Electrochemical Impedance Spectroscopy (EIS) has been used to develop a methodology able to identify and quantify fermentable sugars present in the enzymatic hydrolysis phase of second-generation bioethanol production from pineapple waste. Thus, a low-cost non-destructive system consisting of a stainless double needle electrode associated to an electronic equipment that allows the implementation of EIS was developed. In order to validate the system, different concentrations of glucose, fructose and sucrose were added to the pineapple waste and analyzed both individually and in combination. Next, statistical data treatment enabled the design of specific Artificial Neural Networks-based mathematical models for each one of the studied sugars and their respective combinations. The obtained prediction models are robust and reliable and they are considered statistically valid (CCR% > 93.443%). These results allow us to introduce this EIS-based technique as an easy, fast, non-destructive, and in-situ alternative to the traditional laboratory methods for enzymatic hydrolysis monitoring.

The use of a voltammetric electronic tongue for the quantitative analysis of quality parameters in influent wastewater from a wastewater treatment plant (WWTP) that treats domestic and industrial wastewater is proposed. The electronic voltammetric tongue consists of a set of four noble electrodes (iridium, rhodium, platinum and gold) housed inside a stainless steel cylinder. These noble metals have high durability and are low maintenance‐demanding, as required for developing future automated equipment. A pulse voltammetry study was conducted in 35 wastewater samples to determine ammonia (NH4+‐N), nitrates (NO3−‐N), total phosphate (tot‐P), soluble chemical oxygen demand (CODs) and conductivity. These parameters were also determined in these samples by routine analytical methods in the WWTP laboratory. A partial least squares (PLS) analysis was run to obtain a model to predict each parameter. Twenty‐five samples were included in the calibration set and 10 in the validation set. Calibration and validation sets were selected randomly, except for the extreme values of each parameter, which were included in the calibration set. Variable selection was performed on the voltammetric data using Genetic Algorithms in the calibration data set for each parameter. The electronic tongue showed good predictive power to determine the concentrations of NH4+‐N, NO3−‐N and tot‐P and CODs.