Electromagnetic imaging and sensing for food quality and safety assessment [Guest Editorial]

The six articles in this special present to the antennas and propagation community some of the emerging research activities on the application of EM-based technologies in such a societally relevant topic. The articles address food industry applications as different as sensing food quality and food spoilage indicators and monitoring food items to detect contaminants

A Microwave Imaging Device for Detecting Contaminants in Water-based Food Products

Food and beverage industries are paying an increasing attention to the development of new technologies for non-invasive assessment of food products. In particular, there is a need of deploying tools to detect low-density plastic, rubber, wood and glass that are unlikely to be detected by X-Rays, currently used in the most powerful commercial systems. To this end, we propose a microwave-based device, exploiting the dielectric contrast between potential intrusions (e.g., plastic fragments) and the surrounding medium, represented by the food/beverage product. In particular, this work aims to numerically assess this principle of detection to water-based products that are, due to the medium losses, a challenging category at microwaves. An antennas array surrounds the object moving along the production line, to monitor the electromagnetic signal variations with respect to a reference case. The working frequency is chosen by selecting a proper trade-off between penetration depth and image resolution. Then, a procedure, based on the application of the distorted-Born approximation is applied to reconstruct a 3-D image of the contaminant position. Finally, the successful detection of a millimetric-sized plastic sphere is presented in the case of a common commercial bottle filled with water

Microwave Tomography for Food Contamination Monitoring

The security of packaged food needs to be guaranteed to safeguard customers health. A raise of complaints of physical contaminations into food products pushes for the development of additional monitoring techniques to prevent any kind of hazards, but also to protect brands from customers trust loss. In this work, a prototype working at microwave frequencies is assessed and tested in a significant environment. It exploits the dielectric contrast between contaminants and food content, and it is it is mainly focused on two classes of intrusions matters, i.e. plastic and glass fragments of few mm size, that have limited detection by the existing in-line technologies, such as X-rays systems. The measurements and the resulting 3-D image reconstructions are encouraging and allow to aim at the development of an industrial prototype, monitoring packaged food in real-time along a production line

A non-conformal multi-resolution preconditioner in the MoM solution of large multi-scale structures

The extension of the surface integral equations (SIEs) [1] to non-conforming meshes has ignited intense research in the last years with the goal of finding a versatile and accurate method to address large and multi-scale complex problems, greatly simplifying computer-aided-design (CAD) generation and meshing processes

Neural Network and Microwave Sensing for Food Contamination Monitoring

In this paper, a technique covering the lacks in currently adopted food safety devices is presented, through a microwave-sensing prototype combined with a neural network. An antennas array, composed by 6 low-cost printed circuit boards, surrounds the product to analyze. Their positions and number have been chosen as a trade-off between an optimal coverage of the volume of interest and the physical constraints of an industrial device, i.e., the conveyor belt, moving at production speed. The signals are recorded and used to train a neural network, resulting in an overall 99.45% success rate in classification

A non-conformal multi-resolution preconditioner in the MoM solution of large multi-scale structures

An efficient method to improve the convergence in non-conformal meshes including an automatic quasi-Helmholtz decomposition has been developed for the simulation of nonconforming meshes using the novel Multibranch Rao-Wilton-Glisson (MB-RWG) basis functions. Numerical experiments will be shown to illustrate the great flexibility of this approach for the solution of small-frequency and large multi-scale objects

Experimental Validation of a 3D Microwave Imaging Device for Brain Stroke Monitoring

In this work we present the experimental validation of a 3D microwave imaging device to brain observation. The device is conceived as a way to monitor stroke development, supporting physicians in the follow-up of patients in the aftermath of cerebrovascular accidents, and giving to them extra information for decision-making and application of therapies. The device acquires data through antennas placed around the patient head, in a low-complexity system that guarantees that available information is enough for reliable outcome. Experimental testing is performed on a 3-D human-like head phantom with promising results

Innovative Rotman lens setup for extended scan range array antennas

The aim of this work is to design a smart and cost effective 24 GHz Short Range Radar (SRR) array antenna system for automotive applications. The beam forming network consists of a hybrid solution including an analog phase shifter, realized with a Rotman lens, and an additional digital phase shifting stage on array side allowing to select between two states, and consequently to enhance the scan angle. This paper will demonstrate that this new concept allows to double the scanning capability of the array with respect to a design employing only the Rotman lens

Moving Forward to Real-time Imaging-based Monitoring of Cerebrovascular Diseases Using a Microwave Device: Numerical and Experimental Validation

This paper introduces a numerical and experimental assessment of the microwave device capabilities to perform continuous real-time imaging-based monitoring of a brain stroke, exploiting a differential measuring scheme of the scattering matrices and the distorted Born approximation. The device works around 1 GHz and consists of a low-complexity 22-antenna-array composed of custom-made wearable elements. The imaging kernel is built using an average-head reference scenario computed off-line via accurate numerical models and an in-house finite element method electromagnetic solver. The validation follows the progression of emulated evolving hemorrhagic stroke condition, including tests with both an average single-tissue head model and a multi-tissue one in the numerical part and the average scenario in the experimental one. The results show the system's capacity to localize and track the shape changes of the stroke-affected area in all studied cases