Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging

Ting An; Siyao Yu; Wenqian Huang; Guanglin Li; Xi Tian; Shuxiang Fan; Chunwang Dong; Chunjiang Zhao

doi:10.1016/j.saa.2021.120791

Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging

Spectrochim Acta A Mol Biomol Spectrosc. 2022 Mar 15:269:120791. doi: 10.1016/j.saa.2021.120791. Epub 2021 Dec 22.

Authors

Ting An¹, Siyao Yu², Wenqian Huang³, Guanglin Li⁴, Xi Tian³, Shuxiang Fan³, Chunwang Dong⁵, Chunjiang Zhao⁶

Affiliations

¹ College of Engineering and Technology, Southwest University, Chongqing 400715, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China.
² College of Mechanical and Electrical Engineering Shihezi University, Shihezi 832000, China.
³ Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
⁴ College of Engineering and Technology, Southwest University, Chongqing 400715, China.
⁵ Tea Research Institute, The Chinese Academy of Agricultural Sciences, Hangzhou 310008, China. Electronic address: dongchunwang@163.com.
⁶ College of Engineering and Technology, Southwest University, Chongqing 400715, China; Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China. Electronic address: zhaocj@nercita.org.cn.

PMID: 34968835
DOI: 10.1016/j.saa.2021.120791

Abstract

The rapid and non-destructive detection of moisture in withering leaves is an unsolved problem because the leaves are stacked together and have random orientation. To address this issue, this study aimed to establish more robust and accurate models. The performance of front side, back side and multi-region models were compared, and the front side model showed the worst transferability. Therefore, five effective wavelength (EW) selection algorithms were combined with a successive projection algorithm (SPA) to select EWs. It was found that the shuffled frog leaping algorithm (SFLA) combined with SPA was the best method for the front side model for moisture analyses. Based on the selected EWs, the extreme learning machine (ELM) became the model with the best self-verification result. Subsequently, moisture distribution maps of withering leaves were successfully generated. Considering the processing demand of withering leaves, local region models developed based on partial least squares and the SFLA-SPA method were applied to predict the moisture of withering leaves in the local and stacked region. The results showed that the RPD, R_cv and R_p values were above 1.6, 0.870 and 0.897, respectively. These results provide a useful reference for the non-destructive detection of moisture in withering leaves.

Keywords: Back side; Front side; Hyperspectral images; Moisture content; Robust model; Withering leaves.

MeSH terms

Camellia sinensis*
Hyperspectral Imaging
Least-Squares Analysis
Plant Leaves
Tea*

Substances

Tea