Principal Component Artificial Neural Network Research Articles

Nondestructive Detection of Pesticide Residue (Chlorpyrifos) on Bok Choi (Brassica rapa subsp. Chinensis) Using a Portable NIR Spectrometer Coupled with a Machine Learning Approach.

The contamination of agricultural products, such as vegetables, by pesticide residues has received considerable attention worldwide. Pesticide residue on vegetables constitutes a potential risk to human health. In this study, we combined near infrared (NIR) spectroscopy with machine learning algorithms, including partial least-squares discrimination analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and principal component artificial neural network (PC-ANN), to identify pesticide residue (chlorpyrifos) on bok choi. The experimental set comprised 120 bok choi samples obtained from two small greenhouses that were cultivated separately. We performed pesticide and pesticide-free treatments with 60 samples in each group. The vegetables for pesticide treatment were fortified with 2 mL/L of chlorpyrifos 40% EC residue. We connected a commercial portable NIR spectrometer with a wavelength range of 908-1676 nm to a small single-board computer. We analyzed the pesticide residue on bok choi using UV spectrophotometry. The most accurate model correctly classified 100% of the samples used in the calibration set in terms of the content of chlorpyrifos residue on samples using SVM and PC-ANN with raw data spectra. Thus, we tested the model using an unknown dataset of 40 samples to verify the robustness of the model, which produced a satisfactory F1-score (100%). We concluded that the proposed portable NIR spectrometer coupled with machine learning approaches (PLS-DA, SVM, and PC-ANN) is appropriate for the detection of chlorpyrifos residue on bok choi.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

Modulation format/bit rate recognition based on principal component analysis (PCA) and artificial neural networks (ANNs): erratum

The aim of this erratum is to add an omitted reference.

Development and Deployment of Organizational Culture Maturity Model (TMKC) for the Business Excellence Success

The paper describes the development of the maturity-based organisational culture model (TMKC - team bonding-motivation-knowledge management-commitment for improvement). Each level of the model has practices for the improvements. The model deployment in 11 different organisations indicates the earnings before interest, depreciation, taxes and amortisation (EBITDA) are significantly higher. Delphi method-based survey, dipstick survey and correlation analysis were applied to discover the extent of the problem. The cluster analysis, principal components analysis and artificial neural network were used to prioritise organisation-based cultural factors from 13 to 4. The impact of TMKC model was validated through pilots and statistical tests. The higher maturity levels of 4 and 5 have significantly increased business excellence (BE) initiative goal achievement rate from 34% to 86%. The successful TMKC model deployment positively impacts % EBITDA change. The model has been piloted in 11 cases and its finding can change with deployments in varied instances.

Prediction of wind drift and evaporation losses of a sprinkler irrigation system using principal component analysis and artificial neural network technique

Principal component analysis was merged with the artificial neural network (ANN) technique to predict wind drift and evaporation losses (WDEL) from a sprinkler irrigation system. For this purpose, field experiments were conducted to determine WDEL under different conditions. Data from field experiments and previous studies were used as sample data to train the ANN model. Three models were developed to predict WDEL. In the first model (ANN1), 9 neurons (riser height, main nozzle diameter, auxiliary nozzle diameter, discharge rate of the main nozzle, discharge rate of the auxiliary nozzle, operating pressure, wind speed, air temperature and relative humidity) were used as the input layer. In the second model (ANN2), 7 neurons (riser height, operating pressure, wind speed, air temperature and relative humidity, diameter ratio and discharge ratio) were used as the input layer. The third model (ANN3) used a multivariate technique (PC1, PC2, and PC3). Results revealed that the ANN3 model had the highest coefficient of determination (R2 = 0.8349). The R2 values for the ANN1 and ANN2 models were 0.7792 and 0.4807, respectively. It can be concluded that the ANN3 model has the highest predictive capacity.

Research on the Prediction Model of Transformer Bidding

Aiming at the problem of transformer manufacturing enterprises bidding is lacking scientific theoretical guidance and low bid probability, in order to predict the next bid price, based on principal component analysis (PCA) and artificial neural network (ANN) pre-tender estimate forecast model is proposed. The model uses PCA to preprocess the original high dimensional data, select principal components (PC) as the radial basis function (RBF) neural network's input. PCA eliminates the correlation of the input variables, at the same time of simplifying the structure of ANN, improving the accuracy of the prediction model. The simulation results show the applicability of the pre-tender estimate forecast model.

Reinforced Concrete Bridge Structural Health Classification using Hybrid Principal Component Analysis and Artificial Neural Network Approach with Wireless Sensor Network

Reinforced Concrete Bridge Structural Health Classification using Hybrid Principal Component Analysis and Artificial Neural Network Approach with Wireless Sensor Network

Classification of Chinese Herbal Medicine by Laser-Induced Breakdown Spectroscopy with Principal Component Analysis and Artificial Neural Network

ABSTRACTChinese herbal medicine has attracted increasing attention because of the unique and significant efficacy in various diseases. In this paper, three types of Chinese herbal medicine, the roots of Angelica pubescens, Codonopsis pilosula, and Ligusticum wallichii with different places of origin or parts, are analyzed and identified using laser-induced breakdown spectroscopy (LIBS) combined with principal component analysis (PCA) and artificial neural network (ANN). The study of the roots of A. pubescens was performed. The score matrix is obtained by principal component analysis, and the backpropagation artificial neural network (BP-ANN) model is established to identify the origin of the medicine based on LIBS spectroscopy of the roots of A. pubescens with three places of origin. The results show that the average classification accuracy is 99.89%, which exhibits better prediction of classification than linear discriminant analysis or support vector machine learning methods. To verify the effectiveness of PCA combined with the BP-ANN model, this method is used to identify the origin of C. pilosula. Meanwhile, the root and stem of L. wallichii are analyzed by the same method to distinguish the medicinal materials accurately. The recognition rate of C. pilosula is 95.83%, and that of L. wallichii is 99.85%. The results present that LIBS combined with PCA and BP-ANN is a useful tool for identification of Chinese herbal medicine and is expected to achieve automatic real-time, fast, and powerful measurements.

Determination of Volatile Organic Compounds Using Quartz Crystal Microbalances Coated with Hexamethyldisiloxane

ABSTRACTChemical sensors generally suffer from the cross-sensitivity problem. For this purpose, the identification of volatile organic compounds (VOCs) using unselective sensor requires a combination of sensors followed by pattern recognition methods. In the current study, four quartz crystal microbalances (QCMs) were coated by plasma-enhanced chemical vapor deposition (PECVD) with the pure vapor of hexamethyldisiloxane (HMDSO) at monomer pressures ranging from 5 to 40 Pa. The coated QCM-based sensors were used for the detection of volatile organic molecules. The sensor responses toward different VOCs showed the possibility to tune the chemical affinity of the sensors by changing only the monomer pressure during the sensitive layer deposition process. Fourier transform infrared (FTIR) spectroscopy and atomic force microscopy (AFM) were used to investigate chemical composition and surface morphology of the coated QCM sensors, respectively. The sensor responses have been used as a database for the identification of VOCs using pattern recognition methods such as principal component analysis (PCA) and artificial neural networks (ANNs). PCA is a linear method that allowed the identification of ethanol only among two other VOCs. Complete identification and accurate quantification of the studied VOCs have been achieved by the combination of PCA for data preprocessing and ANN for pattern recognition.

Exploring Quantitative Structure-Activity Relationships (QSARs) of Non-Tri cyclic Cyclooxygenase-2 (COX-2) Inhibitors by MLR and PC-ANN

Quantitative structure–activity relationship study using principal component artificial neural network (PC-ANN) methodology was conducted to predict the inhibitory activities expressed as pIC50 of 73 non-tri cyclic cyclooxygenase-2 (COX-2) inhibitors. The results obtained by MLR shows that the best two models are close to each other with regression coefficient of 0.85. These optimal models were further analyzed by PC-ANN and the best model obtained was with regression coefficient of 0.823 for the test set. The lowest prediction sum of squares (PRESS) value obtained for the prediction set is 4.727 which accounts for predictability of the model. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.

Credit Risk Assessment Model Based Using Principal component Analysis And Artificial Neural Network

Credit risk assessment for bank customers has gained increasing attention in recent years. Several models for credit scoring have been proposed in the literature for this purpose. The accuracy of the model is crucial for any financial institution’s profitability. This paper provided a high accuracy credit scoring model that could be utilized with small and large datasets utilizing a principal component analysis (PCA) based breakdown to the significance of the attributes commonly used in the credit scoring models. The proposed credit scoring model applied PCA to acquire the main attributes of the credit scoring data then an ANN classifier to determine the credit worthiness of an individual applicant. The performance of the proposed model was compared to other models in terms of accuracy and training time. Results, based on German dataset showed that the proposed model is superior to others and computationally cheaper. Thus it can be a potential candidate for future credit scoring systems.

QSAR Analysis of Some Antagonists for p38 map kinase Using Combination of Principal Component Analysis and Artificial Intelligence.

Quantitative relationships between structures of a set of p38 map kinase inhibitors and their activities were investigated by principal component regression (PCR) and principal componentartificial neural network (PC-ANN). Latent variables (called components) generated by principal component analysis procedure were applied as the input of developed Quantitative structure- activity relationships (QSAR) models. An exact study of predictability of PCR and PC-ANN showed that the later model has much higher ability to calculate the biological activity of the investigated molecules. Also, experimental and estimated biological activities of compounds used in model development step have indicated a good correlation. Obtained results show that a non-linear model explaining the relationship between the pIC50s and the calculated principal components (that extract from structural descriptors of the studied molecules) is superior than linear model. Some typical figures of merit for QSAR studies explaining the accuracy and predictability of the suggested models were calculated. Therefore, to design novel inhibitors of p38 map kinase with high potency and low undesired effects the developed QSAR models were used to estimate biological pIC50 of the studied compounds.

Performance optimization of gas refineries by ANN and DEA based on financial and operational factors

This study presents a combined artificial neural network (ANN) and multivariate approach for performance evaluation and optimization of gas refineries. This study introduces standard financial and non-financial indicators for performance evaluation of the gas refineries. Data are collected from gas balance sheets and the detailed statistics of gas refineries. Two cases have been considered for performance evaluation. In the first case the financial indicators and in the second case the financial and non-financial indicators are used and tested over five years period. The refineries are evaluated by data envelopment analysis (DEA), principal component analysis (PCA), numerical taxonomy and artificial neural network (ANN). Finally, a complete sensitivity analysis is performed for each stated method. The results show that DEA is more resistant to noise than other methods. Also, there is slight difference between results of financial and combined financial and operational indicators. This suggests the use of combined financial and operational indicators for future practical studies in gas refineries. This is the first study that presents an integrated approach for combined performance of financial and operational indicators in gas refineries.

Experimental design for simultaneous analysis of malachite green and methylene blue; derivative spectrophotometry and principal component-artificial neural network

In this study, oxidized multiwalled carbon nanotubes (MWCNT) with sizes in the range of 10–30 nm were efficiently applied for simultaneous and competitive adsorption of malachite green (MG) and methylene blue (MB).

Competitive adsorption of methylene blue and brilliant green onto graphite oxide nano particle following: Derivative spectrophotometric and principal component-artificial neural network model methods for their simultaneous determination

In this work, the competitive adsorption of methylene blue (MB) and brilliant green (BG) onto graphite oxide (GO) nanoparticles followed by their accurate and reproducible determination by second order derivative spectrophotometry (SODS) and principal component–artificial neural network model (PCA–ANN) model has been studied. The evaluation of kinetic and isotherm studies was investigated at optimum experimental conditions set as pH=7.0, 8mg of GO and 14min contact time in binary systems. The equilibrium amounts of MB and BG dyes in binary mixture adsorbed onto GO-NP has opposite correlation with their initial concentration. Principal component analysis (PCA) used to minimize the dimensionality of large data sets via reducing the number of spectral data by a three-layered feed-forward artificial neural network (ANN) trained by Levenberg–Marquardt back-propagation algorithm. The ANN model was able to predict the concentrations of both dyes in mixtures with a tangent sigmoid transfer function (tansig) at hidden layer with 20 neurons and a linear transfer function (purelin) at output layer. Several isotherm models were applied to experimental data and the isotherm constants were calculated for BG and MB dyes. Among the applied models, the extended Freundlich isotherm model adequately predicts the multi-component adsorption equilibrium data at moderate ranges of concentration.

Rapid and simultaneous determination of tetracycline and cefixime antibiotics by mean of gold nanoparticles-screen printed gold electrode and chemometrics tools

The screen-printed gold electrode (SPGE) modified with the formation of self-assembly monolayer (SAM) of cysteine (Cys) on gold-nanoparticles (Aunano) was utilized for rapid and simultaneous determination of tetracycline and cefixime antibiotics by square wave voltammetry (SWV). Electrochemical investigation and characterization of the modified electrode was achieved using cyclic voltammetry (CV) and scanning electron microscopy (SEM). A principal component artificial neural network (PCANN) with three layer back-propagation network was utilized for the analysis of the voltammogram data. It is possible to simultaneously determine the tetracycline and cefixime concentrations in the ranges of 10−5and 10−3molL−1, under the optimum conditions. Moreover the SPGE-Aunano-Cys biosensor together with chemometrics tools was successfully applied to the determination of tetracycline and cefixime in biological fluids, which may provide a promising alternative in routine biosensing applications.

Exploring QSARs of Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) Tyrosine Kinase Inhibitors by MLR, PLS and PC-ANN

Quantitative structure-activity relationship study was performed to understand the inhibitory activity of a set of 192 vascular endothelial growth factor receptor-2 (VEGFR-2) compounds. QSAR models were developed using multiple linear regression (MLR) and partial least squares (PLS) as linear methods. While principal component - artificial neural networks (PC-ANN) modeling method with application of eigenvalue ranking factor selection procedure was used as nonlinear method. The results obtained offer good regression models having good prediction ability. The results obtained by MLR and PLS are close and better than those obtained by principal component- artificial neural network. The best model was obtained with a correlation coefficient of 0.87. The strength and the predictive performance of the proposed models was verified using both internal (cross-validation and Y-scrambling) and external statistical validations.

Prediction of antihypertensive activity of pyridazinone derivatives through multivariate image analysis applied to QSAR

The antihypertensive activities of a series of pyridazinone derivatives have been modeled using an image-based approach for quantitative structure–activity relationships (MIA–QSAR). First, multivariate image analysis (MIA) descriptors were achieved from bidimensional images. Then, two regression methods were applied to correlate such descriptors with the antihypertensive activities: principal component regression (PCR) and principal component-artificial neural network (PC-ANN) with back-propagation learning algorithm, respectively. Correlation ranking procedure was used to rank the principal components and entered them into the models. The PC-ANN-based model for this series of compounds demonstrated higher predictive ability than the PCR-based model. The results supported that PCR model could predict the antihypertensive activity in pyridazinone derivatives with R2 ≥ 0.726 for training set and R2 ≥ 0.622 for validation set, whereas nonlinear method PC-ANN could predict the antihypertensive activity with R2 ≥ 0.970 for training set and with R2 ≥ 0.997 and R2 ≥ 0.982 for validation and test sets, respectively. The obtained results indicated that MIA descriptors may be useful to predict antihypertensive activity of pyridazinone derivatives.

The Design of New Soft Sensors Based on PCA and a Neural Network for Parameters Estimation of a Petroleum Reservoir

A new set of soft sensors is presented based on principal component analysis (PCA) and artificial neural network (ANN) methodologies for parameters estimation of a petroleum reservoir. The crude diagrams of reservoir parameters provide valuable evaluation for petrophysical parameters. These parameters, however, are usually difficult to measure due to limitations on cost reliability considerations, inappropriate instrument maintenance, and sensor failures. PCA is utilized to develop new soft sensors to incorporate reliability and prediction capabilities of ANN. For this purpose, a PCA model is derived to reconstruct a parameter from other reservoir parameters using their redundancy relations. The developed soft sensors are applied to reconstruct parameters of Marun reservoir located in Ahwaz, Iran, by utilizing the available geophysical well log data. The experimental results demonstrate that the proposed hybrid PCA-NN algorithm is able to reveal a better performance than the PCA and the conventional back propagation–based NNs.

Forecasting postage traffic using principal component analysis and artificial neural network

Predicting postage traffic and enhancing factors which affect postage traffic are some of the main concerns in postal services. In this paper, we present a combined approach based on principal component analysis (PCA) and artificial neural networks (ANN) for forecasting postage traffic. Due to the large available data set size, PCA is used to reduce the dimension and the required principal components (PCs) are selected based on the given rules. These PCs are then treated as inputs of an ANN to make forecasts for the postage traffic. Real data of Islamic Republic of Iran Postal Company is used to compare the results of the proposed model with that of the traditional regression model. Key words: Forecasting, artificial neural networks, principal component analysis.