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Li C, Wang Y. Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives. Crit Rev Anal Chem 2022:1-20. [PMID: 36409298 DOI: 10.1080/10408347.2022.2148204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.
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Affiliation(s)
- Chaoping Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Shi T, Guan Y, Chen L, Huang S, Zhu W, Jin C. Application of Near-Infrared Spectroscopy Analysis Technology to Total Nucleosides Quality Control in the Fermented Cordyceps Powder Production Process. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2020; 2020:8850437. [PMID: 33354379 PMCID: PMC7737463 DOI: 10.1155/2020/8850437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 09/27/2020] [Accepted: 11/06/2020] [Indexed: 06/12/2023]
Abstract
Product quality control is a prerequisite for ensuring safety, effectiveness, and stability. However, because of the different strain species and fermentation processes, there was a significant difference in quality. As a result, they should be clearly distinguished in clinical use. Among them, the fermentation process is critical to achieving consistent product quality. This study aims to introduce near-infrared spectroscopy analysis technology into the production process of fermented Cordyceps powder, including strain culture, strain passage, strain fermentation, strain filtration, strain drying, strain pulverizing, and strain mixing. First, high performance liquid chromatography (HPLC) was used to measure the total nucleosides content in the production process of 30 batches of fermented Cordyceps powder, including uracil, uridine, adenine, guanosine, adenosine, and the process stability and interbatch consistency were analyzed with traditional Chinese medicine (TCM) fingerprinting, followed by the near-infrared spectroscopy (NIRS) combined with partial least squares regression (PLSR) to establish a quantitative analysis model of total nucleosides for online process monitoring of fermented Cordyceps powder preparation products. The model parameters indicate that the established model with good robustness and high measurement precision. It further clarifies that the model can be used for online process monitoring of fermented Cordyceps powder preparation products.
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Affiliation(s)
- Tiannv Shi
- Key Laboratory of Modern Chinese Medicine Preparation, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
| | - Yongmei Guan
- Key Laboratory of Modern Chinese Medicine Preparation, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
| | - Lihua Chen
- Key Laboratory of Modern Chinese Medicine Preparation, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
| | - Shiyu Huang
- Key Laboratory of Modern Chinese Medicine Preparation, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
| | - Weifeng Zhu
- Key Laboratory of Modern Chinese Medicine Preparation, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
| | - Chen Jin
- Key Laboratory of Modern Chinese Medicine Preparation, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang 330004, China
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Liu X, Zhang S, Ni H, Xiao W, Wang J, Li Y, Wu Y. Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 218:33-39. [PMID: 30954796 DOI: 10.1016/j.saa.2019.03.113] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/26/2019] [Accepted: 03/29/2019] [Indexed: 06/09/2023]
Abstract
Characteristic variables are essential and necessary basis in model construction, and are related to the prediction result closely in near infrared spectroscopy (NIRS) analysis. However, the same compound usually has different characteristic variables for different analysis and it would be lower correlation between variables and structure in many researches. So, the accuracy and reliability are expected to improve by exploring characteristic variables in different spectrum analysis. In this study, competitive adaptive weighted resampling method (CARS) was applied to select characteristic variables related to baicalin from NIRS analysis data, which were applied to analysis of baicalin in three different processes including the herb, extraction process and concentration process of Scutellaria baicalensis. After application of CARS method, 70, 50 and 50 variables were selected respectively from three processes above. The selected variables were firstly analyzed by statistical methods that they were found to be consistent and correlated among three different processes after one-way analysis of variance test and Kendall's W. Partial least-squares (PLS) regression and extreme learning machine (ELM) models were constructed based on optimized data. Models after variable selection were less complicated and had better prediction results than global models. After comparison, CARS-PLS was suitable for the prediction of extraction process, while for the concentration process and herb, CARS-ELM performed better. The Rc value of the herb, extraction and concentration model were 0.9469, 0.9841 and 0.9675, respectively. The RSEP values were 4.54%, 6.96% and 8.37%, respectively. The results help to frame a theoretical basis for characteristic variables of baicalin.
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Affiliation(s)
- Xuesong Liu
- Institute of Modern Chinese Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Siyu Zhang
- Institute of Modern Chinese Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Hongfei Ni
- Institute of Modern Chinese Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China
| | - Wei Xiao
- Jiangsu Kanion Pharmaceutical Co., Ltd., Lianyungang 222001, PR China
| | - Jun Wang
- Suzhou ZeDaXingBang Pharmaceutical Co., Ltd., Suzhou 215000, PR China
| | - Yerui Li
- Suzhou ZeDaXingBang Pharmaceutical Co., Ltd., Suzhou 215000, PR China
| | - Yongjiang Wu
- Institute of Modern Chinese Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China.
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Luan X, Jin M, Liu F. Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process. APPLIED SPECTROSCOPY 2018; 72:1199-1204. [PMID: 29786449 DOI: 10.1177/0003702818776022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The fault detection problem of the oil desalting process is investigated in this paper. Different from the traditional fault detection approaches based on measurable process variables, near-infrared (NIR) spectroscopy is applied to acquire the process fault information from the molecular vibrational signal. With the molecular spectra data, principal component analysis was explored to calculate the Hotelling T2 and squared prediction error, which act as fault indicators. Compared with the traditional fault detection approach based on measurable process variables, NIR spectra-based fault detection illustrates more sensitivity to early failure because of the fact that the changes in the molecular level can be identified earlier than the physical appearances on the process. The application results show that the detection time of the proposed method is earlier than the traditional method by about 200 min.
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Affiliation(s)
- Xiaoli Luan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China
| | - Minjun Jin
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China
| | - Fei Liu
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi, China
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Wang H, Suo T, Wu X, Zhang Y, Wang C, Yu H, Li Z. Near infrared spectroscopy based monitoring of extraction processes of raw material with the help of dynamic predictive modeling. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2018; 192:222-227. [PMID: 29149693 DOI: 10.1016/j.saa.2017.11.023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 11/01/2017] [Accepted: 11/08/2017] [Indexed: 06/07/2023]
Abstract
The control of batch-to-batch quality variations remains a challenging task for pharmaceutical industries, e.g., traditional Chinese medicine (TCM) manufacturing. One difficult problem is to produce pharmaceutical products with consistent quality from raw material of large quality variations. In this paper, an integrated methodology combining the near infrared spectroscopy (NIRS) and dynamic predictive modeling is developed for the monitoring and control of the batch extraction process of licorice. With the spectra data in hand, the initial state of the process is firstly estimated with a state-space model to construct a process monitoring strategy for the early detection of variations induced by the initial process inputs such as raw materials. Secondly, the quality property of the end product is predicted at the mid-course during the extraction process with a partial least squares (PLS) model. The batch-end-time (BET) is then adjusted accordingly to minimize the quality variations. In conclusion, our study shows that with the help of the dynamic predictive modeling, NIRS can offer the past and future information of the process, which enables more accurate monitoring and control of process performance and product quality.
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Affiliation(s)
- Haixia Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Tongchuan Suo
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Xiaolin Wu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Yue Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Chunhua Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China
| | - Heshui Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China; Tianjin Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.
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