1
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Song G, Zeng M, Chen S, Lyu Z, Jiang N, Wang D, Yuan T, Li L, Mei G, Shen Q, Gong J. Exploring molecular mechanisms underlying changes in lipid fingerprinting of salmon (Salmo salar) during air frying integrating machine learning-guided REIMS and lipidomics analysis. Food Chem 2024; 460:140770. [PMID: 39121777 DOI: 10.1016/j.foodchem.2024.140770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 07/21/2024] [Accepted: 08/03/2024] [Indexed: 08/12/2024]
Abstract
Lipid oxidation in air-fried seafood poses a risk to human health. However, the effect of a prooxidant environment on lipid oxidation in seafood at different air frying (AF) temperatures remains unknown. An integrated machine learning (ML) - guided REIMS and lipidomics method was applied to explore lipid profiles, lipid oxidation, and lipid metabolic pathways of salmons under different AF temperatures (140, 160, 180, and 200 °C). A significant difference in the lipidomic fingerprinting of air-dried salmon at different temperatures was shown by the main ML methods (neural networks, support vector machines, ensemble learning, and naïve bayes). In total, 773 differential expression metabolites (DEMs) were identified, including glycerophospholipids (GPs), glycerides (GLs), and sphingolipids. A total of 34 DEMs with p values <0.05 and variable importance of projection values >1.0 were analyzed, belonging to linoleic acid metabolism, GL metabolism, and GP metabolism pathways. Correlation network analysis revealed that some characteristic DEMs (phosphatidylcholine, lyso-phosphatidylcholine, triglycerides, fatty acids, and phosphatidylethanolamine) were highly correlated with lipid oxidation. In addition, variations of volatile compounds, color values, texture characteristics, and thiobarbituric acid-reactive substance values were analyzed to corroborate the oxidation characteristics.
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Affiliation(s)
- Gongshuai Song
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China; Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China; Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310012, Zhejiang, China
| | - Mingwei Zeng
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
| | - Shengjun Chen
- Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China.
| | - Zhangfan Lyu
- School of Human Nutrition, McGill University, Montreal, QC H9X 3V9, Canada
| | - Nengliang Jiang
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
| | - Danli Wang
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
| | - Tinglan Yuan
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
| | - Ling Li
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
| | - Guangming Mei
- Zhejiang Marine Fisheries Research Institute, Zhoushan, 316021, China.
| | - Qing Shen
- Collaborative Innovation Center of Seafood Deep Processing, Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310012, Zhejiang, China.
| | - Jinyan Gong
- Zhejiang Provincial Key Lab for Biological and Chemical Processing Technologies of Farm Product, School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China.
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2
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Wang M, Wang W, Zhang X, Dai G, Tang K. Formulation analysis of functional fragrance via polar-gradient extraction method and chemometrics pattern recognition. Talanta 2024; 275:126121. [PMID: 38688086 DOI: 10.1016/j.talanta.2024.126121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/11/2024] [Accepted: 04/14/2024] [Indexed: 05/02/2024]
Abstract
In this study, characteristic components of 15 natural flavors was analyzed by the polar-gradient extraction (PGE) technique in combination with GC-MS and chemometrics pattern recognition. The obtained results were utilized for the traceability of 4 functional fragrance formulations. The optimal PGE system consisting of 5 different polar solvents, was developed based on similarity-intermiscibility theory. Four chemometrics pattern recognition models including PCA, HCA, PLS-DA, and OPLS-DA were constructed based on the characteristic component database constituting 15 natural flavors. These models were used to trace 4 functional fragrance formulations. The experimental results obtained were found to be satisfactory and accurate. The combination of PGE technique and chemometric pattern recognition methods provides theoretical guidance for the analysis of characteristic components of natural flavors and the traceability of functional fragrance formulations. This approach can be promoted in various fields such as food, traditional Chinese medicine, and cosmetics.
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Affiliation(s)
- Meijin Wang
- Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang, China
| | - Wanru Wang
- Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang, China
| | - Xiaohua Zhang
- Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang, China.
| | - Guilin Dai
- Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang, China
| | - Kewen Tang
- Department of Chemistry and Chemical Engineering, Hunan Institute of Science and Technology, Yueyang, China.
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3
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d’Aquino L, Cozzolino R, Malorni L, Bodhuin T, Gambale E, Sighicelli M, Della Mura B, Matarazzo C, Piacente S, Montoro P. Light Flux Density and Photoperiod Affect Growth and Secondary Metabolism in Fully Expanded Basil Plants. Foods 2024; 13:2273. [PMID: 39063357 PMCID: PMC11275332 DOI: 10.3390/foods13142273] [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: 06/26/2024] [Revised: 07/12/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024] Open
Abstract
Indoor production of basil (Ocimum basilicum L.) is influenced by light spectrum, photosynthetic photon flux density (PPFD), and the photoperiod. To investigate the effects of different lighting on growth, chlorophyll content, and secondary metabolism, basil plants were grown from seedlings to fully expanded plants in microcosm devices under different light conditions: (a) white light at 250 and 380 μmol·m-2·s-1 under 16/8 h light/dark and (b) white light at 380 μmol·m-2·s-1 under 16/8 and 24/0 h light/dark. A higher yield was recorded under 380 μmol·m-2·s-1 compared to 250 μmol·m-2·s-1 (fresh and dry biomasses 260.6 ± 11.3 g vs. 144.9 ± 14.6 g and 34.1 ± 2.6 g vs. 13.2 ± 1.4 g, respectively), but not under longer photoperiods. No differences in plant height and chlorophyll content index were recorded, regardless of the PPFD level and photoperiod length. Almost the same volatile organic compounds (VOCs) were detected under the different lighting treatments, belonging to terpenes, aldehydes, alcohols, esters, and ketones. Linalool, eucalyptol, and eugenol were the main VOCs regardless of the lighting conditions. The multivariate data analysis showed a sharp separation of non-volatile metabolites in apical and middle leaves, but this was not related to different PPFD levels. Higher levels of sesquiterpenes and monoterpenes were detected in plants grown under 250 μmol·m-2·s-1 and 380 μmol·m-2·s-1, respectively. A low separation of non-volatile metabolites based on the photoperiod length and VOC overexpression under longer photoperiods were also highlighted.
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Affiliation(s)
- Luigi d’Aquino
- Italian National Agency for New Technologies Energy and Sustainable Economic Development (ENEA), Portici Research Centre, Piazzale E. Fermi 1, 80055 Portici, Italy;
| | - Rosaria Cozzolino
- Institute of Food Science, National Council of Research (CNR), Via Roma 64, 83100 Avellino, Italy; (L.M.); (C.M.)
| | - Livia Malorni
- Institute of Food Science, National Council of Research (CNR), Via Roma 64, 83100 Avellino, Italy; (L.M.); (C.M.)
| | | | - Emilia Gambale
- Italian National Agency for New Technologies Energy and Sustainable Economic Development (ENEA), Portici Research Centre, Piazzale E. Fermi 1, 80055 Portici, Italy;
| | - Maria Sighicelli
- Italian National Agency for New Technologies Energy and Sustainable Economic Development (ENEA), Casaccia Research Centre, Via Anguillarese 301, Santa Maria di Galeria, 00060 Roma, Italy;
| | - Brigida Della Mura
- Department of Science, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, Italy;
| | - Cristina Matarazzo
- Institute of Food Science, National Council of Research (CNR), Via Roma 64, 83100 Avellino, Italy; (L.M.); (C.M.)
| | - Sonia Piacente
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 84084 Fisciano, Italy; (S.P.); (P.M.)
| | - Paola Montoro
- Department of Pharmacy, University of Salerno, Via Giovanni Paolo II, 84084 Fisciano, Italy; (S.P.); (P.M.)
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4
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Chen B, Wang L, Wang L, Han Y, Yan G, Chen L, Li C, Zhu Y, Lu J, Han L. A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:14865-14873. [PMID: 38912709 DOI: 10.1021/acs.jafc.4c00607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.
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Affiliation(s)
- Biying Chen
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, P. R. China
| | - Li Wang
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Liming Wang
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, P. R. China
| | - Yueran Han
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Guokai Yan
- Guizhou Guotai Liquor Group Co., Ltd., Renhuai 564500, P. R. China
| | - Liangjie Chen
- Guizhou Guotai Liquor Group Co., Ltd., Renhuai 564500, P. R. China
| | - Changwen Li
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Yu Zhu
- Department of Clinical Laboratory, Nankai University Affiliated Third Central Hospital, Tianjin 300170, P. R. China
- Department of Clinical Laboratory, The Third Central Hospital of Tianjin, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Artificial Cell Engineering Technology Research Center of Tianjin, Tianjin Institute of Hepatobiliary Disease, Tianjin 300170, P. R. China
| | - Jun Lu
- Guotai Research Academy, Guizhou Guotai Liquor Group Co., Ltd., 1 Tingjiang Road, Tianjin 300410, P. R. China
| | - Lifeng Han
- State Key Laboratory of Component-based Chinese Medicine, Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, P. R. China
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Cafarella C, Mangraviti D, Rigano F, Dugo P, Mondello L. Rapid evaporative ionization mass spectrometry: A survey through 15 years of applications. J Sep Sci 2024; 47:e2400155. [PMID: 38772742 DOI: 10.1002/jssc.202400155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
Rapid evaporative ionization mass spectrometry (REIMS) is a relatively recent MS technique explored in many application fields, demonstrating high versatility in the detection of a wide range of chemicals, from small molecules (phenols, amino acids, di- and tripeptides, organic acids, and sugars) to larger biomolecules, that is, phospholipids and triacylglycerols. Different sampling devices were used depending on the analyzed matrix (liquid or solid), resulting in distinct performances in terms of automation, reproducibility, and sensitivity. The absence of laborious and time-consuming sample preparation procedures and chromatographic separations was highlighted as a major advantage compared to chromatographic methods. REIMS was successfully used to achieve a comprehensive sample profiling according to a metabolomics untargeted analysis. Moreover, when a multitude of samples were available, the combination with chemometrics allowed rapid sample differentiation and the identification of discriminant features. The present review aims to provide a survey of literature reports based on the use of such analytical technology, highlighting its mode of operation in different application areas, ranging from clinical research, mostly focused on cancer diagnosis for the accurate identification of tumor margins, to the agri-food sector aiming at the safeguard of food quality and security.
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Affiliation(s)
- Cinzia Cafarella
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Domenica Mangraviti
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Francesca Rigano
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
| | - Paola Dugo
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
| | - Luigi Mondello
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Messina Institute of Technology, former Veterinary School, University of Messina, Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Chromaleont s.r.l., former Veterinary School, University of Messina, Messina, Italy
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6
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Li Y, Logan N, Quinn B, Hong Y, Birse N, Zhu H, Haughey S, Elliott CT, Wu D. Fingerprinting black tea: When spectroscopy meets machine learning a novel workflow for geographical origin identification. Food Chem 2024; 438:138029. [PMID: 38006696 DOI: 10.1016/j.foodchem.2023.138029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 10/29/2023] [Accepted: 11/14/2023] [Indexed: 11/27/2023]
Abstract
Food fraud, along with many challenges to the integrity and sustainability, threatens the prosperity of businesses and society as a whole. Tea is the second most commonly consumed non-alcoholic beverage globally. Challenges to tea authenticity require the development of highly efficient and rapid solutions to improve supply chain transparency. This study has produced an innovative workflow for black tea geographical indications (GI) discrimination based on non-targeted spectroscopic fingerprinting techniques. A total of 360 samples originating from nine GI regions worldwide were analysed by Fourier Transform Infrared (FTIR) and Near Infrared spectroscopy. Machine learning algorithms (k-nearest neighbours and support vector machine models) applied to the test data greatly improved the GI identification achieving 100% accuracy using FTIR. This workflow will provide a low-cost and user-friendly solution for on-site and real-time determination of black tea geographical origin along supply chains.
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Affiliation(s)
- Yicong Li
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Natasha Logan
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Brian Quinn
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Yunhe Hong
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Nicholas Birse
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Hao Zhu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Simon Haughey
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK
| | - Christopher T Elliott
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University (Rangsit Campus), Khlong Luang, Pathum Thani 12120, Thailand
| | - Di Wu
- National Measurement Laboratory: Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast, Northern Ireland BT9 5DL, UK.
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7
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Hong Y, Birse N, Quinn B, Li Y, Jia W, van Ruth S, Elliott CT. MALDI-ToF MS and chemometric analysis as a tool for identifying wild and farmed salmon. Food Chem 2024; 432:137279. [PMID: 37657341 DOI: 10.1016/j.foodchem.2023.137279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/03/2023]
Abstract
In this study, the difference between wild and farmed salmon production was successfully profiled and differentiated by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-ToF MS) combined with chemometric analysis. The established method based on multivariate analysis mainly involved principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) as the screening and verifying tools to provide insights into the distinctive features found in wild and farmed salmon products, respectively. The discrimination between farmed and wild salmon was accomplished with 100% classification accuracy using chemometric models, 100% identification accuracy was also achieved in distinguishing wild Salmo salar and Oncorhynchus nerka samples. The results of the present work suggest that the proposed method could serve as a reference for detecting salmon fraud relating to wild or farmed production and expand the application of MALDI-ToF technology further into food authenticity applications.
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Affiliation(s)
- Yunhe Hong
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Nicholas Birse
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom.
| | - Brian Quinn
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Yicong Li
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Wenyang Jia
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom
| | - Saskia van Ruth
- Food Quality and Design Group, Wageningen University and Research, Wageningen, Netherlands; School of Agriculture and Food Science, University College Dublin, Dublin 4, Ireland
| | - Christopher T Elliott
- National Measurement Laboratory, Centre of Excellence in Agriculture and Food Integrity, Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, United Kingdom; School of Food Science and Technology, Faculty of Science and Technology, Thammasat University, 99 Mhu 18, Pahonyothin Road, Khong Luang, Pathum Thani 12120, Thailand
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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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