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.

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.

Microbiomics and metabolomics insights into the microbial regulation on the formation of flavor components in the traditional fermentation process of Chinese Hongqu aged vinegar

This study aimed to investigate microbial succession and metabolic dynamics during the traditional fermentation of Hongqu aged vinegar,and explore the core functional microbes closely related to the formation of flavor components.Microbiome analysis demonstrated that Lactobacillus,Acetobacter,Bacillus,Enterobacter,Lactococcus,Leuconostoc and Weissella were the predominant bacterial genera,while Aspergillus piperis,Aspergillus oryzae,Monascus purpureus,Candida athensensis,C.xylopsoci,Penicillium ochrosalmoneum and Simplicillium aogashimaense were the predominant fungal species.Correlation analysis revealed that Acetobacter was positively correlated with the production of tetramethylpyrazine,acetoin and acetic acid,Lactococcus showed positive correlation with the production of 2-nonanone,2-heptanone,ethyl caprylate,ethyl caprate,1-hexanol,1-octanol and 1-octen-3-ol,C.xylopsoci and C.rugosa were positively associated with the production of diethyl malonate,2,3-butanediyl diacetate,acetoin,benzaldehyde and tetramethylpyrazine.Correspondingly,non-volatile metabolites were also detected through ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.A variety of amino acids and functional dipeptides were identified during the traditional brewing of Hongqu aged vinegar.Correlation analysis revealed that Lactobacillus was significantly associated with DL-lactate,indolelactic acid,D-(+)-3-phenyllactic acid,pimelic acid,pregabalin and 3-aminobutanoic acid.This study is useful for understanding flavor formation mechanism and developing effective strategies for the suitable strains selection to improve the flavor quality of Hongqu aged vinegar.

Near-Infrared Spectroscopy Combined with Partial Least Squares Discriminant Analysis Applied to Identification of Liquor Brands

The identification of liquor brands is very important for food safety. Most of the fake liquors are usually made into the products with the same flavor and alcohol content as regular brand, so the identification for the liquor brands with the same flavor and the same alcohol content is essential. However, it is also difficult because the components of such liquor samples are very similar. Near-infrared (NIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA) was applied to identification of liquor brands with the same flavor and alcohol content. A total of 160 samples of Luzhou Laojiao liquor and 200 samples of non-Luzhou Laojiao liquor with the same flavor and alcohol content were used for identification. Samples of each type were randomly divided into the modeling and validation sets. The modeling samples were further divided into calibration and prediction sets using the Kennard-Stone algorithm to achieve uniformity and representativeness. In the modeling and validation processes based on PLS-DA method, the recognition rates of samples achieved 99.1% and 98.7%, respectively. The results show high prediction performance for the identification of liquor brands, and were obviously better than those obtained from the principal component linear discriminant analysis method. NIR spectroscopy combined with the PLS-DA method provides a quick and effective means of the discriminant analysis of liquor brands, and is also a promising tool for large-scale inspection of liquor food safety.

基于PCA-HCA联合PLS回归模型的蚯蚓粪肥品质等级划分

蚯蚓粪肥理化特性涉及指标多,如何从众多易检测的指标中筛选出能够反映蚯蚓粪肥特点的关键指标,进而用于构建评价模型,高效、快速地评价蚯蚓粪肥的品质等级,是蚯蚓粪肥应用前亟需解决的重要问题与难点。研究针对不同原料类型、不同蚯蚓堆肥时间获得的蚯蚓粪肥,采用统计学与化学计量学对蚯蚓粪肥23个主要指标开展描述统计与相关分析,筛选出了13个蚯蚓粪肥特异性指标。以13个关键指标为基础,首先,结合主成分分析(PCA)与分层聚类分析(HCA)对不同蚯蚓粪肥样品开展品质初级划分;其次,采用偏最小二乘回归(PLS)-判别分析(DA)对分级结果进行效果判定;最后,整体构建基于PLS模型的蚯蚓粪肥等级评价方法并开展验证分析。结果表明:PCA与HCA分析法可将蚯蚓粪肥划分为3个品质等级,通过PLS-DA判别该划分结果合理有效,形成了基于PLS蚯蚓粪肥等级评价模型:蚯蚓粪肥品质等级(Y)=3.0796+0.0026×TOC-0.1381×HS-0.1446×HA-0.1378×TN-0.1355×TP-0.1494×AK-0.1324×AN-0.1402×AP+0.0004×EOC+0.03985×ROC+0.07685×C/N-0.0049×Kos-0.1481×HI(TOC、HS、HA、TN、TP、AK、AN、AP、EOC、ROC、C/N、Kos、HI分别代表总有机碳、腐殖质碳、胡敏酸、总氮、总磷、速效钾、碱解氮、有效磷、易氧化有机碳、难氧化有机碳、碳氮比、氧化稳定系数、腐殖化指数),分级标准为:若Y在0.45~1.56之间,品质等级为一等品;Y在1.63~2.20之间,为二等品;Y在2.28~3.72之间,为三等品。变量权重值表明影响蚯蚓粪肥品质前5的关键指标顺序为HI>TN>HS>HA>AN。研究成功建立了一套“PCA+HCA+PLS”的蚯蚓粪肥品质评价方法,对蚯蚓粪肥分级应用与规范蚯蚓产业市场具有重要意义。

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.

基于PLS-DA和LS-SVM的可见/短波近红外光谱鉴定港种四九、十月红和九月鲜菜心种子的可行性研究

目前市面上菜心的品种复杂,不同菜心种子的品质与发芽率不同,但菜心种子单从外观上差别不大,因此区分菜心种子的类别成为了一大难题。为了实现菜心种子类别的快速区分,探究了基于可见/短波近红外光谱分析菜心种子类别的可行性。从南昌市种子交易场所购买了港种四九、十月红和九月鲜三个品种的菜心种子,从中挑选出品相较好且大小适中的子粒,将每种菜心种子均匀分为30份,按照2∶1划分为建模集和预测集,所有样本共计90份。通过近红外光谱仪获取采样间隔为1 nm的菜心种子的光谱反射率,波长覆盖范围325~1075 nm,将原始光谱数据采用多元散射校正(MSC)、卷积平滑(S-G)和标准正态变换(SNV)三种预处理方法进行预处理,预处理后的光谱变量建立偏最小二乘回归(PLSR)模型,确定了SNV是最佳预处理方法。采用主成分分析(PCA)对菜心种子进行了聚类分析,从前三个主成分因子(PCs)得分图可知三种菜心种子存在光谱特征差异。将原始光谱变量、前三个PCs(累计贡献97.15%)和基于随机蛙跳(RF)算法挑选的13个特征波长作为偏最小二乘判别(PLS-DA)和最小二乘支持向量机(LS-SVM)模型的输入变量,从模型结果可知:三种输入变量中,采用RF筛选特征波长作为模型输入变量时,模型预测效果最好,PCs建立的模型最差,相比于PCA分析,采用RF筛选出的特征波长更能够反映原始光谱信息。比较不同模型预测效果,LS-SVM模型比PLS-DA模型得到的预测精度更好,其中RF-LS-SVM模型是所有模型中最佳的预测模型,建模集和预测集均为100%。采用可见/短波近红外光谱研究菜心种子的类别可行,并且能够获得很好地预测效果,为菜心种子的快速区分提供了理论依据。

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。

基于iPLS-KPCA的高温燃气红外光谱特征提取方法研究

高温燃气红外光谱特征是判断燃气成分和浓度的有效途径。针对高温燃气红外辐射特性复杂、建模难度高的问题,研究了一种基于间隔偏最小二乘(interval Partial Least Squares,iPLS)和核主成分分析(Kernel Principal Component Analysis,KPCA)的特征提取算法。首先通过iPLS进行预筛选,确定具有最优预测能力的特征光谱波段,避免单个子区间建模过程中有用吸收峰信息的遗失;其次,利用KPCA降低数据维度,保留贡献率高的关键特征,降低成分预测模型的复杂度。仿真结果表明,经过iPLS-KPCA方法特征提取后,预测模型的复杂度大幅下降,且预测能力显著提升。

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.

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。

基于KPLS模型的间歇过程产品质量控制

针对间歇过程所具有的非线性特性,提出了一种基于核偏最小二乘(KPLS)模型的最终产品质量控制策略。利用初始条件、批次展开后的过程数据以及最终产品质量建立了间歇过程的KPLS模型;采用基于主成分分析(PCA)映射的预估方法对未知的过程数据进行补充,实现了最终产品质量的在线预测。为了解决最终产品质量的控制,利用T2统计量确定KPLS模型的适用范围,并作为约束引入产品质量控制问题,提高控制策略的可行性;采用粒子群优化(PSO)算法实现了优化问题的高效求解。仿真结果表明,与基于偏最小二乘(PLS)模型的控制策略相比,所提出的方法具有更高的预测精度,且能有效解决产品质量控制中出现的各种问题。

基于PLS的信息特征压缩算法

提出了基于偏最小二乘 (PLS)方法的信息特征压缩算法 较主成分分析 (PCA)方法 ,该算法具有简单、稳健、易于定性解释等优点 ,对于多重共线性资料 ,尤其当解释变量多 ,而样本量少时很有效 由于在考虑压缩数据矩阵X的信息的同时 ,顾及了与目标矩阵Y的最大相关性等优点 ,使之更符合实际 数值实例研究表明 ,文中算法是可行的、有效的 。

基于近红外光谱和OPLS-DA的不同牌号卷烟分类识别方法研究

为了对卷烟牌号进行准确分类鉴别,提出了一种基于近红外光谱(NIRS)分析技术结合有监督的模式识别快速鉴别卷烟牌号的新方法。利用标准正态变量变换(SNV)、多元散射校正(MSC)、一阶导数(FD)、二阶导数(SD)和Savitzky-Golay平滑(SG)及其相结合的光谱预处理方法对烟丝光谱进行预处理,通过近红外光谱结合主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)和正交偏最小二乘判别分析(OPLS-DA)3种模式识别方法对不同牌号烟丝进行分类识别研究,并采用分类识别正确率作为评价指标。实验结果表明:(1)烟丝近红外光谱主成分得分图交叉重叠,区分不明显,PCA无法识别出5种牌号的成品烟丝;(2)烟丝光谱经MSC+FD预处理后的PLS-DA模型可得到较好的识别效果,校正集和测试集的分类识别正确率分别为100%和98.3%;(3)烟丝光谱经MSC+SD预处理后的OPLS-DA模型的模式识别效果最好,模型对自变量拟合指数(R2X),因变量的拟合指数(R2Y)和模型预测指数(Q2)分别为0.485、0.907和0.748,近红外光谱校正集和测试集的分类识别正确率均为100%。说明近红外光谱技术结合有监督模式识别方法OPLS-DA建立的烟丝牌号分类模型具有高效快速、准确无损的优点,为卷烟烟丝分类提供了一种新的快速鉴别方法。

DPLS和SVM的掺假花椒粉近红外光谱定性鉴别

花椒是我国的八大调味料之一。目前花椒市场掺假现象较为多见,为实现掺假花椒粉的快速定性鉴别,采用了近红外光谱结合化学计量学方法进行了探讨。将麦麸粉、稻糠粉、玉米粉和松香粉以1 Wt/Wt.%的递增梯度分别掺入红花椒粉和青花椒粉中,制备掺假浓度范围为1~54 Wt/Wt.%的掺假花椒粉样品,以掺假花椒粉和纯花椒粉共462份样品依次采集其800~2 500nm范围的漫反射近红外光谱。采用主成分分析法(PCA)对光谱数据进行分析,前3个主成分累计贡献率达98.72%,做出的得分图表明PCA法对掺假的花椒粉具有较好的区域划分。347份样本作为校正集,以特征谱区2 000~2 200nm范围的257个采样点的光谱信号作为输入,采用判别偏最小二乘法(DPLS)和支持向量机(SVM)建立定性鉴别模型,经不同光谱预处理,对115份验证集样本进行预测,总体鉴别正确率在97.39%~100%之间,表明该方法是快速定性鉴别掺假花椒粉的一个有效手段。

基于过程数据的动态PLS建模

许多工业过程中获得的输入输出数据在时间、空间上是高度相关的,且含有测量噪声。针对此类数据,提出采用动态PLS方法,最大化输入和输出变量矩阵之间的协方差,保留大部分有用信息,去除测量噪声,把高维数据空间降维,建立较为精确的工业过程数学模型。提出对MIMO系统进行研究,推导了可直接用于控制的动态PLS模型数学公式。对一个工业过程实例进行仿真,分别应用动态PLS回归和线性回归MLR方法,得出的结果经分析验证了此算法的有效性。

基于PLS的热连轧过程故障检测与诊断

为了解决在生产过程中进行故障检测和诊断问题,讨论了基于主元分析(PCA)和偏最小二乘(PLS)的过程故障检测与诊断的原理,运用T^2统计、Q统计方法结合贡献图对轧钢过程进行了分析。结果表明,PLS方法比PCA方法能更好地检测与诊断故障。

HPLC指纹图谱技术结合PLS-DA在养阴清肺颗粒质量控制中的应用

目的:建立养阴清肺颗粒的HPLC指纹图谱,结合偏最小二乘法-判别分析(PLS-DA),为其质量控制提供参考。方法:采用ZORBAX Eclipse XDB-C18色谱柱(250 mm×4.6 mm,5μm);流动相乙腈(A)-0.12%磷酸水溶液(B)梯度洗脱;体积流量:0.9 ml·min^(-1);分段检测波长为230 nm(检测芍药苷、甘草苷)、280 nm(检测麦冬甲基黄烷酮A)、254 nm(检测丹皮酚)、210 nm(检测梓醇、哈巴苷及哈巴俄苷)、330 nm(检测毛蕊花糖苷);柱温:30℃;进样量:10μl。用中药色谱指纹图谱相似度评价系统(2012A版)建立指纹图谱共有模式和相似度计算,用SIMCA14.1软件建立PLS-DA模型做统计分析。结果:建立12批养阴清肺颗粒的指纹图谱,共确定20个共有指纹峰,通过与对照品指认了8个成分;12批样品指纹图谱相似度为0.959~0.982,通过聚类分析(CA)可将12批样品聚成3类,结合主成分分析(PCA)、偏最小二乘法-判别分析(PLS-DA)发现8个成分是造成不同批次样品差异性的主要标记物。结论:该研究建立指纹图谱方法有助于养阴清肺颗粒整体质量控制,同时为其质量评价提供一种有效手段。

基于PCR和PLSR的油气两相流空隙率测量

本文利用12电极ECT系统提供的电容测量信息,基于主成分回归(PCR)和偏最小二乘回归(PLSR)技术,提出了一种油气两相流空隙率测量的新方法。该方法针对不同的油气两相流流型,分别基于PCR和PLSR建立两组空隙率测量模型。实际测量时,根据流型辨识结果分别选用相应的基于PCR的空隙率测量模型和基于PLSR的空隙率测量模型,通过计算获得空隙率值并进行对比。研究结果表明,本文提出的空隙率测量方法是有效的,两组空隙率测量模型的测量结果精度相当,在典型流型下的测量误差均在5%以内,但基于PLSR技术所获空隙率测量模型的降维效果优于基于PCR技术的相应模型。同时,由于该方法直接利用ECT系统提供的电容测量值来建立空隙率测量模型,省去了以往ECT系统测量空隙率所必需的复杂费时的图像重建过程,提高了空隙率测量的实时性,整个测量时间小于0.1s。

改进的PLS-NIR光谱法非破坏定量分析Norvasc药片

本文用改进的偏最小二乘法(PLS)解析Norvasc药片的近红外(NIR)漫反射光谱,实现了对其中的有效成分苯磺酸氨氯地平的精确的非破坏定量分析。用样品的主成分得分趋势图选择PLS的最佳主成分数。分别讨论了最佳波长范围以及NIR原始光谱、NIR一阶导数光谱、NIR二阶导数光谱对测定结果的影响。用HPLC法的测定结果作标准,苯磺酸氨氯地平预测值的相对误差RE(%)<2.53%,可以满足Norvasc药片实际生产中的质量控制要求。