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Geographical traceability of wolfberry pulp: Integrating stable isotopes, minerals, nutrients, and chemometric  ( SCI-EXPANDED收录)   被引量:4

文献类型:期刊文献

英文题名:Geographical traceability of wolfberry pulp: Integrating stable isotopes, minerals, nutrients, and chemometric

作者:Peng, Qi[1];Huang, Jiaxin[1];Li, Shanshan[1];Massou, Beatrice Bassilekin[1];Xie, Guangfa[2]

机构:[1]Shaoxing Univ, Sch Life & Environm Sci, Natl Engn Res Ctr Chinese CRW Branch Ctr, 900 Chengnan Rd, Shaoxing 312000, Peoples R China;[2]Zhejiang Shuren Univ, Coll Biol & Environm Engn, Key Lab Pollut Exposure & Hlth Intervent Zhejiang, Hangzhou 310015, Peoples R China

年份:2024

卷号:134

外文期刊名:JOURNAL OF FOOD COMPOSITION AND ANALYSIS

收录:SCI-EXPANDED(收录号:WOS:001271137800001)、、Scopus(收录号:2-s2.0-85198310613)、WOS

基金:This work was supported by the Zhejiang Shaoxing Huangjiu Industry Innovation Service Complex - Technological Innovation Project in Zhejiang Province (No. 2023KJ082); Foundation of Public Projects of Zhejiang Province, China (No. LGN22C200008); Program Foundation of Public Projects of Shaoxing City, Zhejiang Province, China (No. 2018C30010); Foundation of Public Projects of Zhejiang Province, China (No. 2017C32101); Shaoxing University Fund (No. 08021066), Shaoxing University Fund (No. 08220102213).

语种:英文

外文关键词:Wolfberry pulp; Stable isotopes; Multi -element; Chemometric; Machine learning; Origin identification

外文摘要:The determination of the origin of wolfberry ( Lycium barbarum ) pulp in China remains a challenge despite its nutritional significance and popularity among consumers. To address this gap, 144 samples from four distinct regions in China were analyzed using nutrient elements, stable isotopes combined with multi-element assessments. Chemometric analyses successfully demonstrated the initial separation of wolfberry pulp samples. Notably, a nutrient element (Glucose) along with 12 mineral elements (Tb, Co, Fe, Cd, Pr, V, Mo, Gd, Al, Mg, As, Zn) emerged as pivotal factors for origin identification. Furthermore, employing a random forest algorithm resulted in the highest classification accuracy of 100 %, surpassing support vector machine (96.43 %) and Knearest (71.43 %) methods. The study 's findings underscore the efficacy of utilizing stable isotopes, mineral elements, and nutritional composition as effective markers for tracing the origin of wolfberry pulp. Moreover, this methodology offers promising insights into the potential identification of origins for other agricultural products.

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