Lycium species and variety recognition technology based on electrochemical sensing of leaf signals

Xin SHI; Junjie MAN; Weiting YE; Jiangwei ZHU; Li FU; Yuhong ZHENG; Yue YIN; Yan NIU; Xiaojing WANG

doi:10.15835/nbha51113054

Authors

Xin SHI Ningxia Institute of Quality Standards and Testing Technology for Agricultural Products, Yinchuan 750002 (CN)
Junjie MAN Hangzhou Dianzi University, College of Materials and Environmental Engineering, Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou, 310018 (CN)
Weiting YE Hangzhou Dianzi University, College of Materials and Environmental Engineering, Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou, 310018 (CN)
Jiangwei ZHU Nanjing Forestry University, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing 210037 (CN)
Li FU Hangzhou Dianzi University, College of Materials and Environmental Engineering, Key Laboratory of Novel Materials for Sensor of Zhejiang Province, Hangzhou, 310018 (CN)
Yuhong ZHENG Jiangsu Province & Chinese Academy of Sciences (Nanjing Botanical Garden Mem. Sun Yaseen), Institute of Botany, Nanjing 210014 (CN)
Yue YIN Ningxia Academy of Agriculture and Forestry Sciences, National Wolfberry Engineering Research Cente, Yinchuan 750002 (CN)
Yan NIU Ningxia Institute of Quality Standards and Testing Technology for Agricultural Products, Yinchuan 750002 (CN)
Xiaojing WANG Ningxia Institute of Quality Standards and Testing Technology for Agricultural Products, Yinchuan 750002 (CN)

DOI:

https://doi.org/10.15835/nbha51113054

Keywords:

electrochemical fingerprint, goji berry, species identification, machine learning, phytochemistry

Abstract

Identification of plant species and variety has important application value in the process of agricultural production. In this work, we try to use electrochemical fingerprinting technology to collect the electrochemical behavior of electrochemically active substances in plant leaf tissues. Twenty Lycium species and varieties were specifically selected to investigate the recognition ability of electrochemical fingerprinting. Two different extraction solvents and electrolytes were used to create different collection environments. The results show that different Lycium spp. can exhibit different electrochemical fingerprints. Different species of the same species exhibit relatively similar electrochemical fingerprints. After the second derivative processing, the electrochemical fingerprint of plants can be used for classification and recognition by different machine learning models. Partial least squares discriminant analysis (PLS-DA), k-nearest neighbor, (KNN), support vector machine (SVM), random forest (RF) and stochastic gradient boosting (SGB) were used to establish recognition model of Lycium spp. The results show that SGB has the best identification accuracy for electrochemical fingerprint after second derivative treatment.

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References

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