Partial Least Squares Regression

The issue of multicollinearity has long been acknowledged in statistical modelling; however, it is often untreated in the most of published papers. Indeed, the use of methods for multicollinearity correction is still scarce. One important reason is that despite many proposed methods, little is known about their strength or performance. We compare the statistical properties and performance of four main techniques to correct multicollinearity, i.e., Ridge Regression (R-R), Principal Components Regression (PC-R), Partial Least Squares Regression (PLS-R), and Lasso Regression (L-R), in both a simulation study and two real data examples used for modelling volumes of heart and Thyroid as a function of clinical and anthropometric parameters. We find that when the statistical approaches were used to address different levels of collinearity, we observed that R-R, PC-R and PLS-R appeared to have a somewhat similar behavior, with a slight advantage for the PLS-R. Indeed, in all implemented cas...

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

The purpose of this paper is to examine an extended DSS success model, the model explore the effects of quality features of DSS systems including system quality, information quality, accompanied with perceived ease of use and perceived usefulness on decision support satisfaction and DSS net benefits.
A detailed questionnaire was developed to measure the relationship between the aforementioned variables and data was collected from employees in Royal Jordanian Airlines in Jordan who had experience using DSS at their workplace. Partial Least Squares-Structural Equation Modelling (PLS-SEM) methods were employed to test the research model. The results revealed that system quality had positive effects on both perceived usefulness and decision support satisfaction. Information quality had positive effects on decision support satisfaction; ease of use had positive effects on perceived usefulness, and decision support satisfaction positive effects on net benefits. However, information quality effects on perceived usefulness ease of use effects on decision support satisfaction, perceived usefulness effects on decision support satisfaction and benefits were not significant.  The findings provide several important implications for DSS research and practice. This paper concludes by discussing the limitations of the study, which should be addressed in future research.

On-line analyzers are widely used in chemical and oil-industry to estimate product properties and monitor production process. Partial Least Squares regression (PLS) is known as bi-linear factor model as it projects input (X) and output (Y) data into low dimensional spaces. We present how this projection can be utilised in process monitoring and validation of on-line anal-ysers. We apply the proposed methodology in a diesel fuel mixer where main product properties are estimated from near infrared spectra. Results show that the developed 2 Dimensional Par-tial Least Squares (2DPLS) model not only gives better property estimation performance than the currently applied Topological Near Infrared modelling tool (TOPNIR), but it is also able to provide informative map of operating regimes of the process. Keywords on-line analyser · PLS · multi-dimensional scaling (MDS) · near infrared spectrum Acknowledgement We acknowledge the financial support of the Hungarian State and the European Union under the TAMOP-4.2.2.A-11/1/KONV-2012-0071. This work was presented at the Conference 1 Introduction Control of measured process values (e.g. temperature, pres-sure, flow rate) does not always ensure that product properties (e.g. density, cloud point, flash point) will be in desired ranges. Some of these properties in chemical and oil-industry are not measured online (e.g. cetane index, aromatic field, sulfur con-tent) or not at the frequency necessary for real time control (e.g. flash point, density, cold filter plugging point). The objective of the development of software sensors and online analysers is to support the control of product properties which cannot be mea-sured online or offline measurements would be expensive. Interaction of signals like temperatures, pressures, flow rates or absorption intensities can be used for calculating unmeasured product properties (flash point, density etc.). Soft sensors are es-pecially useful in data fusion, where measurements of different characteristics and dynamics are combined. Near infrared spectroscopy is a widely used on-line mea-surement technique. There are several multivariate models and methods to support the prediction of product properties based on Near-Infrared (NIR) spectra. These methods can be separated into parametric models (e.g. linear regression, multi-linear re-gression, Partial Least Squares regression (PLS)) and nonpara-metric methods (e.g. k-NN[1], False Nearest Neighbors (FNN), Neural Networks, Topological Near-Infrared Modeling [2, 3] -TOPNIR). The main difference between these two classes is that the nonparametric techniques cannot extrapolate. The key idea of the paper is the utilisation of the multivariate signal of NIR analysers not only for building models to estimate product quality but also to use it in process monitoring and val-idation of models used in on-line analysers. A PLS based prediction model has been developed to support both prediction and visalisation (monitoring)[4]. Datasets taken from the Dune Refinery of MOL Ltd were analysed. The PLS model is applied to estimate cold filter plugging point, density and one property of distillation. For monitoring the latent space of the PLS model is used. A special orthogonalisation algorithm was applied. The presented mapping is able to visualise the data and give information about the distribution of operating regimes and the quality of the model.

In the following study, total sugar concentrations before and during alcoholic fermentation, as well as ethanol concentrations and pH levels after fermentation, of red and white wine grapes were successfully predicted using Raman spectroscopy. Fluorescing compounds such as anthocyanins and pigmented phenolics found in red wine present one of the primary limitations of enological analysis using Raman spectroscopy. Unlike the spontaneous Raman effect, fluorescence is a highly efficient process and consequently emits a much stronger signal than spontaneous Raman scattering. For this reason, many enological applications of Raman spectroscopy are impractical as the more subtle Raman spectrum of any red wine sample is in large part masked by fluorescing compounds present in the wine. This work employs a simple extraction method to mitigate fluorescence in finished red wines. Ethanol and total sugars (fructose plus glucose) of wines made from red (Cabernet Sauvignon) and white (Chardonnay,...

In this work a procedure for determining the shelf-life of products by merging near-infrared (NIR) spectroscopy, multivariate techniques of data analysis, and kinetic theory is presented. This procedure allows information from several sources (sensory, physical chemical, and instrumental) to be merged via the multivariate accelerated shelf-life test (MASLT) algorithm. The MASLT is a multivariate approach that relies on soft modeling via an unfolding principal component analysis (u-PCA) and hard modeling, through the conventional kinetic theory, for determining the final shelf-life of products. The procedure was successfully applied to a consumer goods product (a body lotion), whose shelf-life was determined to be 3 years and 9 months, in accordance with results previously obtained using conventional analytical techniques and univariate methods of data analysis.

Purpose – The authors aim to present partial least squares (PLS) as an evolving approach to structural equation modeling (SEM), highlight its advantages and limitations and provide an overview of recent research on the method across various fields. Design/methodology/approach – In this review article, the authors merge literatures from the marketing, management, and management information systems fields to present the state-of-the art of PLS-SEM research. Furthermore, the authors meta-analyze recent review studies to shed light on popular reasons for PLS-SEM usage. Findings – PLS-SEM has experienced increasing dissemination in a variety of fields in recent years with nonnormal data, small sample sizes and the use of formative indicators being the most prominent reasons for its application. Recent methodological research has extended PLS-SEM's methodological toolbox to accommodate more complex model structures or handle data inadequacies such as heterogeneity. Research limitation...

The data of organoleptic evaluation of the quality of yogurt are treated with methods “Correspondence analysis”, “Principle component analysis” and the findings of this analysis are shown. The influence of experts Is reported in evaluating the organoleptic indicators of the examined products. Presented are theoretical bases of creation of predictive models, their types and methods for validation. Obtained are predictive models that express the measurement methods and these models are used to check the possibility of predicting data from expert opinions.

Pelchem, a commercial subsidiary of Necsa (South African Nuclear Energy Corporation), produces a range of commercial fluorocarbon products while driving research and development initiatives to support the fluorine product portfolio. One such initiative is to develop improved analytical techniques to analyse product composition during development and to quality assure produce. Generally the C-F type products produced by Necsa are in a solution of anhydrous HF, and cannot be directly analyzed with traditional techniques without derivatisation. A technique such as vibrational spectroscopy, that can analyze these products directly without further preparation, will have a distinct advantage. However, spectra of mixtures of similar compounds are complex and not suitable for traditional quantitative regression analysis. Multivariate data analysis (MVA) can be used in such instances to exploit the complex nature of spectra to extract quantitative information on the composition of mixtures. ...

Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for