This work reports the development of a low cost in-line color sensor for turbid liquids based on the transmission and scattering phenomena of light from RGB and IR LED sources, gathering multidimensional data. Three different methodologies to discriminate color from the turbidity influence are presented as a proof of concept approach. They are based in regression models, expectation maximization Gaussian mixtures and artificial neural networks applied to labeled measurements. Each methodology presents advantages and disadvantages which will depend on the intended implementation. Regression models revealed to be best suited for standard or occasional measurements, the EM Gaussian mixture will perform better for well-known controlled range of colors and turbidities and the neural networks have easy implementation and potential suited for real-time IoT platforms.

这项工作报告了一种基于来自RGB和IR LED光源的光的透射和散射现象，收集多维数据的，用于混浊液体的低成本在线颜色传感器的开发。提出了三种从浊度影响中区分颜色的方法，以作为概念验证方法。它们基于回归模型，期望最大化高斯混合和应用于标记测量的人工神经网络。每种方法都有优点和缺点，这取决于预期的实现方式。回归模型显示最适合标准或偶然测量，