A low-cost tap water fingerprinting technique was evaluated using the coffee-ring effect, a phenomenon by which tap water droplets leave distinguishable "fingerprint" residue patterns after water evaporates. Tap waters from communities across southern Michigan dried on aluminum and photographed with a cell phone camera and 30× loupe produced unique and reproducible images. A convolutional neural network (CNN) model was trained using the images from the Michigan tap waters, and despite the small size of the image dataset, the model assigned images into groups with similar water chemistry with 80% accuracy. Synthetic solutions containing only the majority species measured in Detroit, Lansing, and Michigan State University tap waters did not display the same residue patterns as collected waters; thus, the lower concentration species also influence the tap water "fingerprint". Residue pattern images from salt mixtures with an array of sodium, calcium, magnesium, chloride, bicarbonate, and sulfate concentrations were analyzed by measuring features observed in the photographs as well as using principal component analysis (PCA) on the image files and particles measurements. These analyses together highlighted differences in the residue patterns associated with the water chemistry in the sample. The results of these experiments suggest that the unique and reproducible residue patterns of tap water samples that can be imaged with a cell phone camera and a loupe contain a wealth of information about the overall composition of the tap water, and thus, the phenomenon should be further explored for potential use in low-cost tap water fingerprinting.

中文翻译：

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使用根据咖啡环效果图像构建的卷积神经网络对自来水进行指纹识别。

使用咖啡环效果评估了一种低成本的自来水指纹识别技术，该现象是自来水在水蒸发后会留下明显的“指纹”残留图案的现象。来自密歇根州南部各社区的自来水用铝干燥，并用手机摄像头和30英寸放大镜拍照，产生了独特且可复制的图像。使用来自密歇根州自来水的图像对卷积神经网络（CNN）模型进行了训练，尽管图像数据集的大小很小，但是该模型将图像按相似的化学成分以80％的精度分组。仅包含底特律，兰辛和密歇根州立大学自来水中测得的大多数物种的合成溶液未显示出与收集的水相同的残留模式。因此，较低浓度的物质也会影响自来水的“指纹”。通过测量照片中观察到的特征以及使用图像文件和颗粒测量中的主成分分析（PCA），分析了盐混合物中的钠，钙，镁，氯化物，碳酸氢盐和硫酸盐浓度阵列的残留图案图像。这些分析共同突出了与样品中水化学有关的残留模式的差异。这些实验的结果表明，可以用手机相机和放大镜成像的自来水样品的独特且可重现的残留模式包含有关自来水整体成分的大量信息，因此，