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1.
基于高光谱数据的叶面积指数监测是快速获取冬小麦叶面积指数的重要方法。为了探究回归方法和高光谱数据变换对冬小麦叶面积指数反演精度的影响,采用逐步回归和偏最小二乘回归方法,分别建立基于冬小麦拔节期冠层高光谱数据、一阶导数光谱数据、二阶导数光谱数据和对数光谱数据的叶面积指数多元线性回归模型。结果显示,导数和对数变换能够提高冬小麦LAI反演精度,以蓝紫光、绿光、红光和近红外波段建立的一阶导数光谱数据逐步回归模型最优,建立回归模型的决定系数R2为0.974,交叉验证的RMSE为0.131,可为冬小麦LAI估算的方法选择和数据处理提供依据和参考。  相似文献   
2.
In this work, investigations dealing with the determination of hydrocarbons in contaminated soil water are presented. The hydrocarbons under investigation range from low to high volatility compounds. A GC‐FID method was developed that due to its efficiency, routine suitability, relative rapidity, and low cost is suitable for the analysis of complex chemical mixtures of highly volatile hydrocarbons (with boiling points between 69 and 190°C). The standard used was a gasoline mixture with boiling points ranging from 100 to 190°C. For this standard, no supplementary preparation is needed and it is suitable for the whole range of hydrocarbons under investigation. The determination of the hydrocarbon content of the samples was performed applying univariate and multivariate statistical analysis to the experimental data. In the characterization of a contamination with highly volatile hydrocarbons of soil water originating from different depth layers from the chemistry location Leuna (Sachsen‐Anhalt, Germany), the advantages of a multivariate method are demonstrated in exemplary manner.  相似文献   
3.
In 2013, Chang'E-3 program will develop lunar mineral resources in-situ detection. A Visible and Near-infrared Imaging Spectrometer (VNIS) has been selected as one payload of CE-3 lunar rover to achieve this goal. It is critical and urgent to evaluate VNIS' spectrum data quality and validate quantification methods for mineral composition before its launch. Ground validation experiment of VNIS was carried out to complete the two goals, by simulating CE-3 lunar rover's detection environment on lunar surface in the laboratory. Based on the hyperspectral reflectance data derived, Correlation Analysis and Partial Least Square (CA-PLS) algorithm is applied to predict abundance of four lunar typical minerals (pyroxene, plagioclase, ilmenite and olivine) in their mixture. We firstly selected a set of VNIS' spectral parameters which highly correlated with minerals' abundance by correlation analysis (CA), and then stepwise regression method was used to find out spectral parameters which make the largest contri- butions to the mineral contents. At last, functions were derived to link minerals' abundance and spectral parameters by partial least square (PLS) algorithm. Not considering the effect of maturity, agglutinate and Fe~, we found that there are wonderful correlations between these four minerals and VNIS' spectral parameters, e.g. the abundance of pyroxene correlates positively with the mixture's absorption depth, the value of absorption depth added as the in- creasing of pyroxene's abundance. But the abundance of plagioclase correlates negatively with the spectral parame- ters of band ratio, the value of band ratio would decrease when the abundance of plagioclase increased. Similar to plagioclase, the abundance of ilmenite and olivine has a negative correlation with the mixture's reflectance data, if the abundance of ilmenite or olivine increase, the reflectance values of the mixture will decrease. Through model validation, better estimates of pyroxene, plagioclase and ilmenite's abundances are given. It is concluded that VNIS has the capability to be applied on lunar minerals' identification, and CA-PLS algorithm has the potential to be used on lunar surface's in-situ detection for minerals' abundance prediction.  相似文献   
4.
The Non-linear lterative Partial Least Squares(NIPALS)algorithm is used in principal componentanalysis to decompose a data matrix into score vectors and eigenvectors(loading vectors)plus a residualmatrix.N1PALS starts with some guessed starting vector.The principal components obtained by NIPALSdepends on the starting vector;the first principal component could not always be computed.Wold hassuggested a starting vector for NIPALS,but we have found that even if this starting vector is used,thefirst principal component cannot be obtained in all cases.The reason why such a situation occurs isexplained by the power method.A simple modification of the original NIPALS procedure to avoid gettingsmaller eigenvalues is presented.  相似文献   
5.
A statistical study of the dependence between various critical fusion temperatures of a certain kind ofcoal and its chemical components is carried out.As well as using classical dependence techniques(multiple,stepwise and PLS regression,principal components,canonical correlation,etc.)together withthe corresponding inference on the parameters of interest,non-parametric regression and bootstrapinference are also performed.  相似文献   
6.
For the calibration of chromatographic systems,different methods can be used.One class of methodsutilizes three-way approaches.The calibration problem is stated in such a way that the decompositionof a three-way array can serve for the prediction of retention on new stationary phases.Two three-way approaches are presented:the Unfold-PCA and PARAFAC models.The theory ofboth methods is presented and the differences are highlighted,the main difference being that PARAFACis a trilinear decomposition whereas Unfold-PCA is not.Both three-way methods are evaluated on asmall data set consisting of retention measurements of eight solutes at six mobile phase compositions onsix stationary phases.The differences in performance of the two models are minor,For calibration purposes,two variants of the methods are discussed:three-way PLS and an extensionof PARAFAC.Again the theory and differences between the two methods are explained.The predictiveperformance of the two methods is compared using the same data set as earlier.The differences inpredictive performance,however,are minor.Both methods are capable of predicting 98% of thevariation in the test sets.Yet,there are other considerations when comparing methods than predictiveperformance,e.g.the quality of the predictions.  相似文献   
7.
龙梅  裴世桥 《岩矿测试》2004,23(1):6-10
利用偏最小二乘法回归的多变量校正方式,建立了应用近红外反射光谱学方法无损快速测定各种地质样品中有机质的模型.设计了多重散射光校正、标准正常变量转换及导数光谱,扣除额外基线和重叠信号的影响,分离出与有机质含量有关的光谱信息.大多数地质样品的有机质近红外反射光谱估算结果与化学法符合.  相似文献   
8.
The standard deviation of prediction errors(SDEP)is used to evaluate and compare the predictive abilityof some regression models,namely MLR,ACE and linear and non-linear PLS,the last being the bestone.The parameter is determined by a cross-validation approach as an average of several runs obtainedon forming groups in a random way.The variation in SDEP with the number of latent variables in PLSis also discussed.  相似文献   
9.
A general framework for manipulating spectra as functions in traditional multivariate methods such asPCA and PLS is described.The functional representation is very convenient for compression,ensuringsmoothness and continuity.There are two fundamentally different types of representations:(a)byfunctions and(b)by function coefficients.The use of coefficients is the most practical way of analysis.  相似文献   
10.
The use of a new multi model integration method of Partial Least Squares regression (PLS) can completely eliminate the multicollinearity features to improve multi model’s integrated forecasting results of the humidity and temperature. Based on the four centers’ ensemble forecast results, namely, the European Center for Medium-Range Weather Forecasts (ECMWF), Chinese Meteorological Administration (CMA), the Japan Meteorological Agency (JMA) and the UK Met Office (UKMO), we built a 2012 multi mode (25°~60°N, 60°~150°E) 24 ~168 hours forecast time (interval 24 hours) multi model for humidity and temperature and used the four methods, like ensemble average (BREM) for eliminating the deviation, a simple set of average (EMN), Super Ensemble (SUP) and Partial Least Squares regression (PLS) for ground temperature multi model integration. We used the Root-Mean-Square Error (RMSE) and anomaly correlation coefficient (cor) to determine the effect of more modes of integration and to predict a short course of cold. The two prediction results showed that the Partial Least Squares regression (PLS) was the best multi model integrated method, more superior than the other three single modes and compared with the other three methods, it showed better prediction performance, which has certain value and application prospect.  相似文献   
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