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Liang Y, Wang Z, Zhang L, Dai H, Wu W, Zheng Z, Lin F, Xu J, Huang Y, Sun W. Characterization of volatile compounds and identification of key aroma compounds in different aroma types of Rougui Wuyi rock tea. Food Chem 2024; 455:139931. [PMID: 38850976 DOI: 10.1016/j.foodchem.2024.139931] [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: 02/27/2024] [Revised: 05/24/2024] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
In this study, we characterized the aroma profiles of different Rougui Wuyi rock tea (RGWRT) aroma types and identified the key aroma-active compounds producing these differences. The roasting process was found to have a considerable effect on the aroma profiles. Eleven aroma compounds, including linalool, β-ionone, geraniol, indole, and (E)-nerolidol, strongly affected the aroma profiles. An RGWRT aroma wheel was constructed. The rich RGWRT aroma was found to be dominated by floral, cinnamon-like, and roasty aromas. Human olfaction was correlated with volatile compounds to determine the aromatic characteristics of these compounds. Most key aroma-active compounds were found to have floral, sweet, and herbal aromas (as well as some other aroma descriptors). The differences in key compounds of different aroma types were found to result from the methylerythritol phosphate, mevalonic acid and shikimate metabolic pathways and the Maillard reaction. Linalool, geraniol, and (E,E)-2,4-heptadienal were found to spontaneously bind to olfactory receptors.
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Affiliation(s)
- Yilin Liang
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zhihui Wang
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Lingzhi Zhang
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Haomin Dai
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Weiwei Wu
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Zhiqiang Zheng
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Fuming Lin
- Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 362406, China
| | - Jie Xu
- Wuyi Star Tea Industrial Company Limited, Wuyishan 354301, China
| | - Yan Huang
- Anxi College of Tea Science, Fujian Agriculture and Forestry University, Quanzhou 362406, China.
| | - Weijiang Sun
- College of Horticulture, Fujian Agriculture and Forestry University, Fuzhou 350002, 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 L, He M, Wang X, Guo S, Zhang Y, Li W. Fast Discrimination and Quantification Analysis of Atractylodis rhizoma Using NIR Spectroscopy Coupled with Chemometrics Tools. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:7707-7715. [PMID: 38530236 DOI: 10.1021/acs.jafc.3c08812] [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: 03/27/2024]
Abstract
In this study, near-infrared (NIR) spectroscopy and high-performance liquid chromatography (HPLC) combined with chemometrics tools were applied for quick discrimination and quantitative analysis of different varieties and origins of Atractylodis rhizoma samples. Based on NIR data, orthogonal partial least squares discriminant analysis (OPLS-DA) and K-nearest neighbor (KNN) models achieved greater than 90% discriminant accuracy of the three species and two origins of Atractylodis rhizoma. Moreover, the contents of three active ingredients (atractyloxin, atractylone, and β-eudesmol) in Atractylodis rhizoma were simultaneously determined by HPLC. There are significant differences in the content of the three components in the samples of Atractylodis rhizoma from different varieties and origins. Then, partial least squares regression (PLSR) models for the prediction of atractyloxin, atractylone, and β-eudesmol content were successfully established. The complete Atractylodis rhizoma spectra gave rise to good predictions of atractyloxin, atractylone, and β-eudesmol content with R2 values of 0.9642, 0.9588, and 0.9812, respectively. Based on the results of this present research, it can be concluded that NIR is a great nondestructive alternative to be applied as a rapid classification system by the drug industry.
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Affiliation(s)
- Le Peng
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Mulan He
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Xi Wang
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Shubo Guo
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Yazhong Zhang
- Anhui Institute for Food and Drug Control, Hefei 230051, China
| | - Wenlong Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
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He G, Yang SB, Wang YZ. A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition. J Food Sci 2024; 89:2316-2331. [PMID: 38369957 DOI: 10.1111/1750-3841.16989] [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: 10/12/2023] [Revised: 01/20/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024]
Abstract
Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.
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Affiliation(s)
- Gang He
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
- College of Food Science and Technology, Yunnan Agricultural University, Kunming, China
| | - Shao-Bing Yang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
| | - Yuan-Zhong Wang
- Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, China
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Wu B, Tang W, Zhou J, Jia H, Shen H, Qi Z. Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades. Heliyon 2024; 10:e27732. [PMID: 38486786 PMCID: PMC10938135 DOI: 10.1016/j.heliyon.2024.e27732] [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: 08/06/2023] [Revised: 12/10/2023] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Mee tea, one of the major types of green tea in China, is often used for export because of its elegant appearance, high fragrance and strong taste. However, the quality of tea differs greatly due to the difference in raw material selection and production technology level. In order to accurately and quickly differentiate different grades of Mee tea, fuzzy fast pseudoinverse linear discriminant analysis (FFPLDA) was proposed based on fast pseudoinverse linear discriminant analysis (FPLDA) for extracting discriminant information from near-infrared (NIR) spectra. Firstly, NIR spectra of Mee tea samples were acquired, and then they were preprocessed by multiplicative scatter correlation (MSC). Secondly, the compression of data was achieved by principal component analysis (PCA). Thirdly, linear discriminant analysis (LDA), FPLDA, FFPLDA and fuzzy Foley-Sammon transformation (FFST) were respectively performed to retrieve discriminant information from NIR data. Finally, the K-nearest neighbor (KNN) was utilized to classify Mee tea grades. In this study, experimental results showed that the accuracy of FFPLDA was higher than that of LDA, FFST and FPLDA. Therefore, NIR spectroscopy coupled with FFPLDA and KNN has a good effect in discrimination of Mee tea grades and also a great application potential.
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Affiliation(s)
- Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China
| | - Wenbo Tang
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Jin Zhou
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Hongwen Jia
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou, 239000, China
| | - Hualei Shen
- Institute of Talented Engineering Students, Jiangsu University, Zhenjiang, 212013, China
| | - Zuxuan Qi
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, 212013, China
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Ni W, Yu Y, Gao X, Han Y, Zhang W, Zhang Z, Xiao W, Hu Q, Zhang Y, Huang H, Li F, Chen M, Han J. Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay. Anal Chem 2024; 96:301-308. [PMID: 38102984 DOI: 10.1021/acs.analchem.3c04107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.
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Affiliation(s)
- Weiwei Ni
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Yang Yu
- School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 211109, China
| | - Xu Gao
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Yang Han
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211109, China
| | - Wenhui Zhang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Zerui Zhang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Wenqi Xiao
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Qin Hu
- Department of Laboratory Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Yanliang Zhang
- Nanjing Research Center for Infectious Diseases of Integrated Traditional Chinese and Western Medicine, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing 211109, China
| | - Hui Huang
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Fei Li
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Mingqi Chen
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
| | - Jinsong Han
- State Key Laboratory of Natural Medicines, College of Engineering, China Pharmaceutical University, Nanjing 211109, China
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