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Zhang D, Huang Y, Fan X, Zeng X. Effects of solid-state fermentation with Aspergillus cristatus (MK346334) on the dynamics changes in the chemical and flavor profile of dark tea by HS-SPME-GC-MS, HS-GC-IMS and electronic nose. Food Chem 2024; 455:139864. [PMID: 38833862 DOI: 10.1016/j.foodchem.2024.139864] [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: 02/18/2024] [Revised: 05/04/2024] [Accepted: 05/26/2024] [Indexed: 06/06/2024]
Abstract
Aspergillus cristatus, the predominant microbe of Fuzhuan brick tea (FBT), is responsible for the creation of distinctive golden flower and unique floral aroma of FBT. The present study examined the alterations in chemical and aromatic components of raw dark tea by solid-state fermentation using A. cristatus (MK346334), the strain isolated from FBT. As results, catechins, total ployphenols, total flavonoids, theaflavins, thearubigins and antioxidant activity were significantly reduced after fermentation. Moreover, 112 and 76 volatile substances were identified by HS-SPME-GC-MS and HS-GC-IMS, respectively, primarily composed of alcohols, ketones, esters and aldehydes. Furthermore, the calculation of odor activity values revealed that 19 volatile chemicals, including hexanal, heptanal, linalool and methyl salicylate, were the main contributors to the floral, fungal, woody and minty aroma of dark tea. The present research highlights the pivotal role played by the fermentation with A. cristatus in the chemical composition, antioxidant property and distinctive flavor of dark tea.
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Affiliation(s)
- Di Zhang
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
| | - Yujie Huang
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
| | - Xia Fan
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
| | - Xiaoxiong Zeng
- College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.
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Wang Z, Han Y, Zhang L, Ye Y, Wei L, Li L. The utilization of a data fusion approach to investigate fingerprint profiles of dark tea from China's different altitudes. Food Chem X 2024; 22:101447. [PMID: 38779497 PMCID: PMC11108843 DOI: 10.1016/j.fochx.2024.101447] [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: 01/02/2024] [Revised: 04/21/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024] Open
Abstract
Dark tea refers to a kind of post-fermented product, and its quality and price vary owing to the distinct altitudes at which it grows. In this study, a novel method based on high performance liquid chromatography with a diode-array detector (HPLC-DAD) and an evaporative light scattering detector (HPLC-ELSD) was proposed for the classification of dark teas from distinct altitudes in China. Through implementing a strategy fusing feature-level data to construct a combined dataset, the classification performance of dark teas from distinct altitudes in China was evaluated after preprocessing. The results suggested that, through the feature fusion strategy, the identification accuracy rate increased from <70% of a single detector to 76.923%. After the implementation of preprocessing, the identification accuracy rate was further improved. Typically, the model identification accuracy rate after short-time Fourier Transform (STFT) treatment reached 92.85%, and the AUROC value was higher than 0.84, exhibiting a favorable generalization ability. This study provides a new thinking for the identification technology of dark teas from different altitudes in China.
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Affiliation(s)
- Zhenhong Wang
- Resources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
| | - Yuanxi Han
- Food Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China
| | - Liyou Zhang
- Resources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
| | - Yongxiang Ye
- Food Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China
| | - Liping Wei
- Resources & Environment College, Tibet Agriculture & Animal Husbandry University; Tea Industry Engineering Center of Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
| | - Liang Li
- Food Science College, Tibet Agriculture & Animal Husbandry University; R&D Center of Agricultural Products with Tibetan Plateau Characteristics; The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Tibet Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China
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Peng B, Xie Y, Lai Q, Liu W, Ye X, Yin L, Zhang W, Xiong S, Wang H, Chen H. Pesticide residue detection technology for herbal medicine: current status, challenges, and prospects. ANAL SCI 2024; 40:581-597. [PMID: 38367162 DOI: 10.1007/s44211-024-00515-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/17/2024] [Indexed: 02/19/2024]
Abstract
The domains of cancer therapy, disease prevention, and health care greatly benefit from the use of herbal medicine. Herbal medicine has become the mainstay of developing characteristic agriculture in the planting area increasing year by year. One of the most significant factors in affecting the quality of herbal medicines is the pesticide residue problem caused by pesticide abuse during the cultivation of herbal medicines. It is urgent to solve the problem of detecting pesticide residues in herbal medicines efficiently and rapidly. In this review, we provide a comprehensive description of the various methods used for pesticide residue testing, including optical detection, the enzyme inhibition rate method, molecular detection methods, enzyme immunoassays, lateral immunochromatographic, nanoparticle-based detection methods, colorimetric immunosensor, chemiluminescence immunosensor, smartphone-based immunosensor, etc. On this basis, we systematically analyze the mechanisms and some of the findings of the above detection strategies and discuss the challenges and prospects associated with the development of pesticide residue detection tools.
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Affiliation(s)
- Bin Peng
- Guangzhou Huashang Vocational College, Guangzhou, 510000, China
| | - Yueliang Xie
- Guangdong Agriculture Industry Business Polytechnic, Guangzhou, 510000, China
| | - Qingfu Lai
- Guangzhou Huashang Vocational College, Guangzhou, 510000, China
| | - Wen Liu
- Guangdong Agriculture Industry Business Polytechnic, Guangzhou, 510000, China
| | - Xuelan Ye
- Guangzhou Huashang Vocational College, Guangzhou, 510000, China
| | - Li Yin
- Guangzhou Huashang Vocational College, Guangzhou, 510000, China
| | - Wanxin Zhang
- Guangzhou Huashang Vocational College, Guangzhou, 510000, China
| | - Suqin Xiong
- Guangzhou Huashang Vocational College, Guangzhou, 510000, China
| | - Heng Wang
- Guangdong Haid Group Co., Ltd, Guangzhou, 510000, China.
| | - Hui Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.
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Deng H, Liu J, Xiao Y, Wu JL, Jiao R. Possible Mechanisms of Dark Tea in Cancer Prevention and Management: A Comprehensive Review. Nutrients 2023; 15:3903. [PMID: 37764687 PMCID: PMC10534731 DOI: 10.3390/nu15183903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Tea is one of the most popular drinks in the world. Dark tea is a kind of post-fermented tea with unique sensory characteristics that is produced by the special fermentation of microorganisms. It contains many bioactive substances, such as tea polyphenols, theabrownin, tea polysaccharides, etc., which have been reported to be beneficial to human health. This paper reviewed the latest research on dark tea's potential in preventing and managing cancer, and the mechanisms mainly involved anti-oxidation, anti-inflammation, inhibiting cancer cell proliferation, inducing cancer cell apoptosis, inhibiting tumor metastasis, and regulating intestinal flora. The purpose of this review is to accumulate evidence on the anti-cancer effects of dark tea, the corresponding mechanisms and limitations of dark tea for cancer prevention and management, the future prospects, and demanding questions about dark tea's possible contributions as an anti-cancer adjuvant.
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Affiliation(s)
- Huilin Deng
- Department of Food Science and Engineering, Institute of Science and Technology, Jinan University, 601 Huangpu Road, Guangzhou 510632, China; (H.D.); (J.L.)
| | - Jia Liu
- Department of Food Science and Engineering, Institute of Science and Technology, Jinan University, 601 Huangpu Road, Guangzhou 510632, China; (H.D.); (J.L.)
| | - Ying Xiao
- Faculty of Medicine, Macau University of Science and Technology, Macau, China;
| | - Jian-Lin Wu
- State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China;
| | - Rui Jiao
- Department of Food Science and Engineering, Institute of Science and Technology, Jinan University, 601 Huangpu Road, Guangzhou 510632, China; (H.D.); (J.L.)
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Cao Q, Zhao C, Bai B, Cai J, Chen L, Wang F, Xu B, Duan D, Jiang P, Meng X, Yang G. Oolong tea cultivars categorization and germination period classification based on multispectral information. FRONTIERS IN PLANT SCIENCE 2023; 14:1251418. [PMID: 37705705 PMCID: PMC10495989 DOI: 10.3389/fpls.2023.1251418] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
Recognizing and identifying tea plant (Camellia sinensis) cultivar plays a significant role in tea planting and germplasm resource management, particularly for oolong tea. There is a wide range of high-quality oolong tea with diverse varieties of tea plants that are suitable for oolong tea production. The conventional method for identifying and confirming tea cultivars involves visual assessment. Machine learning and computer vision-based automatic classification methods offer efficient and non-invasive alternatives for rapid categorization. Despite advancements in technology, the identification and classification of tea cultivars still pose a complex challenge. This paper utilized machine learning approaches for classifying 18 oolong tea cultivars based on 27 multispectral characteristics. Then the SVM classification model was executed using three optimization algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The results revealed that the SVM model optimized by GWO achieved the best performance, with an average discrimination rate of 99.91%, 93.30% and 92.63% for the training set, test set and validation set, respectively. In addition, based on the multispectral information (h, s, r, b, L, Asm, Var, Hom, Dis, σ, S, G, RVI, DVI, VOG), the germination period of oolong tea cultivars can be completely evaluated by Fisher discriminant analysis. The study indicated that the practical protection of tea plants through automated and precise classification of oolong tea cultivars and germination periods is feasible by utilizing multispectral imaging system.
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Affiliation(s)
- Qiong Cao
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Hunan Agricultural University College of Mechanical and Electronical Engineering, Changsha, Hunan, China
| | - Chunjiang Zhao
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
- Hunan Agricultural University College of Mechanical and Electronical Engineering, Changsha, Hunan, China
| | - Bingnan Bai
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jie Cai
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Longyue Chen
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Fan Wang
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Bo Xu
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Dandan Duan
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Ping Jiang
- Hunan Agricultural University College of Mechanical and Electronical Engineering, Changsha, Hunan, China
| | - Xiangyu Meng
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guijun Yang
- Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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