Machine learning techniques for microwave food contamination detection

Food manufacturers still struggle with the contamination of their products by foreign bodies today. They must first assure customers that their pur- chases are safe. Second, contamination can harm a company’s brand and result in pricey recall efforts. Lastly, because accurate foreign body detec- tion technologies enable manufacturers to acquire essential food certifications that may boost their revenues. The issue of metal object detection is a major concern for a variety of industries. To address this issue, a number of strategies have been imple- mented, such as signal processing filters, detection systems, X-ray scanning, and near-infrared imaging. However, these strategies have some limitations, such as susceptibility to water attenuation, and a trade-off between low spatial resolution and high penetration depth. Moreover, low-density, non- metallic objects, such as plastic, glass, and wood, remain undetectable. To overcome these drawbacks, microwave sensing technology provides a cost- effective, easy-to-use and non-ionizing solution that is contactless, real-time, and destructive-free. This technology has the potential to detect metal ob- jects as well as other low-density, non-metallic objects that are otherwise undetectable. Furthermore, using this technology can provide improved ac- curacy, speed, and reliability. It also offers a number of advantages, such as reduced power consumption, low cost, and improved safety. Thus, microwave sensing technology can be a reliable and cost-effective solution to the issue of foriegn object detection. Neural Networks is the ideal tool for categorizing contaminated packaged food items using microwave sensing. A powerful neural network model that can precisely categorize the food items can be simply developed using the vast number of test samples produced by production lines. This method en- ables neural network techniques and machine learning to precisely identify contaminated food items and contribute to raising food safety standards. Additionally, this technology can assist producers in identifying possible prob- lems during the production process and implementing remedial actions to guarantee they are producing safe and superior food items. A number of datasets of gathered data were used to train the model. The model was initially trained on a dataset of around 1260 testing items, gathered at speed rates between 20 and 30 m/min. These dataset was used to validate the concept of using neural networks to detect foreign bodies though microwave sensing. Data from a variety of materials and speeds were used to evaluate the model. The model was additionally tested on a range of various foreign bodies, such as metal, plastic, and glass. The model’s capacity to reliably and accurately identify foreign bodies illustrates the potency of the our neural model. The model metrics of our classification model, such as accuracy, recall, and precision, were examined through a variety of trials. To evaluate the model’s performance, we tested it on a variety of balanced and unbalanced datasets. The model’s precision, recall, and accuracy was assessed after the experiments were conducted. Also, we looked at how the model responded to various kinds of data and how the outcomes varied depending on the data set. Moreover, we used the experiments to verify the model for practical application and confirm its effectiveness. Additionally, we used the experiment findings to determine areas that needed improvement and confirm that the model was executing as predicted.