Combination of LEDs and cognitive modeling to quantify sheep cheese whey in watercourses

Abstract

The concentration of sheep cheese whey (CW) in water obtained from two Spanish reservoirs, two Spanish rivers, and distilled water has been estimated by combining spectroscopic measurements, obtained with light-emitting diodes (LEDs), and linear or non-linear algorithms. The concentration range of CW that has been studied covers from 0 to 25% in weight. Every sample was measured by six different types of LEDs possessing different emission wavelengths (blue, orange, green, pink, white, and UV). 1,800 fluorescence measurements were carried out and used to design different types of models to estimate the concentration of CW in water. The fluorescence spectra provided by the pink LED originated the most accurate mathematical models, with mean square errors lower than 3.3% and 2.5% for the linear and non-linear approaches, respectively. The pink LED combined with the non-linear model, which was an artificial neural network, was further validated through a k-fold cross-validation and an internal validation. It should be noted that the sensor used here has been designed and produced by a 3D printer and has the potential of being implemented in situ for real-time and cost-effective analysis of natural watercourses.