A novel technique to control ice cream freezing by electrical characteristics analysis

A review of bio-impedance devices

Bio-impedance measurement analysis primarily refers to a safe and a non-invasive technique to analyze the electrical changes in living tissues on the application of low-value alternating current. It finds applications both in the biomedical and the agricultural fields. This paper concisely reviews the origin and measurement approaches for concepts and fundamentals of bio-impedance followed by a critical review on bio-impedance portable devices with main emphasis on the embedded system approach which is in demand due to its miniature size and present lifestyle preference of monitoring health in real time. The paper also provides a comprehensive review of various bio-impedance circuits with emphasis on the measurement and calibration techniques.

Identifying adulteration of raw bovine milk with urea through electrochemical impedance spectroscopy coupled with chemometric techniques

This study aimed to evaluate the applicability of electrochemical impedance spectroscopy to identify raw bovine milk adulteration with urea. Three batches of raw milk adulterated with urea were studied. Hierarchical clustering indicated that the samples could be split in three groups corresponding to low adulteration (less than 7 wt%), medium adulteration (between 8 and 16 wt%) and high adulteration (over than 16 wt%). A linear discriminant analysis was performed resulting in 90% of accuracy in classifying between groups. Besides, a partial least squares model containing three directions provided good accuracy in quantitatively predicting the urea mass fraction added to raw bovine milk. Finally, calculations using an approximated electric circuit model suggested the formation of urea aggregates that hinder charge transportation within the milk thus diminishing the solution conductivity. Results indicate that electrochemical impedance spectroscopy can be a useful, low cost and rapid tool to identify milk adulteration with urea.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text], F[Formula: see text] and F[Formula: see text]-score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Energy Harvesting Strategies for Wireless Sensor Networks and Mobile Devices: A Review

Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body where the battery replacement is very impractical. Moreover, the depleted battery must be properly disposed of in accordance with national and international regulations to prevent environmental pollution. A very interesting alternative to power mobile devices is energy harvesting where energy sources naturally present in the environment (such as sunlight, thermal gradients and vibrations) are scavenged to provide the power supply for sensor nodes and mobile systems. Since the presence of these energy sources is discontinuous in nature, electronic systems powered by energy harvesting must include a power management system and a storage device to store the scavenged energy. In this paper, the main strategies to design a wireless mobile sensor system powered by energy harvesting are reviewed and different sensor systems powered by such energy sources are presented.

Capacitive-Coupling Impedance Spectroscopy Using a Non-Sinusoidal Oscillator and Discrete-Time Fourier Transform: An Introductory Study

In this study, we propose a new short-time impedance spectroscopy method with the following three features: (1) A frequency spectrum of complex impedance for the measured object can be obtained even when the measuring electrodes are capacitively coupled with the object and the precise capacitance of the coupling is unknown; (2) the spectrum can be obtained from only one cycle of the non-sinusoidal oscillation waveform without sweeping the oscillation frequency; and (3) a front-end measuring circuit can be built, simply and cheaply, without the need for a digital-to-analog (D-A) converter to synthesize elaborate waveforms comprising multiple frequencies. We built the measurement circuit using the proposed method and then measured the complex impedance spectra of 18 resistive elements connected in series with one of three respective capacitive couplings. With this method, each element's resistance and each coupling's capacitance were estimated independently and compared with their nominal values. When the coupling capacitance was set to 10 nF or 1.0 nF, estimated errors for the resistive elements in the range of 2.0-10.0 kΩ were less than 5%.

Assessing heat treatment of chicken breast cuts by impedance spectroscopy

The aim of this work was to develop a new system based on impedance spectroscopy to assess the heat treatment of previously cooked chicken meat by two experiments; in the first, samples were cooked at different temperatures (from 60 to 90 ℃) until core temperature of the meat reached the water bath temperature. In the second approach, temperature was 80 ℃ and the samples were cooked for different times (from 5 to 55 min). Impedance was measured once samples had cooled. The examined processing parameters were the maximum temperature reached in thermal centre of the samples, weight loss, moisture and the integral of the temperature profile during the cooking–cooling process. The correlation between the processing parameters and impedance was studied by partial least square regressions. The models were able to predict the studied parameters. Our results are essential for developing a new system to control the technological, sensory and safety aspects of cooked meat products on the whole meat processing line.