Establishment of a Hyperspectral Spectroscopy-Based Biochemical Component Detection Model for Green Tea Processing Materials

XUE Yiwei; WANG Yu; WANG Huan; DING Shibo; WANG Mengqi; CHEN Sizhou; DING Zhaotang; ZHAO Liqing

doi:10.13386/j.issn1002-0306.2020070110

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May. 2023

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JOURNAL OF MECHANICAL ENGINEERING > 2023 > 44(10): 280-289.

XUE Yiwei, WANG Yu, WANG Huan, et al. Establishment of a Hyperspectral Spectroscopy-Based Biochemical Component Detection Model for Green Tea Processing Materials[J]. Science and Technology of Food Industry, 2023, 44(10): 280−289. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020070110

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Establishment of a Hyperspectral Spectroscopy-Based Biochemical Component Detection Model for Green Tea Processing Materials

doi: 10.13386/j.issn1002-0306.2020070110

XUE Yiwei^1
,,
WANG Yu²,
WANG Huan²,
DING Shibo³,
WANG Mengqi³,
CHEN Sizhou²,
DING Zhaotang^{2,*
,
,},
ZHAO Liqing^{1,*
,
,}

1.
College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
2.
College of Horticulture, Qingdao Agricultural University, Qingdao 266109, China
3.
Rizhao Tea Science Research Institute, Rizhao 276801, China

Received Date: 13 Jul 2022
Issue Publish Date: 15 May 2023

Abstract

Abstract

Objective: To establish a method for rapid detection of biochemical components of green tea processing materials by hyperspectral technique. Methods: The hyperspectral camera was employed to capture real-time images of the tea raw materials during the processing procedure in order to collect the spectral data of the tea raw materials. The samples' moisture content, free amino acids, tea polyphenols, and caffeine content were all found. After spectral data preprocessing, three feature extraction methods, uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA) and partial least-squares (PLS), support vector machine (SVM) and random forest (RF) were combined to predict the water content, free amino acids, polyphenols and caffeine content of tea raw materials. Result: The best combination models of water content, free amino acids, tea polyphenols and caffeine of tea raw materials were UVE-RF, CARS-SVM, UVE-SVM and UVE-PLS, with the coefficient of determination (R²) of 0.99, 0.92, 0.97 and 0.87, and the root mean square error of cross validation (RMSECV) of 0.7615%, 0.723 μg·g⁻¹, 0.3701% and 0.1197%, respectively, the relative percent difference (RPD) was 10.2093%, 25.446 μg·g⁻¹, 3.5851% and 2.5284%, respectively. Conclusion: High correlation, appropriate modeling error, outstanding model effect, and the ability to accurately identify the biochemical components of raw materials throughout processing are all characteristics of the model. This technique is not only quick and precise but also non-destructive. In the processing of tea, it is anticipated to be widely employed.
- hyperspectral imaging,
- tea processing,
- machine learning,
- non-destructive testing,
- biochemical components of tea

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References(36)

References

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Establishment of a Hyperspectral Spectroscopy-Based Biochemical Component Detection Model for Green Tea Processing Materials

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