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Chen Y, Guo M, Chen K, Jiang X, Ding Z, Zhang H, Lu M, Qi D, Dong C. Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion. Talanta 2024; 273:125892. [PMID: 38493609 DOI: 10.1016/j.talanta.2024.125892] [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: 12/30/2023] [Revised: 02/16/2024] [Accepted: 03/07/2024] [Indexed: 03/19/2024]
Abstract
In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.
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Affiliation(s)
- Yong Chen
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China
| | - Mengqi Guo
- College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou, 450002, China; Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Kai Chen
- Shangrao Normal University, The Innovation Institute of Agricultural Technology, College of Life Science, Shangrao, 334001, China
| | - Xinfeng Jiang
- Jiangxi Institute of Economic Crops, Nanchang, 330046, China
| | - Zezhong Ding
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Haowen Zhang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Min Lu
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Dandan Qi
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
| | - Chunwang Dong
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
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Guo H, Pan Y, Li C, Fu Y, Cao Y, Chu Q, Chen P. Influence of Various Tea Utensils on Sensory and Chemical Quality of Different Teas. PLANTS (BASEL, SWITZERLAND) 2024; 13:669. [PMID: 38475515 DOI: 10.3390/plants13050669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
The choice of tea utensils used for brewing significantly impacts the sensory and chemical attributes of tea. In order to assess the influence of various tea sets on the flavor and chemical composition of different tea varieties, a combination of sensory evaluation and high-performance liquid chromatography was employed. The results showed that the content of amino acids in the tea liquid brewed with tin tea utensils was relatively higher, which could exhibit freshness in taste, thus suitable for brewing green tea and white tea. The content of polyphenols, soluble carbohydrates, and water extract in the tea liquid brewed with a porcelain tea set was relatively higher; the sweetness and thickness of the tea liquid were increased, so it was more beneficial to brew black tea. The purple sand tea set was suitable for brewing oolong tea and dark tea, and could endow their respective quality characteristics. Ultimately, these research findings provide a scientific basis for the selection of tea utensils tailored to different types of tea.
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Affiliation(s)
- Haowei Guo
- Tea Research Institute, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Yani Pan
- Tea Research Institute, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Chunlin Li
- Institute of Agro-Product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
| | - Yi Fu
- Department of Cultural Heritage and Museology, Zhejiang University, Hangzhou 310028, China
| | - Yanyan Cao
- Tea Research Institute, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Qiang Chu
- Tea Research Institute, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China
| | - Ping Chen
- Tea Research Institute, College of Agriculture & Biotechnology, Zhejiang University, Hangzhou 310058, China
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3
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Revilla I, Hernández Jiménez M, Martínez-Martín I, Valderrama P, Rodríguez-Fernández M, Vivar-Quintana AM. The Potential Use of Near Infrared Spectroscopy (NIRS) to Determine the Heavy Metals and the Percentage of Blends in Tea. Foods 2024; 13:450. [PMID: 38338587 PMCID: PMC10855971 DOI: 10.3390/foods13030450] [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: 12/21/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 02/12/2024] Open
Abstract
The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95-85%, 85-75%, or 75-50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation.
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Affiliation(s)
- Isabel Revilla
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Miriam Hernández Jiménez
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Iván Martínez-Martín
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Patricia Valderrama
- Department of Chemistry, Universidade Tecnológica Federal do Paraná (UTFPR), Via Rosalina Maria dos Santos 1233, Campo Mourão 87301-899, Paraná, Brazil
| | - Marta Rodríguez-Fernández
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
| | - Ana M. Vivar-Quintana
- Food Technology, Universidad de Salamanca, E.P.S. de Zamora, Avenida Requejo 33, 49022 Zamora, Spain; (I.R.); (M.H.J.); (I.M.-M.)
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Drees A, Bockmayr B, Bockmayr M, Fischer M. Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning. Foods 2023; 12:2939. [PMID: 37569208 PMCID: PMC10418458 DOI: 10.3390/foods12152939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023] Open
Abstract
Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0-10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg.
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Affiliation(s)
- Alissa Drees
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany;
| | | | - Michael Bockmayr
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany;
- Research Institute Children’s Cancer Center Hamburg, Martinistr. 52, 20251 Hamburg, Germany
| | - Markus Fischer
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany;
- Center for Hybrid Nanostructures (CHyN), Department of Physics, University of Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
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Haruna SA, Li H, Wei W, Geng W, Luo X, Zareef M, Yao-Say Solomon Adade S, Ivane NMA, Isa A, Chen Q. Simultaneous quantification of total flavonoids and phenolic content in raw peanut seeds via NIR spectroscopy coupled with integrated algorithms. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121854. [PMID: 36162210 DOI: 10.1016/j.saa.2022.121854] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/14/2022] [Accepted: 09/03/2022] [Indexed: 06/16/2023]
Abstract
Peanuts are nutritionally valuable for both humans and animals due to their high content of flavonoids and phenolic compounds. Herein, we explored the potential of near-infrared (NIR) spectroscopy coupled with efficient variable selection algorithms for quantitative prediction of total flavonoids (TFC) and total phenolics content (TPC) in raw peanut seeds. Spectrophotometrically, the reference results of the extracts for TFC and TPC were analysed and recorded. The integrated application of the synergy interval coupled competitive adaptive reweighted sampling-partial least squares (Si-CARS-PLS) were used for prediction. The model performance appraisal was based on the correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD). The Si-CARS-PLS performed optimally for TFC (Rp = 0.9137, RPD = 2.49) and TPC (Rp = 0.9042, RPD = 2.31), respectively. Moreover, the model (Si-CARS-PLS) was found to have an acceptable fit for the analytes under study since it achieved 0.88 for TFC and 0.86 for TPC based on the external validation. Therefore, these results showed that NIR coupled with Si-CARS-PLS could be used for the quantitative prediction of flavonoids and phenolic contents in raw peanut seeds.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Xiaofeng Luo
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | | | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China
| | - Adamu Isa
- Department of Food Science & Technology, Kano University of Science & Technology, Wudil, P.M.B 3244 Kano, Kano State, Nigeria
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China; College of Food and Biological Engineering, Jimei University, Xiamen 361021, PR China.
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Jin G, Xu Y, Cui C, Zhu Y, Zong J, Cai H, Ning J, Wei C, Hou R. Rapid identification of the geographic origin of Taiping Houkui green tea using near-infrared spectroscopy combined with a variable selection method. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2022; 102:6123-6130. [PMID: 35474316 DOI: 10.1002/jsfa.11964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 03/24/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy. RESULTS First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set. CONCLUSION The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry.
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Affiliation(s)
- Ge Jin
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Yifan Xu
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Chuanjian Cui
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Yuanyuan Zhu
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Jianfa Zong
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Huimei Cai
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Chaoling Wei
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Ruyan Hou
- State Key Laboratory of Tea Plant Biology and Utilization, School of Tea and Food Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
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NIR Spectrometric Approach for Geographical Origin Identification and Taste Related Compounds Content Prediction of Lushan Yunwu Tea. Foods 2022; 11:foods11192976. [PMID: 36230052 PMCID: PMC9563823 DOI: 10.3390/foods11192976] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/16/2022] [Accepted: 09/18/2022] [Indexed: 11/29/2022] Open
Abstract
Lushan Yunwu Tea is one of a unique Chinese tea series, and total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio models (TP/FAA) represent its most important taste-related indicators. In this work, a feasibility study was proposed to simultaneously predict the authenticity identification and taste-related indicators of Lushan Yunwu tea, using near-infrared spectroscopy combined with multivariate analysis. Different waveband selections and spectral pre-processing methods were compared during the discriminant analysis (DA) and partial least squares (PLS) model-building process. The DA model achieved optimal performance in distinguishing Lushan Yunwu tea from other non-Lushan Yunwu teas, with a correct classification rate of up to 100%. The synergy interval partial least squares (siPLS) and backward interval partial least squares (biPLS) algorithms showed considerable advantages in improving the prediction performance of TP, FAA, and TP/FAA. The siPLS algorithms achieved the best prediction results for TP (RP = 0.9407, RPD = 3.00), FAA (RP = 0.9110, RPD = 2.21) and TP/FAA (RP = 0.9377, RPD = 2.90). These results indicated that NIR spectroscopy was a useful and low-cost tool by which to offer definitive quantitative and qualitative analysis for Lushan Yunwu tea.
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Lu J, Cao Y, Pan Y, Mei S, Zhang G, Chu Q, Chen P. Sensory-Guided Identification and Characterization of Kokumi-Tasting Compounds in Green Tea ( Camellia sinensis L.). MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27175677. [PMID: 36080442 PMCID: PMC9458127 DOI: 10.3390/molecules27175677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/21/2022] [Accepted: 07/23/2022] [Indexed: 11/19/2022]
Abstract
The chemical substances responsible for the kokumi taste of green tea infusion are still unclear. Here, we isolated the kokumi compound-containing fractions from green tea infusion through ultrafiltration, and the major kokumi compounds were characterized as γ-Glu-Gln and γ-Glu-Cys-Gly (GSH) through ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS). The results indicated that peptides and amino acids were essential compounds in the kokumi-enriched fractions for conducting the sense of kokumi. L-theanine had an enhancing effect on the kokumi taste of green tea infusion, which was confirmed in the sensory reconstitution study. Thus, peptides, especially γ-Glu-Gln and GSH, are the major kokumi compounds in green tea infusion, which has the potential of improving the flavor of tea beverages.
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Affiliation(s)
| | | | | | | | | | | | - Ping Chen
- Correspondence: ; Tel.: +86-18857183162
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Haruna SA, Li H, Wei W, Geng W, Yao-Say Solomon Adade S, Zareef M, Ivane NMA, Chen Q. Intelligent evaluation of free amino acid and crude protein content in raw peanut seed kernels using NIR spectroscopy paired with multivariable calibration. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:2989-2999. [PMID: 35916118 DOI: 10.1039/d2ay00875k] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Given the nutritional importance of peanuts, this study examined the free amino acid (FAA) and crude protein (CP) content in raw peanut seeds. Near-infrared spectroscopy (NIRS) was employed in combination with variable selection algorithms after successful reference data analysis using colorimetric and Kjeldahl methods. Ensuing the application of partial least squares (PLS) as a full spectral model, the genetic algorithm (GA), bootstrapping soft shrinkage (BOSS), uninformative variable elimination (UVE), and random frog (RF) models were tested and assessed. A comparison of correlation coefficients of prediction (Rp), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) was performed to appraise the performance of the built models. Using RF-PLS, an unsurpassed outcome was achieved for FAA (Rp = 0.937, RPD = 3.38) and CP (Rp = 0.9261, RPD = 3.66). These findings demonstrated that NIR in combination with RF-PLS could be utilized for quantitative, rapid, and nondestructive prediction of FAA and CP in raw peanut seed samples.
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Affiliation(s)
- Suleiman A Haruna
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
- Department of Food Science and Technology, Kano University of Science and Technology, Wudil, P. M. B 3244, Kano, Kano State, Nigeria
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Wenya Wei
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Wenhui Geng
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | | | - Muhammad Zareef
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Ngouana Moffo A Ivane
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, 212013, P. R. China.
- College of Food and Biological Engineering, Jimei University, Xiamen, 361021, P. R. China
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Rapid determination of free amino acids and caffeine in matcha using near-infrared spectroscopy: A comparison of portable and benchtop systems. J Food Compost Anal 2022. [DOI: 10.1016/j.jfca.2022.104868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Ma Q, Qin M, Song L, Sun H, Zhang H, Wu H, Ren Z, Liu H, Duan G, Wang Y, Ding Z. Molecular Link in Flavonoid and Amino Acid Biosynthesis Contributes to the Flavor of Changqing Tea in Different Seasons. Foods 2022; 11:foods11152289. [PMID: 35954056 PMCID: PMC9368528 DOI: 10.3390/foods11152289] [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: 06/22/2022] [Revised: 07/21/2022] [Accepted: 07/27/2022] [Indexed: 02/04/2023] Open
Abstract
The present study was aimed to elucidate the flavor formation mechanism of Changqing tea. High-performance liquid chromatography (HPLC) analysis showed that the total catechins of Changqing tea was 65–160 mg/g, with 16–34 mg/g non-galloyated catechins and 49–126 mg/g galloylated catechins. Tea polyphenols and free amino acids account for 286–312 mg/g and 35–89 mg/g, respectively. Transcriptome of Changqing tea during different seasons revealed 316, 130 and 12 DEGs in comparisons of spring vs. autumn, spring vs. summer, and summer vs. autumn, respectively. Compared to spring, the genes involved in flavonoid biosynthesis and bitter imparted amino acids were up-regulated in summer and autumn. Metabolome analysis was conducted by using HPLC-MS; the result indicated that umami and kokumi contributing amino acids were decreased in summer and autumn compared with spring. It could be concluded that the coordination of flavonoid biosynthesis and amino acids biosynthesis resulted in the special flavor of Changqing tea.
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Affiliation(s)
- Qingping Ma
- College of Agronomy, Liaocheng University, Liaocheng 252000, China; (Q.M.); (M.Q.); (L.S.)
| | - Mengyao Qin
- College of Agronomy, Liaocheng University, Liaocheng 252000, China; (Q.M.); (M.Q.); (L.S.)
| | - Laichao Song
- College of Agronomy, Liaocheng University, Liaocheng 252000, China; (Q.M.); (M.Q.); (L.S.)
| | - Haiwei Sun
- Taian Academy of Agricultural Sciences, Taian 271000, China; (H.S.); (H.Z.); (H.W.); (Z.R.)
| | - Hong Zhang
- Taian Academy of Agricultural Sciences, Taian 271000, China; (H.S.); (H.Z.); (H.W.); (Z.R.)
| | - Huanhuan Wu
- Taian Academy of Agricultural Sciences, Taian 271000, China; (H.S.); (H.Z.); (H.W.); (Z.R.)
| | - Zhihong Ren
- Taian Academy of Agricultural Sciences, Taian 271000, China; (H.S.); (H.Z.); (H.W.); (Z.R.)
| | - Hui Liu
- Jinan Bureau of Agriculture and Rural Areas, Jinan 271100, China; (H.L.); (G.D.)
| | - Gang Duan
- Jinan Bureau of Agriculture and Rural Areas, Jinan 271100, China; (H.L.); (G.D.)
| | - Yu Wang
- College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China;
| | - Zhaotang Ding
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Rizhao 276800, China
- Correspondence: ; Tel.: +86-15853260396
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Establishment of a rapid detection model for the sensory quality and components of Yuezhou Longjing tea using near-infrared spectroscopy. Lebensm Wiss Technol 2022. [DOI: 10.1016/j.lwt.2022.113625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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Ding Y, Yan Y, Li J, Chen X, Jiang H. Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM. Foods 2022; 11:foods11111658. [PMID: 35681408 PMCID: PMC9180160 DOI: 10.3390/foods11111658] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/27/2022] [Accepted: 05/31/2022] [Indexed: 02/04/2023] Open
Abstract
In this paper, we propose a method for classifying tea quality levels based on near-infrared spectroscopy. Firstly, the absorbance spectra of Huangshan Maofeng tea samples were obtained in a wavenumber range of 10,000~4000 cm−1 using near-infrared spectroscopy. The spectral data were then converted to transmittance and smoothed using the Savitzky–Golay (SG) algorithm. The denoised transmittance spectra were dimensionally reduced using principal component analysis (PCA). The characteristic variables obtained using PCA were used as the input variables and the tea level was used as the output to establish a support vector machine (SVM) classification model. The penalty factor c and the kernel function parameter g in the SVM model were optimized using particle swarm optimization (PSO) and comprehensive-learning particle swarm optimization (CLPSO) algorithms. The final experimental results show that the CLPSO-SVM method had the best classification performance, and the classification accuracy reached 99.17%.
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Affiliation(s)
- Yuhan Ding
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Institute of High-Performance Electrical Machine System and Intelligent Control, Jiangsu University, Zhenjiang 212013, China
| | - Yuli Yan
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Jun Li
- Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China; (Y.D.); (J.L.)
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- School of Automation, Southeast University, Nanjing 210096, China
| | - Xu Chen
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
| | - Hui Jiang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China; (Y.Y.); (X.C.)
- Correspondence:
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14
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Gharibzahedi SMT, Barba FJ, Zhou J, Wang M, Altintas Z. Electronic Sensor Technologies in Monitoring Quality of Tea: A Review. BIOSENSORS 2022; 12:bios12050356. [PMID: 35624658 PMCID: PMC9138728 DOI: 10.3390/bios12050356] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 05/14/2022] [Accepted: 05/19/2022] [Indexed: 05/27/2023]
Abstract
Tea, after water, is the most frequently consumed beverage in the world. The fermentation of tea leaves has a pivotal role in its quality and is usually monitored using the laboratory analytical instruments and olfactory perception of tea tasters. Developing electronic sensing platforms (ESPs), in terms of an electronic nose (e-nose), electronic tongue (e-tongue), and electronic eye (e-eye) equipped with progressive data processing algorithms, not only can accurately accelerate the consumer-based sensory quality assessment of tea, but also can define new standards for this bioactive product, to meet worldwide market demand. Using the complex data sets from electronic signals integrated with multivariate statistics can, thus, contribute to quality prediction and discrimination. The latest achievements and available solutions, to solve future problems and for easy and accurate real-time analysis of the sensory-chemical properties of tea and its products, are reviewed using bio-mimicking ESPs. These advanced sensing technologies, which measure the aroma, taste, and color profiles and input the data into mathematical classification algorithms, can discriminate different teas based on their price, geographical origins, harvest, fermentation, storage times, quality grades, and adulteration ratio. Although voltammetric and fluorescent sensor arrays are emerging for designing e-tongue systems, potentiometric electrodes are more often employed to monitor the taste profiles of tea. The use of a feature-level fusion strategy can significantly improve the efficiency and accuracy of prediction models, accompanied by the pattern recognition associations between the sensory properties and biochemical profiles of tea.
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Affiliation(s)
- Seyed Mohammad Taghi Gharibzahedi
- Institute of Chemistry, Faculty of Natural Sciences and Maths, Technical University of Berlin, Straße des 17. Juni 124, 10623 Berlin, Germany;
- Institute of Materials Science, Faculty of Engineering, Kiel University, 24143 Kiel, Germany
| | - Francisco J. Barba
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain; (F.J.B.); (J.Z.); (M.W.)
| | - Jianjun Zhou
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain; (F.J.B.); (J.Z.); (M.W.)
| | - Min Wang
- Nutrition and Food Science Area, Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Faculty of Pharmacy, University of Valencia, 46100 Valencia, Spain; (F.J.B.); (J.Z.); (M.W.)
| | - Zeynep Altintas
- Institute of Chemistry, Faculty of Natural Sciences and Maths, Technical University of Berlin, Straße des 17. Juni 124, 10623 Berlin, Germany;
- Institute of Materials Science, Faculty of Engineering, Kiel University, 24143 Kiel, Germany
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15
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Geographical origin identification and chemical markers screening of Chinese green tea using two-dimensional fingerprints technique coupled with multivariate chemometric methods. Food Control 2022. [DOI: 10.1016/j.foodcont.2021.108795] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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16
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Tong Y, Zhu R, Li C, Guo H, Huang C, Zhou S, Gong S, Fan F. A novel application of
check‐all‐that‐apply
with semi‐trained assessors for tea sensory characterization and preference: Using Longjing tea as a case study. J SENS STUD 2022. [DOI: 10.1111/joss.12742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Yi‐Lin Tong
- Zhejiang University Tea Research Institute Hangzhou China
| | - Ruo‐Lan Zhu
- Zhejiang University Tea Research Institute Hangzhou China
| | - Chun‐Lin Li
- Zhejiang University Tea Research Institute Hangzhou China
| | - Hao‐Wei Guo
- Zhejiang University Tea Research Institute Hangzhou China
| | | | - Sen‐Jie Zhou
- Zhejiang University Tea Research Institute Hangzhou China
| | - Shu‐Ying Gong
- Zhejiang University Tea Research Institute Hangzhou China
| | - Fang‐Yuan Fan
- Zhejiang University Tea Research Institute Hangzhou China
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17
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Ou Q, Li J, Yang X, Yang W, Liu G, Shi Y. Identification of Pu'er raw tea with different storage years by infrared spectroscopy. J FOOD PROCESS PRES 2021. [DOI: 10.1111/jfpp.16103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Quanhong Ou
- School of Physics and Electronic Information Yunnan Normal University Kunming China
| | - Jianmei Li
- School of Physics and Electronic Information Yunnan Normal University Kunming China
| | - Xien Yang
- School of Physics and Electronic Information Yunnan Normal University Kunming China
| | - Weiye Yang
- School of Physics and Electronic Information Yunnan Normal University Kunming China
| | - Gang Liu
- School of Physics and Electronic Information Yunnan Normal University Kunming China
| | - Youming Shi
- School of Physics and Electronic Engineering Qujing Normal University Qujing China
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18
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Analysis of total phenolic compounds and caffeine in teas using variable selection approach with two-dimensional fluorescence and infrared spectroscopy. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106570] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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19
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Zuo Y, Tan G, Xiang D, Chen L, Wang J, Zhang S, Bai Z, Wu Q. Development of a novel green tea quality roadmap and the complex sensory-associated characteristics exploration using rapid near-infrared spectroscopy technology. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 258:119847. [PMID: 33940571 DOI: 10.1016/j.saa.2021.119847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 04/11/2021] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
Nondestructive instrumental identification of the green tea quality instead of professional human panel tests is highly desired for industrial application recently. The special flavor is a key quality-trait that influence consumer preference. However, flavonoids, as well as sensory-associated compounds, which play a critical role in the quality-traits profile of green tea samples have been poorly investigated. In this study, we were proposing an objective and accurate near infrared spectroscopy (NIRS) profile to support quality control within the entire green tea sensory evaluation chain, the complexity of green tea samples' sensory analysis was performed by two complementary methods: the standard calculation and the novel NIRS roadmap coupled with chemometrics. The green tea samples' physical quality, gustatory index, and nutritional index were measured respectively, which taking into consideration the gustatory evaluation of green tea for five commercially representative overall quality ("very bad", "bad", "regular", "good" and "excellent"). Our findings highlight the underexplored role of NIRS in chemical-to-sensory relationships and its widespread importance and utility in green tea quality improvement. Collectively, the comprehensive characterization of sensory-associated attribution allowed the identification of a wide array of spectrometric features, mostly related to moisture, soluble solids (SS), tea polyphenol (TPP), epigallocatechin gallate (EGCG), epicatechin (EC) and tea polysaccharide (TPS), which can be used as putative biomarkers to rapidly evaluate the green tea flavor variations related to rank differences. Otherwise, the NIRS' data were split into the calibration (n = 80) and prediction (n = 40) set independently, which showed high correlation coefficient with Rp-values of 0.9024, 0.9020 in physical and total cup quality, respectively. In this research, we demonstrated that NIRS was an easily-generated strategy and able to close the loop to feedback into the process for advanced process control. However, the established models should be improved by more green tea samples from different regions.
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Affiliation(s)
- Yamin Zuo
- School of Basic Medical Sciences, Hubei Key Laboratory of Wudang Local Chinese Medicine Research, Hubei University of Medicine, 30 Renmin South Rd, Shiyan, Hubei 442000, China; Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Gaohao Tan
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Di Xiang
- The Yunnan Tea Chamber of Commerce, Panlong District, Kunming, Yunnan 650051, China
| | - Ling Chen
- The Department of Tea, Guizhou Vocational College of Agriculture, 3 Huangshi Rd, Qingzhen, Guizhou 551400, China
| | - Jiao Wang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Shengsheng Zhang
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China
| | - Zhiwen Bai
- The Guizhou Gui Tea (Group) Co. Ltd, Huaxi District, Guiyang, Guizhou 550001, China.
| | - Qing Wu
- Guizhou Key Laboratory for Information System of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, 116 Baoshan North Rd, Guiyang, Guizhou 550001, China; Innovation Laboratory, the Third Experiment Middle School in Guiyang, Guiyang, Guizhou 550001, China.
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20
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Cozzolino D. From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy. ADVANCES IN FOOD AND NUTRITION RESEARCH 2021; 97:119-146. [PMID: 34311898 DOI: 10.1016/bs.afnr.2021.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.
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Affiliation(s)
- Daniel Cozzolino
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia.
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21
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Dai W, Chen C, Feng H, Li G, Peng W, Liu X, Yang J, Hu X. Protection of Ficus pandurata Hance against acute alcohol-induced liver damage in mice via suppressing oxidative stress, inflammation, and apoptosis. JOURNAL OF ETHNOPHARMACOLOGY 2021; 275:114140. [PMID: 33915134 DOI: 10.1016/j.jep.2021.114140] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 04/06/2021] [Accepted: 04/19/2021] [Indexed: 05/16/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Ficus pandurata Hance (FPH) is a traditional Chinese herbal medicine, which is commonly used for liver protection in the folk of Southeast China. However, the medicinal part and pharmacological mechanism have not been clarified yet. AIM OF THE STUDY This study aims to investigate the medicinal part of FPH for liver protection and uncover the potential mechanism. MATERIALS AND METHODS Acute alcoholic liver damage (ALD) mice model induced by intragastric administration with 50% alcohol was used to evaluate the liver protection of FPH. Different parts of FPH, including the root (FPHR), stem (FPHS), leaf (FPHL), and whole plant (FPHWP), were selected to investigate the liver-protected efficacy and determine which part is the medicinal part. Acute oral toxicity (AOT) test was performed to determine the acute toxicity of FPH on Kunming mice. The liver-protected effect of FPH was determined by evaluating the liver function, liver morphological changes, and liver pathological changes. The underlying mechanism was investigated by evaluating the effect on oxidative stress, inflammation, and apoptosis in liver tissues via ELISA, H&E staining, Western Blot, and TUNEL staining assays. RESULTS In the screening test for medicinal parts of FPH, all of the extracts from FPHR, FPHS, FPHL, and FPHWP could alleviate the acute ALD of mice, including reducing abnormal levels of AST, ALT, and relative liver weight. Especially, the alleviated efficacies of FPHS and FPHL were better than those of FPHWP and FPHR, showing that the aerial part (FPHAP, including the stem and leaf), is probably the medicinal part of FPH against acute ALD. In the AOT test, FPHAP at the maximum administration dosage (480 g/kg, calculated based on the quantity of crude material) did not induce obvious abnormality and death of mice, and had no significant influence on body weight, as well as the relative organ weight, showing that the maximum tolerated dose (MTD) of FPHAP was 480 g/kg on Kunming mice. In the anti-acute ALD study, FPHAP significantly reduced the levels of AST, ALT, LDH, ROS, MDA, TNF-α, IL-1β, IL-18, and IL-6, alleviated the morphology of liver injury, increased the levels of SOD and GSH, up-regulated the expressions of Nrf-2, HO-1 and NQO1, and reduced apoptosis of liver cells in acute ALD mice, indicating that FPHAP could significantly alleviate acute ALD by suppressing oxidative stress, inflammation, and apoptosis. CONCLUSIONS FPH could protect acute alcohol-induced liver damage of mice by suppressing oxidative stress, inflammation, and apoptosis. Our study provides scientific evidence for the therapeutic effect of Ficus pandurata Hance in acute ALD mice and suggests its potential development in humans for liver protection, supporting its traditional application.
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Affiliation(s)
- Weibo Dai
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Chang Chen
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Huiting Feng
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Guangru Li
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Weiwen Peng
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China.
| | - Xin Liu
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Jing Yang
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China
| | - Xianjing Hu
- Pharmacology Laboratory, Zhongshan Hospital, Guangzhou University of Chinese Medicine, Zhongshan, China; Centre for Cancer and Inflammation Research, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China; Biotechnological Institute of Chinese Materia Medical, Jinan University, Guangzhou, China.
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22
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Ong P, Chen S, Tsai CY, Chuang YK. Prediction of tea theanine content using near-infrared spectroscopy and flower pollination algorithm. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 255:119657. [PMID: 33744842 DOI: 10.1016/j.saa.2021.119657] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 02/16/2021] [Accepted: 02/28/2021] [Indexed: 06/12/2023]
Abstract
In this study, near-infrared (NIR) spectroscopy was exploited for non-destructive determination of theanine content of oolong tea. The NIR spectral data (400-2500 nm) were correlated with the theanine level of 161 tea samples using partial least squares regression (PLSR) with different wavelengths selection methods, including the regression coefficient-based selection, uninformative variable elimination, variable importance in projection, selectivity ratio and flower pollination algorithm (FPA). The potential of using the FPA to select the discriminative wavelengths for PLSR was examined for the first time. The analysis showed that the PLSR with FPA method achieved better predictive results than the PLSR with full spectrum (PLSR-full). The developed simplified model using on FPA based on 12 latent variables and 89 selected wavelengths produced R-squared (R2) value and root mean squared error (RMSE) of 0.9542, 0.8794 and 0.2045, 0.3219 for calibration and prediction, respectively. For PLSR-full, the R2 values of 0.9068, 0.8412 and RMSEs of 0.2916, 0.3693, were achieved for calibration and prediction. Also, the optimized model using FPA outperformed other wavelengths selection methods considered in this study. The obtained results indicated the feasibility of FPA to improve the predictability of the PLSR and reduce the model complexity. The nonlinear regression models of support vector machine regression and Gaussian process regression (GPR) were further utilized to evaluate the superiority of using the FPA in the wavelength selection. The results demonstrated that utilizing the wavelength selection method of FPA and nonlinear regression model of GPR could improve the predictive performance.
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Affiliation(s)
- Pauline Ong
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan; Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
| | - Suming Chen
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Chao-Yin Tsai
- Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan
| | - Yung-Kun Chuang
- Master Program in Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; School of Food Safety, College of Nutrition, Taipei Medical University, Taipei, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei, Taiwan
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23
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Cardoso VGK, Poppi RJ. Non-invasive identification of commercial green tea blends using NIR spectroscopy and support vector machine. Microchem J 2021. [DOI: 10.1016/j.microc.2021.106052] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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24
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Accumulation pattern of catechins and flavonol glycosides in different varieties and cultivars of tea plant in China. J Food Compost Anal 2021. [DOI: 10.1016/j.jfca.2020.103772] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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25
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Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy. Food Chem 2021; 353:129372. [PMID: 33725540 DOI: 10.1016/j.foodchem.2021.129372] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 02/01/2021] [Accepted: 02/12/2021] [Indexed: 12/29/2022]
Abstract
Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (Rp) of Rp > 0.97, Rp > 0.98 and Rp > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.
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26
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Wang YJ, Li TH, Li LQ, Ning JM, Zhang ZZ. Evaluating taste-related attributes of black tea by micro-NIRS. J FOOD ENG 2021. [DOI: 10.1016/j.jfoodeng.2020.110181] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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27
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Lin H, Jiang H, Lin J, Chen Q, Ali S, Teng SW, Zuo M. Rice Freshness Identification Based on Visible Near-Infrared Spectroscopy and Colorimetric Sensor Array. FOOD ANAL METHOD 2021. [DOI: 10.1007/s12161-021-01963-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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28
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Wang Y, Li M, Li L, Ning J, Zhang Z. Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer. Food Chem 2020; 345:128816. [PMID: 33316713 DOI: 10.1016/j.foodchem.2020.128816] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 11/07/2020] [Accepted: 12/02/2020] [Indexed: 01/14/2023]
Abstract
Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality.
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Affiliation(s)
- Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Menghui Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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29
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Sanaeifar A, Huang X, Chen M, Zhao Z, Ji Y, Li X, He Y, Zhu Y, Chen X, Yu X. Nondestructive monitoring of polyphenols and caffeine during green tea processing using Vis-NIR spectroscopy. Food Sci Nutr 2020; 8:5860-5874. [PMID: 33282238 PMCID: PMC7684591 DOI: 10.1002/fsn3.1861] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 11/06/2022] Open
Abstract
Increasing consumption of green tea is attributed to the beneficial effects of its constituents, especially polyphenols, on human health, which can be varied during leaf processing. Processing technology has the most important effect on green tea quality. This study investigated the system dynamics of eight catechins, gallic acid, and caffeine in the processing of two varieties of tea, from fresh leaves to finished tea. It was found that complex biochemical changes can occur through hydrolysis under different humidity and heating conditions during the tea processing. This process had a significant effect on catechin composition in the finished tea. The potential application of visible and near-infrared (Vis-NIR) spectroscopy for fast monitoring polyphenol and caffeine contents in tea leaves during the processing procedure has been investigated. It was found that a combination of PCA (principal component analysis) and Vis-NIR spectroscopy can successfully classify the two varieties of tea samples and the five tea processing procedures, while quantitative determination of the constituents was realized by combined regression analysis and Vis-NIR spectra. Furthermore, successive projections algorithm (SPA) was proposed to extract and optimize spectral variables that reflected the molecular characteristics of the constituents for the development of determination models. Modeling results showed that the models had good predictability and robustness based on the extracted spectral characteristics. The coefficients of determination for all calibration sets and prediction sets were higher than 0.862 and 0.834, respectively, which indicated high capability of Vis-NIR spectroscopy for the determination of the constituents during the leaf processing. Meanwhile, this analytical method could quickly monitor quality characteristics and provide feedback for real-time controlling of tea processing machines. Furthermore, the study on complex biochemical changes that occurred during the tea processing would provide a theoretical basis for improving the content of quality components and effective controlling processes.
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Affiliation(s)
- Alireza Sanaeifar
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xinyao Huang
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Mengyuan Chen
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Zhangfeng Zhao
- College of Mechanical EngineeringZhejiang University of TechnologyHangzhouChina
| | - Yifan Ji
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xiaoli Li
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Yong He
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Yi Zhu
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xi Chen
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
| | - Xinxin Yu
- College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina
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30
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Sun Y, Wang Y, Huang J, Ren G, Ning J, Deng W, Li L, Zhang Z. Quality assessment of instant green tea using portable NIR spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 240:118576. [PMID: 32535491 DOI: 10.1016/j.saa.2020.118576] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (-)-epigallocatechin, (-)-epigallocatechin 3-gallate, (-)-epicatechin, and (-)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea.
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Affiliation(s)
- Yemei Sun
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jing Huang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Guangxin Ren
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Weiwei Deng
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China.
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Lin X, Sun DW. Recent developments in vibrational spectroscopic techniques for tea quality and safety analyses. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2020.06.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang YJ, Li TH, Li LQ, Ning JM, Zhang ZZ. Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 237:118403. [PMID: 32361319 DOI: 10.1016/j.saa.2020.118403] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/10/2020] [Accepted: 04/20/2020] [Indexed: 05/25/2023]
Abstract
Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.
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Affiliation(s)
- Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Tie-Han Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Lu-Qing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jing-Ming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
| | - Zheng-Zhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China.
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Wang YJ, Li LQ, Shen SS, Liu Y, Ning JM, Zhang ZZ. Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2020; 100:3803-3811. [PMID: 32201954 DOI: 10.1002/jsfa.10393] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/25/2020] [Accepted: 03/21/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea-leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea-leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea-leaf quality. © 2020 Society of Chemical Industry.
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Affiliation(s)
- Yu-Jie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Lu-Qing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Shan-Shan Shen
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Ying Liu
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Jing-Ming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
| | - Zheng-Zhu Zhang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei, China
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Guo Z, Barimah AO, Shujat A, Zhang Z, Ouyang Q, Shi J, El-Seedi HR, Zou X, Chen Q. Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm. Lebensm Wiss Technol 2020. [DOI: 10.1016/j.lwt.2020.109510] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Huang J, Ren G, Sun Y, Jin S, Li L, Wang Y, Ning J, Zhang Z. Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics. Food Sci Nutr 2020; 8:2015-2024. [PMID: 32328268 PMCID: PMC7174226 DOI: 10.1002/fsn3.1489] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/11/2020] [Accepted: 02/04/2020] [Indexed: 01/24/2023] Open
Abstract
The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near-infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA-SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades.
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Affiliation(s)
- Jing Huang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Guangxin Ren
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Yemei Sun
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Shanshan Jin
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
| | - Zhengzhu Zhang
- State Key Laboratory of Tea Plant Biology and UtilizationAnhui Agricultural UniversityHefeiChina
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Kucharska-Ambrożej K, Karpinska J. The application of spectroscopic techniques in combination with chemometrics for detection adulteration of some herbs and spices. Microchem J 2020. [DOI: 10.1016/j.microc.2019.104278] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Li C, Zong B, Guo H, Luo Z, He P, Gong S, Fan F. Discrimination of white teas produced from fresh leaves with different maturity by near-infrared spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2020; 227:117697. [PMID: 31699592 DOI: 10.1016/j.saa.2019.117697] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 10/15/2019] [Accepted: 10/24/2019] [Indexed: 06/10/2023]
Abstract
White tea is a special tea product with increasing market demand. The assessment of white tea quality is mainly based on panel sensory by sensory evaluation experts, which is time costly and is limited by many uncertainties. This study established a rapid and accurate method for classification of white teas produced from buds and young leaves and that produced from mature leaves and shoots using near-infrared spectroscopy (NIR). Back propagation neural network modelling and support vector machine (SVM) modelling were compared with six pre-processing methods. The best performance was provided by SVM with particle swarm optimization combined with Savitzky-Golay filter pre-processing method, achieving the accuracy of 98.92% in test samples. The NIR-related chemical compounds of two categories of white teas produced from fresh leaves with different maturity were analyzed, including catechins, alkaloids, amino acids and flavonol glycosides. Compared with chemical component concentration, NIR absorbance had a distinct advantage in quick classification of white teas based on the principal components analysis. In addition, the sensory characteristics of two categories white teas produced from fresh leaves with different maturity were also assessed by panelist. The result showed that characteristics of "umami-like" and "smooth" were more likely present in white teas produced from buds and young leaves, while "woody" and "coarse" characteristics were usually present in white teas produced from mature leaves and shoots. Thus, NIR technique is a rapid and reliable method for discrimination of white teas produced from fresh leaves with different maturity, and is a potential method to discriminate sensory characteristics of white teas.
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Affiliation(s)
- Chunlin Li
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Bangzheng Zong
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Haowei Guo
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Zhou Luo
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Puming He
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China
| | - Shuying Gong
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.
| | - Fangyuan Fan
- Institute of Tea Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, China.
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Chapman J, Elbourne A, Truong VK, Newman L, Gangadoo S, Rajapaksha Pathirannahalage P, Cheeseman S, Cozzolino D. Sensomics - From conventional to functional NIR spectroscopy - Shining light over the aroma and taste of foods. Trends Food Sci Technol 2019. [DOI: 10.1016/j.tifs.2019.07.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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