Analysis of the suitable thawing endpoint of the frozen chicken breast using video recording analysis, shear force, and bioelectrical impedance measurement

This study focuses on analyzing the texture properties and bioelectrical impedance characteristics of frozen chicken breasts during low-temperature thawing, meanwhile, we also compared the differences in physiochemical properties. Frozen chicken breasts were thawed at 4 ± 2°C for 2, 4, 6, 8, and 10 h separately, then the physiochemical properties (color, pH, water-holding capacity, water distribution), the texture properties (easy-to-cut level), and the bioelectrical impedance were determined and analyzed. The easy-to-cut level of the samples was evaluated by the sensory panel and two indexes, one is Warner-Bratzler shear force measured by texture analysis machine, and the other is cutting speed value calculated by the consumer-oriented cutting behavior analysis using frame-by-frame video recording analysis method. These two methods were used to characterize the easy-to-cut level of the frozen samples during thawing from the industrial processing and home cooking standpoint. Strong correlations were observed between the easy-to-cut level and the bioelectrical impedance of the frozen chicken breasts during thawing. The impedance magnitude at 100 kHz showed a high correlation coefficient (R2  = .9417) with Warner-Bratzler shear force, and the impedance magnitude at 50 Hz showed a high correlation coefficient (R2  = .8658) with cutting speed. Our results indicated the acceptability of using bioelectrical impedance to evaluate the easy-to-cut thawing endpoint for both industry processing and home cooking.

Electric Impedance Spectroscopy in Trees Condition Analysis: Theory and Experiment

Electric impedance spectroscopy is an alternative technology to existing methods that shows promising results in the agro-food industry and plant physiology research. For example, this technology makes it possible to monitor the condition of plants, even in the early stages of development, and to control the quality of finished products. However, the use of electric impedance spectroscopy is often associated with the need to organize special laboratory conditions for measurements. Our aim is to extract information about the state of health of the internal tissues of a plant's branches from impedance measurements. Therefore, we propose a new technique using the device and model developed by us that makes it possible to monitor the condition of tree branch tissues in situ. An apple tree was chosen as the object under study, and the dependence of the impedance of the apple tree branch on the signal frequency and branch length was analyzed. The change in the impedance of an apple tree branch during drying was also analyzed. It was shown that, when a branch dries out, the conductivity of the xylem mainly decreases. The developed technique was also applied to determine the development of the vascular system of an apple tree after grafting. It was shown that the processing of the scion and rootstock sections with the help of cold atmospheric plasma and a plasma-treated solution contributes to a better formation of graft unions.

UHT Milk Characterization by Electrical Impedance Spectroscopy

Ultra-High Temperature (UHT) pasteurized milk is the most diffused variety of milk in Europe. In this paper, a method is presented, employing Electrical Impedance Spectroscopy to characterize the different commercial milks commonly available in grocery stores and supermarkets. The curves of the measured admittance allow for the classification of the type of milk (whole, semi-skimmed, fat-free) and to distinguish lactose-free milk. An electrical circuit model has been derived and different values of circuit parameters add interesting information on the classification of the samples. Furthermore, the characterization allows for the identification of the degradation of the milk before it is visible to the eye, thus highlighting the difference between storage in the fridge and at room temperature, and identifying expired milk.

Improving FPGA Based Impedance Spectroscopy Measurement Equipment by Means of HLS Described Neural Networks to Apply Edge AI

The artificial intelligence (AI) application in instruments such as impedance spectroscopy highlights the difficulty to choose an electronic technology that correctly solves the basic performance problems, adaptation to the context, flexibility, precision, autonomy, and speed of design. Present work demonstrates that FPGAs, in conjunction with an optimized high-level synthesis (HLS), allow us to have an efficient connection between the signals sensed by the instrument and the artificial neural network-based AI computing block that will analyze them. State-of-the-art comparisons and experimental results also demonstrate that our designed and developed architectures offer the best compromise between performance, efficiency, and system costs in terms of artificial neural networks implementation. In the present work, computational efficiency above 21 Mps/DSP and power efficiency below 1.24 mW/Mps are achieved. It is important to remark that these results are more relevant because the system can be implemented on a low-cost FPGA.

Monitoring Freeze-Damage in Grapefruit by Electric Bioimpedance Spectroscopy and Electric Equivalent Models

Grapefruit is a cold-sensitive citrus fruit, and freezing can spoil the harvest when the fruit is still on the tree and even later during manufacturing and transport due to inappropriate postharvest management. This study performed a specific Electric Impedance Spectroscopy (EIS) analysis and statistical data treatment to obtain an EIS and Artificial Neural Networks (ANN)-based model for early freeze-damage detection in grapefruit showing a Correct Correlation Rate of 100%. Additionally, Cryo-Field Emission Scanning Electron Microscopy observations were conducted on both fresh and frozen/thawed samples, analyzing the different impedance responses in order to understand the biological changes in the tissue. Finally, a modified Hayden electric equivalent model was parameterized to simulate the impedance response electrically and link the electric behavior of biological tissue to the change in its properties due to freezing. The developed technique is introduced as an alternative to the traditional ones, as it is fast, economic, and easy to carry out.

Bioimpedance-based Authentication of Defrosted Versus Fresh Pork at the End of Refrigerated Shelf Life

Correct food labeling is a legal requirement and helps consumers to make informed purchasing choices. Mislabeling defrosted meat as fresh is illegal in the EU. However, there are no standardized technologies to authenticate fresh versus defrosted meat. We address this by testing if bioimpedance-based measurements can separate defrosted meat from refrigerated-only meat at the end of shelf life, i.e., when also fresh meat shows deterioration. Pork sirloin samples from 20 pigs were first tested at 12 days postmortem ('fresh group'). This time point was chosen to represent a typical use-by date for refrigerated storage of fresh pork. Then, all samples were transferred to a -24°C freezer for 3 days and thawed for 2 days before final testing ('frozen-thawed group'). Bioimpedance analyses (BIA) were done in a frequency range of [10²-10⁶ Hz]. Weight, pH and electrode positioning were assessed to test for potential confounding effects. Statistics for treatment dependent differences were based on the established P_y parameter and phase angle, which were extracted from the BI spectra. We found that using bioimpedance testing with tetrapolar electrodes, P_y and phase angle allowed almost complete separation of fresh and previously frozen samples. However, within the whole sample population, there was some overlap between the spectra of fresh and frozen samples. Yet, based on P_y, only one fresh sample (5% of N_total=20) fell in the lowest P_y class with all the frozen samples. We used a multifactorial design that allowed to test the effects of potential confounding factors, such as electrode positioning and meat quality parameters. We found a relatively low explained variance for the P_y parameter, indicating that confounding effects from other factors or quality defects in fresh pork may affect the detection capacity of bioimpedance-based authentication of fresh pork. Our data, therefore, suggest that reliable fresh-label authentication with bioimpedance testing should be based on testing a small number of samples to represent a specific lot of pork that is to be inspected.

Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)

The performances of three non-destructive sensors, based on different principles, bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR) and time domain reflectometry (TDR), were studied to discriminate between unfrozen and frozen-thawed fish. Bigeye tuna (Thunnus obesus) was selected as a model to evaluate these technologies. The addition of water and additives is usual in the fish industry, thus, in order to have a wide range of possible commercial conditions, some samples were injected with different water solutions (based on different concentrations of salt, polyphosphates and a protein hydrolysate solution). Three different models, based on partial least squares discriminant analysis (PLS-DA), were developed for each technology. This is a linear classification method that combines the properties of partial least squares (PLS) regression with the classification power of a discriminant technique. The results obtained in the evaluation of the test set were satisfactory for all the sensors, giving NIR the best performance (accuracy = 0.91, error rate = 0.10). Nevertheless, the classification accomplished with BIA and TDR data resulted also satisfactory and almost equally as good, with accuracies of 0.88 and 0.86 and error rates of 0.14 and 0.15, respectively. This work opens new possibilities to discriminate between unfrozen and frozen-thawed fish samples with different non-destructive alternatives, regardless of whether or not they have added water.

Quality Assessment of Deep-Frying Palm Oil by Impedimetric Sensing with a Simple and Economic Electrochemical Cell

Quality control of deep-frying oil is a global public health concern. A simple and economic electrochemical chamber composed of two bare screen-printed carbon electrodes (working area: 78.54 × 10² cm²; distance: 0.0055 cm; cell constant: 0.70 × 10^-2 cm^-1) was constructed for precisely acquiring the impedimetric responses of a high-resistance palm oil sample (RSD < 7%, n = 3). Good correlations between the measured impedance data (charge transfer resistance and logarithmic output impedance (Log Z) obtained in the frequency region <0.1 Hz) and the regulatory quality indicators (total polar compounds and acid value) were achieved (R² > 0.97), suggesting that the proposed impedimetric sensing method is useful for accurately assessing the deteriorated condition of repeated frying oil. Applications for rapid screening can also be realized because the measurement times of Log Z at any given perturbation frequency from 0.01-1 Hz were all less than 3 min.

Prediction of carcass composition and meat and fat quality using sensing technologies: A review

Consumer demand for high-quality healthy food is increasing, &nbsp;thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

Extending the double-dispersion Cole-Cole, Cole-Davidson and Havriliak-Negami electrochemical impedance spectroscopy models

Double-dispersion impedance models are important for the accurate fitting of spectral impedance measurements in Electrical Impedance Spectroscopy (EIS). While the Cole-Cole model is the most widely known, it is possible to define double-dispersion Cole-Davidson and Havriliak-Negami models as well. In this work, we show that more freedom can be exercised when these three models are combined together and that this combination can be done in various forms. Experimental results using a two-stage optimization algorithm applied on the suggested models are provided.

Using segmental bioimpedance analysis to estimate soft tissue and chemical composition of retail cuts and carcasses of lambs

This study evaluated the potential of segmental bioimpedance analysis (SBIA) to estimate the composition of retail cuts and their predictability to infer on the carcass composition in lambs. Leg, rib, shoulder, neck, and loin from thirty-one lamb carcasses were evaluated. A single-frequency bioelectrical impedance analyzer at 50 kHz was used to perform measurements. The models for estimating soft tissue showed the highest accuracy in the retail cuts. Lean and fat weight of the lamb cuts or of the carcasses were predicted with R² of calibration ranging from 86.6 to 99.1% and from 67.5 to 95.4%, respectively. Segmental bioimpedance analysis is an accurate technology to assess physical and chemical components in retail cuts of lamb. Despite that, shoulder was the most representative cut; all cuts evaluated through SBIA were valuable to estimate the components of the edible portion of lamb carcasses.

Development of Two-Dimensional Nanomaterials Based Electrochemical Biosensors on Enhancing the Analysis of Food Toxicants

In recent times, food safety has become a topic of debate as the foodborne diseases triggered by chemical and biological contaminants affect human health and the food industry's profits. Though conventional analytical instrumentation-based food sensors are available, the consumers did not appreciate them because of the drawbacks of complexity, greater number of analysis steps, expensive enzymes, and lack of portability. Hence, designing easy-to-use tests for the rapid analysis of food contaminants has become essential in the food industry. Under this context, electrochemical biosensors have received attention among researchers as they bear the advantages of operational simplicity, portability, stability, easy miniaturization, and low cost. Two-dimensional (2D) nanomaterials have a larger surface area to volume compared to other dimensional nanomaterials. Hence, researchers nowadays are inclined to develop 2D nanomaterials-based electrochemical biosensors to significantly improve the sensor's sensitivity, selectivity, and reproducibility while measuring the food toxicants. In the present review, we compile the contribution of 2D nanomaterials in electrochemical biosensors to test the food toxicants and discuss the future directions in the field. Further, we describe the types of food toxicity, methodologies quantifying food analytes, how the electrochemical food sensor works, and the general biomedical properties of 2D nanomaterials.

Utilization of Electrical Impedance Spectroscopy and Image Classification for Non-Invasive Early Assessment of Meat Freshness

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.

Rapid and Efficient Determination of Relative Water Contents of Crop Leaves Using Electrical Impedance Spectroscopy in Vegetative Growth Stage

Crop water stress is a deficiency in plants in water supply when the transpiration rate becomes higher than the water absorption capacity. The stress may be detected by a reduction in soil water content, or by the change in physiological properties of the crop. The leaf water content (LWC) is commonly used to assess the water status of plants, which is one of the indicators of crop water stress. In this work, the leaf relative water contents of four different crops: canola, wheat, soybeans, and corn—all in vegetative growth stage—were determined by a noninvasive tool called, electrical impedance spectroscopy (EIS). Using a frequency range of 5–15 kHz, a strong correlation between leaf water contents and leaf impedances was obtained using multiple linear regression. The trained dataset was validated by analysis of variance tests. Regression results were obtained using the least square method. The optimized regression model coefficients for different crops were proposed by selecting features using the wrapper backward elimination method. Multi-collinearity among the features was considered and individual T-tests were made in the feature selection. A maximum correlation coefficient (R) of 0.99 was obtained for canola compared to the other crops; the corresponding coefficient of determination (R2) of 0.98, an adjusted R2 of 0.93, and root mean square error (rmse) of 0.30% were obtained for 36 features. Therefore, the results show that the proposed technique using EIS can be used to develop a low-cost and effective tool for determining the leaf water contents rapidly and efficiently in multiple crops.

Determination of Leaf Nitrogen Concentrations Using Electrical Impedance Spectroscopy in Multiple Crops

In this work, crop leaf nitrogen concentration (LNC) is predicted by leaf impedance measurements made by electrical impedance spectroscopy (EIS). This method uses portable equipment and is noninvasive, as are other available nondestructive methods, such as hyperspectral imaging, near-infrared spectroscopy, and soil-plant analyses development (SPAD). An EVAL-AD5933EBZ evaluation board is used to measure the impedances of four different crop leaves, i.e., canola, wheat, soybeans, and corn, in the frequency range of 5 to 15 kHz. Multiple linear regression using the least square method is employed to obtain a correlation between leaf nitrogen concentrations and leaf impedances. A strong correlation is found between nitrogen concentrations and measured impedances for multiple features using EIS. The results are obtained by PrimaXL Data Analysis ToolPak and validated by analysis of variance (ANOVA) tests. Optimized regression models are determined by selecting features using the backward elimination method. After a comparative analysis among the four different crops, the best multiple regression results are found for canola with an overall correlation coefficient (R) of 0.99, a coefficient of determination (R2) of 0.98, and root mean square (RMSE) of 0.54% in the frequency range of 8.7–12 kHz. The performance of EIS is also compared with an available SPAD reading which is moderately correlated with LNC. A high correlation coefficient of 0.94, a coefficient of determination of 0.89, and RMSE of 1.12% are obtained using EIS, whereas a maximum correlation coefficient of 0.72, a coefficient of determination of 0.53, and RMSE of 1.52% are obtained using SPAD for the same number of combined observations. The proposed multiple linear regression models based on EIS measurements sensitive to LNC can be used on a very local scale to develop a simple, rapid, inexpensive, and effective instrument for determining the leaf nitrogen concentrations in crops.

Nondestructive Evaluation of Wet Aged Beef by Novel Electrical Indexes: A Preliminary Study

The aim of this study was to investigate the suitability of electrical impedance spectroscopy (EIS) as a nondestructive quality monitoring tool of aged beef, focusing on the development of accurate electrical indexes. The relationship between the electrical indexes derived from the impedance ratio (IR) or admittance was established. Quality parameters such as the drip loss, cooking loss, water-holding capacity, and shear force of beef loin wet-aged for 0 to 21 days were evaluated to develop the new electrical indexes. In addition, the predictive capability of EIS was trialed using different indexes and frequencies. This study revealed that the most appropriate choice is to use electrical parameters at a lower frequency to determine or predict the physical properties of aged beef. The IR was derived from the ratio between the electrical impedance measured parallel to and perpendicular to the muscle fibers in the low-frequency domain. Furthermore, the degradation of muscle fibers was observed by optical microscopy. The investigated electrical indexes had higher correlations with shear force (0.52 ≤ R² ≤ 0.58) compared to correlations with aging days (0.34 ≤ R² ≤ 0.39). The findings of the study could be used for meat quality inspection in slaughterhouses as well as during aging.

Microwave Imaging Sensor Using Low Profile Modified Stacked Type Planar Inverted F Antenna

Microwave imaging is the technique to identify hidden objects from structures using electromagnetic waves that can be applied in medical diagnosis. The change of dielectric property can be detected using microwave antenna sensor, which can lead to localization of abnormality in the human body. This paper presents a stacked type modified Planar Inverted F Antenna (PIFA) as microwave imaging sensor. Design and performance analysis of the sensor antenna along with computational and experimental analysis to identify concealed object has been investigated in this study. The dimension of the modified PIFA radiating patch is 40 × 20 × 10 mm³. The reflector walls used, are 45 mm in length and 0.2-mm-thick inexpensive copper sheet is considered for the simulation and fabrication which addresses the problems of high expenses in conventional patch antenna. The proposed antenna sensor operates at 1.55–1.68 GHz where the maximum realized gain is 4.5 dB with consistent unidirectional radiation characteristics. The proposed sensor antenna is used to identify tumor in a computational human tissue phantom based on reflection and transmission coefficient. Finally, an experiment has been performed to verify the antenna’s potentiality of detecting abnormality in realistic breast phantom.

Microwave Sensors for Breast Cancer Detection

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.