Jianyi Zhang1, Jiyun Nie2, Liangbin Zhang3, Guofeng Xu1, Haidong Zheng1, Youming Shen1, Lixue Kuang1, Xiaoqin Gao1, Hui Zhang1. 1. Laboratory of Quality and Safety Risk Assessment for Fruit, Ministry of Agriculture and Rural Affairs, Quality Inspection and Test Center for Fruit and Nursery Stocks, Ministry of Agriculture and Rural Affairs, Research Institute of Pomology, Chinese Academy of Agricultural Sciences, Xingcheng, China. 2. College of Horticulture, Qingdao Agricultural University/Qingdao Key Lab of Modern Agriculture Quality and Safety Engineering, Qingdao, China. 3. Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Baotou Academy of Agriculture and Animal Husbandry Science, Baotou, China.
Abstract
BACKGROUND: Half of all apple production worldwide comes from China. However, the geographic authentication of Chinese apples has not been well studied. We highlight the multi-element-based geographical discrimination of apples from the southwest cold highlands (SCH) of China. 565 samples from the SCH (138) and others (427) were obtained, and the content of fifteen elements were applied to construct models for discrimination. RESULTS: The SCH apples from 2017 to 2019 had higher concentrations of Mn, Zn, Cr, Cd, Se, Pb, and Fe, but lower concentrations of Na, B, Ni, and P. With sufficient training, linear discriminant analysis (LDA) discriminated the SCH, and the testing accuracy averaged 92.5% and 92.2%. Nonlinear discrimination models were more suitable than the linear models. Optimized random forest analysis was the model with the best fit, and with averaged training and testing it obtained a level of accuracy of 98.2% and 98.5%. CONCLUSION: The multielement-based discrimination of SCH apples could aid further studies of geographical origins.
BACKGROUND: Half of all apple production worldwide comes from China. However, the geographic authentication of Chineseapples has not been well studied. We highlight the multi-element-based geographical discrimination of apples from the southwest cold highlands (SCH) of China. 565 samples from the SCH (138) and others (427) were obtained, and the content of fifteen elements were applied to construct models for discrimination. RESULTS: The SCHapples from 2017 to 2019 had higher concentrations of Mn, Zn, Cr, Cd, Se, Pb, and Fe, but lower concentrations of Na, B, Ni, and P. With sufficient training, linear discriminant analysis (LDA) discriminated the SCH, and the testing accuracy averaged 92.5% and 92.2%. Nonlinear discrimination models were more suitable than the linear models. Optimized random forest analysis was the model with the best fit, and with averaged training and testing it obtained a level of accuracy of 98.2% and 98.5%. CONCLUSION: The multielement-based discrimination of SCHapples could aid further studies of geographical origins.
Authors: Wojciech Koch; Wirginia Kukula-Koch; Marcin Czop; Tomasz Baj; Janusz Kocki; Piotr Bawiec; Roser Olives Casasnovas; Anna Głowniak-Lipa; Kazimierz Głowniak Journal: Molecules Date: 2021-10-04 Impact factor: 4.411