ArticleSensors (Basel, Switzerland)2021
Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Non-Destructive Prediction of NaCl Content in Pork During Ultrasound-Assisted Marination: Multiphysics Simulation and Electrical Impedance Spectroscopy.Foods (Basel, Switzerland) · 2026Article
- A Non-Stationary Model for Analysis of Impedance Spectra of Biological Samples.Entropy (Basel, Switzerland) · 2026Article
- Label-Free Electrochemical Impedance Spectroscopy for Biosensing: Evolving Interfaces and Mechanistic Insights.Small science · 2025Review
- A Low-Cost Implementation of a Potato (Sensors (Basel, Switzerland) · 2025Article
- A Novel Electromagnetic Response Measurement System for Continuous Monitoring of Meat Aging.Foods (Basel, Switzerland) · 2025Article
- Non-Destructive Detection of Chilled Mutton Freshness Using a Dual-Branch Hierarchical Spectral Feature-Aware Network.Foods (Basel, Switzerland) · 2025Article
- Electrical Impedance Spectroscopy for Moisture and Oil Content Prediction in Oil Palm (Plants (Basel, Switzerland) · 2022Article
- Electrical impedance spectroscopy (EIS) in plant roots research: a review.Plant methods · 2021Review
- A Technique for Nonmicrobial Freshness Evaluation of Broiler Breast Meat Using a Handheld Electrical Impedance Meter.Animal science journal = Nihon chikusan GakkaihoArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This study presents a system for assessing the freshness of meat with electrical impedance spectroscopy (EIS) in the frequency range of 125 Hz to 128 kHz combined with an image classifier for non-destructive and low-cost applications. The freshness standard is established by measuring the aerobic plate count (APC), 2-thiobarbituric acid reactive substances (TBARS), and composition analysis (crude fat, crude protein, and moisture) values of the microbiological detection to represent the correlation between EIS and meat freshness. The EIS and images of meat are combined to predict the freshness with the Adaboost classification and gradient boosting regression algorithms. As a result, when the elapsed time of beef storage for 48 h is classified into three classes, the time prediction accuracy is up to 85% compared to prediction accuracy of 56.7% when only images are used without EIS information. Significantly, the relative standard deviation (RSD) of APC and TBARS value predictions with EIS and images datum achieves 0.890 and 0.678, respectively.
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Registered trials
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