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.

A porcine model was used to investigate the feasibility of using VIS-NIR spectroscopy to differentiate between degrees of ischemia–reperfusion injury in the small intestine. Ten pigs were used in this study and four segments were created in the small intestine of each pig: (1) control, (2) full arterial and venous mesenteric occlusion for 8 h, (3) arterial and venous mesenteric occlusion for 2 h followed by reperfusion for 6 h, and (4) arterial and venous mesenteric occlusion for 4 h followed by reperfusion for 4 h. Two models were built using partial least square discriminant analysis. The first model was able to differentiate between the control, ischemic, and reperfused intestinal segments with an average accuracy of 99.2% with 10-fold cross-validation, and the second model was able to discriminate between the viable versus non-viable intestinal segments with an average accuracy of 96.0% using 10-fold cross-validation. Moreover, histopathology was used to investigate the borderline between viable and non-viable intestinal segments. The VIS-NIR spectroscopy method together with a PLS-DA model showed promising results and appears to be well-suited as a potentially real-time intraoperative method for assessing intestinal ischemia–reperfusion injury, due to its easy-to-use and non-invasive nature.

Electrical impedance spectroscopy is a well-established tool for monitoring changes in the electrical properties of tissue. Most tissue and organ types have been investigated in various studies. As for the small intestine, there are several published studies conducted on pig and rat models. This study investigates the changes in passive electrical properties of the complete wall of the human intestine non-invasively during ischemia. We aim to use the passive electrical properties to assess intestinal viability. The bioimpedance measurements were performed using a two-electrode set-up with a Solartron 1260 Impedance/gain-phase analyser. The small intestinal samples were resected from patients who underwent pancreaticoduodenectomy. Impedance measurements were conducted following resection by placing the electrodes on the surface of the intestine. A voltage was applied across the intestinal sample and the measured electrical impedance was obtained in the ZPlot software. Impedance data were further fitted into a Cole model to obtain the Cole parameters. The P_y value was calculated from the extracted Cole parameters and used to assess the cell membrane integrity, thus evaluate the intestinal viability. Eight small intestinal segments from different patients were used in this study and impedance measurements were performed once an hour for a ten-hour period. One hour after resection, the impedance decreased, then increased the next two hours, before decreasing until the end of the experiment. For all the intestinal segments, the P_y values first increased and reached a plateau which lasted for 1 - 2 hours, before it decreased irreversibly. The time interval where P_y value reached the maximum is consistent with reported viable/non-viable limits from histological analysis.

Intestinal ischemia is a serious condition where the surgeon often has to make important but difficult decisions regarding resections and resection margins. Previous studies have shown that 3 h (hours) of warm full ischemia of the small bowel followed by reperfusion appears to be the upper limit for viability in the porcine mesenteric ischemia model. However, the critical transition between 3 to 4 h of ischemic injury can be nearly impossible to distinguish intraoperatively based on standard clinical methods. In this study, permittivity data from porcine intestine was used to analyze the characteristics of various degrees of ischemia/reperfusion injury. Our results show that dielectric relaxation spectroscopy can be used to assess intestinal viability. The dielectric constant and conductivity showed clear differences between healthy, ischemic and reperfused intestinal segments. This indicates that dielectric parameters can be used to characterize different intestinal conditions. In addition, machine learning models were employed to classify viable and non-viable segments based on frequency dependent dielectric properties of the intestinal tissue, providing a method for fast and accurate intraoperative surgical decision-making. An average classification accuracy of 98.7% was obtained using only permittivity data measured during ischemia, and 96.2% was obtained with data measured during reperfusion. The proposed approach allows the surgeon to get accurate evaluation from the trained machine learning model by performing one single measurement on an intestinal segment where the viability state is questionable.

There is a shortage of donor livers and patients consequently die on waiting lists worldwide. Livers are discarded if they are clinically judged to have a high risk of non-function following transplantation. With the aim of extending the pool of available donor livers, we assessed the condition of porcine livers by monitoring the microwave dielectric properties. A total of 21 livers were divided into three groups: control with no injury (CON), biliary injury by hepatic artery occlusion (AHEP), and overall hepatic injury by static cold storage (SCS). All were monitored for four hours in vivo, followed by ex vivo plurithermic machine perfusion (PMP). Permittivity data was modeled with a two-pole Cole–Cole equation, and dielectric properties from one-hour intervals were analyzed during in vivo and normothermic machine perfusion (NMP). A clear increasing trend in the conductivity was observed in vivo in the AHEP livers compared to the control livers. After four hours of NMP, separations in the conductivity were observed between the three groups. Our results indicate that dielectric relaxation spectroscopy (DRS) can be used to detect and differentiate liver injuries, opening for a standardized and reliable point of evaluation for livers prior to transplantation.

Biological tissues have variable passive electrical properties depending on their cellular constitution. Electrical impedance spectroscopy is commonly used to monitor cell and tissue characteristics. By measuring the impedance of a sample at various frequencies, it is possible to collect information regarding cell size and shape, cell membrane properties or cytoplasm conductivity. In the perspective of longitudinal structural monitoring, bioimpedance measurements outrank traditional tissue analysis methods such as fixation and slicing, owing to their non-destructive nature. Machine learning can be used to automatically process the impedance data and make real-time classifications of tissue types. Here, we present preliminary results on ex-vivo mouse adipose tissue measurements using electrical impedance spectroscopy and further data processing and classification using machine learning models.