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Chen Y, Zhang J, Feng J, Chen W, Liu W, Chen J, Ye J, Li W. Holistic quality evaluation method of Epimedii Folium based on NIR spectroscopy and chemometrics. PHYTOCHEMICAL ANALYSIS : PCA 2024; 35:771-785. [PMID: 38273442 DOI: 10.1002/pca.3327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/26/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024]
Abstract
INTRODUCTION There are some problems in the quality control of Epimedii Folium (leaves of Epimedium brevicornum Maxim.), such as the mixed use of Epimedii Folium from different harvesting periods and regions, incomplete quality evaluation, and time-consuming analysis methods. OBJECTIVE Near-infrared (NIR) spectroscopy was conducted to establish a rapid overall quality evaluation method for Epimedii Folium. MATERIALS AND METHODS Quantitative models of the total solid, moisture, total flavonoid, and flavonol glycoside (Epimedin A, Epimedin B, Epimedin C, Icariin) contents of Epimedii Folium were established by partial least squares regression (PLSR). The root mean square error (RMSE) and correlation coefficient (R) were used to evaluate the performance of models. The qualitative models of Epimedii Folium from different geographic origins and harvest periods were established based on K-nearest neighbor (KNN), back-propagation neural network (BPNN), and random forest (RF). Accuracy and Kappa values were used to evaluate the performance of models. A new multivariable signal conversion strategy was proposed, which combines NIR spectroscopy with the PLSR model to predict the absorbance values of retention time points in the high-performance liquid chromatography (HPLC) fingerprint to obtain the predicted HPLC fingerprint. The Pearson correlation coefficient and cosine coefficient were used to evaluate the similarity between real and predicted HPLC fingerprints. RESULTS Qualitative models, quantitative models, and the similarity between real and predicted HPLC fingerprints are satisfactory. CONCLUSION The method serves as a fast and green analytical quality evaluation method of Epimedii Folium and can replace traditional methods to achieve the overall quality evaluation of Epimedii Folium.
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Affiliation(s)
- Yuru Chen
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Jianyu Zhang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Jiahao Feng
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
| | - Wei Chen
- Shanghai Zhen Ren Tang Pharmaceutical Co., Ltd, Shanghai, People's Republic of China
| | - Wengang Liu
- Chengdu Kanghong Pharmaceutical Co., Ltd., Chengdu, People's Republic of China
| | - Jingchao Chen
- Chengdu Kanghong Pharmaceutical Co., Ltd., Chengdu, People's Republic of China
| | - Jianming Ye
- Chengdu Kanghong Pharmaceutical Co., Ltd., Chengdu, People's Republic of China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin, China
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Li X, Zhong Y, Li J, Lin Z, Pei Y, Dai S, Sun F. Rapid identification and determination of adulteration in medicinal Arnebiae Radix by combining near infrared spectroscopy with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124437. [PMID: 38772180 DOI: 10.1016/j.saa.2024.124437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 05/23/2024]
Abstract
The medicinal Arnebia Radix (AR) is one of widely-used Chinese herbal medicines (CHMs), usually adulterated with non-medicinal species that seriously compromise the quality of AR and affect patients' health. Detection of these adulterants is usually performed by using expensive and time-consuming analytical instruments. In this study, a rapid, non-destructive, and effective method was proposed to identify and determine the adulteration in the medicinal AR by near-infrared (NIR) spectroscopy coupled with chemometrics. 37 batches of medicinal AR samples originated from Arnebia euchroma (Royle) Johnst., 11 batches of non-medicinal AR samples including Onosma paniculatum Bur. et Franch and Arnebia benthamii (Wall. ex G. Don) Johnston, and 72 batches of adulterated AR samples were characterized by NIR spectroscopy. The data driven-soft independent modeling by class analogy (DD-SIMCA) and partial least squares-discriminant analysis (PLS-DA) were separately used to differentiate the authentic from adulterated AR samples. Then the PLS and support vector machine (SVM) were applied to predict the concentration of the adulteration in the adulterated AR samples, respectively. As a result, the classification accuracies of DD-SIMCA and PLS-DA models were 100% for the calibration set, and 96.7% vs. 100% for the prediction set. Moreover, the relative prediction deviation (RPD) values of PLS models reached 11.38 and 7.75 for quantifying two adulterants species, which were obviously superior to the SVM models. It can be concluded that the NIR spectroscopy coupled with chemometrics is feasible to identify the authentic from adulterated AR samples and quantify the adulteration in adulterated AR samples.
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Affiliation(s)
- Xiaolong Li
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yongqi Zhong
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China
| | - Jiaqi Li
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China
| | - Zhaozhou Lin
- Beijing Zhongyan Tongrentang Medicine R&D Co. Ltd, Beijing, China
| | - Yanling Pei
- Hebei Xinminhe Pharmaceutical Technology Development Co., Ltd, Hebei, China
| | - Shengyun Dai
- National Institutes for Food and Drug Control, Beijing, China.
| | - Fei Sun
- School of Chinese Materia Medica, Guangdong Pharmaceutical University, Guangzhou, China.
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Pan L, Li H, Zhao J. Improvement of the prediction of a visual apple ripeness index under seasonal variation by NIR spectral model correction. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123075. [PMID: 37423101 DOI: 10.1016/j.saa.2023.123075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 06/09/2023] [Accepted: 06/21/2023] [Indexed: 07/11/2023]
Abstract
Apple ripeness assessment is essential to ensure its post-harvest commercial value, and the visible/near-infrared(NIR) spectral models that are effective in achieving this goal are prone to failure due to seasonal or instrumental factors. This study has proposed a visual ripeness index (VRPI) determined by parameters such as soluble solids, titratable acids, etc., which vary during the ripening period of the apple. The R and RMSE of the index prediction model based on the 2019 sample were 0.871 to 0.913 and 0.184 to 0.213 respectively. The model failed to predict the next two years of the sample, which was effectively addressed by model fusion and correction. For the 2020 and 2021 samples, the revised model improves R by 6.8% and 10.6% and reduces RMSE by 52.2% and 32.2% respectively. The results showed that the global model is adapted to the correction of the VRPI spectral prediction model under seasonal variation.
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Affiliation(s)
- Liulei Pan
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
| | - Hao Li
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
| | - Juan Zhao
- Northwest A&F University, College of Mechanical and Electronic Engineering, Yangling, Shaanxi 712100, China; Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China; Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China.
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4
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Chen R, Liu F, Zhang C, Wang W, Yang R, Zhao Y, Peng J, Kong W, Huang J. Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1128300. [PMID: 37025139 PMCID: PMC10072231 DOI: 10.3389/fpls.2023.1128300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.
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Affiliation(s)
- Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Wei Wang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Rui Yang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Yiying Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
| | - Jiyu Peng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Wenwen Kong
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, China
| | - Jing Huang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
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5
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Rapid quantification of adulterated Panax notoginseng powder by ultraviolet-visible diffuse reflectance spectroscopy combined with chemometrics. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1016/j.cjac.2022.100055] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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6
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Tu Y, Li L, Wang Z, Yang L. Advances in analytical techniques and quality control of traditional Chinese medicine injections. J Pharm Biomed Anal 2021; 206:114353. [PMID: 34562802 DOI: 10.1016/j.jpba.2021.114353] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 07/27/2021] [Accepted: 08/29/2021] [Indexed: 12/24/2022]
Abstract
Traditional Chinese medicine injections (TCMIs) are a new pharmaceutical form in the modernization of traditional Chinese medicines (TCMs). Its efficacy is rapid, the curative effect is improved, and is widely used in critical and acute diseases, complicated and severe diseases, and other treatment. However, with the broad applications of TCMIs, clinical adverse reactions frequently occur, and safety problems become more prominent. Therefore, the quality control of TCMIs is essential. Chemical analysis methods and biological analysis methods are widely used in the quality control of TCMIs. This article describes the current status of TCMIs, the analytical techniques, and methods currently used, and the quality control of TCMIs. A summary of the advantages and disadvantages of the current analysis methods is presented. An overview of the quality control of TCMIs is introduced. In addition, emerging techniques of the quality control of TCMIs are introduced.
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Affiliation(s)
- Yujia Tu
- The MOE Key Laboratory of Standardization of Chinese Medicines and the SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Linnan Li
- The MOE Key Laboratory of Standardization of Chinese Medicines and the SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Zhengtao Wang
- The MOE Key Laboratory of Standardization of Chinese Medicines and the SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Shanghai R&D Center for Standardization of Chinese Medicines, Shanghai 201203, China
| | - Li Yang
- The MOE Key Laboratory of Standardization of Chinese Medicines and the SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China; Shanghai R&D Center for Standardization of Chinese Medicines, Shanghai 201203, China.
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Guo HJ, Weng WF, Zhao HN, Wen JF, Li R, Li JN, Zeng CB, Ji SG. Application of Fourier transform near-infrared spectroscopy combined with GC in rapid and simultaneous determination of essential components in Amomum villosum. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 251:119426. [PMID: 33485242 DOI: 10.1016/j.saa.2021.119426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 12/31/2020] [Accepted: 01/01/2021] [Indexed: 06/12/2023]
Abstract
A method is described using rapid and sensitive Fourier transform near-infrared spectroscopy combined with Gas Chromatograpy internal standard method detection for the simultaneous identification and determination of three bioactive compounds in Amomum villosum samples. Partial least squares regression is selected as the analysis type and multiplicative scatter correction, second derivative, and SNV were adopted for the spectral pretreatment. The correlation coefficients (R) of the calibration models were above 0.95 and the root mean square error of predictions were under 0.8. The developed models were applied to unknown samples with satisfantory results. The established method was validated and can be applied to the intrinsic quality control of Amomum villosum.
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Affiliation(s)
- Huan-Jia Guo
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Wen-Feng Weng
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Hong-Ning Zhao
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Jin-Feng Wen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Rong Li
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Jun-Ni Li
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Chan-Biao Zeng
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China
| | - Sheng-Guo Ji
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangdong, China.
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8
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Ren G, Li T, Wei Y, Ning J, Zhang Z. Estimation of Congou black tea quality by an electronic tongue technology combined with multivariate analysis. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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9
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Zhao J, Tian G, Qiu Y, Qu H. Rapid quantification of active pharmaceutical ingredient for sugar-free Yangwei granules in commercial production using FT-NIR spectroscopy based on machine learning techniques. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 245:118878. [PMID: 32919149 DOI: 10.1016/j.saa.2020.118878] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 08/13/2020] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
Rapid quantification methods for sugar-free Yangwei granules were developed based on near-infrared (NIR) spectroscopy combined with machine learning approaches as a quality control strategy for Chinese medicine granules (CMGs). Different machine learning approaches-i.e., interval partial least squares optimized by the genetic algorithm (GA-iPLS), the backpropagation artificial neural network (BP-ANN), and the particle swarm optimization-support vector machine (PSO-SVM)-were used to develop prediction models for three active pharmaceutical ingredients (APIs), namely, albiflorin, paeoniflorin, and benzoylpaeoniflorin. The partial least squares (PLS) algorithm was used for linear model calibration and comparison of the prediction performance of these developed models. The performance of the final models was assessed by the correlation coefficient (R), root mean square error of calibration set (RMSEC), and root mean square error of prediction set (RMSEP). All models performed well in model fitting and provided satisfactory prediction accuracy. The results indicate that the machine learning approaches are more stable, predictable, and suitable for CMGs when a high-accuracy analysis is required. In summary, NIR spectroscopy coupled with machine learning techniques is a suitable tool for the straightforward quantification of CMGs.
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Affiliation(s)
- Jie Zhao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Geng Tian
- National & Local United Engineering Laboratory of TCM Advanced Manufacturing Technology, Tasly Pharmaceutical Group Co., Ltd., Tianjin 300400, China
| | - Yanyan Qiu
- Chiatai Qingchunbao Pharmaceutical Co., Ltd., Hangzhou 310023, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
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Ren G, Gan N, Song Y, Ning J, Zhang Z. Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105600] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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11
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Vahedi N, Mohammadhosseini M, Nekoei M. QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches. CURR ANAL CHEM 2020. [DOI: 10.2174/1573411016999200518083359] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily
present in eukaryotes.
Methods:
In the present report, some efficient linear and non-linear methods including multiple linear
regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully
used to develop and establish quantitative structure-activity relationship (QSAR) models
capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP
inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set
and selection of the training and test sets. A genetic algorithm (GA) variable selection method was
employed to select the optimal subset of descriptors that have the most significant contributions to
the overall inhibitory activity from the large pool of calculated descriptors.
Results:
The accuracy and predictability of the proposed models were further confirmed using crossvalidation,
validation through an external test set and Y-randomization (chance correlations) approaches.
Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed
models. The results revealed that non-linear modeling approaches, including SVM and ANN
could provide much more prediction capabilities.
Conclusion:
Among the constructed models and in terms of root mean square error of predictions
(RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for
the training set, the predictive power of the GA-SVM approach was better. However, compared with
MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.
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Affiliation(s)
- Nafiseh Vahedi
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Majid Mohammadhosseini
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
| | - Mehdi Nekoei
- Department of Chemistry, College of Basic Sciences, Shahrood Branch, Islamic Azad University, Shahrood, Iran
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Qiu Y, Pan X, Su L, Lui H, Li YD. Effects and safety of Tanreqing injection on viral pneumonia: A protocol for systematic review and meta-analysis. Medicine (Baltimore) 2020; 99:e22022. [PMID: 32925736 PMCID: PMC7489681 DOI: 10.1097/md.0000000000022022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Influenza-related viral pneumonia is a severe threat to human health, which has caused high morbidity and mortality each year. The objective of this study was to assess the efficacy and safety of Tanreqing Injection therapy in patients with viral pneumonia. MATERIALS AND METHODS This protocol established in this study has been reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Web of Science, PubMed, EMBASE and the Cochrane Library were searched for clinical randomized trials in cases with viral pneumonia until 1st of July 2020. We will use a combination of Medical Subject Heading and free-text terms with various synonyms to search based on the Eligibility criteria. Two investigators independently reviewed the included studies and extracted relevant data. The relative risk (RR) and 95% confidence intervals (CIs) of were used as effect estimate. I-square (I) test, substantial heterogeneity, sensitivity analysis and publication bias assessment will be performed accordingly. Stata 14.0 and Review Manger 5.3 are used for meta-analysis and systematic review. RESULTS The results will be published in a peer-reviewed journal. CONCLUSION The results of this review will be widely disseminated through peer-reviewed publications and conference presentations. This evidence may also provide helpful evidence of whether Tanreqing Injection therapy was efficient and safe in patients with viral pneumonia. PROSPERO REGISTRATION NUMBER CRD42020164164.
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Affiliation(s)
- Yue Qiu
- Department of General Internal Medicine, The Third Affiliated Hospital of Beijing University of Chinese Medicine
| | - Xue Pan
- Beijing University of Chinese Medicine
| | - Lin Su
- Department of Chinese Medicine, Rehabilitation Hospital affiliated to National Research Center For Rehabilitation Technical Aids
| | - Hui Lui
- Department of General Internal Medicine, The Third Affiliated Hospital of Beijing University of Chinese Medicine
| | - Ya-Dong Li
- School of Life Science, Beijing University of Chinese Medicine, Beijing, China
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Zhu Y, Zhang J, Li M, Ren H, Zhu C, Yan L, Zhao G, Zhang Q. Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different matrices. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 232:117997. [PMID: 32062401 DOI: 10.1016/j.saa.2019.117997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/21/2019] [Accepted: 12/25/2019] [Indexed: 06/10/2023]
Abstract
Clostridium perfringens (C. perfringens) has the ability to form metabolically-dormant spores that can survive food preservation processes and cause food spoilage and foodborne safety risks upon germination outgrowth. This study was conducted to investigate the effects of different AGFK concentrations (0, 50, 100, 200 mM/mL) on the spore germination of C. perfringens in four matrices, including Tris-HCl, FTG, milk, and chicken soup. C. perfringens spore germinability was investigated using near infrared spectroscopy (NIRS) combined with chemometrics. The spore germination rate (S), the OD600%, and the Ca2+-DPA% were measured using traditional spore germination methods. The results of spore germination assays showed that the optimum germination rate was obtained using 100 mM/L concentrations of AGFK in the FTG medium, and the S, OD600% and Ca2+-DPA% were 98.6%, 59.3% and 95%, respectively. The best prediction models for the S, OD600% and Ca2+-DPA% were obtained using SNV as the preprocessing method for the original spectra, with the competitive adaptive weighted resampling method (CARS) as the characteristic variables related to the selected spore germination methods from NIRS data. The results of the S showed that the optimum model was built by CARS-PLSR (RMSEV = 0.745, Rc = 0.897, RMSEP = 0.769, Rp = 0.883). For the OD600%, interval partial least squares regression (CARS-siPLS) was performed to optimize the models. The calibration yielded acceptable results (RMSEV = 0.218, Rc = 0.879, RMSEP = 0.257, Rp = 0.845). For the Ca2+-DPA%, the optimum model with CARS-siPLS yielded acceptable results (RMSEV = 44.7, Rc = 0.883, RMSEP = 50.2, Rp = 0.872). This indicated that quantitative determinations of the germinability of C. perfringens spores using NIR technology is feasible. A new method based on NIR was provided for rapid, automatic, and non-destructive determination of the germinability of C. perfringens spores.
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Affiliation(s)
- Yaodi Zhu
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Jiaye Zhang
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Miaoyun Li
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China.
| | - Hongrong Ren
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Chaozhi Zhu
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Longgnag Yan
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Gaiming Zhao
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
| | - Qiuhui Zhang
- College of Food Science and Technology, Henan Key Laboratory of Meat Processing and Quality Safety Control, Henan Agricultural University, Zhengzhou 450000, PR China
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Li J, Wen J, Tang G, Li R, Guo H, Weng W, Wang D, Ji S. Development of a comprehensive quality control method for the quantitative analysis of volatiles and lignans in Magnolia biondii Pamp. by near infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 230:118080. [PMID: 31982656 DOI: 10.1016/j.saa.2020.118080] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 06/10/2023]
Abstract
The quality of drug is vital to its curative effect, thus it is important to develop a comprehensive quality control method for commonly used drugs. In this study, we developed a Gas chromatography-mass spectrometry separation method for the qualitative and quantitative analysis of volatiles, together with a High-performance liquid chromatography-mass spectrometry separation method for lignans in Magnolia biondii Pamp.. 79 volatiles and 11 lignans were identified via comparing their chromatographic behavior and mass spectra data with those in the literature. The methods were then used to determine the contents of volatiles (1, 8-cineole, d-Limonene, α-terpineol, linalool, L-camphor brain and bornyl acetate) and lignans (epieudesmin, magnolin, epi-magnolin A and fargesin) in Magnolia biondii Pamp.. Subsequently, 13 qualitative models including volatiles (1, 8-cineole, d-Limonene, α-terpineol, linalool, L-camphor brain and bornyl acetate), water-soluble extractive, lignans (pinoresinol dimethyl ether, magnolin, epi-magnolin A and fargesin) and moisture were developed by Near-Infrared Spectroscopy based on partial least square regression herein. The reference values were obtained by High-performance liquid chromatography, Gas chromatography and etc., while the predicted values were attained from the NIR spectrum. Compared with the traditional detection methods, NIR technique methodology significantly improved the ability to evaluate the quality of Magnolia biondii Pamp., which had the advantages of convenience, celerity, highly efficiency, low cost, no harm to samples, no reagent consumption, and no pollution to the environment. Moreover, the systematic analysis method combined pharmaceutical analysis with pharmacochemistry was proposed to prepare volatiles, water-soluble extractive and lignans parts from the same sample. This way could extract more index components to be beneficial in the quality control of Magnolia biondii Pamp. roundly.
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Affiliation(s)
- Junni Li
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Jinfeng Wen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Gengqiu Tang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Rong Li
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Huanjia Guo
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Wenfeng Weng
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Dong Wang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China
| | - Shengguo Ji
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, No. 280, Outer Ring Road East, Higher Education Mega Center, 510006 Guangdong, PR China.
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15
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Yan X, Zhang S, Fu H, Qu H. Combining convolutional neural networks and on-line Raman spectroscopy for monitoring the Cornu Caprae Hircus hydrolysis process. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 226:117589. [PMID: 31634714 DOI: 10.1016/j.saa.2019.117589] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 09/29/2019] [Accepted: 09/30/2019] [Indexed: 06/10/2023]
Abstract
Cornu Caprae Hircus (goat horn, GH) is one of the frequently used medicinal animal horns in traditional Chinese medicine (TCM). Hydrolysis is one of the key steps for GH pretreatment in pharmaceutical manufacturing. However, the physicochemical complexity of the hydrolysis samples imposes a challenge for hydrolysis process analysis and monitoring. In this study, convolutional neural networks (CNNs), one of the most popular deep learning methods, were used to develop quantitative calibration models based on on-line Raman spectroscopy for monitoring the GH hydrolysis process. Partial least squares (PLS) calibration models were also developed for model performance comparison. For CNN modeling, raw Raman spectra were used as inputs and hyperparameters in the CNN structure were optimized. Results show for four of the seven analytes, the optimized CNN models using raw spectra as inputs outperform the optimized PLS models developed with preprocessed spectra. Therefore, compared with the commonly used PLS algorithm, CNN modeling is also a practicable regression method and can be employed for the analytical purpose of this study. Models with better performance are expected to be obtained by improving the CNN model structure and using more effective hyperparameter optimization approaches in further studies. To the best of our knowledge, this is the first reported case study of combining CNNs and on-line Raman spectroscopy for a regression task.
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Affiliation(s)
- Xu Yan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Sheng Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Hao Fu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
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16
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Zhang ZY. The statistical fusion identification of dairy products based on extracted Raman spectroscopy. RSC Adv 2020; 10:29682-29687. [PMID: 35518240 PMCID: PMC9056169 DOI: 10.1039/d0ra06318e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 07/28/2020] [Indexed: 11/21/2022] Open
Abstract
At present, practical and rapid identification techniques for dairy products are still scarce. Taking different brands of pasteurized milk as an example, they are all milky white in appearance, and their Raman spectra are very similar, so it is not feasible to identify them directly using the naked eye. In the current work, a clear feature extraction and fusion strategy based on a combination of Raman spectroscopy and a support vector machine (SVM) algorithm was demonstrated. The results showed a 58% average recognition accuracy rate for dairy products as based on the original Raman full spectral data and up to nearly 70% based on a single spectral interval. Data normalization processing effectively improved the recognition accuracy rate. The average recognition accuracy rate of dairy products reached 91% based on the normalized Raman full spectral data or nearly 85% based on a normalized single spectral interval. The fusion of multispectral feature regions yielded high accuracy and operation efficiency. After screening and optimizing based on SVM algorithm, the best spectral feature intervals were determined to be 335–354 cm−1, 435–454 cm−1, 485–540 cm−1, 820–915 cm−1, 1155–1185 cm−1, 1300–1414 cm−1, and 1415–1520 cm−1 under the experimental conditions, and the average identification accuracy rate here reached 93%. The developed scheme has the advantages of clear feature extraction and fusion, and short identification time, and it provides a technical reference for food quality control. At present, practical and rapid identification techniques for dairy products are still scarce.![]()
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Affiliation(s)
- Zheng-Yong Zhang
- State Key Laboratory of Dairy Biotechnology
- Shanghai Engineering Research Center of Dairy Biotechnology
- Dairy Research Institute
- Bright Dairy & Food Co., Ltd
- Shanghai 200436
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17
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Yan X, Fu H, Zhang S, Qu H. Combining convolutional neural networks and in-line near-infrared spectroscopy for real-time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins. J Sep Sci 2019; 43:663-670. [PMID: 31674130 DOI: 10.1002/jssc.201900874] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/17/2019] [Accepted: 10/25/2019] [Indexed: 11/10/2022]
Abstract
The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in-line near-infrared spectroscopy for notoginsenoside R1 , ginsenosides Rg1 , Re, Rb1 and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in-line near-infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.
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Affiliation(s)
- Xu Yan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
| | - Hao Fu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
| | - Sheng Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
| | - Haibin Qu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, P. R. China
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