Geographical origin traceability of tea based on multi-element spatial distribution and the relationship with soil in district scale. (August 2018)

• Record Type: Journal Article
• Authors: Li, Lei; Wen, Bo; Zhang, Xiaolei; Zhao, Yue; Duan, Yu; Song, Xiangfei; Ren, Shuang; Wang, Yuhua; Fang, Wanping; Zhu, Xujun
• Is Part Of: Food control. Volume 90(2018)
• Page Start: 18
• Page End: 28
• Publication Date: 2018-08
• Subjects: Tea leaves -- Soil -- Mineral elements -- Correlation -- Traceability; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2018.02.031
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: In this study, a discriminant model was established by determining mineral element contents in tea leaves and the soil, collected from Lishui, Jiangsu Province, China. The contents of 12 elements (Se, Zn, Ni, Mn, Cr, Pb, Mg, Ca, Cu, Al, Na, and K) were determined in both tea leaves and soil samples. Cluster analysis and principal component analysis (PCA) were employed for regional classification of tea samples. After data conversion and correlation analysis, spatial and quantitative prediction models were established by ordinary Kriging interpolation and multiple linear regressions. The results indicated a corresponding relationship of elements between tea and soil, and the cluster analysis and PCA showed a clear distinction between tea from the north to that from the middle and south of Lishui. Kriging interpolation predicted the levels of 12 elements, and among them, Se, Ca, and Cr showed a related spatial distribution. Three linear regression equations were established using Mn, Al, Ni, and K contents and soil pH, and these equations fitted well between predicted and actual values. The established linear equations can be used to identify the predominant mineral elements in tea plants and soil from Lishui and to identify the geographical origin of the tea product. Highlights: GIS was applied in the spatial distribution of mineral elements. Mineral elements of tea leaves and soils in district scale had correspondence. Tea regions were classified with unsupervised classifications methods. Mn, Al, Ni, K and pH could establish good linear regressions for traceability.