Study of adsorption processes of model drugs at supercritical conditions using partial least squares regression

The characterisation of adsorption or impregnation processes using conventional or supercritical fluid technologies becomes an increasing part of the research on drug formulations. The complexity of the relationships between these adsorption processes and the experimental variables potentially influencing them, however, makes these studies more problematic. In this paper, a chemometric approach based on nonlinear partial least squares (NL-PLS) modelling is applied to characterise the effect of the experimental variables on the supercritical impregnation process. Various adsorbent materials such as silica gel, zeolite and amberlite were investigated using the following model compounds as adsorbates: benzoic, salicylic and acetylsalicylic acids.     

Cross fitted partial least squares (CF‐PLS): an alternative algorithm for a more reliable PLS

This paper presents a modified version of the NIPALS algorithm for PLS regression with one single response variable. This version, denoted a CF‐PLS, provides significant advantages over the standard PLS. First of all, it strongly reduces the over‐fit of the regression. Secondly, R² for the null hypothesis follows a Beta distribution only function of the number of observations, which allows the use of a probabilistic framework to test the validity of a component. Thirdly, the models generated with CF‐PLS have comparable if not better prediction ability than the models fitted with NIPALS. Finally, the scores and loadings of the CF‐PLS are directly related to the R², which makes the model and its interpretation more reliable. Copyright © 2011 John Wiley & Sons, Ltd.     

On estimating model complexity and prediction errors in multivariate calibration: generalized resampling by random sample weighting (RSW)

The present paper focuses on determining the number of PLS components by using resampling methods such as cross validation (CV), Monte Carlo cross validation (MCCV), bootstrapping (BS), etc. To resample the training data, random non‐negative weights are assigned to the original training samples and a sample‐weighted PLS model is developed without increasing the computational burden much. Random weighting is a generalization of the traditional resampling methods and is expected to have a lower risk of getting an insufficient training set. For prediction, only the training samples with random weights less than a threshold value are selected to ensure that the prediction samples have less influence on training. For complicated data, because the optimal number of PLS components is often not unique or readily distinguished and there might exist an optimal region of model complexity, the distribution of prediction errors can be more useful than a single value of root mean squared error of prediction (RMSEP). Therefore, the distribution of prediction errors are estimated by repeated random sample weighting and used to determine model complexity. RSW is compared with its traditional counterparts like CV, MCCV, BS and a recently proposed randomization test method to demonstrate its usefulness. Copyright © 2010 John Wiley & Sons, Ltd.     

Incorporating interactions in multi‐block sequential and orthogonalised partial least squares regression

This paper is about how to incorporate interaction effects in multi‐block methodologies. The method proposed is inspired by polynomial regression modelling in the case with only a few independent variables but extends/generalises the idea to situations where the blocks are potentially very large with respect to the number of variables. The method follows a so‐called type I sums of squares strategy where the linear effects (main effects) are incorporated sequentially and before the interactions. The sequential and orthogonalised partial least squares (SO‐PLS) technique is used as a basis for the proposal. The SO‐PLS method is based on sequential estimation of each new block by the PLS regression method after orthogonalisation with respect to blocks already fitted. The new method preserves the invariance already established for SO‐PLS and can be used for blocks with different dimensionality. The method is tested on one real data set with two independent blocks with different complexity and on a simulated data set with a large number of variables in each block. Copyright © 2011 John Wiley & Sons, Ltd.     

Multiblock redundancy analysis: interpretation tools and application in epidemiology

For the purpose of exploring and modeling the relationships between a dataset Y and several datasets () measured on the same individuals, multiblock Partial Least Squares is a regression technique which is widely used, particularly in process monitoring, chemometrics and sensometrics. In the same vein, a new multiblock method, called multiblock Redundancy Analysis, is proposed. It is introduced by maximizing a criterion that reflects the objectives to be addressed. The solution of this maximization problem is directly derived from the eigenanalysis of a matrix. In addition, this method is related to other multiblock methods. Multiblock modeling methods provide to the user a large spectrum of interpretation indices for the investigation of the relationships among variables and among datasets. They are related to the criterion to maximize and therefore directly derived from the maximization problem under consideration. The interest of multiblock Redundancy Analysis and the associated interpretation tools are illustrated using a dataset in the field of veterinary epidemiology. Copyright © 2011 John Wiley & Sons, Ltd.     

Variable-weighted least-squares support vector machine for multivariate spectral analysis

Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.     

A complex multi-attribute large-group PLS decision-making method in the interval-valued intuitionistic fuzzy environment

In the complex multi-attribute large-group decision-making (CMALGDM) problems in interval-valued intuitionistic fuzzy (IVIF) environment, attributes of the alternatives are often stratified and correlated. This paper proposes a decision-making method for these problems based on partial least squares (PLS) path modelling, which not only fully exploits the decision information of decision makers (DMs), but also effectively addresses the relativity problem in the decision attributes and objectively assigned weights to the primary decision attributes (i.e., “latent variables for decision making”). The method can be outlined in three steps. First, a two-stage method is proposed to transform the interval-valued intuitionistic fuzzy number (IVIFN) samples into single-valued samples. In this step, an improved C-OWA operator is first given to transform the IVIFN samples into intuitionistic fuzzy number (IFN) samples, which makes the preference information of the DMs more objectively aggregated. Then a proposed membership-based method is applied to reduce the information loss and transform the IFN samples into single-valued samples. Second, the estimated values and weights of the “latent variables for decision-making” are obtained by means of the PLS path modelling algorithm. Finally, a multi-alternative sorting method is devised in accordance with the estimated values and weights. An example is provided to illustrate the proposed technique and evaluate its feasibility and validity.     

基于岩石光谱吸收特征的白云母含量反演

岩石是由多种矿物组成,其反射率光谱吸收特征与矿物含量之间存在紧密联系,矿物光谱在特定波段处的光谱吸收特征是定量估算含量的重要指标之一。为提升岩石光谱吸收特征定量反演矿物含量的准确度与精度,以白云母为研究对象,分析岩石光谱在2.2 μm附近的光谱吸收特征及其白云母含量,采用Savitzky-Golay平滑滤波和连续统去除法对岩石光谱反射率进行处理,进而提取光谱吸收特征参数（吸收深度、吸收宽度、吸收面积）,分析岩石光谱在2.2 μm附近吸收特征与白云母含量之间的相关性。研究中采用单一吸收特征建立统计模型、多维吸收特征建立偏最小二乘法（PLS）和多层感知器（MLP）模型,对岩石中白云母含量与光谱吸收特征参数进行分析,进而提出一种非线性预测岩石中矿物含量的方法。研究结果表明,岩石光谱在2.2 μm附近的光谱吸收特征中,吸收深度与白云母含量之间的相关性最高。基于单一吸收特征的统计模型中,二次曲线模型对吸收深度拟合的效果最佳,R2为0.935 0,RMSE为0.063 0,岩石光谱的吸收深度随白云母丰度满足二次曲线变化,岩石中白云母的含量越高,岩石光谱吸收深度值越大;基于多维光谱吸收特征的PLS模型相较于MLP模型拟合的效果更佳,其R2为0.947 7高于MLP的0.901 2,RMSE为0.002 7低于MLP的0.005 1;整体上,多维模型优于单一维度模型,PLS模型反演能力最佳,该模型在预测白云母含量上具有运算量小、精度高的特点。通过分析岩石在诊断特征处的光谱吸收特征,为其矿物组分的含量等进行定量反演提供理论参考,为矿产资源监测与评估提供快速高效便捷的方法。     

Intelligent resource allocation for transmission security on IRS-assisted spectrum sharing systems with OFDM

In this paper, intelligent reflecting surface (IRS) technology is employed to enhance physical layer security (PLS) for spectrum sharing communication systems with orthogonal frequency division multiplexing (OFDM). Aiming to improve the secondary users’ secrecy rates, a design problem for jointly optimizing the transmission beamforming of secondary base station (SBS), the IRS’s reflecting coefficient and the channel allocation is formulated under the constraints of the requirements of minimum data rates of primary users and the interference between users. As the scenario is highly complex, it is quite challenging to address the non-convexity of the optimization problem. Thus, a deep reinforcement learning (DRL) based approach is taken into consideration. Specifically, we use dueling double deep Q networks (D3QN) and soft Actor–Critic (SAC) to solve the discrete and continuous action space optimization problems, respectively, taking full advantage of the maximum entropy RL algorithm to explore all possible optimal paths. Finally, simulation results show that our proposed approach has a great improvement in security transmission rate compared with the scheme without IRS and OFDM, and our proposed D3QN-SAC approach is more effective than other approaches in terms of maximum security transmission rate.