دورية أكاديمية

Novel consensus quantitative structure-retention relationship method in prediction of pesticides retention time in nano-LC

التفاصيل البيبلوغرافية
العنوان: Novel consensus quantitative structure-retention relationship method in prediction of pesticides retention time in nano-LC
المؤلفون: Zahra Pahlavan Yali, Mohammad Hossein Fatemi
المصدر: Nanochemistry Research, Vol 3, Iss 2, Pp 205-211 (2018)
بيانات النشر: Iranian Chemical Society, 2018.
سنة النشر: 2018
المجموعة: LCC:Chemical technology
LCC:Chemistry
مصطلحات موضوعية: quantitative structure-retention relationship, pesticides, nano-lc retention time, genetic algorithm-multiple linear regression, average consensus model, Chemical technology, TP1-1185, Chemistry, QD1-999
الوصف: In this study, quantitative structure-retention relationship (QSRR) methodology employed for modeling of the retention times of 16 banned pesticides in nano-liquid chromatography (nano-LC) column. Genetic algorithm-multiple linear regression (GA-MLR) method employed for developing global and consensus QSRR models. The best global GA-MLR model was established by adjusting GA parameters. Three descriptors of SpMax2_Bhp, Mor31u and, MATS6c appeared in this model. Consensus QSRR models developed as an average consensus model (ACM) and weighted consensus model (WCM) by a combination of a subset of the GA-MLR models. Comparison of statistical parameters of developed models indicated that an ACM which is combining of the best global QSRR model with four-descriptor sub-model can be selected as the best consensus QSRR model. CrippenLogP, RDF070m, Lop, and HASA1 descriptors appeared in four-descriptor sub-model. In ACM, the square of correlation coefficients (R2) was 0.973 and 0.939, and the SE was 0.49 and 0.40, for the training and test sets, respectively. The ACM was assessed by leave one out cross-validation ("Q2 cv" = 0.935) as well as internal validation. Descriptors which appeared in this model suggest electrostatic, steric and hydrophobic interactions play the main role in the chromatographic retention of studied pesticides in nano-LC conditions.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2538-4279
2423-818X
العلاقة: http://www.nanochemres.org/article_81111_d1b8f8d90d738cdd27ae763761309e09.pdfTest; https://doaj.org/toc/2538-4279Test; https://doaj.org/toc/2423-818XTest
DOI: 10.22036/ncr.2018.02.010
الوصول الحر: https://doaj.org/article/7d2f88b74fb64caaa2f7302f3a8b8c36Test
رقم الانضمام: edsdoj.7d2f88b74fb64caaa2f7302f3a8b8c36
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:25384279
2423818X
DOI:10.22036/ncr.2018.02.010