title: Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology.
abstract: The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyperspectral 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 microspectroscopy 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.
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China; Institute of Photonics and Bio-Medicine, Graduate School of Science, East China University of Science and Technology, Shanghai, China.
authors: Yongni
authors: Y
authors: Shao
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
authors: Yuan
authors: Y
authors: Li
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
authors: Linjun
authors: L
authors: Jiang
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China.
authors: Jian
authors: J
authors: Pan
authors: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, 310058, China. Electronic address: yhe@zju.edu.cn.
authors: Yong
authors: Y
authors: He
authors: Institute of Photonics and Bio-Medicine, Graduate School of Science, East China University of Science and Technology, Shanghai, China. Electronic address: xiaomingdou@yeah.net.
authors: Xiaoming
authors: X
authors: Dou
mesh_terms: D000465:Algorithms; D002708:Chlorella; D016002:Discriminant Analysis; D016018:Least-Squares Analysis; D058086:Microalgae; D010575:Pesticides; D019265:Spectroscopy, Near-Infrared
chemical_list: D010575:Pesticides
keywords: Linear discriminant analysis; Pesticide varieties; Raman microspectroscopy; Successive projections algorithm; Visible/Near-infrared hyperspectral imaging