Department of Chemistry, Faculty of Science, Al-Azhar University, Nasr City, Cairo, 11884, Egypt.
Sci Rep. 2022 Dec 30;12(1):22584. doi: 10.1038/s41598-022-27054-5.
Numerous scientific, health care, and industrial applications are showing increasing interest in developing optical pH sensors with low-cost, high precision that cover a wide pH range. Although serious efforts, the development of high accuracy and cost-effectiveness, remains challenging. In this perspective, we present the implementation of the machine learning technique on the common pH paper for precise pH-value estimation. Further, we develop a simple, flexible, and free precise mobile application based on a machine learning algorithm to predict the accurate pH value of a solution using an available commercial pH paper. The common light conditions were studied under different light intensities of 350, 200, and 20 Lux. The models were trained using 2689 experimental values without a special instrument control. The pH range of 1: 14 is covered by an interval of ~ 0.1 pH value. The results show a significant relationship between pH values and both the red color and green color, in contrast to the poor correlation by the blue color. The K Neighbors Regressor model improves linearity and shows a significant coefficient of determination of 0.995 combined with the lowest errors. The free, publicly accessible online and mobile application was developed and enables the highly precise estimation of the pH value as a function of the RGB color code of typical pH paper. Our findings could replace higher expensive pH instruments using handheld pH detection, and an intelligent smartphone system for everyone, even the chef in the kitchen, without the need for additional costly and time-consuming experimental work.
许多科学、医疗保健和工业应用都越来越关注开发低成本、高精度、覆盖广泛 pH 值范围的光学 pH 传感器。尽管已经做出了认真的努力，但开发高精度和具有成本效益的传感器仍然具有挑战性。在这方面，我们提出了将机器学习技术应用于常见 pH 试纸，以实现精确的 pH 值估计。此外，我们开发了一个简单、灵活且免费的基于机器学习算法的精确移动应用程序，该应用程序可以使用市售的 pH 试纸预测溶液的准确 pH 值。研究了在不同光强为 350、200 和 20 Lux 下的常见光线条件。该模型使用 2689 个实验值进行训练，无需特殊仪器控制。pH 值范围为 1:14，间隔约为 0.1 pH 值。结果表明，pH 值与红色和绿色之间存在显著关系，而与蓝色的相关性较差。K 近邻回归模型提高了线性度，并显示出与最低误差相结合的 0.995 的显著决定系数。我们开发了免费的、公开访问的在线和移动应用程序，可根据典型 pH 试纸的 RGB 颜色码实现 pH 值的高精度估计。我们的发现可以替代使用手持式 pH 检测的更高成本的 pH 仪器，以及适用于每个人的智能智能手机系统，即使是厨房的厨师也无需进行额外的昂贵且耗时的实验工作。
