کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6390686 | 1628403 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Determining the geographical origin of Chinese green tea by linear discriminant analysis of trace metals and rare earth elements: Taking Dongting Biluochun as an example
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
دانش تغذیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Geographic origin of food is topic of interest for both consumers and producers since the prices of agricultural products can vary greatly according to their geographic origins. Determining the geographic origins of green teas produced in specific regions is very important. Here, we take one of the Chinese famous green teas Dongting Biluochun (DTBLC) as a model to classify its geographic origin based on the metal element analysis combined with the statistical analysis. A total of 37 metal elements including rare earth elements were analyzed by ICP-MS and PCA, CA and LDA were used to describe the relationship of BLC tea profiles from different geographical origins. High recognition (98.2%) and satisfactory predictive ability (96.4%) of the developed LDA model proved that the proposed method is a powerful, practical tool which is suitable for tracing unclassified BLC samples. Moreover, the authentic DTBLC from different protection areas also achieved as high as 95.2% classification rate. Besides, the method of determining the geographic origin of DTBLC was also suitable for other green teas from close regions. Our study provides a high classification rate, good sensitivities and powerful tool to address the quality control and food authenticity concerns regarding green tea which is of great importance for the green tea industry.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Control - Volume 59, January 2016, Pages 714-720
Journal: Food Control - Volume 59, January 2016, Pages 714-720
نویسندگان
Guicen Ma, Yingbin Zhang, Jianyang Zhang, Guoqing Wang, Liyan Chen, Minglu Zhang, Ting Liu, Xin Liu, Chengyin Lu,