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Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy

文献类型： 外文期刊

作者： Chen, Jiayou 1 ; Yang, Chongshan 2 ; Yuan, Changbo 4 ; Li, Yang 2 ; An, Ting 2 ; Dong, Chunwang 2 ;

作者机构： 1.Liming Vocat Univ, Quanzhou 362007, Fujian, Peoples R China

2.Chinese Acad Agr Sci, Tea Res Inst, Hangzhou 310008, Peoples R China

3.Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China

4.Shandong Acad Agr Sci, Tea Res Inst, Jinan 250033, Peoples R China

期刊名称：SCIENTIFIC REPORTS （ 影响因子：4.6； 五年影响因子：4.9 ）

ISSN： 2045-2322

年卷期： 2022 年 12 卷 1 期

页码：

收录情况： SCI

摘要： Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line=0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (R-p) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process.

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