Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products

Abstract

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools. Considering the prevalence of water and oil as primary components in many food and beverage items, the proposed technique is applied to both media. The approach involves a thorough examination of the MW sensing system, from selecting appropriate frequency bands to characterizing the antenna in its near-field region. The process culminates in the collection of scattering parameters to create the datasets, followed by classification using the Support Vector Machine (SVM) learning algorithm. Binary and multiclass classifications are performed on two types of datasets, including those with complex numbers and amplitude data only. High accuracy is achieved for both water-based and oil-based products.

Keywords: Antenna; Electromagnetic modeling; Machine learning; Microwave sensing; Near-field sensing; Non-destructive technique.

Figure 1

The complete measurement system comprises: (1) the conveyor belt, (2) the jar under test, (3) the antennas, (4) the vector network analyzer (VNA), (5) the shielding box.

Figure 2

The EM characteristics of water and oil. (a) Relative permittivity and conductivity of water. (b) Relative permittivity and conductivity of oil. (c) Penetration depth within water. (d) Penetration depth within oil. The dotted lines within (c,d) denote the upper limit for the frequency range selected for carrying out our measurements on the samples.

Figure 3

(a) Front view of the design, (b) rear view of the design, (c) front view after fabrication, (d) rear view after fabrication. Red labels indicate the following: L1 and L2 represent the width and length of the antenna’s transmission line, while R1 and R2 denote the longer and shorter radii of the four ellipses comprising the antenna structure. Additionally, L3 and L4 correspond to the dimensions of the V-cut triangular shape in the ground plane.

Figure 4

The system for near-field measurements. (a) The measurement setup displays the transmitting antenna, denoted as 1, which is the flower antenna used in the setup. (b) The measurement setup also features the receiving wideband horn antenna, labeled as 2.

Figure 5

The measured amplitude of reflection coefficients for the realized antenna in three scenarios: with an empty jar, with a jar filled with oil, and with a jar filled with water.

Figure 6

The measured amplitude of the antenna’s near-field (normalized to the maximum magnitude value across all frequency points) in the absence of any jar at: (a) 2 GHz, (b) 4 GHz, (c) 6 GHz, (d) 8 GHz, and (e) 10 GHz The rectangular shape featured in the plot indicates the anticipated location of the food jar within the measurement system.

Figure 7

The design system used for E-field spatial coverage analysis.

Figure 8

The E-field spatial coverage at 2.5 GHz inside the water jar: (a) incident, (b) total, and (c) scattered.

Figure 9

The simulation system similar to the measurements except for the presence of the foreign body.

Figure 10

Time-domain analysis.

Figure 11

The amplitude of the IFFT results for the measured reflection coefficients.

Figure 12

The time frame of one complete data acquisition (one single measurement).

Figure 13

The normalized amplitude of the scattering matrix $[S_n]$ for two uncontaminated samples under test: (a) water, (b) oil.

Figure 14

Projection of PCA results onto the three most significant eigenvectors. (a) Complex nature dataset (Water). (b) Amplitude-only dataset (Water). (c) Complex nature dataset (Oil). (d) Amplitude-only dataset (Oil).