Ethanol quantification in pineapple waste by an electrochemical impedance spectroscopy-based system and artificial neural networks

- We develop a technique to monitor ethanol in bioethanol production.
- We use a specific device and design the appropriate mathematical models.
- The results on pineapple waste samples demonstrate its robustness and reliability.
- This technique is an alternative to the traditional laboratory analytical methods.
- It can be applied to manage bioethanol production from other lignocellulosic wastes.

Electrochemical impedance spectroscopy (EIS) technique has been applied to determine the ethanol concentration in pineapple waste samples. To do this, six different concentrations of ethanol were added to the pineapple samples and were analyzed using the system designed by our research group and consisting of the Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) device associated to a stainless steel double needle electrode. Results indicated that phase data in frequencies between 6.0×105 Hz and 8.0×105 Hz showed the highest sensitivity to ethanol concentrations. A principal component analysis (PCA) confirmed the potential discrimination and partial least squares (PLS) regression showed mathematical models able to quantify ethanol in samples accurately. In order to implement flexible and precise models in programmable equipment, different types of artificial neural networks (ANNs) have been studied: Fuzzy ARTMAP and multi-layer feed-forward (MLFF) algorithms. As a result, a coefficient of determination (R2)=0.996 and a root mean square error of prediction (RMSEP)=0.408 have been obtained. Therefore, it allows us to introduce this technique as an alternative method for ethanol quantification along the fermentation of pineapple waste in an easy, low-cost, rapid and portable way.