Full-range optical pH sensor array based on neural networks

A neural network multivariate calibration is used to predict the pH of a solution in the full-range (0–14) from hue (H) values coming from imaging an optical pH sensor array based on 11 sensing elements with immobilized pH indicators. Different colorimetric acid-base indicators were tested for membrane preparation fulfilling the following conditions: 1) no leaching; 2) change in tonal coordinate by reaction and 3) covering the full pH range with overlapping between their pH responses. The sensor array was imaged after equilibration with a solution using a scanner working in transmission mode. Using software developed by us, the H coordinate of the colour space HSV was calculated from the RGB coordinates of each element.The neural network was trained with the calibration data set using the Levenberg–Marquardt training method. The network structure has 11 input neurons (each one matching the hue of a single element in the sensor array), 1 output (the pH approximation value) and 1 hidden layer with 10 hidden neurons. The network provides an MSE = 0.0098 in the training data, MSE = 0.0183 in the validation data and MSE = 0.0426 in the test data coming from a set of real water samples. The resulting correlation coefficient R obtained in the Pearson correlation test is R = 0.999.