Food safety, especially edible oils, has attracted more and more attention recently. Many methods and instruments have emerged to detect the edible oils, which include oils classification and adulteration. It is well known than the adulteration is based on classification. Then, in this paper, a portable detection system, based on laser induced fluorescence, is proposed and designed to classify the various edible oils, including (olive, rapeseed, walnut, peanut, linseed, sunflower, corn oils). 532 nm laser modules are used in this equipment. Then, all the components are assembled into a module (100*100*25mm). A total of 700 sets of fluorescence data (100 sets of each type oil) are collected. In order to classify different edible oils, principle components analysis and support vector machine have been employed in the data analysis. The training set consisted of 560 sets of data (80 sets of each oil) and the test set consisted of 140 sets of data (20 sets of each oil). The recognition rate is up to 99%, which demonstrates the reliability of this potable system. With nonintrusive and no sample preparation characteristic, the potable system can be effectively applied for food detection.
Unlike traditional THz imaging system, we first report a design of 0.2THz stepped frequency radar system,
and prove its feasibility by simulation. The stepped frequency radar working from 200GHz to 210GHz can
provide centimeter accuracy. To demonstrate the feasibility of our design, we simulate our system by using
Advanced Design System (ADS) and Simulink in Matlab. The transmitter line is simulated in ADS, while
system-level simulation is carried out in Matlab. The simulation of transmitter is implemented by using
parameters from actual products, which can ensure the reality of simulation. In this paper, we will present
the methods and results of our simulation. From the results, we can conclude that our design is feasible.