ArticleSensors (Basel, Switzerland)2016
An Electrochemical Impedance Spectroscopy System for Monitoring Pineapple Waste Saccharification.
Article in Sensors (Basel, Switzerland), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Optimizing unsupervised clustering of electrochemical impedance spectra via normalization and dimensionality reduction.Scientific reports · 2026Article
- Plant impedance spectroscopy: a review of modeling approaches and applications.Frontiers in plant science · 2023Review
- Electric Impedance Spectroscopy in Trees Condition Analysis: Theory and Experiment.Sensors (Basel, Switzerland) · 2022Article
- Article
- Comparison of Chemometric Problems in Food Analysis Using Non-Linear Methods.Molecules (Basel, Switzerland) · 2020Review
- Freeze-Damage Detection in Lemons Using Electrochemical Impedance Spectroscopy.Sensors (Basel, Switzerland) · 2019Article
- Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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