An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification

Area of Science:

• Biotechnology and Bioengineering
• Analytical Chemistry
• Process Monitoring

Background:

• Enzymatic hydrolysis of pineapple waste generates valuable sugars.
• Traditional sugar analysis methods are often time-consuming and labor-intensive.
• Efficient monitoring of saccharification is crucial for optimizing bioprocesses.

Purpose of the Study:

• To develop and validate an electrochemical impedance spectroscopy (EIS) based method for monitoring pineapple waste hydrolysis.
• To compare the performance of Partial Least Squares (PLS) and Artificial Neural Networks (ANNs) for predicting sugar concentrations.
• To establish EIS combined with ANNs as a viable alternative to conventional sugar analysis techniques.

Main Methods:

• Utilized an Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) with a double needle electrode.
• Performed EIS measurements at various saccharification time points (0-24 hours).
• Employed PLS and ANNs (multilayer feed forward, quick propagation, logistic-type transfer functions) for data modeling and prediction.

Main Results:

• PLS models showed good correlation (R² > 0.944, RMSEP < 1.782) between EIS and sugar content.
• ANN models demonstrated superior predictive performance (R² > 0.973, RMSEP < 0.486) for glucose, fructose, sucrose, and total sugars.
• EIS measurements effectively tracked the enzymatic hydrolysis process over 24 hours.

Conclusions:

• A combination of EIS and ANN models is a powerful and accurate tool for monitoring pineapple waste saccharification.
• This approach offers a promising, rapid, and potentially cost-effective alternative to traditional laboratory sugar analysis.
• The developed method facilitates real-time process control and optimization in biorefineries.