Aggregated functional data model for Near-Infrared Spectroscopy calibration and prediction

Calibration and prediction for NIR spectroscopy data are performed based on a functional interpretation of the Beer-Lambert formula. Considering that, for each chemical sample, the resulting spectrum is a continuous curve obtained as the summation of overlapped absorption spectra from each analyte plus a Gaussian error, we assume that each individual spectrum can be expanded as a linear combination of B-splines basis. Calibration is then performed using two procedures for estimating the individual analytes curves: basis smoothing and smoothing splines. Prediction is done by minimizing the square error of prediction. To assess the variance of the predicted values, we use a leave-one-out jackknife technique. Departures from the standard error models are discussed through a simulation study, in particular, how correlated errors impact on the calibration step and consequently on the analytes' concentration prediction. Finally, the performance of our methodology is demonstrated through the analysis of two publicly available datasets.Comment: 27 pages, 7 figures, 7 table

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

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

Nonlinear multiple regression methods for spectroscopic analysis: application to NIR calibration

Chemometrics has been applied to analyse near-infrared (NIR) spectra for decades. Linear regression methods such as partial least squares (PLS) regression and principal component regression (PCR) are simple and widely used solutions for spectroscopic calibration. My dissertation connects spectroscopic calibration with nonlinear machine learning techniques. It explores the feasibility of applying nonlinear methods for NIR calibration. Investigated nonlinear regression methods include least squares support vec- tor machine (LS-SVM), Gaussian process regression (GPR), Bayesian hierarchical mixture of linear regressions (HMLR) and convolutional neural networks (CNN). Our study focuses on the discussion of various design choices, interpretation of nonlinear models and providing novel recommendations and insights for the con- struction nonlinear regression models for NIR data. Performances of investigated nonlinear methods were benchmarked against traditional methods on multiple real-world NIR datasets. The datasets have differ- ent sizes (varying from 400 samples to 7000 samples) and are from various sources. Hypothesis tests on separate, independent test sets indicated that nonlinear methods give significant improvements in most practical NIR calibrations

Glucose concentration monitoring using near-infrared spectrum of spent dialysis fluid in hemodialysis patients

Uvod/Cilj Dijabetesna nefropatija vodi ka trajnom oštećenju bubrežnog tkiva, dok sam hemodijalizni tretman prouzrokuje oscilacije u nivou glukoze u krvi. Neinvazivni monitoring glukoze, kroz skeniranje otpadnog dijalizata, pružio bi značajne informacije o nivou glukoze u krvi bolesnika. Cilj studije je predikcija koncentracije glukoze u krvi bolesnika na hemodijalizi kroz spektroskopsku karakterizaciju otpadnog dijalizata u području spektra bliskom infracrvenom (NIR). Metode Uzorci krvi i otpadnog dijalizata uzimani su od 15 bolesnika u 15. minutu hemodijalize. Uzorci otpadnog dijalizata skenirani su u regionu NIR, koji se prostirao od 900 do 1300 nm. Da bi se primenila veštačka neuronska mreža, korišćena je funkcija NFTOOL programskog paketa Matlab. Ispitivanje i obuka veštačke neuronske mreže izvedeni su korišćenjem spektra NIR otpadne dijalizne tečnosti kao ulaza i koncentracije glukoze u dijapazonu 3-9 mmol/l kao izlaza. Rezultati Koristeći veštačku neuronsku mrežu, uočili smo značajnu korelaciju između spektra otpadnog dijalizata i koncentracije 3-9 mmol/l glukoze u krvi bolesnika. Zaključak Korelacija od 93% između spektra NIR otpadnog dijalizata i koncentracije glukoze pokazala je da se spektroskopija NIR može smatrati neinvazivnom metodom za pouzdano praćenje nivoa glukoze u krvi kod bolesnika na hemodijalizi.Introduction/Objective Diabetic nephropathy leading to end-stage renal disease is a major health problem worldwide. Hemodialysis (HD) treatment is associated with glycemia variations. Diabetic patients on HD might benefit from a non-invasive online glycemia monitoring system. The aim of this study was to assess the glucose concentration from the matrix of the spent dialysate fluid using near-infrared (NIR) spectroscopy. Methods Blood samples and spent dialysate were collected in the 15th minute of the HD treatment from 15 patients. The spent dialysis fluid was characterized by a NIR spectrometer in the range of 900-1300 nm. In order to apply the artificial neural network (ANN) and train it, the MATLAB NFTOOL program was used. The testing and training of the ANN were executed using the NIR spectrum of the spent dialysis fluid as input, and the glucose concentration as output. Results A significant correlation in excess of 93% between the NIR spectrum of the spent dialysate and the blood glucose concentration (3-9 mmol/l) was found. Conclusions NIR spectroscopy is a non-invasive and reliable method of glycemia monitoring which can be used in maintaining HD patients

Glucose concentration monitoring using near-infrared spectrum of spent dialysis fluid in hemodialysis patients

Uvod/Cilj Dijabetesna nefropatija vodi ka trajnom oštećenju bubrežnog tkiva, dok sam hemodijalizni tretman prouzrokuje oscilacije u nivou glukoze u krvi. Neinvazivni monitoring glukoze, kroz skeniranje otpadnog dijalizata, pružio bi značajne informacije o nivou glukoze u krvi bolesnika. Cilj studije je predikcija koncentracije glukoze u krvi bolesnika na hemodijalizi kroz spektroskopsku karakterizaciju otpadnog dijalizata u području spektra bliskom infracrvenom (NIR). Metode Uzorci krvi i otpadnog dijalizata uzimani su od 15 bolesnika u 15. minutu hemodijalize. Uzorci otpadnog dijalizata skenirani su u regionu NIR, koji se prostirao od 900 do 1300 nm. Da bi se primenila veštačka neuronska mreža, korišćena je funkcija NFTOOL programskog paketa Matlab. Ispitivanje i obuka veštačke neuronske mreže izvedeni su korišćenjem spektra NIR otpadne dijalizne tečnosti kao ulaza i koncentracije glukoze u dijapazonu 3-9 mmol/l kao izlaza. Rezultati Koristeći veštačku neuronsku mrežu, uočili smo značajnu korelaciju između spektra otpadnog dijalizata i koncentracije 3-9 mmol/l glukoze u krvi bolesnika. Zaključak Korelacija od 93% između spektra NIR otpadnog dijalizata i koncentracije glukoze pokazala je da se spektroskopija NIR može smatrati neinvazivnom metodom za pouzdano praćenje nivoa glukoze u krvi kod bolesnika na hemodijalizi.Introduction/Objective Diabetic nephropathy leading to end-stage renal disease is a major health problem worldwide. Hemodialysis (HD) treatment is associated with glycemia variations. Diabetic patients on HD might benefit from a non-invasive online glycemia monitoring system. The aim of this study was to assess the glucose concentration from the matrix of the spent dialysate fluid using near-infrared (NIR) spectroscopy. Methods Blood samples and spent dialysate were collected in the 15th minute of the HD treatment from 15 patients. The spent dialysis fluid was characterized by a NIR spectrometer in the range of 900-1300 nm. In order to apply the artificial neural network (ANN) and train it, the MATLAB NFTOOL program was used. The testing and training of the ANN were executed using the NIR spectrum of the spent dialysis fluid as input, and the glucose concentration as output. Results A significant correlation in excess of 93% between the NIR spectrum of the spent dialysate and the blood glucose concentration (3-9 mmol/l) was found. Conclusions NIR spectroscopy is a non-invasive and reliable method of glycemia monitoring which can be used in maintaining HD patients

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed