Metamaterial-Based Sensor Integrating Microwave Dielectric and Near-Infrared Spectroscopy Techniques for Substance Evaluation

A novel era of smart sensors is emerging, in which they are being used in combination with artificial intelligence (AI) to achieve a better understanding of the world. Such sensors need to be ubiquitous. Accordingly, they should have miniaturized dimensions and low costs. However, there is a tradeoff between cost and accuracy of the sensors, which should be overcome with the use of new types of sensors and techniques. In this article, a metamaterial-based sensor that performs simultaneous microwave dielectric spectroscopy and near-infrared (NIR) spectroscopy measurements is presented. It uses a planar circular complementary split-ring resonator for dielectric measurement with an optical path, and NIR spectroscopy is performed in the same physical structure. A greater diversity of data can be achieved from the electromagnetic spectrum using the data from microwave dielectric spectroscopy and NIR spectroscopy; thus, it becomes possible to enhance the accuracy of substance identification with fewer samples. The proposed sensor was evaluated for the identification of olive oil adulteration and achieved an accuracy of 100% using only 14 training samples. To achieve this same accuracy, other techniques used for the detection of adulteration in olive oil need more than 50 training samples. Therefore, the proposed sensor needs 72% fewer samples for training classifier algorithms used in olive oil adulteration identification. This implies the reduction of computing requirements and wall clock time. The proposed sensor opens new avenues to perform material characterization and identification using a combination of microwave dielectric and NIR spectroscopies in the same structure.

Automated summary

A novel era of smart sensors is emerging, combining with AI to achieve a better understanding of the world. This article presents a metamaterial-based sensor that performs simultaneous measurements of microwave dielectric spectroscopy and NIR spectroscopy, enhancing the accuracy of substance identification with fewer samples. The proposed sensor achieves 100% accuracy in olive oil adulteration identification with only 14 training samples, requiring 72% fewer samples compared to other techniques.