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Facile Cloud Point Extraction for the Separation and Determination of Phenolic Acids from Dandelion.

ABSTRACT: It is significantly crucial to develop a robust pretreatment for the quantitative analysis of herbs. However, the traditional strategies are time-consuming, tedious, and not eco-friendly. In this work, cloud point extraction (CPE) is engineered for the simultaneous separation and enrichment of ferulic acid (FA), chlorogenic acid (CLA), and caffeic acid (CA) from dandelion prior to its determination by high-performance liquid chromatography (HPLC). A famous nonionic surfactant of Triton X-114 was selected as an extractant of CPE, and parameters affecting the extraction, such as surfactant concentration, salt content, pH value, temperature, and incubation time, were investigated carefully. Furthermore, the well-designed CPE with ultrasonic assistance combined with HPLC was developed for the detection of the target analytes in dandelion. The established method having a good linearity in the range of 0.15-26.2 mg L^-1 with R ² more than 0.9979 and the spiked recoveries ranging from 81 to 96% was applied to test real samples of dandelion. The contents of CA in samples were consistent with those assayed by the method (Chinese Pharmacopoeia 2015). The proposed method afforded good analytical performances, shorter pretreatment time (65 min), and less organic solvent consumption (less than 1.0 mL). It was proved that the developed method presented a facile, inexpensive, efficient, and environment-friendly pretreatment and can be used for the quantitative analysis of CLA, CA, and FA in dandelion. As expected, the proposed method would be a promising potential for the quality analysis of herbal medicines.

SUBMITTER: Ji Y

PROVIDER: S-EPMC8158839 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Phenolic acids are a major group of secondary metabolites widely distributed in plants. In the case of maize, the major proportion of these metabolites occurs in the edible grain and their antioxidant activities are associated with improvements in human health. However, conventional extraction of secondary metabolites is very time consuming and generates a substantial amount of solvent waste. One approach to resolve these limitations is the use of microscale approaches, which minimize the quantity of solvents required, as well as the sample amounts and processing times. The objective of this work was to develop an improved microscale method for extraction of phenolic acids from maize and to compare it with a conventional extraction method. RESULTS:The improved microscale extraction method, coupled with an HPLC-DAD detection method, allowed identification of ferulic acid, p-coumaric acid in its free and bound form, and some diferulic acids. In its free form, p-coumaric acid ranged in content from 2.4 to 6.5 µg/g dry weight (dw) using the conventional method and 7.7 to 54.8 µg/g dw using the improved microscale method. Free ferulic acid content ranged from 2.6 to 12.9 µg/g dw for the conventional method and 16.8 to 181.7 µg/g dw for the improved microscale method. In its bound form, p-coumaric acid ranged in content from 6.0 to 30.6 µg/g dw for the traditional method and 34.4 to 138.6 µg/g dw for the improved microscale method. Bound ferulic acid ranged from 131.8 to 427.5 µg/g dw for the conventional method and 673.8 to 1702.7 µg/g dw for the improved microscale method. The coefficient of variation associated was lower for the improved microscale method than for the conventional method, thereby assuring the replicability of the process. CONCLUSIONS:The improved microscale method proposed here increases the extraction power and batch capacity, while reducing the sample quantity, solvent amounts and extraction time. It also achieves a better replicability with a lower coefficient of variation than is possible with conventional extraction.

Project description:ReasonPedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension and high-resolution data could be obtained by building a three-dimensional point cloud. However, the point cloud data collected by laser radar is often massive and contains a lot of redundancy, which is not conducive to transmission and storage. So, the processing speed grows slow when the original point cloud data is used for recognition. On the other hand, the compression processing of many laser radar point clouds could save computing power and speed up the recognition processing.MethodologyThe article utilizes the fusion point cloud data from laser radar to investigate the fast pedestrian recognition algorithm. The focus is to compress the collected point cloud data based on the boundary and feature value extraction and then use the point cloud pedestrian recognition algorithm based on image mapping to detect pedestrians. This article proposes a point cloud data compression method based on feature point extraction and reduced voxel grid.ResultsThe Karlsruhe Institute of Technology and Toyota Technological Institute data set is used to investigate the proposed algorithm experimentally. The outcomes indicate that the peak signal-to-noise ratio of the compression algorithm is improved by 6.02%. The recognition accuracy is improved by 16.93%, 17.2%, and 16.12%, corresponding to simple, medium, and difficult scenes, respectively, when compared with the point cloud pedestrian recognition method based on image mapping, which uses the random sampling method to compress the point cloud data.ConclusionThe proposed method could achieve data compression better and ensure that many feature points are retained in the compressed Point Cloud Data (PCD). Thus, the compressed PCD achieves pedestrian recognition through an image-based mapping recognition algorithm.

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Facile Cloud Point Extraction for the Separation and Determination of Phenolic Acids from Dandelion.

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