Reactions and Distribution of Birch Extractives in Kraft Pulp Oxygen Delignification

Abstract

Extractives composition and distribution was examined around the oxygen delignification stage in a birch kraft pulp mill. Pulp suspensions were divided into different fractions with a dynamic drainage jar device to obtain fibers (+100 mesh), large fines (100–200 mesh), and small fines (200–400 mesh) fractions as well as the colloidal and dissolved fractions. The fractions were extracted and the resin components analyzed by gas chromatography. No significant oxidation reactions of wood resin were found to occur in oxygen delignification. Major removal of resin was achieved in the subsequent washer, where resin is removed with dissolved, colloidal, and fines fractions. Overall deresination in the subsequent washer was 58%.     

Pulping Yield and Delignification Kinetics of Heartwood and Sapwood of Maritime Pine

Abstract

In maritime pine (Pinus pinaster Ait.), heartwood represents a substantial part of the tree stem at final harvest age (80 years) corresponding to 42% at the base of the stem wood diameter and decreasing upward. The rate of heartwood formation was estimated at 0.35 rings/year, beginning at 18 years of age. Differences in the chemical composition between heartwood and sapwood were mainly in the extractives, 19.7% and 5.8%, respectively. The lignin content was 23.1% and 24.5% in the heartwood and sapwood, respectively. Pulping yield of the heartwood was lower than that of the sapwood (40.0% vs. 49.7%) and was negatively correlated with the extractives content. Extraction of heartwood prior to pulping increased the pulp yield and the delignification (lower residual lignin in pulps). Pulping kinetics showed lower yields for heartwood at all pulping stages, the difference occurring especially in the initial reaction phase. However, delignification rate constants were similar for heartwood and sapwood (3.1×10^?2 min^?1 and 2.7×10^?2 min^?1 for the main delignification phase for sapwood and heartwood, respectively), with a lower activation energy for sapwood (68.3 vs. 90.0 kJ · mol^?1). The presence of heartwood decreases the raw‐material quality for pulping and this should be taken into account when harvesting trees for pulping processes.     

Development and Validation of 2D Difference Intensity Analysis for Chemical Library Screening by Protein‐Detected NMR Spectroscopy

An academic chemical screening approach was developed by using 2D protein‐detected NMR, and a 352‐chemical fragment library was screened against three different protein targets. The approach was optimized against two protein targets with known ligands: CXCL12 and BRD4. Principal component analysis reliably identified compounds that induced nonspecific NMR crosspeak broadening but did not unambiguously identify ligands with specific affinity (hits). For improved hit detection, a novel scoring metric—difference intensity analysis (DIA)—was devised that sums all positive and negative intensities from 2D difference spectra. Applying DIA quickly discriminated potential ligands from compounds inducing nonspecific NMR crosspeak broadening and other nonspecific effects. Subsequent NMR titrations validated chemotypes important for binding to CXCL12 and BRD4. A novel target, mitochondrial fission protein Fis1, was screened, and six hits were identified by using DIA. Screening these diverse protein targets identified quinones and catechols that induced nonspecific NMR crosspeak broadening, hampering NMR analyses, but are currently not computationally identified as pan‐assay interference compounds. The results established a streamlined screening workflow that can easily be scaled and adapted as part of a larger screening pipeline to identify fragment hits and assess relative binding affinities in the range of 0.3–1.6 mm . DIA could prove useful in library screening and other applications in which NMR chemical shift perturbations are measured.     

基于主成分分析和偏最小二乘回归的烟煤水分近红外检测

基于近红外光谱技术对烟煤水分分析的快速、无损性。采集了100个烟煤样品,分成验证集和预测集,验证集85个,预测集15个。利用主成分分析对烟煤的近红外光谱数据进行压缩,然后以主成分为输入,采用偏最小二乘回归建立烟煤水分预测模型。烟煤水分平均绝对相对误差为0.0728,表明该方法用于预测烟煤水分含量是可行的。     

差分拉曼光谱结合聚类分析检验电线塑料外皮研究

为建立一种高效准确的差分拉曼光谱检验电线塑料外皮的方法,利用便携式差分拉曼光谱仪测得了35个不同品牌不同类型的电线塑料外皮的差分拉曼光谱数据;再根据样品的主要成分和所含填料的不同,对样品进行初步分类;利用主成分分析对初步分类后的拉曼数据进行降维,再利用系统聚类将样品分组,最后应用K-均值聚类分析对分组结果准确性进行检验...     

Raman spectra of high‐density,low‐density,and linear low‐density polyethylene pellets and prediction of their physical properties by multivariate data analysis

Raman spectra have been measured for pellets of five samples of high‐density polyethylene (HDPE), seven samples of low‐density polyethylene (LDPE), and six samples of linear low‐density polyethylene (LLDPE). The obtained Raman spectra have been compared to find out characteristic Raman bands of HDPE, LDPE, and LLDPE. Principal component analysis (PCA) was applied to the Raman spectra in the 1600–650 cm^?1 region after multiplicative scatter correction (MSC) to discriminate the Raman spectra of the three different PE species. They are classified into three groups by a score plot of PCA factor 1 vs. 2. HDPE with high density and high crystallinity gives high scores on the factor 1 axis, while LDPE with low density and low crystallinity yields negative scores on the same axis. It seems that factor 1 reflects the density or crystallinity. A PC weight loadings plot for factor 1 shows six upward peaks corresponding to the bands arising from the crystalline parts or all‐trans ? (CH₂)_n? groups and seven downward peaks ascribed to the bands of the amorphous or anisotropic regions and those arising from the short branches. Partial least‐squares (PLS‐1) regression was applied to the Raman spectra after MSC to propose calibration models that predict the density, crystallinity, and melting points of the polyethylenes. The correlation coefficient was calculated to be 0.9941, 0.9800, and 0.9709 for the density, crystallinity, and melting point, respectively, and their root‐mean‐square error of cross validation (RMSECV) was found to be 0.0015, 3.3707, and 2.3745, respectively. The loadings plot of factor 2 for the prediction of melting point is largely different from those for the prediction of density and crystallinity. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 86: 443–448, 2002     

Estimating Glucan,Xylan, and Methylglucuronic Acids in Kraft Pulps of Eucalyptus globulus Using FT-NIR Spectroscopy and Multivariate Analysis

Abstract

Fourier transform near infrared (FT-NIR) associated with multivariate analysis was used to estimate glucan, xylan, 4-O-Methyl-α -D-glucuronic acid (MeGlcA) content, and pulp yield in kraft pulps of Eucalyptus globulus Labill. Several models were applied to correlate chemical composition in samples with the NIR spectral data by means of principal components regression (PCR) or partial least square (PLS) algorithms. Calibration models were built and validated by using all the spectral data and cross-validation methodology. The r_c ² values for the best calibration models for quantification of glucan, xylan, MeGlcA contents and pulp yield were between 0.71–0.92. The model was validated using a set of external samples. The amount of glucan (64–77%), xylan (12–18%), and MeGlcA (204–363 mmol kg pulp^–1) in pulps were predicted with a root mean square error of prediction (RMSEP) of 0.91%, 0.46%, and 15.21% for glucan, xylan, and MeGlcA, respectively. Pulp yield (in the range of 46–70%) was also predicted with good accuracy with a RMSEP of 1.63%. These results suggest that glucan, xylan, MeGlcA composition, and pulp yield in kraft pulps of E. globulus can be adequately estimated by NIR spectroscopy for laboratory or industrial applications. NIR predictions can also provide useful and cost-effective tools for the rapid screening of large numbers of samples.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Assessment of resin formulations and determination of the formaldehyde to urea molar ratio by near‐ and mid‐infrared spectroscopy and multivariate data analysis

The molar ratios of formaldehyde (F) to urea (U) of three resin formulations in the range from 0.90 to 1.49 have been analyzed by means of Attenuated Total Reflection‐Fourier Transform Infrared (ATR‐FTIR) spectroscopy and Fourier Transform‐Near‐Infrared (FT‐NIR) spectroscopy. Application of Principal Component Analysis (PCA) to the spectra (MIR and NIR) allowed to separate them according to the molar ratio and to distinguish between two groups of resins. Soft Independent Modeling of Class Analogy (SIMCA) allowed classification of new resin samples with high model distances between the classes. Partial Least Squares Regression (PLS‐R) models based on MIR (NIR) spectra resulted in high coefficients of determination (R²) values, low errors, and high residual prediction deviations (RPD). To confirm the reproducibility of the process and to carefully evaluate twice all multivariate analysis results obtained, different batches of resins have been prepared to have an additional independent sample set. The number of samples required for MIR and possible applications of MIR and NIR spectroscopy in this context including limitations have been discussed. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2013     

柠檬桉叶的HS-SDME-GC/MS谱图及其主成分分析

采用顶空单液滴微萃取-气相色谱/质谱法(HS-SDME-GC/MS)测定了13个不同产地的柠檬桉叶并结合主成分分析法建立判别模型,采用正交法优化其实验条件.结果表明,单液滴微萃取优化条件为:10 mL顶空瓶中1.0 g样品、60℃顶空40 min、苯甲酸乙酿吸附进而吸收柠檬桉叶中的化学物质,比对NIST 14谱库并结合...     

Application of Advanced Non-Linear Spectral Decomposition and Regression Methods for Spectroscopic Analysis of Targeted and Non-Targeted Irradiation Effects in an In-Vitro Model

Irradiation of the tumour site during treatment for cancer with external-beam ionising radiation results in a complex and dynamic series of effects in both the tumour itself and the normal tissue which surrounds it. The development of a spectral model of the effect of each exposure and interaction mode between these tissues would enable label free assessment of the effect of radiotherapeutic treatment in practice. In this study Fourier transform Infrared microspectroscopic imaging was employed to analyse an in-vitro model of radiotherapeutic treatment for prostate cancer, in which a normal cell line (PNT1A) was exposed to low-dose X-ray radiation from the scattered treatment beam, and also to irradiated cell culture medium (ICCM) from a cancer cell line exposed to a treatment relevant dose (2 Gy). Various exposure modes were studied and reference was made to previously acquired data on cellular survival and DNA double strand break damage. Spectral analysis with manifold methods, linear spectral fitting, non-linear classification and non-linear regression approaches were found to accurately segregate spectra on irradiation type and provide a comprehensive set of spectral markers which differentiate on irradiation mode and cell fate. The study demonstrates that high dose irradiation, low-dose scatter irradiation and radiation-induced bystander exposure (RIBE) signalling each produce differential effects on the cell which are observable through spectroscopic analysis.     

Fourier Transform Infrared Microspectroscopy Combined with Principal Component Analysis and Artificial Neural Networks for the Study of the Effect of β-Hydroxy-β-Methylbutyrate (HMB) Supplementation on Articular Cartilage

The potential of Fourier Transform infrared microspectroscopy (FTIR microspectroscopy) and multivariate analyses were applied for the classification of the frequency ranges responsible for the distribution changes of the main components of articular cartilage (AC) that occur during dietary β-hydroxy-β-methyl butyrate (HMB) supplementation. The FTIR imaging analysis of histological AC sections originating from 35-day old male piglets showed the change in the collagen and proteoglycan contents of the HMB-supplemented group compared to the control. The relative amount of collagen content in the superficial zone increased by more than 23% and in the middle zone by about 17%, while no changes in the deep zone were observed compared to the control group. Considering proteoglycans content, a significant increase was registered in the middle and deep zones, respectively; 62% and 52% compared to the control. AFM nanoindentation measurements collected from animals administered with HMB displayed an increase in AC tissue stiffness by detecting a higher value of Young’s modulus in all investigated AC zones. We demonstrated that principal component analysis and artificial neural networks could be trained with spectral information to distinguish AC histological sections and the group under study accurately. This work may support the use and effectiveness of FTIR imaging combined with multivariate analyses as a quantitative alternative to traditional collagenous tissue-related histology.