| 引用本文: | 冯 敏1,2,幸宏伟1,尤琳烽1,2,刘小娟1.洛神花花青素提取工艺及抗氧化活性研究(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2023，40（6）：1-7 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 目的 用超声波辅助法优化提取洛神花中花青素的提取工艺,并通过斑马鱼胚胎氧化应激模型进行抗氧化活
性研究。 方法 正交法进行提取工艺优化,斑马鱼胚胎进行氧化应激干预测试抗氧化水平。 结果 洛神花花青素最
佳提取工艺条件:提取温度 30 ℃ 、超声功率 300 W、料液比 1 ∶ 40(g / mL)、超声时间 90 min,此时得率为 2. 94 mg /
g。 体外抗氧化活性表明,5. 8 mg / mL 洛神花花青素对 DPPH 自由基清除率、ABTS 自由基清除率和羟自由基清除
率分别为 83. 15 %、 63. 32 %和 74. 4 %。 通过斑马鱼胚胎氧化应激模型进行抗氧化活性研究发现,洛神花花青素
能够有效保护由 AAPH 诱导的斑马鱼胚胎氧化损伤,11. 6 μg / mL 剂量组的洛神花花青素极显著降低斑马鱼胚胎
ROS 的产生,抑制脂质过氧化物的生成和降低胚胎细胞死亡率,其作用效果与 2. 9 μg / mL VC 组相近。 结论 超声
辅助正交优化后的工艺能提高洛神花花青素得率,比单因素最高得率提高 37%;良好的体内外抗氧化活性为进一
步开发洛神花提供理论基础。 |
| 关键词: 洛神花 花青素 超声波提取 抗氧化活性 |
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| Multi-spectral Identification of Coal and Gangue Based on Slime Mold Algorithm Extreme Learning Machine |
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FENG Min1 2,XING Hongwei1,YOU Linfeng1 2,LIUXiaojuan1
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1. School of Electrical and Information Engineering Anhui University of Science & Technology Anhui Huainan 232001
China
2. School of Mechanics and Optoelectronic Physics Anhui University of Science & Technology Anhui Huainan 232001
China
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| Abstract: |
| Accurate identification of coal and gangue is an important prerequisite for coal and gangue sorting and clean
and efficient utilization of coal. In view of the many shortcomings of traditional methods such as low efficiency the need to
install radiation isolation and environmental interference a classification model called Slime Mold Algorithm Extreme
Learning Machine SMA-ELM was proposed to recognize coal and gangue based on multi-spectral image characteristics
and spectral characteristics. A multi-spectral data acquisition system was built to complete the acquisition of spectral
images of coal and gangue. The extracted feature vectors were downscaled by LBP and PCA principal component analysis, and input to SMA-ELM classification model Antlion Algorithm Optimized Extreme Learning Machine ALO-ELM
classification model and Whale Algorithm Optimized Extreme Learning Machine WOA-ELM classification model for
comparison. It focused on the recognition accuracy of coal and gangue under different wavelength responses to screen the
best wavelength and the optimized optimal wavelengths were compared by multiple evaluation indexes. The experimental
results showed that SMA-ELM had the best classification effect and the 6th band was the optimal band the average
recognition accuracy of SMA-ELM in this band was 95. 08% and the recognition F1-Scores of coal and gangue were
96. 47% and 92. 68% respectively with a time of 10. 6 s. The proposed method can achieve the accurate recognition of
coal and gangue which has important research significance for the intelligent separation of coal and gangue. |
| Key words: multi-spectral imaging technology slime molds optimization extreme learning machine classification bands selection LBP algorithm |
