Discrimination of Transgenic Rice Based on Near Infrared Reflectance Spectroscopy and Partial Least Squares Regression Discriminant Analysis

Near infrared reflectance spectroscopy （NIRS）, a non-destructive measurement technique, was combined with partial least squares regression discrimiant analysis （PLS-DA） to discriminate the transgenic （TCTP and mi166） and wild type （Zhonghua 11） rice. Furthermore, rice lines transformed with protein gene （OsTCTP） and regulation gene （Osmi166） were also discriminated by the NIRS method. The performances of PLS-DA in spectral ranges of 4 000-8 000 cm-1 and 4 000-10 000 cm-1 were compared to obtain the optimal spectral range. As a result, the transgenic and wild type rice were distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was 100.0% in the validation test. The transgenic rice TCTP and mi166 were also distinguished from each other in the range of 4 000-10 000 cm-1, and the correct classification rate was also 100.0%. In conclusion, NIRS combined with PLS-DA can be used for the discrimination of transgenic rice.

Estimating Wheat Grain Protein Content Using Multi-Temporal Remote Sensing Data Based on Partial Least Squares Regression

Estimating wheat grain protein content by remote sensing is important for assessing wheat quality at maturity and making grains harvest and purchase policies. However, spatial variability of soil condition, temperature, and precipitation will affect grain protein contents and these factors usually cannot be monitored accurately by remote sensing data from single image. In this research, the relationships between wheat protein content at maturity and wheat agronomic parameters at different growing stages were analyzed and multi-temporal images of Landsat TM were used to estimate grain protein content by partial least squares regression. Experiment data were acquired in the suburb of Beijing during a 2-yr experiment in the period from 2003 to 2004. Determination coefficient, average deviation of self-modeling, and deviation of cross- validation were employed to assess the estimation accuracy of wheat grain protein content. Their values were 0.88, 1.30%, 3.81% and 0.72, 5.22%, 12.36% for 2003 and 2004, respectively. The research laid an agronomic foundation for GPC （grain protein content） estimation by multi-temporal remote sensing. The results showed that it is feasible to estimate GPC of wheat from multi-temporal remote sensing data in large area.

Simultaneous Spectrophotometric Determination of Three Components Including Deoxyschizandrin by Partial Least Squares Regression

The computer auxiliary partial least squares is introduced to simultaneously determine the contents of Deoxyschizandin, Schisandrin, r-Schisandrin in the extracted solution of wuweizi. Regression analysis of the experimental results shows that the average recovery of each component is all in the range from 98.9% to 110.3% , which means the partial least squares regression spectrophotometry can circumvent the overlappirtg of absorption spectrums of mlulti-components, so that sctisfactory results can be obtained without any scrapple pre-separation.

Partial Least Squares Regression Model to Predict Water Quality in Urban Water Distribution Systems

The water distribution system of one residential district in Tianjin is taken as an example to analyze the changes of water quality.Partial least squares(PLS) regression model,in which the turbidity and Fe are regarded as control objectives,is used to establish the statistical model.The experimental results indicate that the PLS regression model has good predicted results of water quality compared with the monitored data.The percentages of absolute relative error(below 15%,20%,30%) are 44.4%,66.7%,100%(turbidity) and 33.3%,44.4%,77.8%(Fe) on the 4th sampling point;77.8%,88.9%,88.9%(turbidity) and 44.4%,55.6%,66.7%(Fe) on the 5th sampling point.

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study:principal components analysis vs.partial least squares

With recent advances in biotechnology, genome-wide association study （GWAS） has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression （LR） based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression （PC-LR）, partial least squares-based logistic regression （PLS-LR）, have recently gained much attention in the analysis of high dimensional genomic data. However, the perfor- mance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism （SNP） set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.

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.

Based on Partial Least-squares Regression to Build up and Analyze the Model of Rice Evapotranspiration

During the course of calculating the rice evapotranspiration using weather factors,we often find that some independent variables have multiple correlation.The phenomena can lead to the traditional multivariate regression model which based on least square method distortion.And the stability of the model will be lost.The model will be built based on partial least square regression in the paper,through applying the idea of main component analyze and typical correlation analyze,the writer picks up some component from original material.Thus,the writer builds up the model of rice evapotranspiration to solve the multiple correlation among the independent variables (some weather factors).At last,the writer analyses the model in some parts,and gains the satisfied result.

Quantum partial least squares regression algorithm for multiple correlation problem

Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.

A Novel Extension of Kernel Partial Least Squares Regression

Based on continuum power regression(CPR) method, a novel derivation of kernel partial least squares(named CPR-KPLS) regression is proposed for approximating arbitrary nonlinear functions.Kernel function is used to map the input variables(input space) into a Reproducing Kernel Hilbert Space(so called feature space),where a linear CPR-PLS is constructed based on the projection of explanatory variables to latent variables(components). The linear CPR-PLS in the high-dimensional feature space corresponds to a nonlinear CPR-KPLS in the original input space. This method offers a novel extension for kernel partial least squares regression(KPLS),and some numerical simulation results are presented to illustrate the feasibility of the proposed method.

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.

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.

The Consistency of LSE Estimators in Partial Linear Regression Models under Mixing Random Errors

In this paper,we consider the partial linear regression model y_(i)=x_(i)β^(*)+g(ti)+ε_(i),i=1,2,...,n,where(x_(i),ti)are known fixed design points,g(·)is an unknown function,andβ^(*)is an unknown parameter to be estimated,random errorsε_(i)are(α,β)-mix_(i)ng random variables.The p-th(p>1)mean consistency,strong consistency and complete consistency for least squares estimators ofβ^(*)and g(·)are investigated under some mild conditions.In addition,a numerical simulation is carried out to study the finite sample performance of the theoretical results.Finally,a real data analysis is provided to further verify the effect of the model.

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.

Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall

Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1st grid to 64th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

NIR Hyperspectral Imaging Measurement of Sugar Content in Peach Using PLS Regression

Near infrared （NIR） hyperspectral imaging measurement of sugar content in peach was introauced. NIR spectral images （650～1 000 nm, resolution： 2 nm） of peach samples were captured with developed hyperspectral imaging setup. Partial least square （PLS） regression prediction model was developed to estimate the sugar content in peach; step-wise backward method was utilized to determine optimal wavelength subsets. Experimental results show that the calibration model with optimal wavelength subsets has a correlation coefficient of prediction of 0.97 and a standard error of prediction of 0.19, the prediction accuracy is higher than the calibration model applied over the whole wavelength, which proves that variable selection plays an important role in improving the prediction accuracy of PLS regression model.

Short Term Electric Load Prediction by Incorporation of Kernel into Features Extraction Regression Technique

Accurate load prediction plays an important role in smart power management system, either for planning, facing the increasing of load demand, maintenance issues, or power distribution system. In order to achieve a reasonable prediction, authors have applied and compared two features extraction technique presented by kernel partial least square regression and kernel principal component regression, and both of them are carried out by polynomial and Gaussian kernels to map the original features’ to high dimension features’ space, and then draw new predictor variables known as scores and loadings, while kernel principal component regression draws the predictor features to construct new predictor variables without any consideration to response vector. In contrast, kernel partial least square regression does take the response vector into consideration. Models are simulated by three different cities’ electric load data, which used historical load data in addition to weekends and holidays as common predictor features for all models. On the other hand temperature has been used for only one data as a comparative study to measure its effect. Models’ results evaluated by three statistic measurements, show that Gaussian Kernel Partial Least Square Regression offers the more powerful features and significantly can improve the load prediction performance than other presented models.

基于高光谱成像技术的涌泉蜜桔糖度最优检测位置

本文旨在探索涌泉蜜桔糖度的最优检测位置和最佳预测模型,以便为蜜桔糖度检测分级提供理论依据。本文利用波长为390.2~981.3 nm的高光谱成像系统对涌泉蜜桔糖度最佳检测位置进行研究,将涌泉蜜桔的花萼、果茎、赤道和全局的光谱信息与其对应部位的糖度结合,建立其预测模型。使用标准正态变量变换(SNV)、多元散射校正(MSC)、基线校准(Baseline)和SG平滑(Savitzkv-Golay)4种预处理方法对不同部位的原始光谱进行预处理,用预处理后的光谱数据建立偏最小二乘回归(PLSR)和最小二乘支持向量机(LSSVM)模型。找出蜜桔不同部位的最佳预处理方式,对经过最佳预处理后的光谱数据采用竞争性自适应重加权算法(CARS)和无信息变量消除法(UVE)进行特征波长筛选。最后,用筛选后的光谱数据建立PLSR和LSSVM模型并进行分析比较。研究结果表明,全局的MSC-CARS-LSSVM模型预测效果最佳,其预测集相关系数Rp=0.955,均方根误差RMSEP=0.395,其次是蜜桔赤道部位的SNV-PLSR模型,其预测集相关系数Rp=0.936,均方根误差RMSEP=0.37。两者预测集相关系数相近,因此可将赤道位置作为蜜桔糖度的最优检测位置。本研究表明根据蜜桔不同部位建立的糖度预测模型的预测效果有所差异,研究最优检测位置和最佳预测模型可以为蜜桔进行糖度检测分级提供理论依据。

基于无人机多光谱NDVI值估测玉米产量

【目的】研究基于UAS-8无人机采集数据,运用归一化植被指数(Normalized Difference Vegetation Index)模型估测玉米产量,为大田无人机多光谱预测玉米产量提供理论依据。【方法】以新疆18份春播玉米为研究对象,获取开花期多光谱图像,经过辐射校正、大气校正、建立掩膜、提取NDVI图,计算植被覆盖率,得到区光谱反射率和归一化植被指数实际数值,将NDVI值与田间实测产量值进行模型拟合。【结果】幂函数Y=23411.46-10997.99/X(R^(2)=0.4886),二次函数为Y=39003.00-117963.03X+103130.25X 2(R^(2)=0.562),正反比函数(Inverse Proportional Function)为Y 2=2840.5 X/(1-X)(R^(2)=0.495),利用偏最小二乘回归(Partial Least Squares Regression),其线性函数Y=24458.22X-9620.55(R^(2)=0.521)。【结论】在数值0.5~0.8区间,NDVI与玉米产量具有较高的相关性,线性函数方程NDVI值可预测玉米的产量。

配料组成对高温芝麻饼粕蛋白酶解物制备肉味香精的影响

为分析配料(硫化物、糖)组成对高温芝麻饼粕蛋白酶解物(High temperature sesame cake protein hydrolysate,HTSPH)制备肉味香精的影响,首先固定木糖与HTSPH含量,考察5种硫化物如半胱氨酸(LCys)、甲硫氨酸(L-Met)、硫胺素(VB1)对美拉德反应产物(Maillard reaction products,MRPs)的感官特性、挥发性成分、pH、褐变与糖基化程度的影响,随后对比木糖、核糖、半乳糖等9种糖与L-Cys、HTSPH反应得到MRPs的特性差异,最后,混料试验考察L-Cys、木糖、HTSPH三者组成对MRPs感官特性与挥发性成分影响。结果显示,不同硫化物形成MRPs的挥发性成分组成差异明显,L-Cys的MRPs肉味突出,感官评价得分最高(69.32±1.34)。对比不同糖的MRPs的性质组成发现,戊糖的MRPs中感官评价得分显著高于已糖的MRPs,其挥发性成分中富含肉香味成分,木糖是适宜由HTSPH制作肉味香精的糖类。混料试验表明L-Cys、木糖与HTSPH组成会显著影响MRPs风味特征。偏最小二乘回归分析发现22种关键挥发性成分。5-甲基-2-噻吩甲醛、二糠基二硫醚、5-乙基噻吩-2-甲醛、4,6-二甲基-1H,3H-噻吩并[3,4-c]噻吩、苯甲醛、2-糠基2-甲基-3-呋喃基二硫化物、2-糠硫醇与感官得分正相关。本研究为高温芝麻饼粕制备肉味香精提供理论指导。