Fogang granitic batholith, the largest Late Mesozoic batholith in the Nanling region, has an exposure area of ca. 6000 km2. Wushi diorite-homblende gabbro body is situated at the northeast part of the batholith. Both ...Fogang granitic batholith, the largest Late Mesozoic batholith in the Nanling region, has an exposure area of ca. 6000 km2. Wushi diorite-homblende gabbro body is situated at the northeast part of the batholith. Both the granitic batholith main body and the diorite-homblende gabbro body belong to high-K calc alkaline series. Compared with the granitic main body, the Wushi body has lower Si (49%–55%), higher Fe, Mg, Ca, lower REE, less depletion of Eu, Ba, P, Ti, and obvious depletion of Zr, Hf. Zircon LA-ICP-MS dating and the mineral-whole rock isochron dating reveal that Fogang granitic main body and Wushi body were generated simultaneously at ca. 160 Ma. The Fogang granitic main body has high (87Sr/86Sr)i ratios (0.70871–0.71570) and low ? Nd(t) values (?5.11–?8.93), suggesting the origins of the granitic rocks from crustal materials. Their Nd two-stage model ages range from 1.37–1.68 Ga. The Sr and Nd isotopic compositions and the Nd model ages of the granitic rocks may suggest that the giant Fogang granitic main body was generated from a heterogeneous source, with participation of mantle component. Wushi diorite-homblende gabbro is an unusual intermediate-basic magmatic rock series, with high (87Sr/86Sr)i ratios (0.71256–0.71318) and low ? Nd(t) values (?7.32–?7.92), which was possibly formed through mixing between the mantle-derived juvenile basaltic magma and the magma produced by the dehydration melting of lower crustal basaltic rocks.展开更多
目的建立基于氢核磁共振(1H nuclear magnetic resonance,1H NMR)结合支持向量机分类模型鉴别蜂蜜植物源的方法。方法采集荆条蜜、油菜蜜、洋槐蜜、葵花蜜4种不同植物源的蜂蜜共计122例样品的谱图信息,分全谱(δ0.10~δ9.50)、脂肪区(δ...目的建立基于氢核磁共振(1H nuclear magnetic resonance,1H NMR)结合支持向量机分类模型鉴别蜂蜜植物源的方法。方法采集荆条蜜、油菜蜜、洋槐蜜、葵花蜜4种不同植物源的蜂蜜共计122例样品的谱图信息,分全谱(δ0.10~δ9.50)、脂肪区(δ0.10~δ3.00)、糖类化合物区(δ3.00~δ6.00)、芳香区(δ6.00~δ9.50)4个不同积分区间建立分类模型,结合主成分权值系数筛选特征变量,进一步优化判别模型。结果基于主成分权值系数筛选变量范围δ3.40~δ3.90和δ4.60~δ4.70内共计267个积分变量,以该区域积分变量为输入变量建立的支持向量机分类模型,对训练集的判别正确率为97.53%,对测试集的判别正确率为100%。结论通过主成分权值系数能有效筛选特征变量,减少输入变量的同时提高模型稳健性与准确性,基于氢核磁共振结合支持向量机分类模型能有效鉴别不同植物源蜂蜜。展开更多
基金Supported by the National Natural Science Foundation of China (Grant Nos. 40221301, 40125007 and 40132010)
文摘Fogang granitic batholith, the largest Late Mesozoic batholith in the Nanling region, has an exposure area of ca. 6000 km2. Wushi diorite-homblende gabbro body is situated at the northeast part of the batholith. Both the granitic batholith main body and the diorite-homblende gabbro body belong to high-K calc alkaline series. Compared with the granitic main body, the Wushi body has lower Si (49%–55%), higher Fe, Mg, Ca, lower REE, less depletion of Eu, Ba, P, Ti, and obvious depletion of Zr, Hf. Zircon LA-ICP-MS dating and the mineral-whole rock isochron dating reveal that Fogang granitic main body and Wushi body were generated simultaneously at ca. 160 Ma. The Fogang granitic main body has high (87Sr/86Sr)i ratios (0.70871–0.71570) and low ? Nd(t) values (?5.11–?8.93), suggesting the origins of the granitic rocks from crustal materials. Their Nd two-stage model ages range from 1.37–1.68 Ga. The Sr and Nd isotopic compositions and the Nd model ages of the granitic rocks may suggest that the giant Fogang granitic main body was generated from a heterogeneous source, with participation of mantle component. Wushi diorite-homblende gabbro is an unusual intermediate-basic magmatic rock series, with high (87Sr/86Sr)i ratios (0.71256–0.71318) and low ? Nd(t) values (?7.32–?7.92), which was possibly formed through mixing between the mantle-derived juvenile basaltic magma and the magma produced by the dehydration melting of lower crustal basaltic rocks.
文摘目的建立基于氢核磁共振(1H nuclear magnetic resonance,1H NMR)结合支持向量机分类模型鉴别蜂蜜植物源的方法。方法采集荆条蜜、油菜蜜、洋槐蜜、葵花蜜4种不同植物源的蜂蜜共计122例样品的谱图信息,分全谱(δ0.10~δ9.50)、脂肪区(δ0.10~δ3.00)、糖类化合物区(δ3.00~δ6.00)、芳香区(δ6.00~δ9.50)4个不同积分区间建立分类模型,结合主成分权值系数筛选特征变量,进一步优化判别模型。结果基于主成分权值系数筛选变量范围δ3.40~δ3.90和δ4.60~δ4.70内共计267个积分变量,以该区域积分变量为输入变量建立的支持向量机分类模型,对训练集的判别正确率为97.53%,对测试集的判别正确率为100%。结论通过主成分权值系数能有效筛选特征变量,减少输入变量的同时提高模型稳健性与准确性,基于氢核磁共振结合支持向量机分类模型能有效鉴别不同植物源蜂蜜。
