An Electrochemical Impedance Spectroscopy-Based Technique to Identify and Quantify Fermentable Sugars in Pineapple Waste Valorization for Bioethanol Production

The biosensor based on electrochemical dynamics of fermentation in yeast Saccharomyces cerevisiae

The zymase activity of the yeast Saccharomyces cerevisiae is sensitive to environmental parameters and is therefore used as a microbiological sensor for water quality assessment, ecotoxicological characterization or environmental monitoring. Comparing to bacterial bioluminescence approach, this method has no toxicity, excludes usage of genetically modified microorganisms, and enables low-cost express analysis. This work focuses on measuring the yeast fermentation dynamics based on multichannel pressure sensing and electrochemical impedance spectroscopy (EIS). Measurement results are compared with each other in terms of accuracy, reproducibility and ease of use in field conditions. It has been shown that EIS provides more information about ionic dynamics of metabolic processes and requires less complex measurements. The conducted experiments demonstrated the sensitivity of this approach for assessing biophotonic phenomena, non-chemical water treatments and impact of environmental stressors.

Process Monitoring of Antisolvent Based Crystallization in Low Conductivity Solutions Using Electrical Impedance Spectroscopy and 2-D Electrical Resistance Tomography

Industrial process monitoring is an important field of research where different chemical processes are monitored and controlled. In this work, electrical impedance spectroscopy (EIS) was used to analyze antisolvent based crystallization of sucrose solutions. The impedance and phase spectra were recorded for four known sucrose concentrations in water, and for each case, four predetermined amounts of ethanol were added. As a result, sixteen different solutions involving sucrose solutions of different concentrations and ethanol to water ratios were analyzed. Significant differences were observed in the magnitude and phase spectra of the solutions in the frequency range of 50 kHz to 300 kHz. The experimentally obtained data from the EIS were converted into frequency response models. Three continuous-time transfer function models of the first-order, second-order, and a second-order with a zero were estimated and compared. In addition, a 2-D electrical resistance tomography (ERT) system with a low conductivity sensor unit was designed and tested with demineralized water, tap water and industrial food grade saturated sucrose solution. Non-conducting phantom and sugar crystals were observed within the saturated sucrose solution using the Bayesian reconstruction algorithm. These demonstrations have the potential to be developed into a multi-frequency ERT systems for monitoring the distribution of the crystals in the reactor. The EIS modality can be a complementary process analytical technology (PAT) tool indicating supersaturation status and provide quality assurance.

Complex numbers-partial least-squares applied to the treatment of electrochemical impedance spectroscopy data

This work investigated the application of partial least-squares regression of complex numbers on multivariate data obtained by electrochemical impedance spectroscopy (EIS). The use of complex numbers-PLS was evaluated in the individual determination of two well-known redox probes: ferrocyanide and hydroquinone. The predictive ability of complex numbers-PLS was evaluated for EIS spectra obtained at different applied potentials and perturbation amplitudes, and was also compared to that obtained with PLS applied to EIS data presented as real numbers - only the real or imaginary part of the complex impedance, or the absolute impedance or the phase angle. It is shown that complex numbers-PLS is more efficient (better prediction models) when more complex electrochemical systems (hydroquinone) are probed. Excellent predictions were obtained for the determination of hydroquinone and catechol in the direct analysis of spiked tap water samples with EIS and complex numbers-PLS.

Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy

Lemon is the most sensitive citrus fruit to cold. Therefore, it is of capital importance to detect and avoid temperatures that could damage the fruit both when it is still in the tree and in its subsequent commercialization. In order to rapidly identify frost damage in this fruit, a system based on the electrochemical impedance spectroscopy technique (EIS) was used. This system consists of a signal generator device associated with a personal computer (PC) to control the system and a double-needle stainless steel electrode. Tests with a set of fruits both natural and subsequently frozen-thawed allowed us to differentiate the behavior of the impedance value depending on whether the sample had been previously frozen or not by means of a single principal components analysis (PCA) and a partial least squares discriminant analysis (PLS-DA). Artificial neural networks (ANNs) were used to generate a prediction model able to identify the damaged fruits just 24 hours after the cold phenomenon occurred, with sufficient robustness and reliability (CCR = 100%).

Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy

The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges.

An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification

Electrochemical impedance spectroscopy (EIS) has been used for monitoring the enzymatic pineapple waste hydrolysis process. The system employed consists of a device called Advanced Voltammetry, Impedance Spectroscopy & Potentiometry Analyzer (AVISPA) equipped with a specific software application and a stainless steel double needle electrode. EIS measurements were conducted at different saccharification time intervals: 0, 0.75, 1.5, 6, 12 and 24 h. Partial least squares (PLS) were used to model the relationship between the EIS measurements and the sugar determination by HPAEC-PAD. On the other hand, artificial neural networks: (multilayer feed forward architecture with quick propagation training algorithm and logistic-type transfer functions) gave the best results as predictive models for glucose, fructose, sucrose and total sugars. Coefficients of determination (R²) and root mean square errors of prediction (RMSEP) were determined as R² > 0.944 and RMSEP < 1.782 for PLS and R² > 0.973 and RMSEP < 0.486 for artificial neural networks (ANNs), respectively. Therefore, a combination of both an EIS-based technique and ANN models is suggested as a promising alternative to the traditional laboratory techniques for monitoring the pineapple waste saccharification step.