Department of Chemistry, University of Virginia, Charlottesville, VA, USA.
Department of Mechanical Engineering, University of Virginia, Charlottesville, VA, USA.
Nat Protoc. 2021 Jan;16(1):218-238. doi: 10.1038/s41596-020-00413-0. Epub 2020 Dec 9.
Recently, there has been an explosion of scientific literature describing the use of colorimetry for monitoring the progression or the endpoint result of colorimetric reactions. The availability of inexpensive imaging technology (e.g., scanners, Raspberry Pi, smartphones and other sub-$50 digital cameras) has lowered the barrier to accessing cost-efficient, objective detection methodologies. However, to exploit these imaging devices as low-cost colorimetric detectors, it is paramount that they interface with flexible software that is capable of image segmentation and probing a variety of color spaces (RGB, HSB, Y'UV, Lab*, etc.). Development of tailor-made software (e.g., smartphone applications) for advanced image analysis requires complex, custom-written processing algorithms, advanced computer programming knowledge and/or expertise in physics, mathematics, pattern recognition and computer vision and learning. Freeware programs, such as ImageJ, offer an alternative, affordable path to robust image analysis. Here we describe a protocol that uses the ImageJ program to process images of colorimetric experiments. In practice, this protocol consists of three distinct workflow options. This protocol is accessible to uninitiated users with little experience in image processing or color science and does not require fluorescence signals, expensive imaging equipment or custom-written algorithms. We anticipate that total analysis time per region of interest is ~6 min for new users and <3 min for experienced users, although initial color threshold determination might take longer.
最近，科学文献中涌现出大量关于比色法用于监测比色反应进程或终点结果的描述。廉价成像技术（例如扫描仪、树莓派、智能手机和其他低于 50 美元的数码相机）的普及降低了获取具有成本效益的客观检测方法的门槛。然而，要将这些成像设备用作低成本比色检测器，至关重要的是它们要与能够进行图像分割和探测各种颜色空间（RGB、HSB、Y'UV、Lab* 等）的灵活软件接口。为高级图像处理开发定制软件（例如智能手机应用程序）需要复杂的、定制编写的处理算法、高级计算机编程知识以及物理学、数学、模式识别和计算机视觉方面的专业知识或学习。ImageJ 等免费软件为强大的图像分析提供了另一种经济实惠的途径。在这里，我们描述了一个使用 ImageJ 程序处理比色实验图像的协议。实际上，该协议包含三个不同的工作流程选项。对于没有图像处理或颜色科学经验的新手用户来说，该协议易于上手，且不需要荧光信号、昂贵的成像设备或定制算法。我们预计，新用户每个感兴趣区域的总分析时间约为 6 分钟，而有经验的用户则<3 分钟，尽管初始颜色阈值确定可能需要更长时间。
