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.

Correlation analysis and partial least square modeling to quantify typical minerals with Chang'E-3 visible and near-infrared imaging spectrometer's ground validation data

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 contributions 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 Fe0, 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 increasing of pyroxene's abundance. But the abundance of plagioclase correlates negatively with the spectral parameters 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.

Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration(ROP) of a tunnel boring machine(TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares(PLS) regression and support vector machine(SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and peak slope index(PSI), and also rock mass properties including distance between planes of weakness(DPW) and the alpha angle(α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination(R2) and root mean squares error(RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.

Comparison of Calibration Curve Method and Partial Least Square Method in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

The Laser Induced Breakdown Spectroscopy (LIBS) is a fast, non-contact, no sample preparation analytic technology;it is very suitable for on-line analysis of alloy composition. In the copper smelting industry, analysis and control of the copper alloy concentration affect the quality of the products greatly, so LIBS is an efficient quantitative analysis tech- nology in the copper smelting industry. But for the lead brass, the components of Pb, Al and Ni elements are very low and the atomic emission lines are easily submerged under copper complex characteristic spectral lines because of the matrix effects. So it is difficult to get the online quantitative result of these important elements. In this paper, both the partial least squares (PLS) method and the calibration curve (CC) method are used to quantitatively analyze the laser induced breakdown spectroscopy data which is obtained from the standard lead brass alloy samples. Both the major and trace elements were quantitatively analyzed. By comparing the two results of the different calibration method, some useful results were obtained: both for major and trace elements, the PLS method was better than the CC method in quantitative analysis. And the regression coefficient of PLS method is compared with the original spectral data with background interference to explain the advantage of the PLS method in the LIBS quantitative analysis. Results proved that the PLS method used in laser induced breakdown spectroscopy was suitable for simultaneous quantitative analysis of different content elements in copper smelting industry.

基于MW-MKEPLS的多重时变间歇生产过程质量预测

间歇生产过程的多重时变特性和非线性使得质量预测问题变得复杂。为了提高间歇过程质量预测精度,提出了滑动窗多向核熵偏最小二乘(moving window multiway kernel entropy partial least squares,MW-MKEPLS)方法。首先采用滑动窗进行数据的动态更新获取,构建了滑动窗多重时变模型;然后在滑动窗多重时变模型下通过核函数将数据映射到高维特征空间,采用Renyi熵贡献度进行数据特征提取,更好地获取数据的信息熵和非线性;最后在KECA处理后的高维特征空间进行质量预测。通过青霉素生产发酵过程进行了实验验证,并与MKPLS和MKEPLS进行对比分析,结果表明所提方法的质量预测精度更高。

Factors Affecting Box Office during Broad Spring Festival Based on Partial Least Squares Regression

The box office during the later Spring Festival shows an attractive prospect.This paper studied the factors affecting total box office during the broad Spring Festival which is from the Spring Festival to the Lantern Festival.Data of films released during the broad Spring Festival from the years 2016 to 2019 in China were gathered,and the impact of eight explanatory variables on the box office during the broad Spring Festival was empirically analyzed by partial least squares(PLS)regression with software SIMCA.The results suggest that word-of-mouth has the most positive effect on the box office during the broad Spring Festival.Later propaganda has a positive effect,while early promotion has a negative effect on the box office.Director’s influence has a positive effect,while actor’s influence does not contribute much to the box office.Length of the trailer has a negative effect.The film format of 2D or 3D doesn’t contribute much to the box office.

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.

偏最小二乘法(PLS)及其在分析化学中的应用

本文介绍了一种性能优于其他多元统计方法的新的多元校准法——偏最小二乘法(PLS),研究了其理论基础、算法和性能,评述了其在吸收光谱分析、荧光分光光度分析、ICP—AES、色谱分析、核磁共振谱分析、生物化学、产品质量预测、毒理学、环境化学和地球化学研究等方面的应用以及应用发展动向。引用文献58篇。

PLS-BP法近红外光谱定量分析研究

建立BP模型用于近红外光谱定量分析时,为克服所建模型与训练样本集产生“过拟合”,先用线性算法为其压缩训练数据是必要的。目前多采用主成分法(PCA)和逐步回归法(SRA)。主成分法具有极强的压缩数据能力,用它压缩成的主成分输入BP网所建模型的预测精度一般能满足要求,但它处理数据时未考虑输出变量的影响。逐步回归法根据系统输出选择变量,但所选变量具有自相关性,而且与训练集样品的排列顺序有关,很难选出最好的变量,往往难满足预测精度要求。本研究用偏最小二乘法(PLS),根据输出变量将原始数据压缩为主成分,输入BP网并用所建模型预测30个小麦样品的蛋白质含量。结果表明,与PCA-BP模型的预测决定系数(R2)从92.50提高到97.10,训练迭代次数从12 000减少到4 500。

PLS回归法分析多因素对卷烟燃烧温度及主流烟气有害成分释放量的影响

为了考察卷烟材料(卷烟纸、接装纸、成型纸和滤棒)和膨胀梗丝多因素作用对卷烟燃烧锥最高温度以及焦油、烟碱和主流烟气中7种有害成分(CO,HCN,NNK,NH3,B[a]P,苯酚和巴豆醛)释放量的影响,采用偏最小二乘(PLS)回归法建立了卷烟燃烧锥最高温度以及焦油、烟碱和主流烟气中7种有害成分释放量的多因素预测模型。结果表明:1卷烟燃烧锥最高温度的主要影响因素为卷烟纸助燃剂含量。随卷烟纸助燃剂含量增加,卷烟燃烧锥最高温度有降低趋势。2焦油、烟碱和主流烟气7种有害成分释放量的主要影响因素为接装纸透气度。随接装纸透气度增加,焦油、烟碱和主流烟气7种有害成分释放量有降低趋势。3随卷烟纸助燃剂和膨胀梗丝含量的增加,焦油、烟碱及主流烟气7种有害成分的释放量有降低趋势。适当增加接装纸透气度、卷烟纸助燃剂含量和膨胀梗丝含量,可在一定程度上调控卷烟燃烧锥温度,降低卷烟烟气有害成分释放量。

PLS-DA法判别分析木材生物腐朽的研究

利用近红外光谱结合PLS-DA判别分析方法可用于食品、药品和农产品等的快速识别或检测,因此,研究利用近红外光谱结合PLS-DA方法来检测木材的生物腐朽。研究结果表明:应用近红外光谱结合PLS-DA方法对培训集样本建立的判别模型,其校正及验证结果与实际分类变量的相关系数均超过0.94,SEC和SEP都低于0.17;利用模型对未参与建模的样本进行检测,发现该模型对未腐朽、白腐和褐腐三种类型样本的判别准确率均为100%(偏差均小于0.5);与SIMCA法相比,PLS-DA法对木材生物腐朽样本的判别准确率更高,说明应用近红外光谱结合PLS-DA方法能快速地检测到木材的生物腐朽,并能准确地判别出木材的生物腐朽类型。

PLS法在同时分光光度测定痕量金属离子中的应用

本文研究了新显色剂4-(2-苯并噻唑偶氮)邻苯二酚-Mo(Ⅵ)、W(Ⅶ)-二苯胍三元配合物的高灵敏显色反应以及偏最小二乘法(PLS)在同时分光光度测定痕量钼和钨中的应用。与共轭梯度法和线性规划法相比,PLS法的运行时间短,结果更准确可靠,尤其适用于处理成批试样,为带微处理机的分光光度计提供了一种新的计算方法。

近红外光谱-BP神经网络-PLS法用于橄榄油掺杂分析

橄榄油兼有食用和保健的作用,价值与价格远远高于其他食用油,所以橄榄油中以劣充好的现象十分普遍。可采用近红外光谱法测定初榨橄榄油中掺杂芝麻油、大豆油和葵花籽油的光谱数据,运用改进的BP算法——Levenberg-Marquardt方法,建立PCA-BP人工神经网络方法对其进行定性判别。同时采用偏最小二乘法(PLS)建立了初榨橄榄油中芝麻油、大豆油、葵花籽油含量的近红外光谱定标模型,用交互验证法进行验证。结果表明,BP人工神经网络有很好的定性鉴别能力,PLS建立的芝麻油、大豆油、葵花籽油定标模型的相关系数分别为98.77,99.37,99.44,交叉验证的均方根误差分别为1.3,1.1,1.04。该方法无损、快速、简便,为橄榄油掺杂的检测提供了一种新的途径。

PLS-BPN法用于7005铝合金力学性能与工艺参数的定量研究

用偏最小二乘法(PLS)结合反向传播人工神经网络(BPN)方法对7005铝合金力学性能与工艺参数之间的关系进行定性分析和计算。结果表明:用PLS法对实验数据作模式识别优化处理的结果与实验很吻合,能够指明该合金工艺参数优化的方向;用BPN定量计算的结果与实验测定值符合也较好;将PLS与BPN法有机地联系起来,有利于克服过拟合,提高BPN预报的准确性。用留一(LOO)交叉验证法分别对3种模型PLS、BPN和PLS-BPN的合金性能预报结果进行验证,其中PLS-BPN模型预测的均方根误差(RMSE)和平均相对误差(MRE)均最低,更适合于7005铝合金性能预报。

非线性迭代PLS信息模式识别算法

对偏最小二乘(PLS)回归的基本方法进行了分析研究,提出了基于非线性迭代偏最小二乘(NIPLS)的信息模式识别算法。该算法实现了模式识别中特征提取与分类器设计的有机结合。NIPLS较Fisher判别分析、Bayes判别分析等经典的模式识别算法,具有更强的信息识别能力,且对数据本身的分布要求不高,尤其对于多重共线性资料或解释变量多而样本数量少时更为有效。将该算法应用于土地质量的分类识别,结果表明,该文所建立的算法是有效的、可靠的。

荧光光谱PLS法同时测定氨基酸混合物

根据酪氨酸、色氨酸和苯丙氨酸具有荧光 ,且荧光光谱相互重叠的性质 本文用偏最小二乘法 (PLS)解析 3种氨基酸的荧光光谱 ,克服了组分之间的重叠及非线性干扰 ,建立了一种新的同时测定 3种氨基酸分析方法 用于混合样中色氨酸、酪氨酸和苯丙氨酸的分析 。

基于PLS算法的生物质秸秆元素分析NIRS快速检测

为探讨利用近红外光谱技术快速检测生物质秸秆中N、C、H、S和O 元素的可行性,采集并制备水稻、小麦、油菜和玉米秸秆样本199个,采用近红外光谱（NIRS）分析技术,结合偏最小二乘（PLS）化学计量学算法,在7400-5550cm^-1波段范围内,比较不同光谱预处理方法的定标效果,建立最优的生物质秸秆中N、C、H、S和O 元素的定量分析模型,并用独立的验证集样本对模型进行验证.验证结果表明所建立的N元素的定量分析模型可用于实际检测;O 元素的定量分析模型可进行实际估测;采用近红外技术用于C元素定量分析是可行的,但模型需要进一步优化;H、S元素采用NIRS技术无法进行定量分析.

基于iPLS原理最优化信息区间的桃糖度组合权重PLS模型研究

采用反向区间偏最小二乘法和组合区间偏最小二乘法优化桃糖度可见/近红外光谱的信息区间组合,在选择的信息区间的基础上建立了一种线性组合权重PLS模型.对近红外光谱进行二阶导数处理、卷积平滑校正后,发现在区间分割数为15时筛选结果最优,BiPLS所选择的信息区间为742～770nm和862～920nm,SiPLS所选择的信息区间为742～770nm、832～860nm和892～920nm.直接组合信息区间BiPLS和SiPLS模型的RMSEP值分别为0.386和0.308,线性组合权重PLS模型的RMSEP值分别为0.351和0.364.结果说明在近红外定量分析中线性组合权重模型的建立克服了复杂样品各信息区间对PLS建模贡献率不一样的问题.

基于RPLS的造纸废水处理过程软测量建模

偏最小二乘(PLS)软测量预测模型在预测造纸废水处理过程中的出水化学需氧量(COD_(Cr))和固体悬浮物(SS)时,易受过程非线性特性和系统外部干扰等因素的影响而失效。针对以上问题,研究了递归偏最小二乘(RPLS)算法的造纸废水处理过程软测量建模。计算结果表明,采用PLS模型预测出水CODCr时,平均绝对百分比误差(MAPE)、均方根误差(RMSE)和相关系数(R^2)分别为5.3832%、4.6878和0.5892;采用RPLS模型预测时,MAPE、RMSE、R^2分别为1.3861%、1.8792和0.9221。采用PLS模型预测SS时,MAPE、RMSE和R^2分别为2.5962%、0.7412和0.6651;采用RPLS模型时MAPE、RMSE、R2分别为0.6795%、0.2198和0.9627。以上结果表明,RPLS预测模型比PLS预测模型具有更好的预测性能和更高的精度。

基于PLS-LDA和拉曼光谱快速定性识别食用植物油

以6种食用油共计23个样本为分析对象,采用偏最小二乘线性判别分析法(PLS-LDA)和拉曼光谱进行单一种类(橄榄油、花生油和玉米油)食用油快速定性检测,通过自适应迭代惩罚最小二乘法(airPLS)对拉曼信号进行背景扣除,以及蒙特卡洛无信息变量消除法筛选波长变量,不但有效减少了波长点数,降低了建模运算量,而且提高了单一种类食用油的识别率,使得总体识别率均高于90%,并在此基础上进一步提出了采用PLS-LDA进行多种类食用油识别的检测流程。实验结果表明PLS-LDA在食用油定性识别检测中具有较好的应用前景和可行性,该方法也可为定性检测食品及农产品品质提供借鉴。