Xiabing Kong, Qiusheng Chen, Min Xu, Yihui Liu, Xiaoming Li, Lingxi Han, Qiang Zhang, Haoliang Wan, Lu Liu, Xubo Zhao, Jiyun Nie
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引用次数: 0
Abstract
Winter jujube ( ‘Dongzao’) is greatly appreciated by consumers for its excellent quality, but brand infringement frequently occurs in the market. Here, we first determined a total of 38 elements in 167 winter jujube samples from the main winter jujube producing areas of China by inductively coupled plasma mass spectrometer (ICP-MS). As a result, 16 elements (Mg, K, Mn, Cu, Zn, Mo, Ba, Be, As, Se, Cd, Sb, Ce, Er, Tl, and Pb) exhibited significant differences in samples from different producing areas. Supervised linear discriminant analysis (LDA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA) showed better performance in identifying the origin of samples than unsupervised principal component analysis (PCA). LDA and OPLS-DA had a mean identification accuracy of 87.84 and 94.64% in the testing set, respectively. By using the multilayer perceptron (MLP) and C5.0, the prediction accuracy of the models could reach 96.36 and 91.06%, respectively. Based on the above four chemometric methods, Cd, Tl, Mo and Se were selected as the main variables and principal markers for the origin identification of winter jujube. Overall, this study demonstrates that it is practical and precise to identify the origin of winter jujube through multi-element fingerprint analysis with chemometrics, and may also provide reference for establishing the origin traceability system of other fruits.
期刊介绍:
Journal of Integrative Agriculture publishes manuscripts in the categories of Commentary, Review, Research Article, Letter and Short Communication, focusing on the core subjects: Crop Genetics & Breeding, Germplasm Resources, Physiology, Biochemistry, Cultivation, Tillage, Plant Protection, Animal Science, Veterinary Science, Soil and Fertilization, Irrigation, Plant Nutrition, Agro-Environment & Ecology, Bio-material and Bio-energy, Food Science, Agricultural Economics and Management, Agricultural Information Science.