Chemical fingerprinting and multivariate analysis of Paeonia ostii leaves based on HPLC-DAD and UPLC-ESI-Q/TOF-MS/MS

Liu, Shao-Jing; Guo, Sen; Hou, YuFei; Zhang, Shan-Shan; Bai, Lu; Ho, Chi‐Tang; Yu, LiLi; Yao, Lin; Zhao, Bing-Nan; Bai, Nai-Sheng

doi:10.1016/j.microc.2022.108169

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…The fragment ion at m/z 125.0236 was generated by the loss of a carboxyl, whose molecular formula was determined as C 7 H 6 O 5 , with a molecular mass of 170 Da. Peak 1 was identified as gallic acid, which was in accordance with the findings of a previous study [61]. Peak 2 displayed the [M − H] − ion at m/z 495.1503; its molecular formula was definitely inferred as C 23 H 28 O 12 from the [M + Na] + ion at m/z 519.1485 and [M + K] + ion at m/z 535.1225.…”

Section: Compositional Analysis Of MC Extractsupporting

confidence: 89%

“…Thus, peak 4 was characterized as paeonol. The molecular formula of peak 5 was designed as C 23 The identification of paeoniflorin was consistent with a previous report [61]. Compared with peak 1, the fragment ions at m/z 171.0287 and m/z 169.0136 were observed, which indicated that compound 6 was a derivative of gallic acid.…”

Section: Compositional Analysis Of MC Extractsupporting

confidence: 84%

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Ultrasound-Assisted Extraction of Paeonol from Moutan Cortex: Purification and Component Identification of Extract

Meng,

Chen,

Zheng

et al. 2024

Molecules

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Moutan Cortex (MC) is a traditional Chinese medicine that contains abundant medicinal components, such as paeonol, paeoniflorin, etc. Paeonol is the main active component of MC. In this study, paeonol was extracted from MC through an ultrasound-assisted extraction process, which is based on single-factor experiments and response surface methodology (RSM). Subsequently, eight macroporous resins of different properties were used to purify paeonol from MC. The main components of the purified extract were identified by ultra-performance liquid chromatography–quadrupole–time of flight–mass spectrometry (UPLC-Q-TOF-MS/MS). The results indicate the optimal parameters are as follows: liquid-to-material ratio 21:1 mL/g, ethanol concentration 62%, ultrasonic time 31 min, ultrasonic temperature 36 °C, ultrasonic power 420 W. Under these extraction conditions, the actual yield of paeonol was 14.01 mg/g. Among the eight tested macroporous resins, HPD-300 macroporous resin was verified to possess the highest adsorption and desorption qualities. The content of paeonol increased from 6.93% (crude extract) to 41.40% (purified extract) after the HPD-300 macroporous resin treatment. A total of five major phenolic compounds and two principal monoterpene glycosides were characterized by comparison with reference compounds. These findings will make a contribution to the isolation and utilization of the active components from MC.

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Section: Compositional Analysis Of MC Extractsupporting

confidence: 89%

Section: Compositional Analysis Of MC Extractsupporting

confidence: 84%

Ultrasound-Assisted Extraction of Paeonol from Moutan Cortex: Purification and Component Identification of Extract

Meng,

Chen,

Zheng

et al. 2024

Molecules

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“…The mass spectrometry analysis revealed the loss of a higher number of water molecules, as well as small CO2 and CO molecules. Additionally, gallic acid and galloyl fragments were found to be easily detached from these compounds, which can be attributed to the presence of hydroxyl and carboxyl groups in the structure of gallic acid (Fernandes et al, 2022;Liu et al, 2023). Therefore, the structure of compound 10 could be deduced as PGG (1,2,3,4,6-Penta-O-galloyl -β-D-glucopyranose) [27].…”

Section: Identification Of the Potential Cox-2 Inhibitors By Uplc-q-t...mentioning

confidence: 99%

Identification of potential anti-inflammatory components in Moutan Cortex by bio-affinity ultrafiltration coupled with ultra-performance liquid chromatography mass spectrometry

Zou,

Chen,

et al. 2024

Front. Pharmacol.

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Moutan Cortex (MC) has been used in treating inflammation-associated diseases and conditions in China and other Southeast Asian countries. However, the active components of its anti-inflammatory effect are still unclear. The study aimed to screen and identify potential cyclooxygenase-2 (COX-2) inhibitors in MC extract. The effect of MC on COX-2 was determined in vitro by COX-2 inhibitory assays, followed by bio-affinity ultrafiltration in combination with ultra-performance liquid chromatography-mass spectrometry (BAUF-UPLC-MS). To verify the reliability of the constructed approach, celecoxib was applied as the positive control, in contrast to adenosine which served as the negative control in this study. The bioactivity of the MC components was validated in vitro by COX-2 inhibitor assay and RAW264.7 cells. Their in vivo anti-inflammatory activity was also evaluated using LPS-induced zebrafish inflammation models. Finally, molecular docking was hired to further explore the internal interactions between the components and COX-2 residues. The MC extract showed an evident COX-2-inhibitory effect in a concentration-dependent manner. A total of 11 potential COX-2 inhibitors were eventually identified in MC extract. The COX-2 inhibitory activity of five components, namely, gallic acid (GA), methyl gallate (MG), galloylpaeoniflorin (GP), 1,2,3,6-Tetra-O-galloyl-β-D-glucose (TGG), and 1,2,3,4,6-Penta-O-galloyl-β-D-glucopyranose (PGG), were validated through both in vitro assays and experiments using zebrafish models. Besides, the molecular docking analysis revealed that the potential inhibitors in MC could effectively inhibit COX-2 by interacting with specific residues, similar to the mechanism of action exhibited by celecoxib. In conclusion, BAUF-UPLC-MS combining the molecular docking is an efficient approach to discover enzyme inhibitors from traditional herbs and understand the mechanism of action.

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“…As technology continues to advance, automated statistical techniques have gradually emerged. For example, near-infrared spectroscopy technology [2], highperformance liquid chromatography [3], remote sensing technology [4][5], image processing [6] and computer vision [7]. In recent years, advances in unmanned aerial vehicle (UAV) technology have provided a means to quickly and efficiently acquire high-throughput plant image data, enabling intelligent monitoring of massive plant growth processes [8].…”

Section: Introductionmentioning

confidence: 99%

YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method

Xiao,

Li,

Liang

et al. 2023

Preprint

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Background: Estimating tobacco leaf yield is a crucial task. The number of leaves is directly related to yield. Therefore, it is important to achieve intelligent and rapid high-throughput statistical counting of field tobacco leaves. Unfortunately, the current method of counting the number of tobacco leaves is expensive, imprecise, and inefficient. It heavily relies on manual labor and also faces challenges of mutual shading among the field tobacco plants during their growth and maturity stage, as well as complex environmental background information. This study proposes an efficient method for counting the number of tobacco leaves in a large field based on unmanned aerial vehicle (UAV) image data. First, a UAV is used to obtain high-throughput vertical orthoimages of field tobacco plants to count the leaves of the tobacco plants. The tobacco plant recognition model is then used for plant detection and segmentation to create a dataset of images of individual tobacco plants. Finally, the improved algorithm YOLOv8 with Squeeze-and-Excitation (SE) and bidirectional feature pyramid network (BiFPN) and GhostNet (YOSBG) algorithm is used to detect and count tobacco leaves on individual tobacco plants. Results: Experimental results show YOSBG achieved an average precision (AP) value of 93.6% for the individual tobacco plant dataset with a model parameter (Param) size of only 2.5 million (M). Compared to the YOLOv8n algorithm, the F1 (F1-score) of the improved algorithm increased by 1.7% and the AP value increased by 2%, while the model Param size was reduced by 16.7%. In practical application discovery, the occurrence of false detections and missed detections is almost minimal. In addition, the effectiveness and superiority of this method compared to other popular object detection algorithms have been confirmed. Conclusions: This article presents a novel method for high-throughput counting of tobacco leaves based on UAV image data for the first time, which has a significant reference value. It solves the problem of missing data in individual tobacco datasets, significantly reduces labor costs, and has a great impact on the advancement of modern smart tobacco agriculture.

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Chemical fingerprinting and multivariate analysis of Paeonia ostii leaves based on HPLC-DAD and UPLC-ESI-Q/TOF-MS/MS

Cited by 7 publications

References 38 publications

Ultrasound-Assisted Extraction of Paeonol from Moutan Cortex: Purification and Component Identification of Extract

Ultrasound-Assisted Extraction of Paeonol from Moutan Cortex: Purification and Component Identification of Extract

Identification of potential anti-inflammatory components in Moutan Cortex by bio-affinity ultrafiltration coupled with ultra-performance liquid chromatography mass spectrometry

YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method

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