title: Comparison of several supervised pattern recognition techniques for detecting additive methamidophos in rotenone preparation by near-infrared spectroscopy.
abstract: In this paper, different supervised pattern recognition methods have been applied to detect the manually additive methamidophos in rotenone preparation. The aim of this paper was to examine the performances of different supervised pattern recognition techniques: soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA), artificial neutral networks (ANN), and support vector machine (SVM). The results obtained show that SVM is the most effective techniques with 100.0% classification accuracy followed by ANN, PLS-DA and with the accuracy of 97.5% and 93.3% respectively while SIMCA yields the poorest result of 85.8%. We hope that the results obtained in this study will help both further chemometric investigations and investigations in the sphere of applied vibrational spectroscopy of sophisticated multicomponent systems. Furthermore, the use of portable instrument and satisfactory classification also indicated the possibility of detecting illicit-addition at scene by near-infrared (NIR) spectroscopy which makes a great sense in pesticide quality control.
authors.affiliation: College of Science, China Agricultural University, Beijing 100193, PR China.
authors.forename: Guo
authors.initials: G
authors.lastname: Tang
authors.affiliation: College of Science, China Agricultural University, Beijing 100193, PR China.
authors.forename: Kuangda
authors.initials: K
authors.lastname: Tian
authors.affiliation: College of Science, China Agricultural University, Beijing 100193, PR China.
authors.forename: Xiangzhong
authors.initials: X
authors.lastname: Song
authors.affiliation: College of Science, China Agricultural University, Beijing 100193, PR China.
authors.forename: Yanmei
authors.initials: Y
authors.lastname: Xiong
authors.affiliation: College of Science, China Agricultural University, Beijing 100193, PR China. Electronic address: minsg@263.net.
authors.forename: Shungeng
authors.initials: S
authors.lastname: Min
mesh_terms: D000465:Algorithms; D009010:Monte Carlo Method; D009946:Organothiophosphorus Compounds / Q000032:analysis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012402:Rotenone / Q000032:analysis*; D019265:Spectroscopy, Near-Infrared
chemical_list: D009946:Organothiophosphorus Compounds; D012402:Rotenone; C014655:methamidophos
keywords: Methamidophos; Near-infrared; Rotenone; Supervised pattern recognition