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    科学领域:

    • 材料科学 材料科学 材料科学
    • 分析化学 分析化学
    • 生物技术是生物技术.

    背景情况:

    • 液晶 (LC) 传感器从分子方向生成复杂的光学图像,这对快速分析提出了挑战.
    • 传统的LC光学图像分析方法耗时,可能缺乏精度.
    • 深度学习 (DL) 为科学传感应用中高级图像分析提供了潜力.

    研究的目的:

    • 利用VGG16深度学习模型加速液晶光学图像的分析.
    • 为了提高基于液晶的传感应用的速度和灵敏度.
    • 通过使用LC光学图像来实现分析物的可视化和精确量化.

    主要方法:

    • 实施VGG16深度学习模型,用于分析液晶传感器的各种光学纹理.
    • 训练DL模型根据LC光学图像对分析物进行分类和量化.
    • 基于DL的分析与传统的灰度尺度强度量化的比较.

    主要成果:

    • 在30秒内达到0.9113的分类准确度,用于检测表面活性物甲 (CTAB) 和二甲硫酸盐 (SDS).
    • 减少了胰岛素特异性胺和胰岛素的定量检测的平均相对误差,分别为3.54%和7.94%.
    • 胰岛素识别和度检测的传感时间从300秒缩短到90秒.

    结论:

    • VGG16深度学习模型显著加速液晶传感器分析,提高速度和准确性.
    • 基于光学图像分析,DL为精确,可视化的传感应用提供了强大的分析工具.
    • 这种方法在检测表面活性剂和量化像胰岛素这样的生物分子方面表现得更好.