Characterizing diversity based on nutritional and bioactive compositions of yam germplasm (Dioscorea spp.) commonly cultivated in China

• Published in: Yàowu shi͡p︡in fenxi Vol. 24; no. 2; pp. 367 - 375
• Main Authors: Wu, Zhi-Gang, Jiang, Wu, Nitin, Mantri, Bao, Xiao-Qing, Chen, Song-Lin, Tao, Zheng-Ming
• Format: Journal Article
• Language: English
• Published: China (Republic : 1949- ) Elsevier B.V 01.04.2016; Food and Drug Administration; Taiwan Food and Drug Administration
• Subjects: Allantoin; Amino acids; bioactive constituents; Chemical composition; China; Chinese medicine; Classification; Clustering; Dioscorea; diversity; Domestication; Eigenvalues; Food; Food Analysis; Genetic Variation; Germplasm; Medical materials; Minerals; nutritional compositions; Organic chemistry; Original; Phylogeny; Plant Tubers; Principal Component Analysis; Principal components analysis; Proteins; Starch; Variability; yam; Yams; United States > US; China; Germany; diversity; yam; nutritional compositions; China; bioactive constituents
• ISSN: 1021-9498; 2224-6614
• DOI: 10.1016/j.jfda.2015.12.003

Yams (Dioscorea spp.) are widely cultivated as edible resources and medical materials in China. Characterizing chemical compositions in yam germplasm is crucial to determine their diversity and suitability for food and medicine applications. In this study, a core germplasm containing 25 yam landraces was used to create an effective classification of usage by characterizing their nutritive and medicinal compositions. All studied landraces exhibited high contents of starch from 60.7% to 80.6% dry weight (DW), protein (6.3–12.2% DW), minerals (especially Mg 326.8–544.7 mg/kg DW), and essential amino acids. Allantoin and dioscin varied considerably, with values of 0.62–1.49% DW and 0.032–0.092% DW, respectively. The quality variability of 25 yam landraces was clearly separated in light of UPGMA clustering and principal component analysis (PCA). Using an eigenvalue ≥1 as the cutoff, the first three principal components accounted for most of the total variability (62.33%). Classification was achieved based on the results of the measured parameters and principal component analysis scores. The results are of great help in determining appropriate application strategies for yam germplasm in China.