紫外可见光谱结合多元分析法在线检测混合染料溶液化学需氧量

为了实现对经过污水处理后的染整废水化学需氧量(COD)的在线监测,同时为了克服国标标准法和快速分光光度法快速在线检测COD的诸多不足,进行了紫外可见光谱结合多元分析方法检测混合染液COD的研究。研究样本为自行配制的混合染液,通过自主研发的紫外可见光纤传感检测系统检测混合染液的紫外可见光谱。该检测系统可实现对较高浓度溶液在线原位检测,而无取样、稀释过程。通过标准化(z-score)、平滑(smoothing)、一阶导数(1st derivation)等方法对原始光谱进行预处理并结合主成分回归(PCR)、偏最小二乘回归法(PLS)等对混合染液建立吸光度-COD的回归预测模型,并利用该回归模型预测预测集COD值。实验结果表明,针对混合染料溶液样本,在PCR算法中,采用3种光谱预处理方法,其中标准化法得到的PCR回归模型预测精度最高,决定系数R2=0.961,预测均方根误差(root mean square error of prediction,RMSEP)=21.8。进一步研究发现,使用标准化法预处理结合PLS算法得到PLS回归模型预测精度(R2=0.974,RMSEP=19.6)高于PCR模型。说明针对该样本,利用标准化法进行光谱预处理后,再建立PLS回归模型,能够比较快速准确地进行混合染液COD预测。同时也说明该自行研制的检测系统能够适用在线检测染液COD。该研究可以为进一步实现在线原位检测染整废水COD以及其他水质参数奠定基础。     

近红外光谱定量分析的改进ELM算法

极限学习机理论(extreme learning machine, ELM)作为一种新的化学计量学方法,在近红外光谱定量分析中的应用研究,已引起学术界的高度重视。然而,由于光谱数据维数较高,建立ELM模型时需要大量的隐节点,导致隐含层输出矩阵维数高且存在高度共线性,用现有的Moore-Penrose广义逆算法求取隐含层输出矩阵与待测性质间的回归模型往往会存在病态问题。基于ELM建立光谱波长变量与性质之间的回归模型,提出以ELM模型隐含层输出矩阵作为新的变量,采用作者最新提出的基于变量投影重要性的改进叠加PLS算法(stacked partial least squares regression algorithm based on variable importance in the projection,VIP-SPLS),建立新变量与待测性质间的回归模型。VIP-SPLS算法充分利用了每个隐节点的输出信息,能有效解决高维共线性问题,同时具有模型集成的优点,从而改进了ELM模型的性能。将提出的改进ELM算法(improved ELM,iELM)应用于标准近红外光谱数据集,结果表明iELM模型的精度相对于现有的PLS模型和ELM模型分别显著提升了29.06%和27.47%。     

紫花苜蓿粗蛋白和粗纤维近红外分析模型的建立

采用近红外漫反射光谱技术,结合偏最小二乘法(PLS),以152个来源不同的紫花苜蓿样品建立了粗蛋白和粗纤维含量的近红外定量分析校正模型。在近红外光谱范围内(950～1 650 nm)对紫花苜蓿样品采集光谱数据时,分别设置了粗磨样、细磨样两种样品的状态和1,2,5 nm三种光谱扫描间隔,对建立的模型进行准确性和重复性的验证,比较其优劣。结果显示：光谱扫描时样品为细磨样,光谱扫描间隔为2 nm时所建立的粗蛋白和粗纤维含量的校正模型最佳,其相关系数(R2_cal)分别是0.97和0.94,最佳因素数时的定标标准差(SECV)分别是0.42和0.78。所建近红外定量分析模型对独立检验集样品粗蛋白和粗纤维含量的预测值与化学值的相关系数(R2_val)分别为0.96和0.92,预测标准差(SEP)分别为0.43和0.79。该研究结果表明：利用近红外漫反射光谱法测定紫花苜蓿内在主要品质性状是可行的,为紫花苜蓿粗蛋白和粗纤维含量的检验提供了新的方法模式。     

工作曲线法和偏最小二乘回归分析在XRF定量分析软玉样品中的应用

由于块状固体标准玉石样品的缺乏, 造成了便携式X射线荧光分析技术(pXRF)利用工作曲线法对玉石文物样品进行无损定量分析的困难。试图寻找一种pXRF可采用, 但不需要块状固体玉石标准样品的定量分析方法。选取24件软玉样品, 其中17件为校准样品, 7件为测试样品。所有软玉样品利用质子激发X射线荧光分析技术(PIXE)获得定量分析结果。根据校准软玉样品的PIXE定量分析结果建立兴趣元素的工作曲线, 利用工作曲线对测试软玉样品进行定量分析;然后, 利用pXRF对所有软玉样品进行定性分析, 获得其定性分析图谱, 利用校准软玉样品的定性分析图谱和PIXE定量分析结果, 采用最小偏二乘法对测试软玉样品兴趣元素含量进行分析。最后, 将工作曲线法、PLS方法和PIXE的定量分析结果进行了相互对比。通过误差分析, 评估了工作曲线法和PLS方法定量分析软玉样品的精确度。结果表明, PLS方法可以代替工作曲线法对玉石类样品进行定量分析。     

激光诱导击穿光谱对U元素的定量分析

铀矿是核领域最重要的矿产资源之一,快速、有效勘探铀矿资源能促进核领域平稳、健康发展。激光诱导击穿光谱(LIBS)技术具备多目标元素现场快速检测的优点,能实现铀矿资源准确、快速的现场分析。本工作基于LIBS技术对铀矿中U元素进行了定量分析,对比了偏最小二乘(PLS)和随机森林(RF)两种机器学习算法的定量效果。结果显示,RF模型的定量线性相关系数为0.996,对三个验证集的相对误差分别是22.33%、12.79%和12.04%;PLS模型的定量线性相关系数为0.997,对三个验证集的相对误差分别是4.33%、6.63%和6.85%。对比结果表明,本研究中的PLS模型定量准确度更高,同RF算法相比,PLS算法更适用于铀矿中U的LIBS定量分析。     

辐照处理对油菜籽光谱特性影响的研究

针对油菜籽经过核辐照处理后其光谱反射特性会发生改变的特点,提出了应用可见／近红外光谱技术进行油菜籽的快速无损鉴别。利用偏最小二乘法和BP神经网络建立鉴别模型,并比较了不同光谱预处理方法、主成分数据变换方式及隐含层节点数对模型预测结果的影响。实验采用五种剂量辐照(50, 100, 150, 200Gy和不经核辐照处理)的油菜籽共135个样本进行建模,49个进行预测。结果显示,最优模型是原始光谱数据先经过中值滤波平滑法、附加散射校正及一阶求导法预处理。经PLS方法提取6个主成分经自然对数变换后,选取神经网络隐含层结点数为4个或9个。最优模型对是否经过核辐照处理的样本识别率达100%,对核辐照剂量预测精度为85.71%, 说明提出的方法可以用于评估核辐照处理对油菜籽光谱特性产生的明显影响。     

Blood species identification using Near-Infrared diffuse transmitted spectra and PLS-DA method

Blood species identification is of great significance for blood supervision and wildlife investigations. The current methods used to identify the blood species are destructive. Near-Infrared spectroscopy method is known for its non-invasive properties. In this research, we combined Near-Infrared diffuse transmitted spectra and Partial Least Square Discrimination Analysis (PLS-DA) to identify three blood species, including macaque, human and mouse. Blind test and external test were used to assess the PLS-DA model. The model performed 100% accuracy in its identification between three blood species. This approach does not require a specific knowledge of biochemical features for each individual class but relies on a spectroscopic statistical differentiation of the whole components. This is the first time to demonstrate Near-Infrared diffuse transmitted spectra have the ability to identify the species of origin of blood samples. The results also support a good potential of absorption and scattering spectroscopy for species identification in practical applications for real-time detection.     

Exploring Omics data from designed experiments using analysis of variance multiblock Orthogonal Partial Least Squares

Many experimental factors may have an impact on chemical or biological systems. A thorough investigation of the potential effects and interactions between the factors is made possible by rationally planning the trials using systematic procedures, i.e. design of experiments. However, assessing factors' influences remains often a challenging task when dealing with hundreds to thousands of correlated variables, whereas only a limited number of samples is available. In that context, most of the existing strategies involve the ANOVA-based partitioning of sources of variation and the separate analysis of ANOVA submatrices using multivariate methods, to account for both the intrinsic characteristics of the data and the study design. However, these approaches lack the ability to summarise the data using a single model and remain somewhat limited for detecting and interpreting subtle perturbations hidden in complex Omics datasets.     

Interpreting evidential distances by connecting them to partial orders: Application to belief function approximation

Distances between mass functions are instrumental tools in evidence theory, yet it is not always clear in which situation a particular distance should be used. Indeed, while the mathematical properties of distances have been well studied, how to interpret them is still a largely open issue. As a step towards answering this question, we propose to interpret distances by looking at their compatibility with partial orders. We formalize this compatibility through some mathematical properties thereby allowing to combine the advantages of both partial orders (clear semantics) and distances (richer structure and access to numerical tools). We explore in particular the case of informational partial orders, and how distances compatible with such orders can be used to approximate initial belief functions by simpler ones through the use of convex optimization. We finish by discussing some perspectives of the current work.     

Solubilities and partial molar volumes of three bis(2-ethoxyethyl) ethanedioate derivatives in supercritical carbon dioxide

Three new CO₂-philic bis(2-ethoxyethyl) ethanedioate derivatives, bis(2-(2-methoxyethoxy)ethyl) ethanedioate (compound 1), bis(2-(2-ethoxyethoxy)ethyl) ethanedioate (compound 2), and bis(2-(2-butoxyethoxy)ethyl) ethanedioate (compound 3), were designed and synthesized and their solubilities in supercritical carbon dioxide (scCO₂) were then measured at different temperatures (313, 323, 333) K over the pressure range (8.9 to 13.9) MPa. The measured solubility data were correlated with five different theoretical semi-empirical models (Chrastil, kJ, SS, MST, JCF) and satisfactory agreements were obtained. The JCF model provided the best fit and the lowest average absolute relative deviation (AARD) varied from (3.58 to 6.80)%. Furthermore, the partial molar volumes of three compounds in the supercritical phase were also calculated according to the Kumar and Johnston theory, and the values were between −(19105 and 3510) cm³ · mol⁻¹.