Abstract-Highly Sensitive Detection of Carbendazim by Using Terahertz Time-Domain Spectroscopy Combined With Metamaterial

 Binyi Qin,  Zhi Li,  Fangrong Hu,  Cong Hu,  Tao Chen,  Huo Zhang,   Yonghong Zhao

http://ieeexplore.ieee.org/document/8252737/

The rapid and sensitive detection of pesticide residue is essential for ensuring the food safety of consumers. However, there are many disadvantages for current approaches to detect pesticide residue, such as complex pre-treatment and low sensitivity. In this paper, we demonstrate a highly sensitive carbendazim detection method by using terahertz time-domain spectroscopy (THz-TDS) combined with metamaterial. The metamaterial composed of metal ohm ring arrays, is applied in detecting different concentrations of carbendazim. Resonant peaks of metamaterial move to a lower frequency as the concentration increases. The results illustrate that metamaterial can detect trace amounts of carbendazim, as small as 5 mg/L, which is about 104 times enhancement compared to the squash method for THz-TDS detection. It also means that metamaterial combined with THz-TDS is a potential new approach for food quality and safety control.

Abstract-Identification of genetically modified cotton seeds by terahertz spectroscopy with MPGA-SVM

• Binyi Qin^a, b, 
• Zhi Li^a, c,  
• Tao Chen^c, 
• Yu Chen^d

• ^a School of Mechano-Electronic Engineering, Xidian University, Xi’an, Shanxi 710071, China
• ^b School of Electronics and Communication Engineering, Yulin Normal University, Yulin, Guangxi 537000, China
• ^c Guangxi Key Laboratory of Automatic Detecting Technology and Instruments, School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi 541004, China
• ^d SZU-NUS Collaborative Innovation Center for Optoelectronic Science & Technology, Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China

http://www.sciencedirect.com/science/article/pii/S003040261730699X

The purpose of this paper was to propose a method that combines support vector machine (SVM) and multi-population genetic algorithm (MPGA) for identifying Genetically modified (GM) cotton seeds with THz spectroscopy. The parameters of SVM were tuned by using MPGA. Comparing previous reports of THz-TDS for GM crops detection, we used a larger sample size and more evaluation criterions (i.e. confusion matrix, average accuracy and 95th percentile of accuracy) in the experiment. Principal component analysis (PCA) was utilized to reduce dimensions of THz absorbance spectra, and then used the result of PCA as the input of different classifiers. When the input dimensionality of MPGA-SVM was 12, recall, precision and F-score were more than 97.9%, 96% and 0.9796, respectively, and accuracy was 99%. To further study the performance of MPGA-SVM with different input dimensionality, different number of principal components (ranged from 2 to 16 at intervals of 2) was selected. In addition, the proposed method was compared with traditional classifiers (decision trees (DT), k-nearest neighbor (KNN) and discriminant analysis (DA)). All the results showed that MPGA-SVM has better performance than other classifier. Thus, MPGA-SVM combined with THz spectroscopy is a potential identification tool for GM cotton seeds detection.