Prediction of retention times of pesticide residues in cultivated lands using linear and nonlinear chemometric methods : Stepwise Multiple Linear Regression and Artifical Neural Network
Publish place: The first national conference on environmental research
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
View: 1,056
This Paper With 18 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
NCER01_009
تاریخ نمایه سازی: 25 اسفند 1392
Abstract:
QSPR approach was done to provide the relationship between the structures and of the retention times of 208 pesticide residues in the cultivatid lands. The molecular structure of the compounds were drawn by ChemDraw 8.0 software, was optimized by HyperChem 7.0 software, after that the molecular discriptors of the molecules were calculated by Dragon 2.1 software. Then the number of variables was decresed by stepwise multiple linear regression method. The data set based on the principal of normal distribution, were divided into two series training and test sets. Multiple linear regression create models with six descriptor which are molar refractivity and spanning tree number and 3D-MoRSE descriptors and 3D Petitjean shape index and 2nd component accessibility directional WHIM index / unweighted and 3rd component accessibility directional WHIM index / weighted by I-state. Statistical values of F, R2Pred, R2Train and R2Test obtained for models using SW-MLR were 106, 0.905, 0.902, and 0.921 respectively. The obtained model was evaluated using the technique of cross-validation leave-one-out (LOO) method. Then, to increase the efficiency of the model, non-linear method artificial neural networks (ANN), was used. Predicted value of retention times and statistical parameters obtained from the two methods were compared. The results showed that the artificial neural network (ANN) is superior to the other methods. R2pred values for the method of artificial neural network was 0.960 respectively.
Keywords:
Quantitative structure property relationship , Retention time , Pesticides , Cultivated land , Multiple linear regression , Artificial neural network
Authors
Navid Fahmi
Department of Chemistry, Faculty of Basic Science, Ardabil Center Branch, Payame Noor University, Ardabil, Iran
Ayob Parchehbaf Jadid
Department of Applied Chemistry, Ardabil Branch, Islamic Azad University, Ardabil, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :