Due to the increasing environmental awareness of society and administration, a great number of regulations became effective over the last years in order to preserve natural resources restricting and limiting industrial waste, especially when spills affect aqueous systems. This fact has contributed to the development of a large amount of research programs to come across new methods and processes to monitor and reduce contaminants present in wastewater. Among the variety of contaminants present in industrial effluents, heavy metals are the most hazardous as this compounds are biomagnified and can reach human organism. One of the methods developed for reducing heavy metal concentration in wastewater is biosorption. Biosorption process monitoring has led to the development of sensor arrays or electronic tongues. These kinds of sensors require exhaustive training through the analysis of huge sets of standards, which is time, effort and reagent consumptive. This project is addressed on the optimization of a Sequential Injection Analysis (SIA) prototype built to prepare automatically random generated known training standards and monitor bioprocess absorption to model sensor’s response. In this phase of optimization a miniature spectrometer is assembled to the SIA tubing to monitor flow response in real time of a colorant solution. Spectroscopic analysis also allows monitoring traces of reagent remaining on the system. Calibration and cleaning routines will be designed to ensure reproducibility. Moreover, automatic preparation of standards will be discussed
