Potential of NIR spectroscopy for predicting cellulose nanofibril quality in commercial bleached Kraft pulp of Eucalyptus.

Multivariate models were developed to classify cellulose nanofibril (CNF) fibrillation by a quality index from near infrared (NIR) spectra. Commercial pulps of Eucalyptus spp. were used to produce cellulose nanofibrils by means of a fibrillator mill. After each of the five passes through the mill, samples were collected and analyzed for energy consumption and fiber classification. As a standard, pulps were oxidized with TEMPO reagent followed by a single pass through the mill to compare the resulting quality of CNFs produced by each method. NIR spectra of CNFs were associated with quality indices determined by conventional laboratory analyses that included morphology, turbidity, mechanical properties, X-ray diffraction and quality index measurements. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to the spectral and experimental data. Fibrillator milling to obtain CNFs was efficient and resulted in gel formation following the third pass through the mill. NIR spectroscopy combined with PLS-DA was used successfully to create a model to classify quality of CNFs with 96 % certainty in 3 wt% solutions. These findings suggest that NIR spectroscopy holds promise for estimating CNF quality in suspension, particularly in real-time industrial applications where reliable estimates are crucial.

Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic data.

The content of water in fiber suspension and affects pulp refining, bleaching and draining operations. Cellulose pulp dryness estimate through near infrared (NIR) spectroscopy coupled with multivariate regressions or artificial neural network (ANN) techniques are not well explored yet. In this study models were developed to estimate cellulose pulp dryness in pads based on the NIR spectra. Thus, the cellulose pulp pads (4 mm thick) were weighed and their NIR spectra were obtained in several stages during desorption from 13.1 to 98.3% of content of solids. Partial least square regression (PLS-R) was developed from whole NIR spectra (1300 Absorbance values) and six spectral variables (from 1300) were selected for developing the PLS-R (6) and the ANN model. Both trained neural network and regression can predict pulp dryness of unknown cellulose pulp pads from their NIR data with an error of 2.5%. PLS-R models based on whole NIR spectra showed accurate predictions (the R² of lab-determined and estimated values plot was 0.99) while the ANN showed the same predictive performance from only six NIR variables. Predictive models developed from full NIR spectra and those based on only 6 variables were compared. Our findings indicate that NIR spectroscopy coupled with multivariate analysis and Artificial neural networks are a promising tool for monitoring the weight variation due to dewatering of the cellulose pulps in real time.

Cirurgia de revascularizaçäo do miocárdio em paciente idoso / Miocardial revascularization surgery in the elderly patient

Avaliar os fatores de risco e as complicaçäes na cirurgia de revascularizaçäo do miocárdio nos pacientes idosos. MATERIAL E METODOS: entre julho de 91 a maio de 94 foram avaliados 124 pacientes submetidos à cirurgia de revascularizaçäo do miocárdio, 76 (61,13 por cento) do sexo masculino e 48 (38,70 por cento) do sexo feminino com idade de 65 a 81 anos (M= 70, 13 anos). Fatores de risco analisados: sexo, diabetes, hipertensäo arterial, creatinina > 3 DPOC e classe funcional...