PMID- 28412664
OWN - NLM
STAT- MEDLINE
DCOM- 20180806
LR  - 20191210
IS  - 1873-3557 (Electronic)
IS  - 1386-1425 (Linking)
VI  - 182
DP  - 2017 Jul 5
TI  - New model for prediction binary mixture of antihistamine decongestant using 
      artificial neural networks and least squares support vector machine by 
      spectrophotometry method.
PG  - 105-115
LID - S1386-1425(17)30261-5 [pii]
LID - 10.1016/j.saa.2017.04.001 [doi]
AB  - In the present study, artificial neural networks (ANNs) and least squares support 
      vector machines (LS-SVM) as intelligent methods based on absorption spectra in 
      the range of 230-300nm have been used for determination of antihistamine 
      decongestant contents. In the first step, one type of network (feed-forward 
      back-propagation) from the artificial neural network with two different training 
      algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and 
      adaptive learning rate back-propagation (GDX) algorithm, were employed and their 
      performance was evaluated. The performance of the LM algorithm was better than 
      the GDX algorithm. In the second one, the radial basis network was utilized and 
      results compared with the previous network. In the last one, the other 
      intelligent method named least squares support vector machine was proposed to 
      construct the antihistamine decongestant prediction model and the results were 
      compared with two of the aforementioned networks. The values of the statistical 
      parameters mean square error (MSE), Regression coefficient (R(2)), correlation 
      coefficient (r) and also mean recovery (%), relative standard deviation (RSD) 
      used for selecting the best model between these methods. Moreover, the proposed 
      methods were compared to the high- performance liquid chromatography (HPLC) as a 
      reference method. One way analysis of variance (ANOVA) test at the 95% confidence 
      level applied to the comparison results of suggested and reference methods that 
      there were no significant differences between them.
CI  - Copyright (c) 2017 Elsevier B.V. All rights reserved.
FAU - Mofavvaz, Shirin
AU  - Mofavvaz S
AD  - Department of Chemistry, Shahreza Branch, Islamic Azad University, Shahreza, 
      Isfahan, Iran. Electronic address: shirin_mofavvaz@yahoo.com.
FAU - Sohrabi, Mahmoud Reza
AU  - Sohrabi MR
AD  - Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran, 
      Iran. Electronic address: sohrabi.m46@yahoo.com.
FAU - Nezamzadeh-Ejhieh, Alireza
AU  - Nezamzadeh-Ejhieh A
AD  - Department of Chemistry, Azad University, Shahreza Branch, Shahreza, Isfahan, 
      Iran. Electronic address: arnezamzadeh@iaush.ac.ir.
LA  - eng
PT  - Journal Article
DEP - 20170405
PL  - England
TA  - Spectrochim Acta A Mol Biomol Spectrosc
JT  - Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
JID - 9602533
RN  - 0 (Histamine Antagonists)
RN  - 0 (Nasal Decongestants)
SB  - IM
EIN - Spectrochim Acta A Mol Biomol Spectrosc. 2017 Oct 5;185:197. PMID: 28575819
MH  - Algorithms
MH  - Histamine Antagonists/*analysis
MH  - Least-Squares Analysis
MH  - Linear Models
MH  - Nasal Decongestants/*analysis
MH  - *Neural Networks, Computer
MH  - Reproducibility of Results
MH  - Sensitivity and Specificity
MH  - Spectrophotometry/*methods
MH  - Support Vector Machine
OTO - NOTNLM
OT  - Antihistamine decongestant
OT  - Artificial neural networks
OT  - Backpropagation GDX algorithm, least square support vector machine
OT  - Lavenberg-Marquardt algorithm
OT  - Spectrophotometric
EDAT- 2017/04/17 06:00
MHDA- 2018/08/07 06:00
CRDT- 2017/04/17 06:00
PHST- 2016/12/30 00:00 [received]
PHST- 2017/03/21 00:00 [revised]
PHST- 2017/04/01 00:00 [accepted]
PHST- 2017/04/17 06:00 [pubmed]
PHST- 2018/08/07 06:00 [medline]
PHST- 2017/04/17 06:00 [entrez]
AID - S1386-1425(17)30261-5 [pii]
AID - 10.1016/j.saa.2017.04.001 [doi]
PST - ppublish
SO  - Spectrochim Acta A Mol Biomol Spectrosc. 2017 Jul 5;182:105-115. doi: 
      10.1016/j.saa.2017.04.001. Epub 2017 Apr 5.