A multivariate approach to investigate the NMR CPMG pulse sequence for analysing low MW species in polymers

Detection and quantification of low molecular weight components in polymeric samples via nuclear magnetic resonance (NMR) spectroscopy can be difficult due to overlapping signal caused by line broadening characteristics of polymers. A way of overcoming this problem could be the exploitation of the difference in relaxation between small molecules and macromolecular species, such as the application of a T2 filter by using the Carr–Purcell–Meiboom–Gill (CPMG) spin-echo pulse sequence. This technique, largely exploited in metabolomics studies, is applied here to material sciences. A Design of Experiments approach was used for evaluating the effect of different acquisition parameters (relaxation delay, echo time and number of cycles) and sample-related ones (concentration and polymer molecular weight) on selected responses, with a particular interest in performing a reliable quantitative analysis. Polymeric samples containing small molecules were analysed by NMR with and without the application of the filter, and analysis of variance was used to identify the most influential parameters. Results indicated that increasing the polymer concentration, hence sample viscosity, further attenuates polymer signals in CPMG experiments because the T2 of those signals tends to decrease with increasing viscosity. The signal-to-noise ratio measured for small molecules can undergo a minimum loss when specific parameters are chosen in relation to the polymer molecular weight. Furthermore, the difference in dynamics between aliphatic and aromatic nuclei, as well as between mobile and stiff polymers, translates into different results in terms of polymer signal reduction, suggesting that the relaxation filter can also be used for obtaining information on the polymer structure

Understanding chemical production processes by using PLS path model parameters as soft sensors

Contains fulltext : 221730.pdf (publisher's version ) (Open Access

Leveraging Multiple Linear Regression for Wavelength Selection

In multivariate calibration, wavelengths selection is often used to lower prediction errors of sample properties. As a result, many methods have been created to select wavelengths. Several of the wavelength selection methods involve many tuning parameters that are typically complex or difficult to work with. The purpose of this poster is to show an easy way to select wavelengths while using few simple tuning parameters. The proposed method uses multiple linear regression (MLR) as an indicator to which wavelengths should be used to create a model. From a collection of random MLR models, those models with an acceptable bias/variance balance are evaluated to determine the wavelengths most frequently used. Portions of the most frequently selected wavelengths are chosen as the final MLR selected wavelengths. These MLR selected wavelengths are used to produce a calibration model by the method of partial least squares (PLS). This proposed wavelength selection method is compared to PLS models containing all wavelengths using several near infrared data sets. The PLS models with the selected wavelengths show an improvement in prediction error, suggesting this method as a simple way to select wavelengths

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling

Na modelagem de deep learning (DL) para dados espectrais, um grande desafio está relacionado à escolha da arquitetura de rede DL e à seleção dos melhores hiperparmetros. Muitas vezes, pequenas mudanças na arquitetura neural ou seu hiperparômetro podem ter uma influência direta no desempenho do modelo, tornando sua robustez questionável. Para lidar com isso, este estudo apresenta uma modelagem automatizada de aprendizagem profunda baseada em técnicas avançadas de otimização envolvendo hyperband e otimização bayesiana, para encontrar automaticamente a arquitetura neural ideal e seus hiperparmetros para alcançar modelos robustos de DL. A otimização requer uma arquitetura neural base para ser inicializada, no entanto, mais tarde, ajusta automaticamente a arquitetura neural e os hiperparmetros para alcançar o modelo ideal. Além disso, para apoiar a interpretação dos modelos DL, foi implementado um esquema de pesagem de comprimento de onda baseado no mapeamento de ativação de classe ponderada por gradiente (Grad-CAM). O potencial da abordagem foi mostrado em um caso real de classificação da variedade de trigo com dados espectrais quase infravermelhos. O desempenho da classificação foi comparado com o relatado anteriormente no mesmo conjunto de dados com diferentes abordagens DL e quimiométrica. Os resultados mostraram que, com a abordagem proposta, foi alcançada uma precisão de classificação de 94,9%, melhor do que a melhor precisão relatada no mesmo conjunto de dados, ou seja, 93%. Além disso, o melhor desempenho foi obtido com uma arquitetura neural mais simples em comparação com o que foi usado em estudos anteriores. O deep learning automatizado baseado na otimização avançada pode suportar a modelagem DL de dados espectrais.info:eu-repo/semantics/publishedVersio

On the relevance of preprocessing in predictive maintenance for dynamic systems

The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way. We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems

Schola Hemsterhusiana: de herleving der Grieksche studiën aan de Nederlandsche universiteiten in de achttiende eeuw van Perizonius tot en met Valckenaer

Contains fulltext : mmubn000001_048326992.pdf (publisher's version ) (Open Access)Promotor : F. Sassen [i.p.v. E.[=J.P.E.] Drerup][XIV], IV, 408 p