NEAR INFRARED SPECTROSCOPY FOR ESTIMATING SUGARCANE BAGASSE CONTENT IN MEDIUM DENSITY FIBERBOARD

Medium density fiberboard (MDF) is an engineered wood product formed by breaking down selected lignin-cellulosic material residuals into fibers, combining it with wax and a resin binder, and then forming panels by applying high temperature and pressure. Because the raw material in the industrial process is ever-changing, the panel industry requires methods for monitoring the composition of their products. The aim of this study was to estimate the ratio of sugarcane (SC) bagasse to Eucalyptus wood in MDF panels using near infrared (NIR) spectroscopy. Principal component analysis (PCA) and partial least square (PLS) regressions were performed. MDF panels having different bagasse contents were easily distinguished from each other by the PCA of their NIR spectra with clearly different patterns of response. The PLS-R models for SC content of these MDF samples presented a strong coefficient of determination (0.96) between the NIR-predicted and Lab-determined values and a low standard error of prediction (similar to 1.5%) in the cross-validations. A key role of resins (adhesives), cellulose, and lignin for such PLS-R calibrations was shown. PLS-DA model correctly classified ninety-four percent of MDF samples by cross-validations and ninety-eight percent of the panels by independent test set. These NIR-based models can be useful to quickly estimate sugarcane bagasse vs. Eucalyptus wood content ratio in unknown MDF samples and to verify the quality of these engineered wood products in an online process.

[O/Fe] ESTIMATES FOR CARBON-ENHANCED METAL-POOR STARS FROM NEAR-INFRARED SPECTROSCOPY

We report on oxygen abundances determined from medium-resolution near-infrared spectroscopy for a sample of 57 carbon-enhanced metal-poor (CEMP) stars selected from the Hamburg/ESO Survey. The majority of our program stars exhibit oxygen-to-iron ratios in the range +0.5 < [O/Fe]< + 2.0. The [O/Fe] values for this sample are statistically compared to available high-resolution estimates for known CEMP stars as well as to high-resolution estimates for a set of carbon-normal metal-poor stars. Carbon, nitrogen, and oxygen abundance patterns for a sub-sample of these stars are compared to yield predictions for very metal-poor asymptotic giant branch (AGB) abundances in the recent literature. We find that the majority of our sample exhibit patterns that are consistent with previously studied CEMP stars having s-process-element enhancements and thus have very likely been polluted by carbon- and oxygen-enhanced material transferred from a metal-poor AGB companion.

Near infrared spectroscopy for soil bulk density assessment

P>Soil bulk density values are needed to convert organic carbon content to mass of organic carbon per unit area. However, field sampling and measurement of soil bulk density are labour-intensive, costly and tedious. Near-infrared reflectance spectroscopy (NIRS) is a physically non-destructive, rapid, reproducible and low-cost method that characterizes materials according to their reflectance in the near-infrared spectral region. The aim of this paper was to investigate the ability of NIRS to predict soil bulk density and to compare its performance with published pedotransfer functions. The study was carried out on a dataset of 1184 soil samples originating from a reforestation area in the Brazilian Amazon basin, and conventional soil bulk density values were obtained with metallic ""core cylinders"". The results indicate that the modified partial least squares regression used on spectral data is an alternative method for soil bulk density predictions to the published pedotransfer functions tested in this study. The NIRS method presented the closest-to-zero accuracy error (-0.002 g cm-3) and the lowest prediction error (0.13 g cm-3) and the coefficient of variation of the validation sets ranged from 8.1 to 8.9% of the mean reference values. Nevertheless, further research is required to assess the limits and specificities of the NIRS method, but it may have advantages for soil bulk density predictions, especially in environments such as the Amazon forest.

Estimation of hexenuronic acids and kappa number in kraft pulps of Eucalyptus globulus by Fourier transform near infrared spectroscopy and multivariate analysis

Fourier transform near infrared (FT-NIR) spectroscopy was evaluated as an analytical too[ for monitoring residual Lignin, kappa number and hexenuronic acids (HexA) content in kraft pulps of Eucalyptus globulus. Sets of pulp samples were prepared under different cooking conditions to obtain a wide range of compound concentrations that were characterised by conventional wet chemistry analytical methods. The sample group was also analysed using FT-NIR spectroscopy in order to establish prediction models for the pulp characteristics. Several models were applied to correlate chemical composition in samples with the NIR spectral data by means of PCR or PLS algorithms. Calibration curves were built by using all the spectral data or selected regions. Best calibration models for the quantification of lignin, kappa and HexA were proposed presenting R-2 values of 0.99. Calibration models were used to predict pulp titers of 20 external samples in a validation set. The lignin concentration and kappa number in the range of 1.4-18% and 8-62, respectively, were predicted fairly accurately (standard error of prediction, SEP 1.1% for lignin and 2.9 for kappa). The HexA concentration (range of 5-71 mmol kg(-1) pulp) was more difficult to predict and the SEP was 7.0 mmol kg(-1) pulp in a model of HexA quantified by an ultraviolet (UV) technique and 6.1 mmol kg(-1) pulp in a model of HexA quantified by anion-exchange chromatography (AEC). Even in wet chemical procedures used for HexA determination, there is no good agreement between methods as demonstrated by the UV and AEC methods described in the present work. NIR spectroscopy did provide a rapid estimate of HexA content in kraft pulps prepared in routine cooking experiments.

Comparative analysis of near and mid-infrared spectroscopy to monitor recombinant cyprosin production

Infrared spectroscopy, either in the near and mid (NIR/MIR) region of the spectra, has gained great acceptance in the industry for bioprocess monitoring according to Process Analytical Technology, due to its rapid, economic, high sensitivity mode of application and versatility. Due to the relevance of cyprosin (mostly for dairy industry), and as NIR and MIR spectroscopy presents specific characteristics that ultimately may complement each other, in the present work these techniques were compared to monitor and characterize by in situ and by at-line high-throughput analysis, respectively, recombinant cyprosin production by Saccharomyces cerevisiae. Partial least-square regression models, relating NIR and MIR-spectral features with biomass, cyprosin activity, specific activity, glucose, galactose, ethanol and acetate concentration were developed, all presenting, in general, high regression coefficients and low prediction errors. In the case of biomass and glucose slight better models were achieved by in situ NIR spectroscopic analysis, while for cyprosin activity and specific activity slight better models were achieved by at-line MIR spectroscopic analysis. Therefore both techniques enabled to monitor the highly dynamic cyprosin production bioprocess, promoting by this way more efficient platforms for the bioprocess optimization and control.

Continuous monitoring of liver oxygenation with near infrared spectroscopy during naso-gastric tube feeding in neonates.

Near infrared spectroscopy (NIRS) is a non-invasive method of estimating the haemoglobin concentration changes in certain tissues. It is frequently used to monitor oxygenation of the brain in neonates. At present it is not clear whether near infrared spectroscopy of other organs (e.g. the liver as a corresponding site in the splanchnic region, which reacts very sensitively to haemodynamic instability) provides reliable values on their tissue oxygenation. The aim of the study was to test near infrared spectroscopy by measuring known physiologic changes in tissue oxygenation of the liver in newborn infants during and after feeding via a naso-gastric tube. The test-retest variability of such measurements was also determined. On 28 occasions in 25 infants we measured the tissue oxygenation index (TOI) of the liver and the brain continuously before, during and 30 minutes after feeding via a gastric tube. Simultaneously we measured arterial oxygen saturation (SaO2), heart rate (HR) and mean arterial blood pressure (MAP). In 10 other newborn infants we performed a test-retest analysis of the liver tissue oxygenation index to estimate the variability in repeated intra-individual measurements. The tissue oxygenation index of the liver increased significantly from 56.7 +/- 7.5% before to 60.3 +/- 5.6% after feeding (p < 0.005), and remained unchanged for the next 30 minutes. The tissue oxygenation index of the brain (62.1 +/- 9.7%), SaO2 (94.4 +/- 7.1%), heart rate (145 +/- 17.3 min-1) and mean arterial blood pressure (52.8 +/- 10.2 mm Hg) did not change significantly. The test-retest variability for intra-individual measurements was 2.7 +/- 2.1%. After bolus feeding the tissue oxygenation index of the liver increased as expected. This indicates that near infrared spectroscopy is suitable for monitoring changes in tissue oxygenation of the liver in newborn infants.

Can near infrared spectroscopy of the liver monitor tissue oxygenation?

Measurement of the hepatic oxygenation index by near infrared spectroscopy is a suitable method to estimate the oxygenation and can be a non-invasive means to continuously monitor tissue perfusion and to detect early haemodynamic disturbances in critically ill children.

Effect of age on intraoperative cerebrovascular autoregulation and near-infrared spectroscopy-derived cerebral oxygenation.

Background. Age is an important risk factor for perioperative cerebral complications such as stroke, postoperative cognitive dysfunction, and delirium. We explored the hypothesis that intraoperative cerebrovascular autoregulation is less efficient and brain tissue oxygenation lower in elderly patients, thus, increasing the vulnerability of elderly brains to systemic insults such as hypotension.Methods. We monitored intraoperative cerebral perfusion in 50 patients aged 18-40 and 77 patients >65 yr at two Swiss university hospitals. Mean arterial pressure (MAP) was measured continuously using a plethysmographic method. An index of cerebrovascular autoregulation (Mx) was calculated based on changes in transcranial Doppler flow velocity due to changes in MAP. Cerebral oxygenation was assessed by the tissue oxygenation index (TOI) using near-infrared spectroscopy. End-tidal CO(2), O(2), and sevoflurane concentrations and peripheral oxygen saturation were recorded continuously. Standardized anaesthesia was administered in all patients (thiopental, sevoflurane, fentanyl, atracurium).Results. Autoregulation was less efficient in patients aged >65 yr [by 0.10 (SE 0.04; P=0.020)] in a multivariable linear regression analysis. This difference was not attributable to differences in MAP, end-tidal CO2, or higher doses of sevoflurane. TOI was not significantly associated with age, sevoflurane dose, or Mx but increased with increasing flow velocity [by 0.09 (SE 0.04; P=0.028)] and increasing MAP [by 0.11 (SE 0.05; P=0.043)].Conclusions. Our results do not support the hypothesis that older patients' brains are more vulnerable to systemic insults. The difference of autoregulation between the two groups was small and most likely clinically insignificant.

Chemical characterization and infrared spectroscopy of soil organic matter from two southern brazilian soils

Soil organic matter from the surface horizon of two Brazilian soils (a Latosol and a Chernosol), in bulk samples (in situ SOM) and in HF-treated samples (SOM), was characterized by elemental analyses, diffuse reflectance (DRIFT) and transmission Fourier transform infrared spectroscopy (T-FTIR). Humic acids (HA), fulvic acids (FA) and humin (HU) isolated from the SOM were characterized additionally by ultraviolet-visible spectroscopy (UV-VIS). After sample oxidation and alkaline treatment, the DRIFT technique proved to be more informative for the detection of "in situ SOM" and of residual organic matter than T-FTIR. The higher hydrophobicity index (HI) and H/C ratio obtained in the Chernosol samples indicate a stronger aliphatic character of the organic matter in this soil than the Latosol. In the latter, a pronounced HI decrease was observed after the removal of humic substances (HS). The weaker aliphatic character, the higher O/C ratio, and the T-FTIR spectrum obtained for the HU fraction in the Latosol suggest the occurrence of surface coordination of carboxylate ions. The Chernosol HU fraction was also oxygenated to a relatively high extent, but presented a stronger hydrophobic character in comparison with the Latosol HU. These differences in the chemical and functional group composition suggest a higher organic matter protection in the Latosol. After the HF treatment, decreases in the FA proportion and the A350/A550 ratio were observed. A possible loss of FA and condensation of organic molecules due to the highly acid medium should not be neglected.

Far-infrared spectroscopy of nanoscopic InAs rings

We have employed time-dependent local-spin-density theory to analyze the far-infrared transmission spectrum of InAs self-assembled nanoscopic rings recently reported [A. Lorke et al., Phys. Rev. Lett. (to be published)]. The overall agreement between theory and experiment is fairly good, which on the one hand confirms that the experimental peaks indeed reflect the ringlike structure of the sample, and on the other hand, asseses the suitability of the theoretical method to describe such nanostructures. The addition energies of one- and two-electron rings are also reported and compared with the corresponding capacitance spectra

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Applications of Fourier transform infrared spectroscopy

This article summarizes the basic principles of Fourier Transform Infrared Spectroscopy, with examples of methodologies and applications to different field sciences.

Predicting rapeseed oil content with near-infrared spectroscopy

The objective of this work was to establish a calibration equation and to estimate the efficiency of near-infrared reflectance (NIR) spectroscopy for evaluating rapeseed oil content in Southern Brazil. Spectral data from 124 half-sib families were correlated with oil contents determined by the chemical method. The accuracy of the equation was verified by coefficient of determination (R²) of 0.92, error of calibration (SEC) of 0.78, and error of performance (SEP) of 1.22. The oil content of ten genotypes, which were not included in the calibration with NIR, was similar to the one obtained by the standard chemical method. NIR spectroscopy is adequate to differentiate oil content of rapeseed genotypes.