Essentially, this three-dimensional (3D) model encompasses all colors of human perception and, by design, more accurately depicts the increased spectral sensitivity of human cone cells to green wavelengths 8. The resultant CIE L*a*b* (see Box 1-‘Color spaces’) model is now widely considered to be the gold-standard model of human color vision 7. In 1976, the CIE elaborated on this idea by translating the CIE RGB to a new model, one that more accurately described the numerical relationship between wavelengths and human physiological response to observed color or color change. In 1931, the International Commission on Illumination (CIE) developed a mathematical model of this trichromic system (i.e., the CIE RGB color space) 5, 6. A color space (also, model or system) is an abstract mathematical representation (coordinate system or subspace) that defines the range of perceivable colors in human vision (Fig. In other words, to ameliorate the inherent uncertainties associated with human vision, an empirical model of the visible color space is required. To achieve objective analysis, the subjective nature of user-dependent visual interpretation must be mitigated. With human vision as the detection method, these deficiencies are largely due to on-site environmental conditions (e.g., poor ambient lighting or the flashing lights of emergency vehicles) and natural, person-to-person perceptual differences 4. However, these field-deployable approaches also suffer from critical operational limitations and are often criticized for susceptibility to incorrect or variable user interpretation, which can lead to inaccurate results. Specifically, these kits present in a variety of forms, including paper, membranes and beads, that bypass the need for specialized instruments (e.g., spectrometer), which are classically associated with colorimetry analysis via absorption, reflection and wavelength measurement. Interpretation of these colorimetric kits is not bound to absorbance measurement via conventional laboratory instrumentation. These instrument-free, colorimetric kits boast simplicity, ease of use and cost-effectiveness-all of which are advantageous over conventional laboratory instrumentation. Many on-site detection methods exploit this visual dynamic range by relying on the human eye as a portable sensor, including commonplace water quality tests, swimming pool pH sensors, law enforcement roadside drug tests and military-issue chemical and explosive tests. In humans with normal trichromatic vision, the brain combines the three independent wavelength inputs to reconstruct the observed color, which falls within a vast kaleidoscope of roughly 2 million distinguishable colors 3. Photopigments within the three classes of cone cell are stimulated by different wavelength ranges that correspond to one of three colors: red, green or blue (RGB) 1, 2. Similar content being viewed by othersĪs described by the Young–Helmholtz theory, human vision is a tristimulus system in which three different types of cone cells function as spectrally sensitive receptors (~6 million per eye). 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. 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. In practice, this protocol consists of three distinct workflow options. Here we describe a protocol that uses the ImageJ program to process images of colorimetric experiments. Freeware programs, such as ImageJ, offer an alternative, affordable path to robust image analysis. 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. 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, L*a*b*, etc.). 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. 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.
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