu

2021

Rev Bras Med Esporte

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Objective: There were many constraints produced by training time and joint injury to analyze the influence of the training intensity on the elbow and knee joints of athletes during the training process. Methods: An improved algorithm-based master component analysis (PCA) modeling method is proposed .1 4 4 athletes were selected in xxx and compared in three groups. Results: The improved PCA models for injury prediction were applied to athletes from group A, the traditional injury models for prediction were adopted for athletes from group B, and athletes from group C received the hospital physical examinations. The results showed that the accuracy of elbow injury in group A due to excessive exercise was 66.86%, the accuracy of hospital physical examination in group C was 67%, and the accuracy of the traditional algorithm in group B was 50%, finding that the accuracy of group A was obviously different from group B (P < 0.05). Compared with other injuries caused by excessive friction, the detection accuracy of knee injuries caused by excessive friction in group A was 62%, that in group B was 44%, and that in group C was 63%. There was a statistically marked difference between groups A and B (P < 0.05). Conclusions: A PCA - based model of athletes’ overtraining injury has high accuracy and adaptability, predicting elbow injury. Level of evidence II; Therapeutic studies - investigation of treatment results.

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Quality evaluation of Lonicerae Japonicae Flos and Lonicerae Flos based on simultaneous determination of multiple bioactive constituents combined with multivariate statistical analysis

Cited by 25 publications

References 31 publications

A comprehensive study of the aerial parts of Lonicera japonica Thunb. based on metabolite profiling coupled with PLS‐DA

A comprehensive study of the aerial parts of Lonicera japonica Thunb. based on metabolite profiling coupled with PLS‐DA

Comparative Analysis of Free Amino Acids and Nucleosides in Different Varieties of Mume Fructus Based on Simultaneous Determination and Multivariate Statistical Analyses

The Improvement of Pca Algorithm and Its Application in the Prediction of Elbow Knee Joint Injury

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