Stable isotope and elemental profiles determine geographical origin of saffron from China and Iran

被引:16
|
作者
Nie J. [1 ,4 ]
Yang J. [2 ]
Liu C. [3 ]
Li C. [1 ,4 ]
Shao S. [1 ,4 ]
Yao C. [5 ]
Chen B. [6 ]
Tao Y. [7 ]
Wang F. [1 ,4 ]
Zhang Y. [1 ,4 ]
Rogers K.M. [1 ,4 ,8 ]
Wang P. [7 ]
Yuan Y. [1 ,4 ]
机构
[1] State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Hangzhou
[2] State Key Laboratory of Dao-di Herbs Breeding Base, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing
[3] Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing
[4] Institute of Agro-Products Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Key Laboratory of Information Traceability for Agricultural Products, Ministry of Agriculture and Rural Affairs of China, Hangzhou
[5] Institute for Agro-food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai
[6] Zhejiang Institute for Food and Drug Control, Hangzhou
[7] College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou
[8] National Isotope Centre, GNS Science, Lower Hutt
关键词
Chemometrics; Geographical origin; Saffron; Stable isotopes; Traceability;
D O I
10.1016/j.foodchem.2022.134733
中图分类号
学科分类号
摘要
Origin verification of high-value saffron is essential for fair trade and to protect consumers' interests and rights. A traceability method using elemental content (% C and % N) and stable isotopes (δ13C, δ2H, δ18O, and δ15N) combined with chemometrics was developed to discriminate saffron from Iran and China and classify major domestic production areas in China. Results showed that Iranian samples had lower % C and % N contents but higher δ13C values than Chinese origin saffron, with δ13C acting as an important variable for origin discrimination. Moreover, δ2H and δ13C isotopes were found to be important variables to classify Chinese regional saffron origin. Two supervised pattern recognition models (PLS-DA) developed to classify Iranian and Chinese saffron, and regional Chinese saffron had a discrimination accuracy of 85.0 % and 80.2 %, respectively. These models provide the basis for a new regulatory inspection procedure to verify saffron origin and label claims, minimizing fraudulent mislabeling and adding value to saffron from specific regions. © 2022 Elsevier Ltd
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