A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region

In several LUCC studies, statistical methods are being used to analyze land use data. A problem using conventional statistical methods in land use analysis is that these methods assume the data to be statistically independent. But in fact, they have the tendency to be dependent, a phenomenon known as multicollinearity, especially in the cases of few observations. In this paper, a Partial Least-Squares （PLS） regression approach is developed to study relationships between land use and its influencing factors through a case study of the Suzhou-Wuxi-Changzhou region in China. Multicollinearity exists in the dataset and the number of variables is high compared to the number of observations. Four PLS factors are selected through a preliminary analysis. The correlation analyses between land use and influencing factors demonstrate the land use character of rural industrialization and urbanization in the Suzhou-Wuxi-Changzhou region, meanwhile illustrate that the first PLS factor has enough ability to best describe land use patterns quantitatively, and most of the statistical relations derived from it accord with the fact. By the decreasing capacity of the PLS factors, the reliability of model outcome decreases correspondingly.

PARTIAL LEAST-SQUARES(PLS)REGRESSION AND SPECTROPHOTOMETRY AS APPLIED TO THE ANALYSIS OF MULTICOMPONENT MIXTURES

The UV absorption spectra of o-naphthol,α-naphthylamine,2,7-dihydroxy naphthalene,2,4-dimethoxy ben- zaldehyde and methyl salicylate,overlap severely;therefore it is impossible to determine them in mixtures by traditional spectrophotometric methods.In this paper,the partial least-squares(PLS)regression is applied to the simultaneous determination of these compounds in mixtures by UV spectrophtometry without any pretreatment of the samples.Ten synthetic mixture samples are analyzed by the proposed method.The mean recoveries are 99.4%,996%,100.2%,99.3% and 99.1%,and the relative standard deviations(RSD) are 1.87%,1.98%,1.94%,0.960% and 0.672%,respectively.

Characterizing and estimating rice brown spot disease severity using stepwise regression,principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

High-Dimensional Regression on Sparse Grids Applied to Pricing Moving Window Asian Options

The pricing of moving window Asian option with an early exercise feature is considered a challenging problem in option pricing. The computational challenge lies in the unknown optimal exercise strategy and in the high dimensionality required for approximating the early exercise boundary. We use sparse grid basis functions in the Least Squares Monte Carlo approach to solve this “curse of dimensionality” problem. The resulting algorithm provides a general and convergent method for pricing moving window Asian options. The sparse grid technique presented in this paper can be generalized to pricing other high-dimensional, early-exercisable derivatives.

Partial least squares regression for predicting economic loss of vegetables caused by acid rain

To predict the economic loss of crops caused by acid rain,we used partial least squares(PLS) regression to build a model of single dependent variable -the economic loss calculated with the decrease in yield related to the pH value and levels of Ca2+,NH4+,Na+,K+,Mg2+,SO42-,NO3-,and Cl-in acid rain. We selected vegetables which were sensitive to acid rain as the sample crops,and collected 12 groups of data,of which 8 groups were used for modeling and 4 groups for testing. Using the cross validation method to evaluate the performace of this prediction model indicates that the optimum number of principal components was 3,determined by the minimum of prediction residual error sum of squares,and the prediction error of the regression equation ranges from -2.25% to 4.32%. The model predicted that the economic loss of vegetables from acid rain is negatively corrrelated to pH and the concentrations of NH4+,SO42-,NO3-,and Cl-in the rain,and positively correlated to the concentrations of Ca2+,Na+,K+ and Mg2+. The precision of the model may be improved if the non-linearity of original data is addressed.

Machine learning of partial differential equations from noise data

Machine learning of partial differential equations(PDEs)from data is a potential breakthrough for addressing the lack of physical equations in complex dynamic systems.Recently,sparse regression has emerged as an attractive approach.However,noise presents the biggest challenge in sparse regression for identifying equations,as it relies on local derivative evaluations of noisy data.This study proposes a simple and general approach that significantly improves noise robustness by projecting the evaluated time derivative and partial differential term into a subspace with less noise.This method enables accurate reconstruction of PDEs involving high-order derivatives,even from data with considerable noise.Additionally,we discuss and compare the effects of the proposed method based on Fourier subspace and POD(proper orthogonal decomposition)subspace.Generally,the latter yields better results since it preserves the maximum amount of information.

稀疏偏最小二乘回归-多项式混沌展开代理模型方法

为解决传统多项式混沌展开方法在高维全局灵敏度和结构可靠度分析当中存在的维数灾难与多重共线性问题,该文提出一种稀疏偏最小二乘回归-多项式混沌展开代理模型方法。该方法首先采用偏最小二乘回归技术得到多项式混沌展开系数的初步估计,然后根据回归误差阈值允许下的最大稀疏度原则,采用带有惩罚的矩阵分解技术自适应地保留与结构响应相关性强的多项式,并采用偏最小二乘回归得到多项式混沌展开系数的更新估计。通过对展开系数进行简单后处理即可得到Sobol灵敏度指数。在此基础上保留重要输入变量并按新方法重新进行回归可实现对代理模型的精简,从而在不增加计算代价的情况下实现高精度结构可靠度分析。算例结果表明在保证精度的情况下,采用新方法进行全局灵敏度和结构可靠度分析比传统方法在计算效率方面有显著优势。

Multivariate analysis between meteorological factor and fruit quality of Fuji apple at different locations in China

China has the largest apple planting area and total yield in the world, and the Fuji apple is the major cultivar, accounting for more than 70％ of apple planting acreage in China. Apple qualities are affected by meteorological conditions, soil types, nutrient content of soil, and management practices. Meteorological factors, such as light, temperature and moisture are key environmental conditions affecting apple quality that are difficult to regulate and control. This study was performed to determine the effect of meteorological factors on the qualities of Fuji apple and to provide evidence for a reasonable regional layout and planting of Fuji apple in China. Fruit samples of Fuji apple and meteorological data were investigated from 153 commercial Fuji apple orchards located in 51 counties of 11 regions in China from 2010 to 2011. Partial least-squares regression and linear programming were used to analyze the effect model and impact weight of meteorological factors on fruit quality, to determine the major meteorological factors influencing fruit quality attributes, and to establish a regression equation to optimize meteorological factors for high-quality Fuji apples. Results showed relationships between fruit quality attributes and meteorological factors among the various apple producing counties in China. The mean, minimum, and maximum temperatures from April to October had the highest positive effects on fruit qualities in model effect loadings and weights, followed by the mean annual temperature and the sunshine percentage, the temperature difference between day and night, and the total precipitation for the same period. In contrast, annual total precipitation and relative humidity from April to October had negative effects on fruit quality. The meteorological factors exhibited distinct effects on the different fruit quality attributes. Soluble solid content was affected from the high to the low row preface by annual total precipitation, the minimum temperature from April to October, the mean temperature from April to October, the temperature difference between day and night, and the mean annual temperature. The regression equation showed that the optimum meteorological factors on fruit quality were the mean annual temperature of 5.5-18°C and the annual total precipitation of 602-1121 mm for the whole year, and the mean temperature of 13.3-19.6°C, the minimum temperature of 7.8-18.5°C, the maximum temperature of 19.5°C, the temperature difference of 13.7°C between day and night, the total precipitation of 227 mm, the relative humidity of 57.5-84.0％, and the sunshine percentage of 36.5-70.0％ during the growing period （from April to October）.

Optimizing process of preparing artificial-similar material for rocky slope with uniform formula design

The determination of material formula needs try-and-error experiment,and consumes large amount of time and fund.In order to solve the problem,a comprehensive method is established,via the experiment of artificial-similar material formula of a mine slope.We controlled the samples by the compactness,and arranged the formula of the test group with the method of the uniform formula experiment.The physical and mechanical parameters of these samples were analyzed using the method of the partial least-squares regression(PLS).And a mathematical model of the indexes of physical and mechanics parameters relating to the factors of formulation constituents was established eventually.We used the model to analyze the effect of each formulation constituent on physical and mechanics parameters of samples.The experiment results and analysis illustrates that1)in the formulation of similar material,the effect of raw materials on the internal friction angleφand cohesion C is opposite;2)The method can highly facilitate the process of the of preparing artificial-similar materials,more economic and effective.

Studies on a Novel Characteristic Atom-pair Holographic Code Applied to Quantitative Structure-chromatographic Retention Relationship of Organic Compounds

6 Atomic fragment types of organic compound have been defined, and the multilevel atom-pair frequency matrix has been constructed according to the occurrence number in pairs of atomic fragments with different bond lengths in the molecule. On the basis of them, a novel molecular coding technique： characteristic atom-pair holographic code （CAHC）, is obtained. To some extent, this method exhibits a large number of benefits at the same time. For example, it can calculate 2D molecular topological descriptor easily, operate without difficulty and possess definite physicochemical meaning of 3D molecular structural characterization methods, and may fetch the complicated information of molecule, etc. Therefore, it is appropriate for the study on quantitative structure-property/activity relationship （QSPR/QSAR） of medicines and biological molecules. We attempt in this paper to utilize the method of CAHC to the quantitative prediction of reversed-phase liquid chromatogram （RPLC） retention data of 33 purine derivatives and 24 steroids. The fitting multiple correlation coefficient R2, cross-validated multiple correlation coefficient Q2 and predicted ability Q^2 pred over test set＇s samples of obtained partial least-square （PLS） regression model are respectively 0.990, 0.893 and 0.977, 0.897, 0.941.

Simultaneous Determination of Verapamil Hydrochloride and Gliclazide in Synthetic Binary Mixture and Combined Tablet Preparation by Chemometric-Assisted Spectroscopy

In this study, the simultaneous determination of verapamil hydrochloride and gliclazide in pharmaceuticals by chemometric approaches using UV spectrophotometry has been reported. Verapamil hydrochloride (VER) (Benzeneacetonitrile, α-[3-[[2-(3,4-dimethoxyphenyl) ethyl] methylamino]propyl]-3, 4-dimethoxy-α-(1-methylethyl) hydrochloride) is an L-type calcium channel blocker of the phenylalkylamine class. It has been used in the treatment of hypertension, angina pectoris, and cardiac arrhythmia. Gliclazide (GLZ) (1-(Hexahydrocyclopenta[c]pyrrol-2(1H)-yl)-3-[(4-methylphenyl) sulphonyl]urea) is an oral hypoglycaemic (anti-diabetic) drug and is classified as a second generation sulfonylurea. Spectra of VER and GLZ were recorded at several concentrations within their linear ranges between wavelengths of 200 nm to 400 nm in 0.1N HCl. Partial least squares regression (PLS) and principle components regression (PCR) were used for chemometric analysis of data and the parameters of the chemometric procedures were optimized. The recoveries were satisfactory and statistically comparable. The method was successfully applied to pharmaceutical formulation, tablet, with no interference from excipients as indicated by the recovery study results. The proposed methods are simple, rapid and can be easily used in the quality control of drugs as alternative analysis tools.

基于迁移子空间学习的偏最小二乘回归软测量方法

针对流程工业中工况改变易导致当前样本与历史样本分布失配,传统软测量模型失准的问题,考虑工业数据时序性、动态性以及存在过程漂移等特性对建模的影响,提出一种基于迁移子空间学习的偏最小二乘回归软测量方法.首先,回归框架采用非线性迭代偏最小二乘方法,对其求解映射向量的目标函数施加基于子空间重构的域适应正则项,映射过程中保证当前工况中每个样本能够被历史工况样本线性重构.在此基础上对重构矩阵施加低秩稀疏约束,保持数据结构的同时使重构矩阵具备块状结构以应对过程漂移特性.将所提出方法在1个数值案例和3个不同的多工况数据集中进行实验,并与现有域适应回归方法进行对比分析.实验表明,所提出方法能够有效提高模型在跨工况条件下的预测精度,减少工况间数据分布差异对模型性能的影响.

基于(稀疏)偏最小二乘的老挝电力消费研究

为探讨老挝电力消费的影响因素,建立了偏最小二乘回归和稀疏偏最小二乘回归模型.首先用偏最小二乘从原始数据中提取最具解释能力的主成分,处理强相关电力数据;然后在此法基础上加入惩罚项,即用稀疏偏最小二乘筛选出重要变量和删除冗余信息;最后比较几种常见的方法的预测精度.研究结果表明:偏最小二乘较之常规的最小二乘回归等方法具有更高的预测精度,老挝国内生产总值、人口等因素对电力消费影响较大.

Spectroscopic measurement approaches in evaluation of dry rubber content of cup lump rubber using machine learning techniques

Dry rubber content(DRC)is an important factor to be considered in evaluating the quality of cup lump rubber.The DRC analysis requires prolonged laboratory validation.To develop fast and effective DRC determination methods,this study proposed methods to evaluate the DRC of cup lump rubber using different spectroscopic measurement approaches.This involved a complete fundamental analysis leading to an efficient measurement method based on either point-based measurement using NIR reflectance spectrometer or area-based measurement using hyperspectral imaging.A dataset was prepared that 120 samples were randomly divided into a calibration set of 90 samples and a validation set of 30 samples.To obtain an average spectrum to represent a cup lump rubber sample,the spectral data were collected by locating and scanning for point-based and area-based measurement,respectively.The spectral data were calibrated using partial least squares regression(PLSR)and the least-squares support vector machine(LS-SVM)methods against the reference values.The experiments showed that the area-based measurement approach with both algorithms performed outstandingly in predicting the DRC of cup lump rubber and was clearly better than the point-based measurement approach.The best predictions of PLSR represented by the coefficient of determination(R2),the root mean square error of prediction(RMSEP)and the residual predictive deviation(RPD)were 0.99,0.72%and 15.17,while the best prediction of LS-SVM were 0.99,0.64%and 16.83,respectively.In summary,the area-based measurement based on the LS-SVM prediction model provided a highly accurate estimate of the DRC of cup lump rubber.