洛神花花青素提取工艺及抗氧化活性研究

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，11.6μg/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2.9μg/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。

Multi-spectral Identification of Coal and Gangue Based on Slime Mold Algorithm Extreme Learning Machine

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.