Hierarchical cluster analysis derived from the most suitable prediction models for the discrimination of soybean samples.

<p>(A) Chinese vs. Korean soybean samples (single linkage), (B) discrimination of Chinese soybean samples (Ward), and (C) discrimination of Korean soybean samples (Ward).</p

List of permutation parameters of the PLSR models obtained using variables selected by vector normalization applied after the second differentiation, UV scaling, and with various VIP cutoff values using different wavenumber areas for the comparison of the three groups of Korean provinces.

<p>List of permutation parameters of the PLSR models obtained using variables selected by vector normalization applied after the second differentiation, UV scaling, and with various VIP cutoff values using different wavenumber areas for the comparison of the three groups of Korean provinces.</p

Morphological characteristics of the 13 Chinese soybean samples.

<p>Morphological characteristics of the 13 Chinese soybean samples.</p

Map showing the origin of the Chinese and Korean soybeans used in the experiments.

<p>(A) Map of China. The Chinese provinces were divided into three regions: northeastern, eastern, and southeastern. The northeastern region comprises four provinces: (a) Neimenggu, (b) Heilongjiang, (c) Jilin, and (d) Liaoning. The eastern region comprises four provinces: (e) Hebei, (f) Shandong, (g) Anhui, and (h) Hubei. The southeastern region comprises five provinces: (i) Zhejiang, (j) Jiangxi, (k) Fujian, (l) Guangdong, and (m) Guangxi. (B) Map of South Korea. The South Korean provinces were divided into three regions: upper, left side, and right side. The upper region comprises three provinces: (1) Gyeonggi-do, (2) Gangwon-do, and (3) Chungcheongbuk-do. The left-side region comprises three provinces: (4) Chungcheongnam-do, (5) Jeollabuk-do, (6) and Jeollanam-do. The right-side region comprises two provinces: (7) Gyeongsangbuk-do and (8) Gyeongsangnam-do.</p

List of permutation parameters of the PLSR models obtained using variables selected by vector normalization applied after the second differentiation, UV scaling, and with various VIP cutoff values using different wavenumber areas for the comparison of Chinese and Korean soybeans.

<p>List of permutation parameters of the PLSR models obtained using variables selected by vector normalization applied after the second differentiation, UV scaling, and with various VIP cutoff values using different wavenumber areas for the comparison of Chinese and Korean soybeans.</p

Flow chart for discrimination of unidentified soybean origin using FT-IR.

<p>Flow chart for discrimination of unidentified soybean origin using FT-IR.</p

FT-IR spectrum band assignments of soybeans cultivated in Neimenggu province.

<p>FT-IR spectrum band assignments of soybeans cultivated in Neimenggu province.</p

Morphological characteristics of the eight Korean soybean samples.

<p>(1) Gyeonggi-do Anseong, (2) Gangwon-do Yeongwol, (3) Chungcheongbuk-do Eumseong, (4) Chungcheongnam-do Cheonan, (5) Jeollabuk-do Imsil, (6) Jeollanam-do Yeonggwang, (7) Gyeongsangbuk-do Uiseong, and (8) Gyeongsangnam-do Geochang.</p

List of permutation parameters of the PLSR models obtained using variables selected by vector normalization applied after the second differentiation, UV scaling, and with various VIP cutoff values using different wavenumber areas for the comparison of the three groups of Chinese provinces.

<p>List of permutation parameters of the PLSR models obtained using variables selected by vector normalization applied after the second differentiation, UV scaling, and with various VIP cutoff values using different wavenumber areas for the comparison of the three groups of Chinese provinces.</p

Effects of coronatine elicitation on growth and metabolic profiles of <i>Lemna paucicostata</i> culture

<div><p>In this study, the effects of coronatine treatment on the growth, comprehensive metabolic profiles, and productivity of bioactive compounds, including phenolics and phytosterols, in whole plant cultures of <i>Lemna paucicostata</i> were investigated using gas chromatography-mass spectrometry (GC-MS) coupled with multivariate statistical analysis. To determine the optimal timing of coronatine elicitation, coronatine was added on days 0, 23, and 28 after inoculation. The total growth of <i>L</i>. <i>paucicostata</i> was not significantly different between the coronatine treated groups and the control. The coronatine treatment in <i>L</i>. <i>paucicostata</i> induced increases in the content of hydroxycinnamic acids, such as caffeic acid, isoferulic acid, <i>ρ</i>-coumaric acid, sinapic acid, and phytosterols, such as campesterol and β-sitosterol. The productivity of these useful metabolites was highest when coronatine was added on day 0 and harvested on day 32. These results suggest that coronatine treatment on day 0 activates the phenolic and phytosterol biosynthetic pathways in <i>L</i>. <i>paucicostata</i> to a greater extent than in the control. To the best of our knowledge, this is the first report to investigate the effects of coronatine on the alteration of metabolism in <i>L</i>. <i>paucicostata</i> based on GC-MS profiling. The results of this research provide a foundation for designing strategies for enhanced production of useful metabolites for pharmaceutical and nutraceutical industries by cultivation of <i>L</i>. <i>paucicostata</i>.</p></div