Discrimination of Storage Time of White Tea Using Hyperspectral Imaging

CHEN Shuyuan; ZHANG Youchao; YANG Jie; CAI Meisheng; ZHANG Qingbi; HE Puming; TU Youying

doi:10.13386/j.issn1002-0306.2020110299

Volume 42 Issue 18

Sep. 2021

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Science and Technology of Food Industry > 2021 > 42(18): 276-283. > DOI: 10.13386/j.issn1002-0306.2020110299

CHEN Shuyuan, ZHANG Youchao, YANG Jie, et al. Discrimination of Storage Time of White Tea Using Hyperspectral Imaging[J]. Science and Technology of Food Industry, 2021, 42(18): 276−283. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2020110299.

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Discrimination of Storage Time of White Tea Using Hyperspectral Imaging

1.
Department of Tea Science, Zhejiang University, Hangzhou 310058, China
2.
Department of Agricultural Engineering, Zhejiang University, Hangzhou 310058, China
3.
Organization Group of Fuding Tea Industry Development, Fuding 355200, China
4.
Fuding Tea Industry Bureau, Fuding 355200, China

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Received Date: December 01, 2020
Available Online: July 19, 2021

Graphical Abstract

Abstract

Abstract

The storage time is a main factor determining the value of white tea. To discriminate the storage time rapidly and nondestructively, a new method was applied in this paper. First, hyperspectral image data were captured from Shoumei of 3, 6 and 10 years storage time. Second, four kinds of algorithms were applied to preprocess the original data, savitzky-golay Filter, standard normal variate, minmaxscaler, and multiplicative scatter correction. Third, support vector machine, partial least squares with linear discriminant analysis and logistic regression were built based on the data of the full spectra. Finally, the best combination of preprocessing algorithm and model could be found by comparing the confusion matrix, precision and recall. The results showed that the best classification performances were obtained with the support vector machine after the pretreatment of standard normal variate. The precision of the calibration sets was 90.83%, and that of the prediction sets was 86.02%. Therefore, it is possible to use hyperspectral imaging to discriminate white tea of different storage time in tea industry.
- white tea,
- hyperspectral imaging,
- discriminant analysis,
- storage time

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References (31)

References

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