JOURNAL OF CHROMATOGRAPHIC SCIENCE
·
2023
·
IF 1.3
Liang Xiaolian,Yang Zhengteng,Ma Jiabao,Wang Minggang,Zhao Fengxian,Zhou Chuhui,Feng Jiaru,Song Haimei,Chen Yong,Gui Xiongbin
Yanyangke mixture (YM) is composed of 12 kinds of traditional Chinese medicine (TCM) used for the treatment of patients with cough, dry throat and other diseases caused by acute or chronic pharyngitis or patients with difficulty in expectoration. With the wide application of YM in clinical practice, its quality control has attracted huge attention. Based on the multi-component characteristics of Chinese herbal medicines, it is pertinent to establish a quality evaluation system. A new idea is to adopt gas chromatography–mass spectrometry (GC–MS) chemical composition identification, GC–MS fingerprint, and GC content determination as a potential quality control index of the volatile oil in YM. In this study, the volatile oil of YM was extracted by steam distillation, and the chemical components of the volatile oil were analyzed by GC–MS, and 43 chemical components were identified. The fingerprint of the volatile oil from YM was established and the similarity evaluation was performed. Combined with chemometric methods, such as cluster analysis, principal component analysis and partial least squares analysis, the chemical composition differences of the volatile oil from different batches of YM were compared and the symbolic components affecting the quality of the volatile oil from different batches of YM were excavated. Finally, three components were selected as the potential active component markers of YM and the GC content determination method of these three components was established. A rapid, reasonable, and effective quality evaluation and control method of YM volatile oil was established, which provided a reference for further development and research on YM, as well as a new idea for research on other TCM prescriptions.
研究领域:
中药学
·
分析化学
·
药理学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
MICROWAVE AND OPTICAL TECHNOLOGY LETTERS
·
2023
·Vol. 65
·期 5
·1433-1438
·
IF 1.5
Qiu-Shi Liu,Ming-Jiang Ma,Chong Lv,Guo-Qing Yang,Yan-Lei Yang,Jia-Hao Wang,Zhi-Xing Gao,Hong-Yu He,Xiao-Hua Zhang,Yuan-Peng Li,Bao-Zhen Zhao
Spatially offset Raman spectroscopy (SORS) is an important branch derived from conventional Raman spectroscopy. It has the characteristics to realize the concealed component identification covered by opaque and translucent media nondestructively and rapidly. This technology gradually plays an important role in basic and applied research of numerous areas. This paper focuses on the SORS potential application in food quality and safety inspection, and a SORS detection system has been setup. The ingredients of various concealed food additives have been identified by SORS and the optimum offset distance for detection has been obtained. Meanwhile, the identification effectiveness of the system for low-concentration additives in real foods has also been evaluated.
研究领域:
分析化学
·
食品科学
·
光谱学
·
食品安全
·
质量控制
✓ 已验证
DOI
高影响因子
JOURNAL OF CHROMATOGRAPHY A
·
2025
·
IF 4
Qianyu Wu,Yu Zhang,Ce Sun,Jun Dang
In this study, we developed an online HPLC-lipoprotein lipase system capable of directly recognizing chromatographic peaks corresponding to lipoprotein lipase affinity agents from Nitraria tangutorum . Notably, using the HPLC-lipoprotein lipase system, six lipoprotein lipase affinity agents were successfully recognized and isolated with purities > 95 %, including one phenylpropanoid, one apigenin, and four quercetin derivatives. In vitro enzymatic assays and molecular docking studies demonstrated that these glycosylated compounds bind effectively to the active site of lipoprotein lipase, with binding energies ranging from -7.2 to -8.8 kcal/mol. The compounds also showed notable affinity, with median inhibitory concentration values between 56.2 μM and 202.3 μM and half-maximal effective concentration values ranging from 106.8 μM to 311.0 μM. These results suggest that quercetin glycosides in Nitraria tangutorum contribute significantly to the regulation of lipoprotein lipase activity. Importantly, the established online HPLC-lipoprotein lipase recognition system demonstrated broad application prospects, particularly by providing a rapid and high-precision method for discovering lipoprotein lipase affinity agents in chemically complex natural products.
研究领域:
天然产物化学
·
药物分析学
·
分子药理学
·
生物色谱技术
·
酶抑制剂研究
✓ 已验证
产品关联
DOI
PubMed
高影响因子
CHEMICAL ENGINEERING JOURNAL
·
2025
·
IF 13.2
Wenbin Li,Gaocan Qi,Haihui Ding,Weibin Yan,Xijun Liu,Zhihao Yuan
The safe use of fuel gases in industrial production and daily life requires prompt and accurate detection of their concentrations in the environment. However, due to the strong reducing properties of the flammable gases used as fuel, traditional chemoresistive gas sensors face challenges in simultaneously detecting multiple flammable gases. To address this issue, we have developed a metal oxide-based electronic nose capable of detecting common fuel gases, including CH 4 , H 2 , and CO. By controlling the composition and structure of metal oxides, as well as introducing noble metal modifications, we optimized the response differences of these gas sensitive materials to CH 4 , H 2 , and CO. Four sensitive materials were selected from 8 candidates using principal component analysis to construct gas sensor array on a compact four-channel circuit board. The response sensitivities from the sensor array to various concentrations of CH 4 , H 2 , CO, and their mixtures were utilized to train a backpropagation artificial neural network. A new component-identification outlet layer was integrated with the traditional concentration-recognition to improve the training speed and enable simultaneous identification of gas components and concentrations. Through combined material optimization and dual-output training, the backpropagation artificial neural network achieved 90% training accuracy within just 50 iterations, with both sparse cross-entropy loss and mean squared error reduced below 0.005 after 300 training epochs. The resulting E -nose can accurately identify gas components and determine their concentrations in single-, binary-, and ternary-component mixtures of CH 4 , H 2 , and CO, with detection accuracies exceeding 91%. This study provides a practical approach for E -nose construction, enabling reliable monitoring of flammable gases.
研究领域:
化学传感
·
人工智能
·
气体检测
·
传感器技术
·
材料科学
✓ 已验证
产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）