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Li B, Sun J, Liu Y, Shi L, Zhong Y, Wu P. Multi-level data fusion strategy based on spectral and image information for identifying varieties of soybean seeds. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 322:124815. [PMID: 39024789 DOI: 10.1016/j.saa.2024.124815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/20/2024] [Accepted: 07/12/2024] [Indexed: 07/20/2024]
Abstract
Rapid identification of soybean seed varieties is crucial for agricultural production and seed quality. Identifying varieties of soybean seed using conventional chemical methods is time-consuming, destructive, and inappropriate for seed quality evaluation. This study utilized hyperspectral imaging technology (HSI) to identify four varieties of soybean seeds. The hyperspectral images of soybean seeds were collected in the spectral range of 400-1000 nm. A multi-level data fusion strategy based on spectral and image information was proposed to improve the accuracy of model. Subsequently, the multi-level data fusion strategy based on partial least squares discriminant analysis (PLS-DA) was used to establish the classification models of soybean seeds. Compared with the models using individual analytical sources, the results demonstrated that the models with multi-level data fusion strategy obtained better prediction performance. The high-level data fusion (HLDF) based on Bayesian consensus provided the optimal results with an accuracy (Acc) and F1-score of 93.13 % and 93.70 % in the prediction phase, respectively. Therefore, the multi-level data fusion strategy can be used as an identification method for soybean seed varieties and an effective approach to enhance the discriminatory capability of models.
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Affiliation(s)
- Bo Li
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Jun Sun
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.
| | - Yang Liu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Lei Shi
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Yuhao Zhong
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
| | - Pengcheng Wu
- School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China
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2
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Han Z, Zhao J, Tang Y, Wang Y. Machine learning integration of multi-modal analytical data for distinguishing abnormal botanical drugs and its application in Guhong injection. Chin Med 2024; 19:2. [PMID: 38163913 PMCID: PMC10759515 DOI: 10.1186/s13020-023-00873-y] [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: 10/10/2023] [Accepted: 12/14/2023] [Indexed: 01/03/2024] Open
Abstract
BACKGROUND Determination of batch-to-batch consistency of botanical drugs (BDs) has long been the bottleneck in quality evaluation primarily due to the chemical diversity inherent in BDs. This diversity presents an obstacle to achieving comprehensive standardization for BDs. Basically, a single detection mode likely leads to substandard analysis results as different classes of structures always possess distinct physicochemical properties. Whereas representing a workaround for multi-target standardization using multi-modal data, data processing for information from diverse sources is of great importance for the accuracy of classification. METHODS In this research, multi-modal data of 78 batches of Guhong injections (GHIs) consisting of 52 normal and 26 abnormal samples were acquired by employing HPLC-UV, -ELSD, and quantitative 1H NMR (q1HNMR), of which data obtained was then individually used for Pearson correlation coefficient (PCC) calculation and partial least square-discriminant analysis (PLS-DA). Then, a mid-level data fusion method with data containing qualitative and quantitative information to establish a support vector machine (SVM) model for evaluating the batch-to-batch consistency of GHIs. RESULTS The resulting outcomes showed that datasets from one detection mode (e.g., data from UV detectors only) are inadequate for accurately assessing the product's quality. The mid-level data fusion strategy for the quality evaluation enabled the classification of normal and abnormal batches of GHIs at 100% accuracy. CONCLUSIONS A quality assessment strategy was successfully developed by leveraging a mid-level data fusion method for the batch-to-batch consistency evaluation of GHIs. This study highlights the promising utility of data from different detection modes for the quality evaluation of BDs. It also reminds manufacturers and researchers about the advantages of involving data fusion to handle multi-modal data. Especially when done jointly, this strategy can significantly increase the accuracy of product classification and serve as a capable tool for studies of other BDs.
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Affiliation(s)
- Zhu Han
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Jiandong Zhao
- Tonghua Guhong Pharmaceutical Co., Ltd., 5099 Jianguo Road, Meihekou, 135099, China
| | - Yu Tang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China.
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou, 310018, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314100, China.
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3
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Huang R, Ma S, Dai S, Zheng J. Application of Data Fusion in Traditional Chinese Medicine: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 24:106. [PMID: 38202967 PMCID: PMC10781265 DOI: 10.3390/s24010106] [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: 12/01/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024]
Abstract
Traditional Chinese medicine is characterized by numerous chemical constituents, complex components, and unpredictable interactions among constituents. Therefore, a single analytical technique is usually unable to obtain comprehensive chemical information. Data fusion is an information processing technology that can improve the accuracy of test results by fusing data from multiple devices, which has a broad application prospect by utilizing chemometrics methods, adopting low-level, mid-level, and high-level data fusion techniques, and establishing final classification or prediction models. This paper summarizes the current status of the application of data fusion strategies based on spectroscopy, mass spectrometry, chromatography, and sensor technologies in traditional Chinese medicine (TCM) in light of the latest research progress of data fusion technology at home and abroad. It also gives an outlook on the development of data fusion technology in TCM analysis to provide references for the research and development of TCM.
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Affiliation(s)
- Rui Huang
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211198, China
| | - Shuangcheng Ma
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
| | - Shengyun Dai
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
| | - Jian Zheng
- National Institutes for Food and Drug Control, Beijing 102629, China; (R.H.); (S.M.)
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4
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Zhang Y, Wang Y. Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects. Food Chem X 2023; 19:100860. [PMID: 37780348 PMCID: PMC10534232 DOI: 10.1016/j.fochx.2023.100860] [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: 07/06/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 10/03/2023] Open
Abstract
The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.
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Affiliation(s)
- Yanying Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
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5
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Ding R, Yu L, Wang C, Zhong S, Gu R. Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review. Crit Rev Anal Chem 2023:1-18. [PMID: 36966435 DOI: 10.1080/10408347.2023.2189477] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2023]
Abstract
The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.
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Affiliation(s)
- Rong Ding
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lianhui Yu
- Chengdu Pushi Pharmaceutical Technology Co., Ltd, Chengdu, China
| | - Chenghui Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Shihong Zhong
- School of Pharmacy, Southwest Minzu University, Chengdu, China
| | - Rui Gu
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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6
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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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7
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Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk. Foods 2023; 12:foods12061199. [PMID: 36981127 PMCID: PMC10048274 DOI: 10.3390/foods12061199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 02/28/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.
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8
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Predicting the dietary fiber content of fresh-cut bamboo shoots using a visible and near-infrared hyperspectral technique. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2023. [DOI: 10.1007/s11694-023-01845-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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9
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Xu Y, Zhang J, Wang Y. Recent trends of multi-source and non-destructive information for quality authentication of herbs and spices. Food Chem 2023; 398:133939. [DOI: 10.1016/j.foodchem.2022.133939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 07/19/2022] [Accepted: 08/10/2022] [Indexed: 11/15/2022]
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10
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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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11
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A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials. Molecules 2022; 27:molecules27227766. [PMID: 36431869 PMCID: PMC9696260 DOI: 10.3390/molecules27227766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.
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12
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Rapid Identification of Wild Gentiana Genus in Different Geographical Locations Based on FT-IR and an Improved Neural Network Structure Double-Net. Molecules 2022; 27:molecules27185979. [PMID: 36144717 PMCID: PMC9506529 DOI: 10.3390/molecules27185979] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 11/17/2022] Open
Abstract
Gentiana Genus, a herb mainly distributed in Asia and Europe, has been used to treat the damp heat disease of the liver for over 2000 years in China. Previous studies have shown significant differences in the compositional contents of wild Gentiana Genus samples from different geographical origins. Therefore, the traceable geographic locations of the wild Gentiana Genus samples are essential to ensure practical medicinal value. Over the last few years, the developments in chemometrics have facilitated the analysis of the composition of medicinal herbs via spectroscopy. Notably, FT-IR spectroscopy is widely used because of its benefit of allowing rapid, nondestructive measurements. In this paper, we collected wild Gentiana Genus samples from seven different provinces (222 samples in total). Twenty-one different FT-IR spectral pre-processing methods that were used in our experiments. Meanwhile, we also designed a neural network, Double-Net, to predict the geographical locations of wild Gentiana Genus plants via FT-IR spectroscopy. The experiments showed that the accuracy of the neural network structure Double-Net we designed can reach 100%, and the F1_score can reach 1.0.
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13
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Li F, Zhang J, Wang Y. Vibrational Spectroscopy Combined with Chemometrics in Authentication of Functional Foods. Crit Rev Anal Chem 2022; 54:333-354. [PMID: 35533108 DOI: 10.1080/10408347.2022.2073433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Many foods have both edible and medical importance and are appreciated as functional foods, preventing diseases. However, due to unscrupulous vendors and imperfect market supervision mechanisms, curative foods are prone to adulteration or some other events that harm the interests of consumers. However, traditional analytical methods are unsuitable and expensive for a broad and complex application. Therefore, people urgently need a fast, efficient, and accurate detection method to protect self-interests. Recently, the study of target samples by vibration spectrum shows strong qualitative and quantitative ability. The model established by platform technology combined with the stoichiometric analysis method can obtain better parameters, which it has good robustness and can detect functional food efficiently, quickly and nondestructive. The review compared and prospect five different vibrational spectroscopic techniques (near-infrared, Fourier transform infrared, Raman, hyperspectral imaging spectroscopy and Terahertz spectroscopy). In order to better solve some of the actual situations faced by certification, we explore and through relevant research and investigation to appropriately highlight the applicability and importance of technology combined with chemometrics in functional food authentication. There are four categories of authentication discussed: functional food authenticated in source, processing method, fraud and ingredient ratio. This paper provides an innovative process for the authentication of functional food, which has a meaningful reference value for future review or scientific research of relevant departments.
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Affiliation(s)
- Fengjiao Li
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- School of Agriculture, Yunnan University, Kunming, China
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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14
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Nano-effect multivariate fusion spectroscopy combined with chemometrics for accurate identification the cultivation methods and growth years of Dendrobium huoshanense. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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15
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Liu C, Zuo Z, Xu F, Wang Y. Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review. Crit Rev Anal Chem 2022; 53:1393-1418. [PMID: 34991387 DOI: 10.1080/10408347.2021.2023460] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.
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Affiliation(s)
- Chunlu Liu
- 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
| | - Furong Xu
- 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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16
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Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples. Microchem J 2022. [DOI: 10.1016/j.microc.2021.106893] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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17
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Liu Z, Yang S, Wang Y, Zhang J. Multi-platform integration based on NIR and UV-Vis spectroscopies for the geographical traceability of the fruits of Amomum tsao-ko. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119872. [PMID: 33957443 DOI: 10.1016/j.saa.2021.119872] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/01/2021] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
Due to the world-wide concern relating to herb quality and safety, there is a momentum to authenticate the geographical origin of herb with multi-platform techniques. This study attempted to assess multi-platform information as a practical strategy for the geographical traceability of the fruits of Amomum tsao-ko. To this aim, one hundred and eighty dried fruits of A. tsao-ko from five geographical regions were analyzed by near infrared (NIR) and ultraviolet visible (UV-Vis) spectroscopy. On this basis, two variable dimension reduction strategies, including principal component analysis (PCA) and sequential and orthogonalized partial-least squares (SO-PLS), and two variables selection strategies, including variable importance in projection (VIP) and sequential and orthogonalized covariance selection (SO-CovSel), were performed to extract the feature information in the two blocks. Partial least squares discriminant analysis (PLS-DA) classification algorithm combined with fused matrices was used to identify the geographical origins. The results of PLS-DA models indicated that SO-PLS and SO-CovSel, taking advantage of the sequential modeling coupled to orthogonalization, could not only identify the common information presented in the two blocks but also provide more concise methods without any loss of classification ability, which could be employed in authenticating the geographical regions of the fruits of A. tsao-ko, effectively.
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Affiliation(s)
- Zhimin Liu
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; School of Agriculture, Yunnan University, Kunming 650500, China
| | - Shaobing Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Yuanzhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
| | - Jinyu Zhang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; School of Agriculture, Yunnan University, Kunming 650500, China.
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18
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Wang L, Li J, Li T, Liu H, Wang Y. Method Superior to Traditional Spectral Identification: FT-NIR Two-Dimensional Correlation Spectroscopy Combined with Deep Learning to Identify the Shelf Life of Fresh Phlebopus portentosus. ACS OMEGA 2021; 6:19665-19674. [PMID: 34368554 PMCID: PMC8340397 DOI: 10.1021/acsomega.1c02317] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 07/09/2021] [Indexed: 05/07/2023]
Abstract
The taste of fresh mushrooms is always appealing. Phlebopus portentosus is the only porcini that can be cultivated artificially in the world, with a daily output of up to 2 tons and a large sales market. Fresh mushrooms are very susceptible to microbial attacks when stored at 0-2 °C for more than 5 days. Therefore, the freshness of P. portentosus must be evaluated during its refrigeration to ensure food safety. According to their freshness, the samples were divided into three categories, namely, category I (1-2 days, 0-48 h, recommended for consumption), category II (3-4 days, 48-96 h, recommended for consumption), and category III (5-6 days, 96-144 h, not recommended). In our study, a fast and reliable shelf life identification method was established through Fourier transform near-infrared (FT-NIR) spectroscopy combined with a machine learning method. Deep learning (DL) is a new focus in the field of food research, so we established a deep learning classification model, traditional support-vector machine (SVM), partial least-squares discriminant analysis (PLS-DA), and an extreme learning machine (ELM) model to identify the shelf life of P. portentosus. The results showed that FT-NIR two-dimensional correlation spectroscopy (2DCOS) combined with the deep learning model was more suitable for the identification of fresh mushroom shelf life and the model had the best robustness. In conclusion, FT-NIR combined with machine learning had the advantages of being nondestructive, fast, and highly accurate in identifying the shelf life of P. portentosus. This method may become a promising rapid analysis tool, which can quickly identify the shelf life of fresh edible mushrooms.
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Affiliation(s)
- Li Wang
- College
of Agronomy and Biotechnology, Yunnan Agricultural
University, Kunming 650201, China
| | - Jieqing Li
- College
of Resources and Environment, Yunnan Agricultural
University, Kunming 650201, China
| | - Tao Li
- College
of Resources and Environment, Yuxi Normal
University, Yuxi 653199, China
| | - Honggao Liu
- College
of Agronomy and Life Sciences, Zhaotong
University, Zhaotong 657000, China
| | - Yuanzhong Wang
- Medicinal
Plants Research Institute, Yunnan Academy
of Agricultural Sciences, Kunming 650200, China
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19
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Qie M, Zhang B, Li Z, Zhao S, Zhao Y. Data fusion by ratio modulation of stable isotope, multi-element, and fatty acids to improve geographical traceability of lamb. Food Control 2021. [DOI: 10.1016/j.foodcont.2020.107549] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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20
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Identification and evaluation of Polygonatum kingianum with different growth ages based on data fusion strategy. Microchem J 2021. [DOI: 10.1016/j.microc.2020.105662] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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21
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Hesami M, Naderi R, Tohidfar M. Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study. Appl Microbiol Biotechnol 2020; 104:10249-10263. [PMID: 33119796 DOI: 10.1007/s00253-020-10978-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 10/13/2020] [Accepted: 10/25/2020] [Indexed: 11/30/2022]
Abstract
Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R2 had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27 mM NH4+, 38.92 mM NO3-, 22.79 mM K+, 5.08 mM Cl-, 3.34 mM Ca2+, 1.67 mM Mg2+, 2.17 mM SO42-, and 1.44 mM H2PO4-. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61 μM thiamine, 5.93 μM nicotinic acid, 0.25 μM biotin, and 0.26 μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.Key points• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.• The new culture medium (HNT) had better efficiency than MS medium.
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Affiliation(s)
- Mohsen Hesami
- Gosling Research Institute for Plant Preservation, Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada.,Department of Horticultural Science, Faculty of Agriculture, University of Tehran, Karaj, Iran
| | - Roohangiz Naderi
- Department of Horticultural Science, Faculty of Agriculture, University of Tehran, Karaj, Iran.
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Sciences & Biotechnology, Shahid Beheshti University, G.C., Tehran, Iran
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22
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Hesami M, Alizadeh M, Naderi R, Tohidfar M. Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases. PLoS One 2020; 15:e0239901. [PMID: 32997694 PMCID: PMC7526930 DOI: 10.1371/journal.pone.0239901] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 09/15/2020] [Indexed: 12/21/2022] Open
Abstract
Optimizing the gene transformation factors can be considered as the first and foremost step in successful genetic engineering and genome editing studies. However, it is usually difficult to achieve an optimized gene transformation protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach such as machine learning models for analyzing gene transformation data. In the current study, three individual machine learning models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Radial Basis Function (RBF) were developed for forecasting Agrobacterium-mediated gene transformation in chrysanthemum based on eleven input variables including Agrobacterium strain, optical density (OD), co-culture period (CCP), and different antibiotics including kanamycin (K), vancomycin (VA), cefotaxime (CF), hygromycin (H), carbenicillin (CA), geneticin (G), ticarcillin (TI), and paromomycin (P). Consequently, best-obtained results were used in the fusion process by bagging method. Results showed that ensemble model with the highest R2 (0.83) had superb performance in comparison with all other individual models (MLP:063, RBF:0.69, and ANFIS: 0.74) in the validation set. Also, ensemble model was linked to Fruit fly optimization algorithm (FOA) for optimizing gene transformation, and the results showed that the maximum gene transformation efficiency (37.54%) can be achieved from EHA105 strain with 0.9 OD600, for 3.8 days CCP, 46.43 mg/l P, 9.54 mg/l K, 18.62 mg/l H, and 4.79 mg/l G as selection antibiotics and 109.74 μg/ml VA, 287.63 μg/ml CF, 334.07 μg/ml CA and 87.36 μg/ml TI as antibiotics in the selection medium. Moreover, sensitivity analysis demonstrated that input variables have a different degree of importance in gene transformation system in the order of Agrobacterium strain > CCP > K > CF > VA > P > OD > CA > H > TI > G. Generally, the developed hybrid model in this study (ensemble model-FOA) can be employed as an accurate and reliable approach in future genetic engineering and genome editing studies.
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Affiliation(s)
- Mohsen Hesami
- Department of Plant Agriculture, Gosling Research Institute for Plant Preservation, University of Guelph, Guelph, ON, Canada
| | - Milad Alizadeh
- Department of Botany, University of British Columbia, Vancouver, BC, Canada
| | - Roohangiz Naderi
- Department of Horticultural Science, Faculty of Agriculture, University of Tehran, Karaj, Iran
- * E-mail:
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Sciences & Biotechnology, Shahid Beheshti University, G.C., Tehran, Iran
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23
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Wang Y, Huang HY, Wang YZ. Authentication of Dendrobium Officinale from Similar Species with Infrared and Ultraviolet-Visible Spectroscopies with Data Visualization and Mining. ANAL LETT 2020. [DOI: 10.1080/00032719.2020.1719126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ye Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, PR China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, PR China
| | - Heng-Yu Huang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, PR China
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, PR China
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24
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Li Y, Shen Y, Yao CL, Guo DA. Quality assessment of herbal medicines based on chemical fingerprints combined with chemometrics approach: A review. J Pharm Biomed Anal 2020; 185:113215. [DOI: 10.1016/j.jpba.2020.113215] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 01/08/2020] [Accepted: 02/26/2020] [Indexed: 12/30/2022]
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25
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Mohammadi M, Khanmohammadi Khorrami M, Vatani A, Ghasemzadeh H, Vatanparast H, Bahramian A, Fallah A. Rapid determination and classification of crude oils by ATR-FTIR spectroscopy and chemometric methods. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 232:118157. [PMID: 32106028 DOI: 10.1016/j.saa.2020.118157] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Revised: 02/08/2020] [Accepted: 02/15/2020] [Indexed: 06/10/2023]
Abstract
Classification based on °API gravity is very important to estimate the parameters related to the extraction, purification, toxicity, and pricing of crude oils. Spectroscopy methods show some advantages over ASTM and API methods for crude oil analysis. The attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with chemometric methods has been applied as a quick and non-destructive method for crude oil analysis. In this work, a new analytical method using ATR-FTIR spectroscopy associated with chemometric methods were proposed for adressing regression and classification tasks for crude oils analysis based on °API gravity values. The designed methods are rapid, economic, and nondestructive ways in production process of oil industry. The spectral data were used for estimation of °API gravity using two approaches according to PLS-R and SVM-R algorithm, separately. The ATR-FTIR spectral data were also analyzed by classification method using the partial least squares-discriminant analysis (PLS-DA) for crude oil classification. The samples were classified into three classes based on their °API gravity values. The SVM-R model showed better results than PLS-R for °API gravity values using the F-test at 95% of confidence. The result of classification, showed about 100% accuracy and a zero classification error for calibration and prediction samples in PLS-DA algorithm.
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Affiliation(s)
- Mahsa Mohammadi
- Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
| | | | - Ali Vatani
- Institute of Liquefied Natural Gas (I-LNG), School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Hossein Ghasemzadeh
- Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
| | - Hamid Vatanparast
- Petroleum Engineering Research Division, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
| | - Alireza Bahramian
- Institute of Petroleum Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Afshin Fallah
- Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
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26
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Wang CY, Tang L, Li L, Zhou Q, Li YJ, Li J, Wang YZ. Geographic Authentication of Eucommia ulmoides Leaves Using Multivariate Analysis and Preliminary Study on the Compositional Response to Environment. FRONTIERS IN PLANT SCIENCE 2020; 11:79. [PMID: 32140161 PMCID: PMC7042207 DOI: 10.3389/fpls.2020.00079] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 01/21/2020] [Indexed: 05/03/2023]
Abstract
To explore the influences of different cultivated areas on the chemical profiles of Eucommia ulmoides leaves (EUL) and rapidly authenticate its geographical origins, 187 samples from 13 provinces in China were systematically investigated using three data fusion strategies (low, mid, and high level) combined with two discrimination model algorithms (partial least squares discrimination analysis; random forest, RF). RF models constructed by high-level data fusion with different modes of different spectral data (Fourier transform near-infrared spectrum and attenuated total reflection Fourier transform mid-infrared spectrum) were most suitable for identifying EULs from different geographical origins. The accuracy rates of calibration and validation set were 92.86% and 93.44%, respectively. In addition, climate parameters were systematically investigated the cluster difference in our study. Some interesting and novel information could be found from the clustering tree diagram of hierarchical cluster analysis. The Xinjiang Autonomous Region (Region 5) located in the high latitude area was the only region in the middle temperate zone of all sample collection areas in which the samples belonged to an individual class no matter their distance in the tree diagram. The samples were from a relatively high elevation in the Shennongjia Forest District in Hubei Province (>1200 m), which is the main difference from the samples from Xiangyang City (78 m). Thus, the sample clusters from region 9 are different from the sample clusters from other regions. The results would provide a reference for further research to those samples from the special cluster.
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Affiliation(s)
- Chao-Yong Wang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - Li Tang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of A & F Science and Technology, Hunan Applied Technology University, Changde, China
| | - Li Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - Qiang Zhou
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
| | - You-Ji Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Chemistry and Chemical Engineering, Jishou University, Jishou, China
| | - Jing Li
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- College of Biological Resources and Environmental Sciences, Jishou University, Jishou, China
- *Correspondence: Jing Li, ; Yuan-Zhong Wang,
| | - Yuan-Zhong Wang
- National & Local United Engineering Laboratory of Integrative Utilization Technology of Eucommia Ulmoides, Jishou University, Jishou, China
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
- *Correspondence: Jing Li, ; Yuan-Zhong Wang,
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27
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Pei Y, Zhang Q, Wang Y. Application of Authentication Evaluation Techniques of Ethnobotanical Medicinal Plant Genus Paris: A Review. Crit Rev Anal Chem 2019; 50:405-423. [DOI: 10.1080/10408347.2019.1642734] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
Affiliation(s)
- Yifei Pei
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Qingzhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China
| | - Yuanzhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming, China
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28
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Pei YF, Zuo ZT, Zhang QZ, Wang YZ. Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis. Molecules 2019; 24:molecules24142559. [PMID: 31337084 PMCID: PMC6680555 DOI: 10.3390/molecules24142559] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 07/08/2019] [Accepted: 07/11/2019] [Indexed: 02/07/2023] Open
Abstract
Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.
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Affiliation(s)
- Yi-Fei Pei
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China
| | - Zhi-Tian Zuo
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China
| | - Qing-Zhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China.
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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29
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Wang Y, Zuo ZT, Huang HY, Wang YZ. Original plant traceability of Dendrobium species using multi-spectroscopy fusion and mathematical models. ROYAL SOCIETY OPEN SCIENCE 2019; 6:190399. [PMID: 31218070 PMCID: PMC6549973 DOI: 10.1098/rsos.190399] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 04/15/2019] [Indexed: 05/13/2023]
Abstract
Dendrobium is the largest genus of orchids most of which have excellent medicinal properties. Fresh stems of some species have been consumed in daily life by Asians for thousands of years. However, there are differences in flavour and clinical efficacy among different species. Therefore, it is necessary for a detector to establish an effective and rapid method controlling botanical origins of these crude materials. In our study, three spectroscopies including mid-infrared (MIR) (transmission and reflection mode) and near-infrared (NIR) spectra were investigated for authentication of 12 Dendrobium species. Generally, two fusion strategies, reflection MIR and NIR spectra, were combined with three mathematical models (random forest, support vector machine with grid search (SVM-GS) and partial least-squares discrimination analysis (PLS-DA)) for discrimination analysis. In conclusion, a low-level fusion strategy comprising two spectra after pretreated by the second derivative and multiplicative scatter correction was recommended for discrimination analysis because of its excellent performance in three models. Compared with MIR spectra, NIR spectra were more responsible for the discrimination according to a bi-plot analysis of PLS-DA. Moreover, SVM-GS and PLS-DA were suitable for accurate discrimination (100% accuracy rates) of calibration and validation sets. The protocol combined with low-level fusion strategy and chemometrics provides a rapid and effective reference for control of botanical origins in crude Dendrobium materials.
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Affiliation(s)
- Ye Wang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, People's Republic of China
| | - Zhi-Tian Zuo
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, People's Republic of China
| | - Heng-Yu Huang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, People's Republic of China
- Authors for correspondence: Heng-Yu Huang e-mail:
| | - Yuan-Zhong Wang
- College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, People's Republic of China
- Authors for correspondence: Yuan-Zhong Wang e-mail:
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30
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Wu XM, Zhang QZ, Wang YZ. Traceability the provenience of cultivated Paris polyphylla Smith var. yunnanensis using ATR-FTIR spectroscopy combined with chemometrics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 212:132-145. [PMID: 30639599 DOI: 10.1016/j.saa.2019.01.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 05/20/2023]
Abstract
The conventional procedures, based on attenuated total reflectance-Fourier transform infrared spectrometry (ATR-FTIR), have been developed for the origins traceability of cultivated Paris polyphylla Smith var. yunnanensis (PPY) samples with the help of partial least square discriminant analysis (PLS-DA) and random forest. In this study, a set of 219 batch cultivated PPY samples, containing the cultivation years of 5, 6 and 7, and covering the municipal districts of Chuxiong, Dali, Honghe, Lijiang and Yuxi in Yunnan Province, China, were used to build the discrimination models. Firstly, a visualized analysis was carried out by t-distributed stochastic neighbor embedding (t-SNE) to reduce each data point in a two-dimensional map and make a knowledge of the sample distribution tendency. Secondly, the single spectra data sets of Paridis rhizome and leaf tissues, and the combination of these two data sets with variable selection (mid-level data fusion strategy), were used to establish PLS-DA and random forest models, and parallelly compared the model performance. Results demonstrated that the discrimination ability of PLS-DA preceded the random forest model, and the classification performance was remarkably improved after mid-level data fusion. These results verified each other by 5-, 6- and 7-year old Paridis samples and indicated that the model performance established in the present study was reliable. Besides, five agronomic characters, including the plant height, dry weight of rhizome and leaf tissues, and the allocation of rhizome and leaf were determined and analyzed, results of which indicated that the dry weight and their allocation was significantly different among various origins and fluctuated with the cultivation years. This study was using a comprehensive and green analytical method to discriminate the cultivated Paridis according to their provenances, which was simultaneously benefited for the appropriate cultivation areas selection based on the dry weight of rhizome tissues.
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Affiliation(s)
- Xue-Mei Wu
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
| | - Qing-Zhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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Geographical Authentication of Macrohyporia cocos by a Data Fusion Method Combining Ultra-Fast Liquid Chromatography and Fourier Transform Infrared Spectroscopy. Molecules 2019; 24:molecules24071320. [PMID: 30987245 PMCID: PMC6479993 DOI: 10.3390/molecules24071320] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 03/28/2019] [Accepted: 04/01/2019] [Indexed: 12/16/2022] Open
Abstract
Macrohyporia cocos is a medicinal and edible fungi, which is consumed widely. The epidermis and inner part of its sclerotium are used separately. M. cocos quality is influenced by geographical origins, so an effective and accurate geographical authentication method is required. Liquid chromatograms at 242 nm and 210 nm (LC242 and LC210) and Fourier transform infrared (FTIR) spectra of two parts were applied to authenticate the geographical origin of cultivated M. cocos combined with low and mid-level data fusion strategies, and partial least squares discriminant analysis. Data pretreatment involved correlation optimized warping and second derivative. The results showed that the potential of the chromatographic fingerprint was greater than that of five triterpene acids contents. LC242-FTIR low-level fusion took full advantage of information synergy and showed good performance. Further, the predictive ability of the FTIR low-level fusion model of two parts was satisfactory. The performance of the low-level fusion strategy preceded those of the single technique and mid-level fusion strategy. The inner parts were more suitable for origin identification than the epidermis. This study proved the feasibility of the data fusion of chromatograms and spectra, and the data fusion of different parts for the accurate authentication of geographical origin. This method is meaningful for the quality control of food and the protection of geographical indication products.
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32
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Pei YF, Zhang QZ, Zuo ZT, Wang YZ. Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics. Molecules 2018; 23:molecules23123343. [PMID: 30563007 PMCID: PMC6320853 DOI: 10.3390/molecules23123343] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 12/12/2018] [Accepted: 12/14/2018] [Indexed: 12/14/2022] Open
Abstract
Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.
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Affiliation(s)
- Yi-Fei Pei
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
- College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China.
| | - Qing-Zhi Zhang
- College of Traditional Chinese Medicine, Yunnan University of Traditional Chinese Medicine, Kunming 650500, China.
| | - Zhi-Tian Zuo
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
| | - Yuan-Zhong Wang
- Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
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Wu XM, Zuo ZT, Zhang QZ, Wang YZ. Classification of Paris species according to botanical and geographical origins based on spectroscopic, chromatographic, conventional chemometric analysis and data fusion strategy. Microchem J 2018. [DOI: 10.1016/j.microc.2018.08.035] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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