Identification of chafers using near-infrared spectra
Author of the article:Lü Xin-Hang1** GAO Chuan-Bu1 TIAN You-Wen2 WANG Xiao-Qi1*** FANG Hong1
Author's Workplace：1. College of Plant Protection, Shenyang Agricultural University, Shenyang 110161, China; 2. College of Electricity Information and Engineering Institute, Shenyang 110161, China
Key Words：chafer, rapid identification, NIRS, SVM, models
Abstract： [Objectives] To explore a new method for the rapid and accurate identification of chafers, which are important agricultural and forestry pests in China, and extend this to other beetles. [Methods] Near-infrared reflectance spectroscopy (NIRS) was used to analyze near-infrared spectrum data from 15 chafers with the support vector machine (SVM) algorithm. The spectra were processed using the methods described by Savitzky-Golay for smoothing and autoscaling, after removal of the noise region. Using the spectra of 150 specimens, 66% of the NIR spectra of chafer samples were used to establish SVM-based discrimination models, and the remaining 34% of NIRS spectra were used to validate these models. The models were self-validated. [Results] The self-validated results of calibration samples are shown below. In the identification model of 4 subfamilies of the Scarabaeidae family, the accuracy rate for identifying the Melolonthinae was 86% and that for other subfamilies 95%. Identification models for species and genera all had accuracy rates of 100%, except that for Protaetia cathaica. (Bates). The self-validated results of prediction samples showed that all accuracy rates reached 100%, except that for Polyphylla laticollis Lewis which was because of the low number of specimens available. These results are consistent with those obtained by morphological identification. [Conclusion] This novel method has a considerable potential for application in the future due to its speed, reliability and easy of use, as well as its veracity and credibility in pest control and inspection and quarantine.