Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy

Diffuse reflectance near-infrared (NIR) spectroscopy is a technique widely used for rapid and non-destructive analysis of solid samples. A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drug has been developed by using artificial neural network (ANN) on near-infrared (NIR) spectroscopy. An ANN containing three layers of nodes was trained. Various ANN models based on pretreated spectra (first-derivative, second-derivative and standard normal variate; SNV) were tested and compared, respectively. In the models the concentration of paracetamol and caffeine as active principles of compound paracetamol and diphenhydramine hydrochloride powder was determined simultaneously. Partial least squares regression (PLS) multivariate calibrations were also used, which were compared with ANN. The best model was obtained at first-derivative spectra. We have also discussed the parameters that affected the networks and predicted the test set (unknown) specimens. The degree of approximation, a new evaluation criteria of the network were employed, which proved the accuracy of the predicted results.     

扑热息痛片剂药品的近红外光谱法非破坏定量分析

现代近红外光谱分析技术将近红外光谱 (NIR)法同计算机科学和化学计量学结合 ,实现了对样品进行无损非破坏性定量分析 .该法具有速度快、操作简单及所需样品少等特点 ,能够实现样品分析的时间同步、地点同步及无损非破坏分析 .为实现生产过程中即时、在线的质量控制提供了新的手段[1 ] .本文应用人工神经网络 [2 ]与近红外漫反射光谱相结合对扑热息痛片剂药品进行了非破坏快速定量分析 .用扑热息痛片剂药品的近红外漫反射光谱数据、一阶导数光谱数据及二阶导数光谱数据分别建立了 ANN模型 ,预测未知样品 ,讨论了影响网络的因素 ,使用了新…     

A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples

Determination of particle size is one of the critical parameters in nanotechnology. The relationship between particle size and diffuse reflectance (DR) spectra in near-infrared region has been applied to introduce a method for estimation of particle size. Back-propagation artificial neural network (BP-ANN) as a nonlinear model was applied to estimate average particle size based on near-infrared diffuse reflectance spectra. Thirty five different nano TiO₂ samples with different particle size were analyzed by DR-FTNIR spectrometry and the obtained data were processed by BP-ANN. The network was trained by 30 samples and was evaluated by the remaining 5 samples. In order to establish whether the new method is applicable for estimation of particle size of nano structured samples, the optimized model was applied to analyze 44 nano TiO₂ samples. It was observed that ANN using the back-propagation algorithm is capable of generalization and could correctly predict the average particle size of nano-sized particles.     

人工神经网络—近红外光谱法用于甲氧苄胺嘧啶粉末药品的非？…

近红外(NIR)光谱分析技术已应用于制药、化妆品、烟草、食品、化学药品、聚合物、纺织品、油漆涂料、煤炭和石油工业等各个领域的质量监控.近年来,NIR光谱分析技术也应用于药品分析中,因该方法具有非破坏性,样品不需要复杂的预处理和分离即可直接测定.它可对药物进行定性和定量测定以及多晶、光学异构体和湿度的测定.近红外光谱法用于无损非破坏测定胶囊类以及片剂的研究已有报道[1,2].NIR光谱在使用中也有一定的局限性,主要是结构复杂,谱图重叠多,在进行定性和定量分析中需采用一定的数据处理才能获得准确可靠的分析结果.在定量分析中,…     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

Broad-Range Optical pH Sensor Based on Binary Mixed-Indicator Doped Sol-Gel Film and Application of Artificial Neural Network

Abstract

In this article, we constructed a pH optical sensor based on m-cresol purple/bromocresol green mixed indicators. The sensor possessed different behaviors at various pH values from ?1.04 to 8.70. The observed behaviors were modeled by means of a PC-feed-forward artificial neural network (ANN) with back-propagation training algorithm. The resulting ANN model was used to predict pH values of new measured spectra from unknown solutions. The results showed very good agreement between true and predicted pH values. The sensor revealed no leaching and excellent reversibility. Other advantages of the sensor include rapid response time, long-term stability, reversibility, high sensitivity, and little hysteresis effect.     

人工神经网络用于近红外光谱测定柴油闪点

采用主成分－人工神经网络对不同留程柴油的近红外光谱进行校正,预测其闪点。采用监控集控制网络训练过程,以避免过训练。探讨了人工神经网络（ＡＮＮ）、直接线性连接人工神经网络（ＬＡＮＮ）的校正效果,并与局部权重回归（ＬＷＲ）、主成分回归（ＰＣＲ）及偏最小二乘（ＰＬＳ）等校正方法进行了比较,认为人工神经及直接线性关联的较好手段。     

Usage of Artificial Neural Network (Back Propagation) in Optimising Salicylic Acid Determination with Ferric(III) Nitrate

Abstract

The use of artificial neural networks (ANN) in optimizing salicylic acid (SA) determination is presented in this paper. A simple and rapid spectrophotometric method for salicylic acid (SA) determination was carried out based on the complexation of salicylic acid–ferric(III) nitrate, SAFe(III). The SA forms a stable purple complex with ferric(III) nitrate at pH 2.45. The useful dynamic linear range is 0.01–0.35 g/L. It has a maximum absorption at 524 nm and the stability is more than 50 hours. The results were used for artificial neural networks (ANNs) training to optimize data. For training and validation purposes, a back‐propagation (BP) artificial neural network (ANN) was used. The results showed that ANN technique was very effective and useful in broadening the limited dynamic linear response range mentioned to an extensive calibration response (0.01–0.70 g/L). It was found that a network with 22 hidden neurons was highly accurate in predicting the determination of SA. This network scores a summation of squared error (SSE) skill and low average predicted error of 0.0078 and 0.00427 g/L, respectively.     

Back‐propagation artificial neural network and attenuated total reflectance‐Fourier transform infrared spectroscopy for diagnosis of basal cell carcinoma by blood sample analysis

A diagnostic method for the cancer, based on investigation of infrared spectra of blood samples, has been developed. The two‐layer modified principal component feed forward back‐propagation artificial neural network (BP‐ANN) was used to classify the attenuated total reflectance‐Fourier transform infrared (ATR‐FTIR) spectra of blood samples obtained from healthy people and those with basal cell carcinoma (BCC). Results showed 98.33% of accuracy, in comparison with the current clinical methods. In the first step, 20 blood samples (10 normal and 10 cancer cases) were applied to construct the calibration model. Spectroscopic studies were performed in 900–1800 cm⁻¹ spectral region with 3.85 cm⁻¹ data space. In order to modify the capability of ANN in prediction of test samples, two different algorithms were applied. The obtained results confirmed the compatibility of the proposed network with the architecture of 20‐8‐2 (input‐hidden‐output) with the pattern model. It was concluded that analysis of blood samples by ATR‐FTIR spectroscopy and ANN chemometric technique would be a reliable approach for detection of BCC. Copyright © 2009 John Wiley & Sons, Ltd.     

Artificial neural network data analysis for classification of soils based on their radionuclide content

The artificial neural network (ANN) data analysis method was used to recognize and classify soils of an unknown geographic origin. A total of 103 soil samples were differentiated into classes according to the regions in Serbia and Montenegro from which they were collected. Their radionuclide (²²⁶Ra, ²³⁸U, ²³⁵U, ⁴⁰K, ¹³⁴Cs, ¹³⁷Cs, ²³²Th, and ⁷Be) activities detected by gamma-ray spectrometry were then used as inputs to ANN. Five different training algorithms with different numbers of samples in training sets were tested and compared in order to find the one with the minimum root mean square error (RMSE). The best predictive power for the classification of soils from the fifteen regions was achieved using a network with seven hidden layer nodes and 2500 training epochs using the online back-propagation randomized training algorithm. With the optimized ANN, most soil samples not included in the ANN training data set were correctly classified at an average rate of 92%. The text was submitted by the authors in English.     

粉末药品安体舒通的无损定量分析——人工神经网络－近红外光谱法的应用

研究了应用人工神经网络进行粉末药品的无损定量分析,使用安体舒通粉末药品的近红外漫反射光谱数据建立人工神经网络模型,预测未知样品,讨论了影响网络的各参数,使用了逼近度作为网络新的评价标准。     

Principal Component‐Wavelet Neural Network as a Multivariate Calibration Method for Simultaneous Determination of Iron,Nickel, and Cobalt

Abstract

Principal component‐artificial neural network (PC‐ANN) and principal component‐wavelet neural network (PC‐WNN) are applied for simultaneous determination of iron(II), nickel(II), and cobalt(II). A simple and selective spectrophotometric method for simultaneous determination of iron(II), nickel(II), and cobalt(II) based on formation of their complexes with 1‐(2‐pyridylazo)‐2‐naphtol (PAN) in micellar media is described. Although the complexes of Fe(II), Ni(II), and Co(II) with reagent show a spectral overlap, they have been simultaneously determined by PC‐ANN and PC‐WNN. The results obtained by the two methods were compared and it was shown that in PC‐WNN, the convergence speed was faster and the root mean square error of prediction set was also smaller than PC‐ANN. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of Fe(II), Ni(II) and Co(II) in synthetic samples.     

近红外光谱-人工神经网络法用于粉末药品阿斯匹林的非破坏定量分析

研究了应用人工神经网络进行粉末药品的非破坏定量分析。使用阿斯匹林粉末药品的近红外漫反射一阶导数光谱数据建立人工神经网络模型,预测未知样品。讨论了影响网络的各参数,使用了新的网络评价标准－逼近度。     

Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy

The pharmaceutical industry faces increasing regulatory pressure to optimize quality control. Content uniformity is a basic release test for solid dosage forms. To accelerate test throughput and comply with the Food and Drug Administration's process analytical technology initiative, attention is increasingly turning to nondestructive spectroscopic techniques, notably near-infrared (NIR) spectroscopy (NIRS). However, validation of NIRS using requisite linearity and standard error of prediction (SEP) criteria remains a challenge. This study applied wavelet transformation of the NIR spectra of a commercial tablet to build a model using conventional partial least squares (PLS) regression and an artificial neural network (ANN). Wavelet coefficients in the PLS and ANN models reduced SEP by up to 60% compared to PLS models using mathematical spectra pretreatment. ANN modeling yielded high-linearity calibration and a correlation coefficient exceeding 0.996.     

Differential Kinetic Spectrophotometric Determination of Methamidophos and Fenitrothion in Water and Food Samples by Use of Chemometrics

A spectrophotometric method for simultaneous analysis of methamidophos and fenitrothion was proposed by application of chemometrics to the spectral kinetic data, which was based upon the difference in the inhibitory effect of the two pesticides on acetylcholinesterase (AChE) and the use of 5,5′‐dithiobis(2‐nitrobenzoic acid) (DTNB) as a chromogenic reagent for the thiocholine iodide (TChI) released from the acetylthiocholine iodide (ATChI) substrate. The absorbance of the chromogenic product was measured at 412 nm. The different experimental conditions affecting the development and stability of the chromogenic product were carefully studied and optimized. Linear calibration graphs were obtained in the concentration range of 0.5–7.5 ng·mL^?1 and 5–75 ng·mL^?1 for methamidophos and fenitrothion, respectively. Synthetic mixtures of the two pesticides were analysed, and the data obtained processed by chemometrics, such as partial least square (PLS), principal component regression (PCR), back propagation‐artificial neural network (BP‐ANN), radial basis function‐artificial neural network (RBF‐ANN) and principal component‐radial basis function‐artificial neural network (PC‐RBF‐ANN). The results show that the RBF‐ANN gives the lowest prediction errors of the five chemometric methods. Following the validation of the proposed method, it was applied to the determination of the pesticides in several commercial fruit and vegetable samples; and the standard addition method yielded satisfactory recoveries.     

Application of artificial neural networks to the classification of soils from São Paulo State using near-infrared spectroscopy

This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter propagation neural network (CP-ANN) and a radial basis function network (RBFN). These strategies were used to classify soil samples from different geographical regions in Brazil by means of their near-infrared (diffuse reflectance) spectra. The results were better with CP-ANN (classification error 8.6%) than with RBFN (classification error 11.0%).     

Orthogonal projection to latent structures combined with artificial neural network for quantitative analysis of phenoxymethylpenicillin potassium powder

A method for quantitative analysis of phenoxymethylpenicillin potassium powder on the basis of near-infrared (NIR) spectroscopy is investigated by using orthogonal projection to latent structures (O-PLS) combined with artificial neural network (ANN). Being a preprocessing method, O-PLS can remove systematic orthogonal variation from a given data set X without disturbing the correlation between X and the response set y. In this paper, O-PLS method was applied to preprocess the original spectral data of phenoxymethylpenicillin potassium powder, and the filtered data was used to establish the ANN model. In this model, the concentration of phenoxymethylpenicillin potassium as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare with O-PLS-ANN model, the calibration models that use the original spectra and different preprocessing methods (first-derivative, second-derivative, standard normal variate (SNV) and multiplicative scatter correction (MSC)) of the spectra were also designed. Experimental results show that O-PLS-ANN model is the best.     

Simultaneous kinetic-spectrophotometric determination of carbidopa, levodopa and methyldopa in the presence of citrate with the aid of multivariate calibration and artificial neural networks

The kinetic methodology based on the difference of reaction rates, is based on the reaction between a common oxidizing agents such as tris(1,10-phenanthroline) and iron(III) complex (ferriin, [Fe (phen)₃]³⁺) in the presence of citrate and spectrophotometrically, monitoring the changes of absorbance at the maximum wavelength of 511 nm. Experimental conditions such as pH, reagents and citrate concentrations were optimized, and the data obtained from the experiments were processed by several chemometric approaches, such as artificial neural network (ANN) and partial least squares (PLS). A set of synthetic mixtures of carbidopa (CD), levodopa (LD) and methyldopa (MD) was evaluated and the results obtained by the applications of these chemometric approaches were discussed and compared. It was found that the back propagation artificial neural network (BP-ANN) method afforded better precision relatively than those of radial basis function artificial neural networks (RBF-ANN) and PLS. The proposed method was also applied satisfactorily to the determination of carbidopa, levodopa and methyldopa in real samples.