Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing

Abstract

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.

Rapid beef quality detection using spectra pre‐processing methods in electrical impedance spectroscopy and machine learning

“…Bacteria can grow in beef samples after repeated freezing and thawing, and they have lower conductivity than beef tissue (Pal et al ., 2016). Furthermore, repeated freezing can damage and break cells, causing ions to leak out with the blood (Abie et al ., 2021). This study removed the blood before testing, which also removed some of the ions, resulting in higher impedance.…”

Bioimpedance-based authentication of defrosted versus fresh pork at the end of refrigerated shelf life

“…Our data corroborates previous bioelectric studies, which have contrasted groups of refrigerated-only with frozen-thawed samples, and proposed bioimpedance-based testing as a suitable method for authentication of fresh meaty foods or fish [ 20 , 21 , 22 , 23 ]. Typically, such bioelectric studies were based on testing for resistance and reactance individually, on the impedance modulus at distinct frequencies, or on parameters that reflect the frequency modulation of the impedance response, such as P y [ 10 , 13 , 23 , 24 ]. The significantly reduced impedance response, expressed as P y , that we found after freezing (compare Fig.…”

“…2 and Table 1) is typically believed to result from an overall destruction of cell membranes and other capacitive elements, as well as from increased amounts of free electrolytes in extracellular spaces [ 9 ]. While data that directly links micro-structural damage with changed bioimpedance response after freezing is scarce, we previously demonstrated the vast extend of ice crystal formation in frozen pork with cryo-scanning electron microscopy and a greatly reduced bioimpedance response measured in similarly freeze-treated samples [ 10 ].…”

“…Electrical impedance measurements are sensitive to structure and composition changes of biological tissues. Several studies have shown that measuring electrical bioimpedance, i.e., the passive electrical properties of meat, allows detecting differences between fresh and frozen-thawed meaty products from fish [ 8 ], chicken [ 9 ], and pork [ 5 , 10 ]. However, information is scarce on potentially confounding effects, on the selection of suitable bioimpedance parameters and test-setups, and on the applicability for different types of meat products and storage history.…”

Rapid recognition of processed milk type using electrical impedance spectroscopy and machine learning

“…EIS combined with machine learning algorithms are widely used in the field of liquid food especially dairy product to detect additives or nutrients and quantify their concentration through the establishment of regression models between the substance and the electrical properties (Żywica & Banach, 2015; Lopes et al ., 2018; Das et al ., 2019; Widodo et al ., 2019; Ghodinde et al ., 2022). Furthermore, its feasibility for constructing classification models is also demonstrated, such as for the ripening degree of avocado (Islam et al ., 2018), fermentation process of black tea (Zhu et al ., 2019) and biological cell damage during freezing and thawing (Abie et al ., 2021). Although there is research using EIS and Support Vector Machine to classify milk with different amounts of fats (Ciosek et al ., 2006), but the study does not use fresh data to stress test the model.…”