Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests

Boreal forests play an important role in global environment systems. Understanding boreal forest ecosystem structure and function requires accurate monitoring and estimating of forest canopy and biomass. We used partial least square regression （PLSR） models to relate forest parameters, i.e. canopy closure density and above ground tree biomass, to Landsat ETM＋ data. The established models were optimized according to the variable importance for projection （VIP） criterion and the bootstrap method, and their performance was compared using several statistical indices. All variables selected by the VIP criterion passed the bootstrap test （p〈0.05）. The simplified models without insignificant variables （VIP 〈1） performed as well as the full model but with less computation time. The relative root mean square error （RMSE%） was 29% for canopy closure density, and 58% for above ground tree biomass. We conclude that PLSR can be an effective method for estimating canopy closure density and above ground biomass.

Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved interval partial least squares

As important components of air pollutant,volatile organic compounds(VOCs)can cause great harm to environment and human body.The concentration change of VOCs should be focused on in real-time environment monitoring system.In order to solve the problem of wavelength redundancy in full spectrum partial least squares(PLS)modeling for VOCs concentration analysis,a new method based on improved interval PLS(iPLS)integrated with Monte-Carlo sampling,called iPLS-MC method,was proposed to select optimal characteristic wavelengths of VOCs spectra.This method uses iPLS modeling to preselect the characteristic wavebands of the spectra and generates random wavelength combinations from the selected wavebands by Monte-Carlo sampling.The wavelength combination with the best prediction result in regression model is selected as the characteristic wavelengths of the spectrum.Different wavelength selection methods were built,respectively,on Fourier transform infrared(FTIR)spectra of ethylene and ethanol gas at different concentrations obtained in the laboratory.When the interval number of iPLS model is set to 30 and the Monte-Carlo sampling runs 1000 times,the characteristic wavelengths selected by iPLS-MC method can reduce from 8916 to 10,which occupies only 0.22%of the full spectrum wavelengths.While the RMSECV and correlation coefficient(Rc)for ethylene are 0.2977 and 0.9999 ppm,and those for ethanol gas are 0.2977 ppm and 0.9999.The experimental results show that the iPLS-MC method can select the optimal characteristic wavelengths of VOCs FTIR spectra stably and effectively,and the prediction performance of the regression model can be significantly improved and simplified by using characteristic wavelengths.

Factors influencing the internet banking adoption decision in North Cyprus: an evidence from the partial least square approach of the structural equation modeling

Purpose:This paper aims to examine how the adoption decision of the internet banking in North Cyprus would be affected based on the following dimensions;the technology features,the personal characteristics,the social environment and the expected risk.Design/methodology/approach:A self-administered survey was conducted with 291 participants responded to it.The partial least square approach of the structural equation modeling(PLS-SEM)is employed to investigate the direct effects of the proposed factors on the adoption decision.Additionally,the mediation test is used to examine indirect effects.Findings:Results showed that even though the participants appreciated the benefits of the online banking as the perceived usefulness factor exerts the greatest direct effect,they would rather use clear and easy-to-use websites,adding to that their assessments of the usefulness of these services are significantly influenced by the surrounding people’s views and prior experience.This is demonstrated by the total effects of the perceived ease of use and the subjective norm factors,which are greater than the direct effect of the perceived usefulness factor since both of these factors have significant direct and indirect effects mediated by the perceived usefulness factor.The negative impact of the perceived risk factor is weak compared to the previous factors.While the personal innovativeness factor showed the weakest effect among the proposed factors.

Partial least square modeling of hydrolysis: analyzing the impacts of pH and acetate

pH and volatile fatty acids both might affect the further hydrolysis of particulate solid waste, which is the limiting-step of anaerobic digestion. To clarify the individual effects of pH and volatile fatty acids, batch experiments were conducted at fixed pH value （pH 5-9） with or without acetate （20 g/L）. The hydrolysis efficiencies of carbohydrate and protein were evaluated by carbon and nitrogen content of solids, amylase activity and proteinase activity. The trend of carbohydrate hydrolysis with pH was not affected by the addition of acetate, following the sequence ofpH 7〉pH 8〉pH 9〉pH 6〉pH 5; but the inhibition of acetate （20 g/L） was obvious by 10%-60 %. The evolution of residual nitrogen showed that the effect of pH on protein hydrolysis was minor, while the acetate was seriously inhibitory especially at alkali condition by 45%-100 %. The relationship between the factors （pH and acetate） and the response variables was evaluated by partial least square modeling （PLS）. The PLS analysis demonstrated that the hydrolysis of carbohydrate was both affected by pH and acetate, with pH the more important factor. Therefore, the inhibition by acetate on carbohydrate hydrolysis was mainly due to the corresponding decline of pH, but the presence of acetate species, while the acetate species was the absolutely important factor for the hydrolysis of protein.

DATA MODELING METHOD BASED ON PARTIAL LEAST SQUARE REGRESSION AND APPLICATIO N IN CORRELATION ANALYSIS OF THE STATOR BARS CONDITION PARAMETERS

Objective To investigate v arious data message of the stator bars condition parameters under the condition that only a few samples are available, especially about correlation information between the nondestructive parameters and residual breakdown voltage of the stat or bars. Methods Artificial stator bars is designed to simulat e the generator bars. The partial didcharge( PD) and dielectric loss experiments are performed in order to obtain the nondestructive parameters, and the residua l breakdown voltage acquired by AC damage experiment. In order to eliminate the dimension effect on measurement data, raw data is preprocessed by centered-compr ess. Based on the idea of extracting principal components, a partial least squar e (PLS) method is applied to screen and synthesize correlation information betwe en the nondestructive parameters and residual breakdown voltage easily. Moreover , various data message about condition parameters are also discussed. Re sults Graphical analysis function of PLS is easily to understand vario us data message of the stator bars condition parameters. The analysis Results ar e consistent with result of aging testing. Conclusion The meth od can select and extract PLS components of condition parameters from sample dat a, and the problems of less samples and multicollinearity are solved effectively in regression analysis.

Boosting the partial least square algorithm for regression modelling

Boosting algorithms are a class of general methods used to improve the general periormance of regression analysis. The main idea is to maintain a distribution over the train set. In order to use the given distribution directly, a modified PLS algorithm is proposed and used as the base learner to deal with the nonlinear multivariate regression problems. Experiments on gasoline octane number prediction demonstrate that boosting the modified PLS algorithm has better general performance over the PLS algorithm.

Model of Hot Metal Silicon Content in Blast Furnace Based on Principal Component Analysis Application and Partial Least Square

In blast furnace （BF） iron-making process, the hot metal silicon content was usually used to measure the quality of hot metal and to reflect the thermal state of BF. Principal component analysis （PCA） and partial least- square （PLS） regression methods were used to predict the hot metal silicon content. Under the conditions of BF rela- tively stable situation, PCA and PLS regression models of hot metal silicon content utilizing data from Baotou Steel No. 6 BF were established, which provided the accuracy of 88.4% and 89.2%. PLS model used less variables and time than principal component analysis model, and it was simple to calculate. It is shown that the model gives good results and is helpful for practical production.

Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

Spatter Rate Estimation of GMAW-S based on Partial Least Square Regression

This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode （GMAW-S） in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes： arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression （PLSR） is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.

Process Characterization of the Transesterification of Rapeseed Oil to Biodiesel Using Design of Experiments and Infrared Spectroscopy

For optimization of production processes and product quality,often knowledge of the factors influencing the process outcome is compulsory.Thus,process analytical technology(PAT)that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality.The present study aims at characterizing a well-known industrial process,the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters(FAME)for usage as biodiesel in a continuous micro reactor set-up.To this end,a design of experiment approach is applied,where the effects of two process factors,the molar ratio and the total flow rate of the reactants,are investigated.The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield.The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression.The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis.A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination(R^(2))of 0.9608.Thus,we applied a PAT approach to generate further insight into this established industrial process.

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

Simultaneous determination of several elements （U, Ta, Mn, Zr and W） with inductively coupled plasma atomic emission spectrometry （ICP-AES） in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network （ANN） and partial least squares regression （PLS） for simultaneous determination in different degrees of overlap was investigated. The emission spectra were recorded at uranium analytical line （263.553 nm） with a 0.06 nm spectral window by ICP-AES. Principal component analysis was applied to data and scores on 5 dominant principal components were subjected to ANN. A 5-5-5 （input, hidden and output neurons） network was used with linear transfer function after both hidden and output layers. The PI,S model was trained with five latent variables and 20 samples in calibration set. The relative errors of predictions （REP） in test set were 3.75% and 3.56% for ANN and PLS respectively.

A Comparison of CNN and PLSR for Glucose Monitoring Using Mid-Infrared Absorption Spectroscopy

With the development of mid-infrared (MIR) photoelectric devices, mid-infrared spectroscopy has become one of the important methods for non-invasive detection of blood glucose. The mid-infrared region (4000 - 400 cm-1) has the well-known fingerprint region (1200 - 800 cm-1) of glucose, which has clearer characteristic absorption peaks and better specificity. There is a lot of molecular information about glucose in the MIR. The non-invasive detection of blood glucose by mid-infrared spectroscopy needs to achieve certain accuracy, and the quantitative model is an important factor affecting the accuracy of glucose detection. In this paper, the samples of imitation solution containing only glucose and the samples of imitation mixed solution are taken as the research objects, and the mid-infrared spectral data of the samples are collected. The full spectrum partial least squares Regression (PLSR) model, SNV + Ctr-PLSR model, MSC + Ctr-PLSR model, and convolutional neural networks (CNN) model of 3000 - 900 cm-1 band were constructed. Full spectrum PLS model and CNN model of 1200 - 900 cm-1 band were constructed. The experimental results show that the optimal model of the two bands is CNN, then the correlation coefficient of prediction set (Rp) of 3000 - 900 cm-1 band is 0.95, and the root mean square error of pre-diction set (RMSEP) value is 22.10. The Rp of 1200 - 900 cm-1 band is 0.95, and the RMSEP value is 22.54. The research results show that CNN is a promising method, which has higher accuracy than PLSR, and is especially suitable for modeling human complex environment. In addition, the study provides a theoretical and practical basis for CNN in feature selection and model interpretation.

Evaluation model for freeway incident management system

In order to evaluate the general situation and find special problems of the freeway incident management system, an evaluation model is proposed. First, the expert appraisal approach is used to select the primary evaluation index. As a result, 81 indices and the hierarchical structures of the index such as the object layer, the sub-object layer, the criterion layer and the index layer are determined. Then, based on the fuzzy characteristics of each index layer, the analytical hierarchy process（AHP）and the fuzzy comprehensive evaluation are applied to generate the weight and the satisfaction of the index and the criterion layers. When analyzing the relationship between the sub-object layer and the object layer, it is easy to find that the number of sub-objects is too large and sub-objects are significantly redundant. The partial least square （PLS） is proposed to solve the problems. Finally, an application example, whose result has already been accepted and employed as the indication of a new project in improving incident management, is introduced and the result verifies the feasibility and efficiency of the model.

Measurement and analysis of soil nitrogen and organic matter content using near-infrared spectroscopy techniques

Near infrared reflectance （N1R） spectroscopy is as a rapid, convenient and simple nondestructive technique useful for quantifying several soil properties. This method was used to estimate nitrogen （N） and organic matter （OM） content in a soil of Zhejiang Province, Hangzhou County. A total of 125 soil samples were taken from the field. Ninety-five samples spectra were used during the calibration and cross validation stage. Thirty samples spectra were used to predict N and OM concentration. NIR spectra of these samples were correlated using partial least square regression. The regression coefficients between measured and predicted values of N and OM was 0.92 and 0.93, and SEP （standard error of prediction） were 3.28 and 0.06, respectively, which showed that NIR method had potential to accurately predict these constituents in this soil. The results showed that NIR spectroscopy could be a good tool for precision farming application.

Application of NIR spectroscopy for firmness evaluation of peaches

The use of near infrared （NIR） spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800-2500 nm by InGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts （including three latitudes and three longitudes） were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes （i, ii, iii） and latitudes （A, B, C）. For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression （PLSR） models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction （MSC） pretreatment, with correlation coefficient of cross-validation γcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of calibration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.

Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis

Powdery mildew （Blumeria graminis） is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis （CWA） was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features （SFs） were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression （MLR） analysis and the partial least square regression （PLSR） analysis were adopted as training methods in model mildew on leaf level were found to be closely related with the development. The spectral characteristics of the powdery spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed （R2=0.77, relative root mean square error RRMSE=0.28） the model composed by 14 optimal conventional SFs （R2---0.69, RRMSE--0.32） in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be promising in providing relatively accurate estimates of disease severity of powdery mildew on leaf level.

Voidage Measurement of Gas-Oil Two-phase Flow

A new method for the voidage measurement of gas-oil two-phase flow was proposed.The voidage measurement was implemented by the identification of flow pattern and a flow pattern specific voidage measure- ment model.The flow pattern identification was achieved by combining the fuzzy pattern recognition technique and the crude cross-sectional image reconstructed by the simple back projection algorithm.The genetic algorithm and the partial least square method were applied to develop the voidage measurement models.Experimental results show that the proposed method is effective.It can overcome the influence of flow pattern on the voidage measure- ment,and also has the advantages of simplicity and speediness.

Application of Wavelet Transform in the Prediction of Navel Orange Vitamin C Content by Near-Infrared Spectroscopy

This study was to search for an approach for rapid measurement of orange vitamin C （Vc） content. By using different decomposing levels of Daubechies 3 wavelet transform, the near-infrared spectra signals obtained from intact fruits of 100 navel orange samples were denoised, and the results of the predicted Vc contents for the corresponding samples determined by the reconstructed spectra after denoising were validated by means of PLS-CV （partial least squared-cross validation）. It was shown that the prediction effects verified by PLS-CV analysis varied when different wavelet transform decomposing levels were employed. At the wavelet decomposing level 4, the best prediction effect was obtained, with the correlation coefficient R between the prediction and true values being 0.9574 and the expected variance RMSECV being as low as 3.9 mg 100 g^-1. Furthermore, the 11 different approaches for the pretreatment of the near-infrared spectrum were compared. It was found that the calibration model established by PLS using spectra pretreated by wavelet transform denoising provided the best prediction for Vc content, exhibiting the highest correlation between the prediction and true values by cross validation. In conclusion, the near infrared spectral model denoised by means of wavelet transform can be used for accurate, rapid, and nondestructive quantitative analysis on navel orange Vc content.

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

Multi-loop Constrained Iterative Model Predictive Control Using ARX -PLS Decoupling Structure

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control(MPC) controller design in original space into the multi-loop single input single output(SISO) MPC controllers design in latent space.An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control(IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.