Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy

Wei Pan; Wenjing Liu; Xiujuan Huang

doi:10.1016/j.crfs.2023.100654

Rapid identification of the geographical origin of Baimudan tea using a Multi-AdaBoost model integrated with Raman Spectroscopy

Curr Res Food Sci. 2023 Dec 9:8:100654. doi: 10.1016/j.crfs.2023.100654. eCollection 2024.

Authors

Wei Pan¹, Wenjing Liu¹, Xiujuan Huang²

Affiliations

¹ Fujian Key Laboratory of Agro-products Quality & Safety, Institute of Agricultural Quality Standards and Testing Technology, Fujian Academy of Agricultural Sciences, Fuzhou, 350003, People's Republic of China.
² Fujian Saifu Food Inspection Institute Co. Ltd., Fuzhou, 350003, People's Republic of China.

Abstract

The potential of Multi-AdaBoost in spectral analysis is substantial, particularly when combined with weak classifiers and trained to develop into a robust classifier. Given the variable quality of Baimudan tea sourced from diverse regions, the novel application of Raman spectroscopy in conjunction with the Multi-AdaBoost model to analyze the geographic origin of Baimudan tea was introduced. Initially, Raman spectra of Baimudan tea from four distinct origins in Fujian province were gathered, namely Fuan (FA), Fuding (FD), Zhenghe (ZH), and Songxi (SX). Decision Tree (DT) and Support Vector Machine (SVM) models were employed as fitting classifiers to construct the Multi-AdaBoost-DT and Multi-AdaBoost-SVM models. The results demonstrated that the Multi-AdaBoost-DT model exhibited significantly improved recognition rates for FA, FD, ZH, and SX origin compared to the DT model, with the average recognition rate increasing from 86.46% to 91.67%. In contrast, the recognition rates for FA and SX origin in the Multi-AdaBoost-SVM model remained unchanged, attributed to the model having reached a local optimum. The recognition rates of FD origin increased from 91.67% to 95.83%, a significant improvement, while those of ZH origin escalated from 83.33% to 87.50%. The average recognition rate increased from 92.71% to 94.79%. Additionally, Multi-AdaBoost-SVM and Multi-AdaBoost-DT enhanced the sensitivity and specificity of the discrimination outcomes. These results corroborated the effectiveness of the proposed Multi-AdaBoost-SVM model in identifying the geographical origin of Baimudan tea. Moreover, the Multi-AdaBoost model demonstrates potential in elevating the discrimination accuracy of weak classifiers, which bodes well for its application in food authentication and quality control.

Keywords: Baimudan tea; Geographical origin; Multi-AdaBoost; Raman spectroscopy.