The primary design of advanced ground-based atmospheric microwave sounder and retrieval of physical parameters

This paper introduces a prototype of ground-based atmospheric microwave sounder that operates in K-band from 22 to 31 GHz and V-band from 51 to 59 GHz. Different from the MP3000A and RPG, the sounder adopts independent dual-band reflectors instead of sharing a dual-band reflector. The direct detect type receiver is applied, which is of smaller size, higher sensitivity, efficient data observing and lower nonlinear error than the widely used superheterodyne receiver. The observing brightness temperatures from this prototype agree well with the simulated brightness temperatures according to the ground-based radiative transfer theory. We use the artificial neural network (ANN) algorithm to retrieve temperature profiles, which has higher spatial resolution especially in the capping inversion when compared with the linear regression algorithm. The temperature retrievals are comparable with the retrievals from RPG and MP3000A retrieval models and have a smaller bias in some certain regions.     

Modeling of Swelling Behaviors of Acrylamide-Based Polymeric Hydrogels by Intelligent System

Hydrogels based on acrylamide (AAm) were synthesized by free radical polymerization in an aqueous solution using N,N’-methylenebisacrylamide (MBAAm) as crosslinker. To obtain anionic hydrogels, 2-acrylamido-2-methylpropanesulfonic acid sodium salt (AMPS) and acrylic acid (AAc) were used as comonomers. The swelling behaviors of all hydrogel systems were modeled using an artificial neural network (ANN) and compared with a multivariable least squares regression (MLSR) model and phenomenal model. The predictions from the ANN model, which associated input parameters, including the amounts of crosslinker (MBA) and comonomer, and swelling values with time, produce results that show excellent correlation with experimental data. The parameters of swelling kinetics and water diffusion mechanisms of the hydrogels were calculated using the obtained experimental data. Model analysis indicated that the ANN models could accurately describe complex swelling behaviors of highly swellable hydrogels.     

An expeditious method for determining particle size distribution by near infrared spectroscopy: Comparison of PLS2 and ANN models

The particle size distribution of a solid product can be crucial parameter considering its application to different kinds of processes. The influence of particle size on near infrared (NIR) spectra has been used to develop effective alternative methods to traditional ones in order to determine this parameter. In this work, we used the chemometrical techniques partial least squares 2 (PLS2) and artificial neural networks (ANNs) to simultaneously predict several variables to the rapid construction of particle size distribution curves. The PLS2 algorithm relies on linear relations between variables, while the ANN technique can model non-linear systems.Samples were passed through sieves of different sieve opening in order to separate several size fractions that were used to construct two types of particle size distribution curves. The samples were recorded by NIR and their spectra were used with PLS2 and ANN to develop two calibration models for each. The correlation coefficients and relative standard errors of prediction (RSEP) have been used to assess the goodness of fit and accuracy of the results.The four calibration models studied provided statistically identical results based on RSEP values. Therefore, the combined use of NIR spectroscopy and PLS2 or ANN calibration models allows determining the particle size distributions accurately. The results obtained by ANN or PLS2 are statistically similar.     

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.     

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.     

Simultaneous determination of fermented milk aroma compounds by a potentiometric sensor array

The paper reports on the application of an electronic tongue for simultaneous determination of ethanol, acetaldehyde, diacetyl, lactic acid, acetic acid and citric acid content in probiotic fermented milk. The αAstree electronic tongue by Alpha M.O.S. was employed. The sensor array comprised of seven non-specific, cross-sensitive sensors developed especially for food analysis coupled with a reference Ag/AgCl electrode. Samples of plain, strawberry, apple-pear and forest-fruit flavored probiotic fermented milk were analyzed both by standard methods and by the potentiometric sensor array. The results obtained by these methods were used for the development of neural network models for rapid estimation of aroma compounds content in probiotic fermented milk.The highest correlation (0.967) and lowest standard deviation of error for the training (0.585), selection (0.503) and testing (0.571) subset was obtained for the estimation of ethanol content. The lowest correlation (0.669) was obtained for the estimation of acetaldehyde content. The model exhibited poor performance in average error and standard deviations of errors in all subsets which could be explained by low sensitivity of the sensor array to the compound. The obtained results indicate that the potentiometric electronic tongue coupled with artificial neural networks can be applied as a rapid method for the determination of aroma compounds in probiotic fermented milk.     

基于BP算法的企业自我实现能力综合评价

针对企业需要的多层次及动态复杂性特征．结合企业需要层次理论。在建立企业自我实现能力指标体系的基础上。构造了基于改进的BP算法的企业自我实现能力综合测评模型。该模型具有较好的收敛速度和自适应能力。具有方便、准确、可靠、快速等特点。实证分析和仿真结果的比较表明该算法是有效的。     

Application of a Multilayer Perceptron Artificial Neural Network for the Prediction and Optimization of the Andrographolide Content in Andrographis paniculata

Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and selecting suitable sites for the cultivation of A. paniculata with high andrographolide content using a multilayer perceptron artificial neural network (MLP-ANN) approach. A total of 150 accessions of A. paniculata collected from different regions of Odisha and West Bengal in eastern India showed a variation in andrographolide content in the range of 0.28–5.45% on a dry weight basis. The MLP-ANN was trained using climatic factors and soil nutrients as the input layer and the andrographolide content as the output layer. The best topological ANN architecture, consisting of 14 input neurons, 12 hidden neurons, and 1 output neuron, could predict the andrographolide content with 90% accuracy. The developed ANN model showed good predictive performance with a correlation coefficient (R²) of 0.9716 and a root-mean-square error (RMSE) of 0.18. The global sensitivity analysis revealed nitrogen followed by phosphorus and potassium as the predominant input variables influencing the andrographolide content. The andrographolide content could be increased from 3.38% to 4.90% by optimizing these sensitive factors. The result showed that the ANN approach is reliable for the prediction of suitable sites for the optimum andrographolide yield in A. paniculata.