文献类型：期刊文献

英文题名：Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology

作者：Shao, Yongni[1,2];Li, Yuan[1];Jiang, Linjun[1];Pan, Jian[1];He, Yong[1];Dou, Xiaoming[2]

机构：[1]Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Zhejiang, Peoples R China;[2]East China Univ Sci & Technol, Inst Photon & Biomed, Grad Sch Sci, Shanghai, Peoples R China

年份：2016

卷号：104

起止页码：432

外文期刊名：WATER RESEARCH

收录：;EI(收录号：20163502760264);WOS:【SCI-EXPANDED(收录号:WOS:000386401900045)】；

基金：The research presented in this paper was partially supported by The National Natural Science Foundation of China (31402318), the Natural Science Foundation of Zhejiang province, China (Q14C130002), and the Higher Education Research Fund for the doctoral program of the new teacher project (20130101120149).

语种：英文

外文关键词：Visible/Near-infrared hyperspectral imaging; Raman microspectroscopy; Pesticide varieties; Successive projections algorithm; Linear discriminant analysis

摘要：The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyper spectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. Successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman micro spectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification. (C) 2016 Elsevier Ltd. All rights reserved.