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Wang Q, Zhang Y, Yang B. Development status of novel spectral imaging techniques and application to traditional Chinese medicine. J Pharm Anal 2023; 13:1269-1280. [PMID: 38174122 PMCID: PMC10759257 DOI: 10.1016/j.jpha.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 01/05/2024] Open
Abstract
Traditional Chinese medicine (TCM) is a treasure of the Chinese nation, providing effective solutions to current medical requisites. Various spectral techniques are undergoing continuous development and provide new and reliable means for evaluating the efficacy and quality of TCM. Because spectral techniques are noninvasive, convenient, and sensitive, they have been widely applied to in vitro and in vivo TCM evaluation systems. In this paper, previous achievements and current progress in the research on spectral technologies (including fluorescence spectroscopy, photoacoustic imaging, infrared thermal imaging, laser-induced breakdown spectroscopy, hyperspectral imaging, and surface enhanced Raman spectroscopy) are discussed. The advantages and disadvantages of each technology are also presented. Moreover, the future applications of spectral imaging to identify the origins, components, and pesticide residues of TCM in vitro are elucidated. Subsequently, the evaluation of the efficacy of TCM in vivo is presented. Identifying future applications of spectral imaging is anticipated to promote medical research as well as scientific and technological explorations.
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Affiliation(s)
- Qi Wang
- Department of Medicinal Chemistry and Natural Medicinal Chemistry, College of Pharmacy, Harbin Medical University, Harbin, 150081, China
| | - Yong Zhang
- Department of Pharmacology (The State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, 150081, China
- Research Unit of Noninfectious Chronic Diseases in Frigid Zone, Chinese Academy of Medical Sciences, Harbin, 150081, China
- Institute of Metabolic Disease, Heilongjiang Academy of Medical Science, Harbin, 150086, China
| | - Baofeng Yang
- Department of Pharmacology (The State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Research, Ministry of Education), College of Pharmacy, Harbin Medical University, Harbin, 150081, China
- Research Unit of Noninfectious Chronic Diseases in Frigid Zone, Chinese Academy of Medical Sciences, Harbin, 150081, China
- Department of Pharmacology and Therapeutics, Melbourne School of Biomedical Sciences, Faculty of Medicine, Dentistry and Health Sciences University of Melbourne, Melbourne, VIC, 3010, Australia
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Wang W, Man Z, Li X, Chen R, You Z, Pan T, Dai X, Xiao H, Liu F. Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress. JOURNAL OF HAZARDOUS MATERIALS 2023; 449:131010. [PMID: 36801724 DOI: 10.1016/j.jhazmat.2023.131010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (Rp=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (Rp=0.9161) for SP and CARS-ELM (Rp=0.9021) for MDA, all with Rp higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.
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Affiliation(s)
- Wei Wang
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Zun Man
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Zhengkai You
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaorong Dai
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo 315100, China
| | - Hang Xiao
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
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3
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Wei K, Teng G, Wang Q, Xu X, Zhao Z, Liu H, Bao M, Zheng Y, Luo T, Lu B. Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy. Foods 2023; 12:foods12081710. [PMID: 37107505 PMCID: PMC10138139 DOI: 10.3390/foods12081710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 04/15/2023] [Accepted: 04/19/2023] [Indexed: 04/29/2023] Open
Abstract
Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R2 was 0.9983. The results showed that the LIBS technique can be adopted to test adulteration in the sample of Fritillaria cirrhosa powder and has potential applications in drug quality control.
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Affiliation(s)
- Kai Wei
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Geer Teng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7LD, UK
| | - Qianqian Wang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Xiangjun Xu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Zhifang Zhao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Haida Liu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Mengyu Bao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yongyue Zheng
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianzhong Luo
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
- Yangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing 314033, China
| | - Bingheng Lu
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Photonic Information Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
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4
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Jiao L, Sun C, Yan N, Yan C, Qu L, Wang Q, Zhang S, Ma L. Discrimination of Salvia miltiorrhiza from Different Geographical Origins by Laser-Induced Breakdown Spectroscopy (LIBS) with Convolutional Neural Network (CNN). ANAL LETT 2023. [DOI: 10.1080/00032719.2023.2180515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Affiliation(s)
- Long Jiao
- College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China
| | - Chengyu Sun
- College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China
| | - Naying Yan
- College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China
| | - Chunhua Yan
- College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China
| | - Le Qu
- Cooperative Innovation Center of Unconventional Oil and Gas Exploration and Development in Shaanxi Province, Xi’an, Shaanxi, China
| | - Qin Wang
- School of Chemistry and Environment Science, Shaanxi University of Technology, Hanzhong, Shaanxi, China
| | - Shengrui Zhang
- School of Chemistry and Environment Science, Shaanxi University of Technology, Hanzhong, Shaanxi, China
| | - Ling Ma
- College of Chemistry and Chemical Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China
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Xia Z, Che X, Ye L, Zhao N, Guo D, Peng Y, Lin Y, Liu X. A Synergetic Strategy for Brand Characterization of Colla Corii Asini (Ejiao) by LIBS and NIR Combined with Partial Least Squares Discriminant Analysis. Molecules 2023; 28:molecules28041778. [PMID: 36838765 PMCID: PMC9965801 DOI: 10.3390/molecules28041778] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
A synergetic strategy was proposed to address the critical issue in the brand characterization of Colla corii asini (Ejiao, CCA), a precious traditional Chinese medicine (TCM). In all brands of CCA, Dong'e Ejiao (DEEJ) is an intangible cultural heritage resource. Seventy-eight CCA samples (including forty DEEJ samples and thirty-eight samples from other different manufacturers) were detected by laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIR). Partial least squares discriminant analysis (PLS-DA) models were built first considering individual techniques separately, and then fusing LIBS and NIR data at low-level. The statistical parameters including classification accuracy, sensitivity, and specificity were calculated to evaluate the PLS-DA model performance. The results demonstrated that two individual techniques show good classification performance, especially the NIR. The PLS-DA model with single NIR spectra pretreated by the multiplicative scatter correction (MSC) method was preferred as excellent discrimination. Though individual spectroscopic data obtained good classification performance. A data fusion strategy was also attempted to merge atomic and molecular information of CCA. Compared to a single data block, data fusion models with SNV and MSC pretreatment exhibited good predictive power with no misclassification. This study may provide a novel perspective to employ a comprehensive analytical approach to brand discrimination of CCA. The synergetic strategy based on LIBS together with NIR offers atomic and molecular information of CCA, which could be exemplary for future research on the rapid discrimination of TCM.
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Affiliation(s)
- Ziyi Xia
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Xiaoqing Che
- Shandong Runzhong Pharmaceutical Co., Ltd., Yantai 256603, China
| | - Lei Ye
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
| | - Na Zhao
- Key Laboratory of Xinjiang Phytomedicine Resources and Utilization in Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832002, China
| | - Dongxiao Guo
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
| | - Yanfang Peng
- Pharmacy Faculty, Hubei University of Chinese Medicine, Wuhan 430065, China
| | - Yongqiang Lin
- Shandong Institute of Food and Drug Inspection, Jinan 250101, China
- Correspondence: (Y.L.); (X.L.)
| | - Xiaona Liu
- College of Integrated Traditional Chinese and Western Medicine, Binzhou Medical University, Yantai 264003, China
- Correspondence: (Y.L.); (X.L.)
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Kabir MH, Guindo ML, Chen R, Sanaeifar A, Liu F. Application of Laser-Induced Breakdown Spectroscopy and Chemometrics for the Quality Evaluation of Foods with Medicinal Properties: A Review. Foods 2022; 11:2051. [PMID: 35885291 PMCID: PMC9321926 DOI: 10.3390/foods11142051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/29/2022] [Accepted: 07/06/2022] [Indexed: 12/05/2022] Open
Abstract
Laser-induced Breakdown Spectroscopy (LIBS) is becoming an increasingly popular analytical technique for characterizing and identifying various products; its multi-element analysis, fast response, remote sensing, and sample preparation is minimal or nonexistent, and low running costs can significantly accelerate the analysis of foods with medicinal properties (FMPs). A comprehensive overview of recent advances in LIBS is presented, along with its future trends, viewpoints, and challenges. Besides reviewing its applications in both FMPs, it is intended to provide a concise description of the use of LIBS and chemometrics for the detection of FMPs, rather than a detailed description of the fundamentals of the technique, which others have already discussed. Finally, LIBS, like conventional approaches, has some limitations. However, it is a promising technique that may be employed as a routine analysis technique for FMPs when utilized effectively.
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Affiliation(s)
- Muhammad Hilal Kabir
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.); (A.S.)
- Department of Agricultural and Bio-Resource Engineering, Abubakar Tafawa Balewa University, Bauchi 740272, Nigeria
| | - Mahamed Lamine Guindo
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.); (A.S.)
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.); (A.S.)
| | - Alireza Sanaeifar
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.); (A.S.)
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; (M.H.K.); (M.L.G.); (R.C.); (A.S.)
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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Bin L, Zhao-yang H, Hui-zhou C, A-kun Y, Ai-guo OY. Identification of different parts of Panax notoginseng based on terahertz spectroscopy. J Anal Sci Technol 2022. [DOI: 10.1186/s40543-022-00328-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
AbstractIn this paper, the combined terahertz time-domain spectroscopy (THz-TDS) and chemometrics method is proposed to identify four different parts of Panax notoginseng rapidly and nondestructively. The research objects of the taproot, scissor, rib, and hairy root of P. notoginseng are taken. The refractive index, absorption coefficient, time-domain, and frequency-domain spectra of the samples are analyzed. It is found that the terahertz spectra of different parts of P. notoginseng are significantly different, so the absorption coefficient of samples is selected to establish models. Firstly, the baseline correction, multiple scattering correction, and normalization algorithms are used to preprocess the absorption coefficient in 0.5–2.0 THz to remove noise. Then, the Kennard–Stone (KS) algorithm is used to divide the model set and the prediction set at the ratio of 3:1, and the successive projection algorithm (SPA) is used to select the characteristic frequency points of the samples. Finally, the chosen characteristic variables are input into the support vector machine (SVM) and linear discriminant analysis (LDA) algorithm to establish the qualitative analysis models, respectively. In the SPA-SVM models, the performance of the SPA-SVM model under the linear kernel function by baseline is best, the accuracy of the training set of it is 99.50%, and the accuracy of the test set of it is 99.25%. In the SPA-LDA models, the performance of the SPA-LDA model by baseline is best, and the accuracy of the training set of it is 100%, and the accuracy of the test set of it is 100%. And the value of cumulative variance contribution is proposed to assess whether the variable is good or bad to model. The results show that the combined THz-TDS and chemometrics method can be used to realize rapid, accurate, and nondestructive identification of different parts of P. notoginseng.
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Chen H, He Y. Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review. THE AMERICAN JOURNAL OF CHINESE MEDICINE 2022; 50:91-131. [PMID: 34931589 DOI: 10.1142/s0192415x22500045] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.
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Affiliation(s)
- Haiyang Chen
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
| | - Yu He
- School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, P. R. China
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WANG Y, LI MG, FENG T, ZHANG TL, FENG YQ, LI H. Discrimination of Radix Astragali according to geographical regions by data fusion of laser induced breakdown spectroscopy (LIBS) and infrared spectroscopy (IR) combined with random forest (RF). CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1016/j.cjac.2022.100057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Képeš E, Vrábel J, Adamovsky O, Střítežská S, Modlitbová P, Pořízka P, Kaiser J. Interpreting support vector machines applied in laser-induced breakdown spectroscopy. Anal Chim Acta 2021; 1192:339352. [DOI: 10.1016/j.aca.2021.339352] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 11/22/2021] [Accepted: 12/01/2021] [Indexed: 02/07/2023]
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Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review. Nutrients 2021; 13:322. [PMID: 33499405 PMCID: PMC7911928 DOI: 10.3390/nu13020322] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/12/2021] [Accepted: 01/18/2021] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.
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Affiliation(s)
- Jarosław Sak
- Chair and Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
- BioMolecular Resources Research Infrastructure Poland (BBMRI.pl), Poland
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Yue J, Zuo Z, Huang H, Wang Y. Application of Identification and Evaluation Techniques for Ethnobotanical Medicinal Plant of Genus Panax: A Review. Crit Rev Anal Chem 2020; 51:373-398. [PMID: 32166968 DOI: 10.1080/10408347.2020.1736506] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Genus Panax, as worldwide medicinal plants, has a medical history for thousands of years. Most of the entire genus are traditional ethnobotanical medicine in China, Myanmar, Thailand, Vietnam and Laos, which have given rise to international attention and use. This paper reviewed more than 210 articles and related books on the research of Panax medicinal plants and their Chinese patent medicines published in the last 30 years. The purpose was to review and summarize the species classification, geographical distribution, and ethnic minorities medicinal records of the genus Panax, and further to review the analytical tools and data analysis methods for the authentication and quality assessment of Panax medicinal materials and Chinese patent medicines. Five main technologies applied in the identification and evaluation of Panax have been introduced and summarized. Chromatography was the most widely used one. Further research and development of molecular identification technology had the potential to become a mainstream identification technology. In addition, some novel, controversial, and worthy methods including electronic noses, electronic eyes, and DNA barcoding were also introduced. At the same time, more than 80% of the researches were carried out by a combination of chemometric pattern-recognition technologies and multi-analysis technologies. All the technologies and methods applied can provide strong support and guarantee for the identification and evaluation of genus Panax, and also conduce to excellent reference value for the development and in-depth research of new technologies in Panax.
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Affiliation(s)
- Jiaqi Yue
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China.,College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Zhitian Zuo
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Hengyu Huang
- 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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13
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Duan H, Han L, Huang G. Quantitative Analysis of Major Metals in Agricultural Biochar Using Laser-Induced Breakdown Spectroscopy with an Adaboost Artificial Neural Network Algorithm. Molecules 2019; 24:molecules24203753. [PMID: 31635230 PMCID: PMC6832405 DOI: 10.3390/molecules24203753] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 10/14/2019] [Accepted: 10/16/2019] [Indexed: 01/26/2023] Open
Abstract
To promote the green development of agriculture by returning biochar to farmland, it is of great significance to simultaneously detect heavy and nutritional metals in agricultural biochar. This work aimed first to apply laser-induced breakdown spectroscopy (LIBS) for the determination of heavy (Pb, Cr) and nutritional (K, Na, Ca, Mg, Cu, and Zn) metals in agricultural biochar. Each batch of collected biochar was prepared to a standardized sample using the separating and milling method. Two types of univariate analysis model were developed using peak intensity and integration area of the sensitive emission lines, but the performance did not satisfy the requirements of practical application because of the poor correlations between the measured values and predicted values, as well as large relative standard deviation of the prediction (RSDP) values. An ensemble learning algorithm, adaboost backpropagation artificial neural network (BP-Adaboost), was then used to develop the multivariate analysis models, which had a more robust performance than traditional univariate analysis, partial least squares regression (PLSR), and backpropagation artificial neural network (BP-ANN). The optimized RSDP values for K, Ca, Mg, and Cu were less than 10%, while the RSDP values for Pb, Cr, Zn, and Na were in the range of 10–20%. Moreover, the pairwise t-test of its prediction set showed that there was no significant difference between the measurements of LIBS and ICP-MS. The promising results indicate that rapid and simultaneous detection of major heavy and nutritional metals in agricultural biochar can be achieved using LIBS and reasonable chemometric algorithms.
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Affiliation(s)
- Hongwei Duan
- Laboratory of Biomass and Bioprocessing Engineering, College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Lujia Han
- Laboratory of Biomass and Bioprocessing Engineering, College of Engineering, China Agricultural University, Beijing 100083, China.
| | - Guangqun Huang
- Laboratory of Biomass and Bioprocessing Engineering, College of Engineering, China Agricultural University, Beijing 100083, China.
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