Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology
Abstract
:1. Introduction
2. Materials and Methods
2.1. Chemicals and Reagents
2.2. Sample Preparation
2.3. Determination of Tea Pigments Content
2.4. Image Information Acquisition
2.5. Color Features Information Extraction
2.6. Data Set Partitioning and Preprocessing
2.7. Establishment of Prediction Models
2.8. Prediction Models Evaluation Indexes
3. Results and Discussion
3.1. Results of Determination of Tea Pigments Content
3.2. Response of Color Feature Variables During Rolling Process
3.3. Correlation Analysis Between Color Features Variables and Tea Pigments
3.4. Data Preprocessing and Principal Component Analysis
3.5. Prediction Models of Tea Pigments Based on Image Information
3.5.1. Prediction Results of TFs
3.5.2. Prediction Results of TRs
3.5.3. Prediction Results of TBs
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Chen, Q.C.; Fu, Y.; Heng, W.T.; Yu, S.; Xie, F.; Dong, F.; Lin, Z.; Dai, W.D.; Fu, H.H. Re-rolling treatment in the fermentation process improves the taste and liquor color qualities of black tea. Food Chem.-X 2024, 21, 101143. [Google Scholar] [CrossRef] [PubMed]
- Wang, A.L.; Lei, Q.Q.; Zhang, B.B.; Wu, J.H.; Fu, Z.Y.; He, J.F.; Wang, Y.B.; Wu, X.Y. Revealing novel insights into the enhancement of quality in black tea processing through microbial intervention. Food Chem.-X 2024, 23, 101743. [Google Scholar] [CrossRef] [PubMed]
- Wu, S.M.; Yu, Q.Y.; Shen, S.; Shan, X.J.; Hua, J.J.; Zhu, J.Y.; Qiu, J.R.; Deng, Y.L.; Zhou, Q.H.; Jiang, Y.W.; et al. Non-targeted metabolomics and electronic tongue analysis reveal the effect of rolling time on the sensory quality and nonvolatile metabolites of congou black tea. Lwt-Food Sci. Technol. 2022, 169, 113971. [Google Scholar] [CrossRef]
- Zhang, S.; Wu, S.M.; Yu, Q.Y.; Shan, X.J.; Chen, L.; Deng, Y.L.; Hua, J.J.; Zhu, J.Y.; Zhou, Q.H.; Jiang, Y.W.; et al. The influence of rolling pressure on the changes in non-volatile compounds and sensory quality of congou black tea: The combination of metabolomics, E-tongue, and chromatic differences analyses. Food Chem.-X 2023, 20, 100989. [Google Scholar] [CrossRef] [PubMed]
- Hua, J.J.; Xu, Q.; Yuan, H.B.; Wang, J.J.; Wu, Z.Q.; Li, X.T.; Jiang, Y.W. Effects of novel fermentation method on the biochemical components change and quality formation of Congou black tea. J. Food Compos. Anal. 2021, 96, 103751. [Google Scholar] [CrossRef]
- Liu, M.; Wang, R.X.; Shi, D.L.; Cao, R.Y. Non-destructive prediction of tea polyphenols during Pu-erh tea fermentation using NIR coupled with chemometrics methods. J. Food Compos. Anal. 2024, 131, 106247. [Google Scholar] [CrossRef]
- Turgut, S.S.; Entrenas, J.A.; Taskin, E.; Garrido-Varo, A.; Pérez-Marín, D. Estimation of the sensory properties of black tea samples using non-destructive near-infrared spectroscopy sensors. Food Control 2022, 142, 109260. [Google Scholar] [CrossRef]
- Luo, W.; Tian, P.; Fan, G.Z.; Dong, W.T.; Zhang, H.L.; Liu, X.M. Non-destructive determination of four tea polyphenols in fresh tea using visible and near-infrared spectroscopy. Infrared Phys. Technol. 2022, 123, 104037. [Google Scholar] [CrossRef]
- Wen, J.; Xu, G.Q.; Zhang, A.; Ma, W.; Jin, G. Emerging technologies for rapid non-destructive testing of grape quality: A review. J. Food Compos. Anal. 2024, 133, 106446. [Google Scholar] [CrossRef]
- Jiang, Y.Y.; Zhang, D.X.; Yang, L.; Cui, T.; He, X.T.; Wu, D.Y.; Dong, J.Q.; Li, C.; Xing, S.L. Design and experiment of non-destructive testing system for moisture content of in-situ maize ear kernels based on VIS-NIR. J. Food Compos. Anal. 2024, 133, 106369. [Google Scholar] [CrossRef]
- Maniwara, P.; Nakano, K.; Ohashi, S.; Boonyakiat, D.; Seehanam, P.; Theanjumpo, P.; Poonlarp, P. Evaluation of NIRS as non-destructive test to evaluate quality traits of purple passion fruit. Sci. Hortic. 2019, 257, 108712. [Google Scholar] [CrossRef]
- Wei, Y.; Wen, Y.Q.; Huang, X.L.; Ma, P.H.; Wang, L.; Pan, Y.; Lv, Y.J.; Wang, H.X.; Zhang, L.; Wang, K.B.; et al. The dawn of intelligent technologies in tea industry. Trends Food Sci. Technol. 2024, 144, 104337. [Google Scholar] [CrossRef]
- Wang, Y.J.; Ren, Z.Y.; Chen, Y.Y.; Lu, C.Y.; Deng, W.W.; Zhang, Z.Z.; Ning, J.M. Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea fermentation. Food Chem. 2023, 401, 134090. [Google Scholar] [CrossRef] [PubMed]
- Zhu, H.K.; Liu, F.; Ye, Y.; Chen, L.; Li, J.Y.; Gu, A.H.; Zhang, J.Q.; Dong, C.W. Application of machine learning algorithms in quality assurance of fermentation process of black tea- based on electrical properties. J. Food Eng. 2019, 263, 165–172. [Google Scholar] [CrossRef]
- Yao, L.H.; Jiang, Y.M.; Caffin, N.; D’Arcy, B.; Datta, N.; Liu, X.; Singanusong, R.; Xu, Y. Phenolic compounds in tea from Australian supermarkets. Food Chem. 2006, 96, 614–620. [Google Scholar] [CrossRef]
- Ouyang, Q.; Wang, L.; Park, B.; Kang, R.; Chen, Q.S. Simultaneous quantification of chemical constituents in matcha with visible-near infrared hyperspectral imaging technology. Food Chem. 2021, 350, 129141. [Google Scholar] [CrossRef]
- Chemov, V.; Alander, J.; Bochko, V. Integer-based accurate conversion between RGB and HSV color spaces. Comput. Electr. Eng. 2015, 46, 328–337. [Google Scholar] [CrossRef]
- Paschos, G. Perceptually uniform color spaces for color texture analysis: An empirical evaluation. IEEE Trans. Image Process. 2001, 10, 932–937. [Google Scholar] [CrossRef]
- Lan, T.M.; Shen, S.; Yuan, H.B.; Jiang, Y.W.; Tong, H.R.; Ye, Y. A Rapid Prediction Method of Moisture Content for Green Tea Fixation Based on WOA-Elman. Foods 2022, 11, 2928. [Google Scholar] [CrossRef]
- Shen, S.; Hua, J.J.; Zhu, H.K.; Yang, Y.Q.; Deng, Y.L.; Li, J.; Yuan, H.B.; Wang, J.J.; Zhu, J.Y.; Jiang, Y.W. Rapid and real-time detection of moisture in black tea during withering using micro-near-infrared spectroscopy. Lwt-Food Sci. Technol. 2022, 155, 112970. [Google Scholar] [CrossRef]
- Zheng, L.; Watson, D.G.; Johnston, B.F.; Clark, R.L.; Edrada-Ebel, R.; Elseheri, W. A chemometric study of chromatograms of tea extracts by correlation optimization warping in conjunction with PCA, support vector machines and random forest data modeling. Anal. Chim. Acta 2009, 642, 257–265. [Google Scholar] [CrossRef]
- Asante, E.A.; Du, Z.; Lu, Y.Z.; Hu, Y.G. Detection and assessment of nitrogen effect on cold tolerance for tea by hyperspectral reflectance with PLSR, PCR, and LM models. Inf. Process. Agric. 2021, 8, 96–104. [Google Scholar] [CrossRef]
- Amsaraj, R.; Mutturi, S. Real-coded GA coupled to PLS for rapid detection and quantification of tartrazine in tea using FT-IR spectroscopy. Lwt-Food Sci. Technol. 2021, 139, 110583. [Google Scholar] [CrossRef]
- Sobhaninia, M.; Mani-Varnosfaderani, A.; Barzegar, M.; Sahari, M.A. Combining ion mobility spectrometry and chemometrics for detecting synthetic colorants in black tea: A reliable and fast method. Food Chem.-X 2024, 21, 101213. [Google Scholar] [CrossRef] [PubMed]
- Xie, G.; Qian, Y.T.; Wang, S.Y. Forecasting Chinese cruise tourism demand with big data: An optimized machine learning approach. Tour. Manag. 2021, 82, 104208. [Google Scholar] [CrossRef]
- Wei, X.; Li, S.; Zhu, S.P.; Zheng, W.Q.; Xie, Y.; Zhou, S.L.; Hu, M.D.; Miao, Y.J.; Ma, L.K.; Wu, W.J.; et al. Terahertz spectroscopy combined with data dimensionality reduction algorithms for quantitative analysis of protein content in soybeans. Spectrochim. Acta Part A-Mol. Biomol. Spectrosc. 2021, 253, 119571. [Google Scholar] [CrossRef]
- Yang, Y.C.; Sun, D.W.; Wang, N.N. Rapid detection of browning levels of lychee pericarp as affected by moisture contents using hyperspectral imaging. Comput. Electron. Agric. 2015, 113, 203–212. [Google Scholar] [CrossRef]
- Li, H.; Hu, Y.; Ma, S.; Haruna, S.A.; Chen, Q.; Zhu, W.; Xia, A. Porphyrin and pH sensitive dye-based colorimetric sensor array coupled chemometrics for dynamic monitoring of tea quality during ultrasound-assisted fermentation. Microchem. J. 2024, 197, 109813. [Google Scholar] [CrossRef]
- Ren, G.X.; Yin, L.L.; Wu, R.; Ning, J.M. Rapid detection of ash content in black tea using a homemade miniature near-infrared spectroscopy. Spectrochim. Acta Part A-Mol. Biomol. Spectrosc. 2024, 308, 123740. [Google Scholar] [CrossRef]
- Dai, F.S.; Shi, J.; Yang, C.S.; Li, Y.; Zhao, Y.; Liu, Z.Y.; An, T.; Li, X.L.; Yan, P.; Dong, C.W. Detection of anthocyanin content in fresh Zijuan tea leaves based on hyperspectral imaging. Food Control 2023, 152, 109839. [Google Scholar] [CrossRef]
- Zhang, L.; Ho, C.T.; Zhou, J.; Santos, J.S.; Armstrong, L.; Granato, D. Chemistry and Biological Activities of Processed Camellia sinensis Teas: A Comprehensive Review. Compr. Rev. Food Sci. Food Saf. 2019, 18, 1474–1495. [Google Scholar] [CrossRef] [PubMed]
Rolling Time (min) | Number of Samples | TFs (%) | TRs (%) | TBs (%) |
---|---|---|---|---|
0 | 15 | 0.03 ± 0.00 | 0.99 ± 0.12 | 1.52 ± 0.19 |
10 | 15 | 0.06 ± 0.01 | 1.52 ± 0.11 | 1.83 ± 0.07 |
20 | 15 | 0.11 ± 0.01 | 1.76 ± 0.07 | 2.06 ± 0.04 |
30 | 15 | 0.14 ± 0.01 | 2.22 ± 0.06 | 2.44 ± 0.11 |
40 | 15 | 0.20 ± 0.01 | 2.57 ± 0.08 | 2.83 ± 0.06 |
50 | 15 | 0.29 ± 0.11 | 3.41 ± 0.19 | 3.40 ± 0.08 |
60 | 15 | 0.38 ± 0.02 | 3.79 ± 0.13 | 3.82 ± 0.10 |
70 | 15 | 0.54 ± 0.01 | 4.23 ± 0.08 | 4.40 ± 0.08 |
80 | 15 | 0.59 ± 0.01 | 4.32 ± 0.11 | 4.60 ± 0.03 |
90 | 15 | 0.63 ± 0.01 | 4.75 ± 0.31 | 4.94 ± 0.14 |
Types of Models | Pretreatment Methods | Training Set | Prediction Set | |||
---|---|---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | RPD | ||
PLSR | Z-score | 0.93 | 0.08 | 0.91 | 0.12 | 2.01 |
Z-score–PCA | 0.94 | 0.07 | 0.94 | 0.10 | 2.19 | |
SVR | Z-score | 0.98 | 0.05 | 0.94 | 0.09 | 2.17 |
Z-score–PCA | 0.95 | 0.07 | 0.95 | 0.07 | 2.67 | |
ELM | Z-score | 0.99 | 0.04 | 0.96 | 0.07 | 3.65 |
Z-score–PCA | 0.99 | 0.04 | 0.97 | 0.06 | 3.97 |
Types of Models | Pretreatment Methods | Training Set | Prediction Set | |||
---|---|---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | RPD | ||
PLSR | Z-score | 0.96 | 0.35 | 0.93 | 0.51 | 2.35 |
Z-score-PCA | 0.95 | 0.41 | 0.94 | 0.44 | 2.71 | |
SVR | Z-score | 0.96 | 0.34 | 0.94 | 0.50 | 2.56 |
Z-score-PCA | 0.96 | 0.35 | 0.95 | 0.44 | 2.73 | |
ELM | Z-score | 0.96 | 0.36 | 0.94 | 0.44 | 3.01 |
Z-score-PCA | 0.97 | 0.34 | 0.96 | 0.36 | 3.56 |
Types of Models | Pretreatment Methods | Training Set | Prediction Set | |||
---|---|---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | RPD | ||
PLSR | Z-score | 0.96 | 0.34 | 0.94 | 0.51 | 2.08 |
Z-score–PCA | 0.94 | 0.38 | 0.94 | 0.41 | 2.45 | |
SVR | Z-score | 0.97 | 0.32 | 0.94 | 0.40 | 2.82 |
Z-score–PCA | 0.95 | 0.37 | 0.95 | 0.37 | 3.02 | |
ELM | Z-score | 0.96 | 0.36 | 0.96 | 0.37 | 3.38 |
Z-score–PCA | 0.97 | 0.31 | 0.96 | 0.35 | 3.60 |
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Zou, H.; Lan, T.; Jiang, Y.; Yu, X.-L.; Yuan, H. Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology. Foods 2024, 13, 3718. https://doi.org/10.3390/foods13233718
Zou H, Lan T, Jiang Y, Yu X-L, Yuan H. Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology. Foods. 2024; 13(23):3718. https://doi.org/10.3390/foods13233718
Chicago/Turabian StyleZou, Hanting, Tianmeng Lan, Yongwen Jiang, Xiao-Lan Yu, and Haibo Yuan. 2024. "Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology" Foods 13, no. 23: 3718. https://doi.org/10.3390/foods13233718
APA StyleZou, H., Lan, T., Jiang, Y., Yu, X.-L., & Yuan, H. (2024). Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology. Foods, 13(23), 3718. https://doi.org/10.3390/foods13233718