Development of an ANN-based soft-sensor to estimate pH variations in Intelligent Packaging Systems with visual indicators (original) (raw)
Scientific Reports, 2022
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 timeconsuming experimental work. The pH value of different solutions is a particularly important point to determine the optimized conditions and quality control for industrial, biological, chemical, and environmental science either outdoor or indoor applications 1,2. Hydrogen ions concentration [H + ] denoted to pH scales from 0 to 14, and the most methods counted for detection are complicated, expensive, and time-consuming as microelectrodes 3 , acid-based indicator 4 , potentiometric titration 1 , colorimetric and fluorescence probes application 5-10. Currently, potentiometric measurements are the most technique used in pH detection. Where the pH of the solution can be calculated by the measurement of the different voltage between the electrodes of the potentiometric device 11. Despite, the high accuracy of conventional potentiometric devices, the operation and calibration process is more complicated and costly, which is not applicable to indoor or outdoor purposes. However, the easy and accessible pH strips are used as an alternative method for visual pH detection, but the strips produce lower precise results. On the other hand, machine learning (ML) techniques give algorithms the ability to predict novel values from training data derived from experiments using Artificial Intelligence (AI). Thus, there are numerous regression or classification algorithms for ML that depend on hyperparameters and mechanisms to achieve their goals and give high performance for planning 12. ML is being used in chemistry such as chemical discovery 13 , molecular representations 14 , synthetic chemistry 15 , materials chemistry 16 , aquatic chemistry research 17,18 , and water pollution 19 .