Biblio

In this work, a measurement system is developed based on acoustic resonance which can be used for classification of materials. Basically, the inspection methods based on acoustic, utilized for containers screening in the field, identification of defective pills hold high significance in the fields of health, security and protection. However, such techniques are constrained by costly instrumentation, offline analysis and complexities identified with transducer holder physical coupling. So a simple, non-destructive and amazingly cost effective technique in view of acoustic resonance has been formulated here for quick data acquisition and analysis of acoustic signature of liquids for their constituent identification and classification. In this system, there are two ceramic coated piezoelectric transducers attached at both ends of V-shaped glass, one is act as transmitter and another as receiver. The transmitter generates sound with the help of white noise generator. The pick up transducer on another end of the V-shaped glass rod detects the transmitted signal. The recording is being done with arduino interfaced to computer. The FFTs of recorded signals are being analyzed and the resulted resonant frequency observed for water, water+salt and water+sugar are 4.8 KHz, 6.8 KHz and 3.2 KHz respectively. The different resonant frequency in case different sample is being observed which shows that the developed prototype model effectively classifying the materials.

Maintaining the security and integrity of our food supply chain has emerged as a critical need. In this paper, we describe a novel authentication approach that can significantly improve the security of the food supply chain. It relies on applying nuclear quadrupole resonance (NQR) spectroscopy to authenticate the contents of packaged food products. NQR is a non-invasive, non-destructive, and quantitative radio frequency (RF) spectroscopic technique. It is sensitive to subtle features of the solid-state chemical environment such that signal properties are influenced by the manufacturing process, thus generating a manufacturer-specific watermark or intrinsic tag for the product. Such tags enable us to uniquely characterize and authenticate products of identical composition but from different manufacturers based on their NQR signal parameters. These intrinsic tags can be used to verify the integrity of a product and trace it through the supply chain. We apply a support vector machine (SVM)-based classification approach that trains the SVM with measured NQR parameters and then authenticates food products by checking their test responses. Measurement on an example substance using semi-custom hardware shows promising results (95% classification accuracy) which can be further improved with improved instrumentation.