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

文献类型： 外文期刊

作者： Pan, Wei 1 ; Liu, Wenjing 1 ; Huang, Xiujuan 2 ;

作者机构： 1.Fujian Acad Agr Sci, Inst Agr Qual Stand & Testing Technol, Fujian Key Lab Agroprod Qual & Safety, Fuzhou 350003, Peoples R China

2.Fujian Saifu Food Inspect Inst Co Ltd, Fuzhou 350003, Peoples R China

3.Fujian Acad Agr Sci, Inst Qual Stand & Testing Technol Agroprod, Fujian Key Lab Agroprod Qual & Safety, 247 Wusi Rd, Fuzhou 350003, Fujian, Peoples R China

4.Chinese Acad Sci, Fujian Saifu Food Inspect Inst Co Ltd, Fujian Inst Phys Construct, Bldg 47 & 51-52,Xihe Pk,155 Yangqiao West Rd, Fuzhou 350003, Fujian, Peoples R China

关键词： Baimudan tea; Raman spectroscopy; Geographical origin; Multi-AdaBoost

期刊名称：CURRENT RESEARCH IN FOOD SCIENCE （ 影响因子：6.3； 五年影响因子：6.3 ）

ISSN：

年卷期： 2024 年 8 卷

页码：

收录情况： SCI

摘要： 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 MultiAdaBoost-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.

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