Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

Dou, Yong; Mi, Hong; Zhao, Lingzhi; Ren, Yang; Ren, Yaxuan

doi:10.1016/j.ab.2005.10.041

Cited by 22 publications

(7 citation statements)

References 27 publications

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“…The predictive abilities of training set and testing set were compared by means of the relative standard error (RSE) (Dou et al., 2006), defined as: where n is the number of samples included in the validation set, y and y n are the encoded values and experimental values of the samples respectively.…”

Section: Methodsmentioning

confidence: 99%

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FT‐IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

Nie

Zhang

Xiao

et al. 2007

Journal of Phytopathology

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A rapid spectroscopic approach for whole-organism fingerprinting of Fourier transform infrared (FT-IR) spectroscopy was used to analyse 16 isolates from five closely related species of Fusarium: F. graminearum, F. moniliforme, F. nivale, F. semitectum and F. oxysporum. Principal components analysis and hierarchical cluster analysis were used to study the clusters in the data. On visual inspection of the clusters from both methods, the spectra were not differentiated into five separate clusters corresponding to species and these unsupervised methods failed to identify these fungal strains. When the data were trained by back propagation algorithm of artificial neural networks (ANNs) with principal components scores of spectra used as input modes, the strains were accurately predicted and recognized. The results in this study show that FT-IR spectroscopy in combination with principal component artificial neural networks (PC-ANNs) is well suited for identifying Fusarium spp. It would be advantageous to establish a comprehensive database of taxonomically well-defined Fusarium species to aid the identification of unknown strains.

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Section: Methodsmentioning

confidence: 99%

“…This process was conducted eight times so as to observe whether training was reproducible. The predictive abilities of training set and testing set were compared by means of the relative standard error (RSE) (Dou et al, 2006), defined as:…”

Section: Ft-ir Measurement and Data Preprocessmentioning

confidence: 99%

FT‐IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

Nie

Zhang

Xiao

et al. 2007

Journal of Phytopathology

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“…The comparative advantage of ANN over conventional models, such as PLS, is that it can model complex, possibly nonlinear relationships without any prior assumptions about the underlying data-generating process. Many previous studies on near infrared spectroscopy modeling have concluded that ANN methods provide better predictions than those of other linear approaches (Dou et al, 2006;Inon et al, 2006;Chalus et al, 2007). In this study, we explored the feasibility of ANN methods in developing NIRS models for estimating nutrient content in poultry manure.…”

Section: Introductionmentioning

confidence: 96%

Quantitative determination of nutrient content in poultry manure by near infrared spectroscopy based on artificial neural networks

Chen¹,

Li²,

Han³

2009

Poultry Science

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Excessively applied manure contains a considerable amount of nutrient content such as nitrogen and phosphorus that could potentially pollute groundwater and soil. The present paper evaluated the use of nonlinear regression methods, such as artificial neural networks (ANN), for developing near infrared reflectance spectroscopy calibration models to predict nutrient content in poultry manure. Four representative nutrient ingredients (ammonia nitrogen, AN; total potassium, TK; total nitrogen, TN; total phosphorus, TP) in poultry manure were selected for evaluating ANN feasibility using 91 diverse samples in which three-fourths of the samples were used as a training set and one-fourth as a validation set. The performance of the ANN models was compared with the partial least squares (PLS) models. We found that the ANN models for all 4 nutrient contents consistently gave better predictions than PLS models. The ratios of prediction to deviation of 2.62 (AN), 1.51 (TK), 2.75 (TN), and 2.01 (TP) with the PLS models were improved to 3.02 (AN), 1.74 (TK), 3.41 (TN), and 2.71 (TP) with the corresponding ANN models. These findings demonstrated that the near infrared reflectance spectroscopy model based on the ANN method may be an appropriate tool to predict nutrient content in poultry manure.

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“…Typically many input/target pairs are used to train a network [ 52 ]. ANN has greater superiority over other classical multivariate methods in modeling linear and non-linear relationship between variables [ 53 , 54 , 55 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative ANNs with Different Input Layers and GA-PLS Study for Simultaneous Spectrofluorimetric Determination of Melatonin and Pyridoxine HCl in the Presence of Melatonin’s Main Impurity

et al. 2013

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Melatonin (MLT) has many health implications, therefore it is important to develop specific analytical methods for the determination of MLT in the presence of its main impurity, N-{2-[1-({3-[2-(acetylamino)ethyl]-5-methoxy-1H-indol-2-yl}methyl)-5-methoxy-1H-indol-3-yl]ethyl}acetaamide (DMLT) and pyridoxine HCl (PNH) as a co-formulated drug. This work describes simple, sensitive, and reliable four multivariate calibration methods, namely artificial neural network preceded by genetic algorithm (GA-ANN), principal component analysis (PCA-ANN) and wavelet transform procedures (WT-ANN) as well as partial least squares preceded by genetic algorithm (GA-PLS) for the spectrofluorimetric determination of MLT and PNH in the presence of DMLT. Analytical performance of the proposed methods was statistically validated with respect to linearity, accuracy, precision and specificity. The proposed methods were successfully applied for the assay of MLT in laboratory prepared mixtures containing up to 15% of DMLT and in commercial MLT tablets with recoveries of no less than 99.00%. No interference was observed from common pharmaceutical additives and the results compared favorably with those obtained by a reference method.

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Determination of compound aminopyrine phenacetin tablets by using artificial neural networks combined with principal components analysis

Cited by 22 publications

References 27 publications

FT‐IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

FT‐IR Spectroscopy and Artificial Neural Network Identification of Fusarium Species

Quantitative determination of nutrient content in poultry manure by near infrared spectroscopy based on artificial neural networks

Comparative ANNs with Different Input Layers and GA-PLS Study for Simultaneous Spectrofluorimetric Determination of Melatonin and Pyridoxine HCl in the Presence of Melatonin’s Main Impurity

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