Application of artificial neural networks in HPLC method development

Agatonović-Kuštrin, Snežana; Zečević, Mira; Živanović, Ljubiša; Tucker, Ian G.

doi:10.1016/s0731-7085(97)00170-2

Cited by 39 publications

(22 citation statements)

References 8 publications

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“…There are some studies in the literature where models were developed based on RSM and ANN using the same experimental design [8][9][10][11][12]. For example, Basri et al (2007) reported the comparison of ANN and RSM in the lipase-catalyzed synthesis of palm-based wax ester, and they suggested the superiority of ANN over RSM.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles

Ghaffari‐Moghaddam¹,

Khajeh²

2011

FNS

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Section: Introductionmentioning

confidence: 99%

Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles

Ghaffari‐Moghaddam¹,

Khajeh²

2011

FNS

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“…Based on the RMS errors of the training and testing sets and high correlation of predicted versus experimental values (R>0.97), it is clear that the correlation between the structure and the chromatographic separation can be predicted by ANNs. Similarly, ANNs can assist in HPLC optimization by finding correlation of the chromatographic behavior of solutes (capacity factors) with mobile phase composition and pH to predict retention times [43], the separation as a function of simultaneous change in pH and solvent strength [44,45], and hydrophobicity coefficients for the prediction of peptide elution profiles [46]. ANNmodel have used in successful prediction of retention values of unanalyzed molecular in studies done by Agatonovic-Kustrin by determining the correlations of chromatograms retention times with mobile phase composition and pH, and with physical chemical properties of amiloride, hydrochloride and methyldopa [47].…”

Section: Anns Applications Analytical Data Analysis and Structure Retmentioning

confidence: 99%

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Sutariya¹,

Groshev²,

Sadana³

et al. 2013

TOBIOIJ

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Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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“…The error or bias in prediction is then propagated through the system and the inter-unit connections are changed to minimize the error in prediction [15]. This is a continuous process with multiple training sets until the minimum error is attained.…”

Section: Introductionmentioning

confidence: 99%

“…However, a major disadvantage of ANN is the difficulty in explaining the relation between independent and response variables resulting from the ambiguously defined weights, which as mentioned earlier, is a black box [17]. According to Agatonovic-Kustrina et al [15], 3 types of data sets that are inclusive of training data, testing data and validation or unseen data, are required for network training, neural network performance monitoring during the training, and measurement of the performance of trained network, respectively. The number of the layers and processing elements in layers vary from one process to another.…”

Section: Introductionmentioning

confidence: 99%

Chicken eggshell as biosorbent: Artificial intelligence as promising approach in optimizing study

Kiew

Ang

Tan

et al. 2016

MATEC Web of Conferences

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Abstract. Response Surface Methodology (RSM) is the most popular approach for optimization study in various biochemical processes nowadays. Artificial Neural Network (ANN) has emerged as one of the most efficient methods in empirical modeling and optimization, particularly for non-linear systems. In this study, the estimation capability of RSM and ANN models was compared in copper removal from aqueous solution. The experiments were carried out based on a 3-level and 4-variable Central Composite Design (CCD). The RSM results revealed that the relationship between the response and independent variable could be represented by the quadratic polynomial model. In the development of ANN model, the optimal configuration of the model was found to be 4-10-1. Estimated responses from both models were compared with the experimentally determined responses to determine predictive capabilities of both techniques. Comparison of two methodologies showed that the ANN model was more accurate and exhibited better generalization capability than RSM, thus indicated a clear superiority than the latter in capturing the non-linear behaviour of the adsorption process using chicken eggshell as biosorbent.

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Application of artificial neural networks in HPLC method development

Cited by 39 publications

References 8 publications

Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles

Comparison of Response Surface Methodology and Artificial Neural Network in Predicting the Microwave-Assisted Extraction Procedure to Determine Zinc in Fish Muscles

Artificial Neural Network in Drug Delivery and Pharmaceutical Research

Chicken eggshell as biosorbent: Artificial intelligence as promising approach in optimizing study

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