Wood identification using pressure DSC data

This work addresses the problem of supervised classification of industrial wood species (seven different types in the present study) through their thermo‐oxidative stability. This is evaluated by pressure differential scanning calorimetry (PDSC) using the ASTM E2009. The maximization of the ratio of correct classification and the reduction of the costs of this activity are intended. This supervised classification problem was carried out using two different proposals: applying novel nonparametric functional data analysis techniques, based on kernel estimation, to the original PDSC curves, and using machine learning classification approaches applied to different multivariate data sets. The multivariate data sets were obtained, on the one hand, by estimating the fractal (Hausdorff) dimension of the PDSC curves by several methods, jointly with selecting the parameters from fitting a nonlinear model to the PDSC curves and, on the other hand, applying principal component analysis or partial linear squares to the thermograms. The results obtained show that the PDSC curves can be used to discriminate wood samples when these innovative and traditional statistical techniques are applied. In the best of the cases, a probability of correct classification that equals to 0.92 was obtained. PDSC represents a new alternative to the use of images, spectra, and other thermal signals as thermogravimetric analysis for classification purposes.Copyright © 2013 John Wiley & Sons, Ltd.     

FTIR spectroscopy coupled with multivariate classification methods to identify different powdered infant formulas adulterated with melamine and cyanuric acid

Fourier transform infrared spectroscopy (FTIR) is a nondestructive, simple, rapid, and cheap measurement technique for analysis of many multicomponent chemical systems, e.g., detection of adulterants in food samples. In this respect, this study proposes combining FTIR spectroscopy with multivariate classification methods for classification and discrimination of different samples of infant formulas adulterated by melamine or/and cyanuric acid. Different parametric and non-parametric multivariate classification methods including the linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and classification and regression tree (CART) approaches were used to classify the recorded FTIR data. Assessing the performance of the multivariate methods according to their sensitivity, specificity and percent of correct prediction results demonstrated that coupling FTIR spectroscopy with multivariate classification can be applied as a rapid and powerful technique to the simultaneous detection of melamine and cyanuric acid in powdered infant formulas. This combinatorial method is efficient for adulterant concentrations as low as 0.0001 w/w%.     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.     

Characterization of fossil Sequoioxylon wood using analytical instrumental techniques

In this study, fossil (Sequoioxylon) wood from the Oligocene–Miocene transition in İstanbul, Turkey was examined using non-destructive test methods to evaluate changes in anatomical and chemical structure. Molecular changes in the cell wall structure of the wood were determined using Fourier transform infrared (FTIR) and FT-Raman spectroscopy, along with the comparison to recent wood [Sequoiadendron giganteum (Lindl.)]. We found that the cell wall carbohydrates of the fossil wood were significantly more degraded compared with lignin; FT-Raman spectroscopy revealed the degradation in more detail compared with FTIR spectroscopy. FT-Raman spectra also demonstrated that hemicellulose and holocellulose were decreased in the fossil wood. Laser-induced breakdown spectroscopy (LIBS) analysis confirmed that the mass loss was due to the decreased H and O content of the fossil wood sample and was caused by decomposition. Light microscopy also showed that fossil and recent woods have similar anatomic structures, and that the micro-morphological structure of the fossil wood was well-preserved.     

Functional nonparametric classification of wood species from thermal data

In this study, thermogravimetric (TG) and differential scanning calorimetry (DSC) curves, obtained by means of a simultaneous TG/DSC analyzer, and statistical functional nonparametric methods are used to classify different wood species. The temperature ranges, where the highest probability of correct classification is reached, are also computed. As each observation is a curve, a nonparametric functional discriminant technique based on the Bayes rule and the Nadaraya–Watson regression estimator is used. It assigns a future observation to the highest probability predefined class (supervised classification). The smoothing parameter needed in this nonparametric method is selected according to the cross-validation technique. The method proposed is applied to a sample of 49 wood items (7 per wood class) and also to classify between hardwoods and softwoods. In all the cases, the samples have been successfully classified, obtaining better results with the TG curves. The results are compared with those obtained with other nonparametric methods based on boosting algorithm. A discussion about the relation of the obtained results with the referenced wood component degradation temperature ranks is presented.     

Thermogravimetric and kinetic methods to date wood finds. First results

Fresh (larch and fir, in its white and red varieties) and ancient wood samples (dating respectively to the 13th, 15th and 17th centuries) were subjected to thermogravimetric analysis (TG and DTG). The resulting thermogravimetric data were then used to construct archeometric curves for the wood varieties tested. In a preliminary approach, it was attempted to correlate the onset temperature of the thermogravimetric step corresponding to cellulose decomposition with the age (expressed in centuries) of the samples, although the results obtained were anything but brilliant. More encouraging results were obtained by examining the relationship between wood sample age and the value of the (percent cellulose/percent lignin) ratio computed from the thermogravimetric data. Lastly, a procedure for processing data obtained from the TG curves was applied to a kinetic analysis of the processes that take place when wood samples are subjected to a temperature regime with a constant heating rate, obtaining values for the activation energy of the TG step corresponding to the decomposition of cellulose. Also using these data it was attempted to construct archeometric curves, obtaining results that varied quite significantly according to the wood species tested.     

Discrimination of genetically modified poplar clones by analytical pyrolysis–gas chromatography and principal component analysis

Analytical pyrolysis combined with gas chromatography and mass spectrometry (Py-GC–MS) is a relatively rapid (1–3 h) method for the investigation of polymers. Various wood tissues from transgenic poplar clones and from control samples have been subjected to a screening test by Py-GC–MS. Pyrolysis products from lignin- and carbohydrate-derived pyrolysis products were subjected to multivariate principal component analysis (PCA). The first three PC accounting for 39–72% of the total variance in the original data set could be attributed to vinyl products from lignin and levoglucosan from cellulose. Samples with gene construct rbcs-rol C were only discriminated by plotting PC1 versus PC3 using the whole data set. However, the wood from trees containing gene construct 35 S-rol C were discriminated in all examined models indicating significant impacts during biosynthesis of the wood. One sample within the data set was further clustered because it turned out that this tree died off after two vegetation periods.     

Characterisation and Classification of Foodborne Bacteria Using Reflectance FTIR Microscopic Imaging

This work investigates the application of reflectance Fourier transform infrared (FTIR) microscopic imaging for rapid, and non-invasive detection and classification between Bacillus subtilis and Escherichia coli cell suspensions dried onto metallic substrates (stainless steel (STS) and aluminium (Al) slides) in the optical density (OD) concentration range of 0.001 to 10. Results showed that reflectance FTIR of samples with OD lower than 0.1 did not present an acceptable spectral signal to enable classification. Two modelling strategies were devised to evaluate model performance, transferability and consistency among concentration levels. Modelling strategy 1 involves training the model with half of the sample set, consisting of all concentrations, and applying it to the remaining half. Using this approach, for the STS substrate, the best model was achieved using support vector machine (SVM) classification, providing an accuracy of 96% and Matthews correlation coefficient (MCC) of 0.93 for the independent test set. For the Al substrate, the best SVM model produced an accuracy and MCC of 91% and 0.82, respectively. Furthermore, the aforementioned best model built from one substrate was transferred to predict the bacterial samples deposited on the other substrate. Results revealed an acceptable predictive ability when transferring the STS model to samples on Al (accuracy = 82%). However, the Al model could not be adapted to bacterial samples deposited on STS (accuracy = 57%). For modelling strategy 2, models were developed using one concentration level and tested on the other concentrations for each substrate. Results proved that models built from samples with moderate (1 OD) concentration can be adapted to other concentrations with good model generalization. Prediction maps revealed the heterogeneous distribution of biomolecules due to the coffee ring effect. This work demonstrated the feasibility of applying FTIR to characterise spectroscopic fingerprints of dry bacterial cells on substrates of relevance for food processing.     

Determination of motor gasoline adulteration using FTIR spectroscopy and multivariate calibration

In this paper, an attempt has been made to develop a feasible procedure for the prediction of quality parameters of motor gasoline and to discriminate between the different adulterated motor gasoline samples using density values, distillation temperatures and Fourier transform infrared (FTIR) analyses along with multivariate calibrations without the need for using chromatographic separation or other expensive instruments such as an octane number analyser. Ten blend mixtures of regular and super motor gasoline were prepared in order to study density, distillation temperatures and FTIR spectra characteristics for each blend. Distillation temperatures for the pure and blend mixtures of regular and super motor gasoline at initial boiling point (IBP) to final boling point (FBP) at 5%Vol. interval were obtained. Accurate and complete distillation data on the uncontaminated fuel would be essential for comparison. Thirteen peaks of the absorbance at the wavenumbers: 434, 461, 484, 673, 694, 1030, 1086, 1217, 1231, 1460, 1497, 1606 and 3028 cm(-1) were chosen to perform the multivariate calibration. The results obtained were expected to be useful in determination and differentiation purposes, providing information on whether the density values, distillation temperatures and FTIR analyses along with multivariate method could be an appropriate feature for differentiating a particular pure motor gasoline sample from the others. The observed differences in the specific spectral bands are investigated and discussed. They have proven to be an effective combination in the pursuit of management's differentiation goals.     

Feasibility of using dendroanalysis of uranium as a biomarker for environmental contamination

A study was initiated to investigate the chronological deposition of uranium in certain species of trees growing on the site of a former uranium metal processing facility. The Feed Materials Production Center (FMPC) is located in Fernald, Ohio, and for roughly 40 years operated as a large scale uranium processing center. Core samples from several species of trees growing in different locations throughout the site were extracted using a 12.5 mm incremental wood boring drill bit. After extraction, each core sample was cut and packaged into individual sections representing 4 annual growth rings and submitted for instrumental neutron activation analysis (INAA). The reaction ²³⁵U(n,f)¹⁴⁰Ba→¹⁴⁰La+γ was evaluated using high resolution germanium gamma-spectroscopy to detect the 1.596 MeV photon emission from the fission product ¹⁴⁰La following a minimum of a 3 week decay. A total of 106 samples representing 7 individual trees of 3 unique species were irradiated. In addition to the tree-core samples, 18 quality control (QC) samples and 18 standard reference material (SRM) Fly Ash samples were irradiated with the core samples for determining neutron flux. The activity in any one sample in a batch was determined by comparison with the amount of natural uranium in the QC standards. No significantly measurable amount of uranium was detected in any of the tree core samples, although 3 tree core samples were in excess of the minimum detectable amount (30 ng).     

Earth pigments in painting: characterisation and differentiation by means of FTIR spectroscopy and SEM-EDS microanalysis

Analytical characterisation of natural earths (ochres, siennas, umbers and green earths) has been carried out using Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) coupled to an energy dispersive X-Ray spectrometer (EDS). The study of these pigments, which are found in works of art, is very important since it can shed light on their source or the pictorial technique used. FTIR spectroscopy is suitable for the identification and differentiation of ochres and siennas. According to the matrix of the sample, FTIR allows the classification of ochres into ochres containing kaolinite and ochres containing sulphate. One of the goals of this research has been to establish a relationship between the matrix and the source of the samples tested. SEM-EDS is probably a better technique than FTIR for characterising umbers and green earths since they do not exhibit significant differences when FTIR studies are performed.     

Characterization of Refuse Derived Fuel Using Thermogravimetric Analysis and Chemometric Techniques

An approach for characterization of refuse derived fuel (RDF) using thermogravimetry and chemometric techniques was developed. For this purpose, a series of samples coming from lignocellulosic products (wood, cardboard, paper, newspaper) and plastics (polyethyleneterephthalate, high density polyethylene, polypropylene, polypropylene, nylon and polyvinylchloride), as well as their mixtures, were investigated by means of thermogravimetry (TG) in a temperature range between 25 and 800°C. The datapoints of TG diagrams (weight loss) were then subjected to principal component analysis in order to unravel similarities/ dissimilarities of the investigated samples. A classification was obtained according to their woody/petroleum derived origination. This classification was more evident if partial least square discriminant analysis was employed. Finally, a partial least square analysis was carried out for the determination of lignocellulosic content in the sample. The model was validated by application to samples with known mass fraction of lignocellulosic products. Finally, the model was applied to two RDF samples using fractions of their particle sizes from 1 mm to less than 0.032 mm, and the results were compared with their ultimate and proximate analysis.     

Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection

An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).     

Characterization of paper finishes by use of infrared spectroscopy in combination with canonical variate analysis

The finishing process used by the paper industry involves subjecting the paper surface to the action of chemicals and physical treatments in a series of operations intended to provide an end-product suitable for its intended use. In this work, we studied various paper finishes by using infrared spectra processed with appropriate chemometric techniques. To this end, we used a wide range of paper samples supplied in various finishes (coated, offset and cast-coated) by several paper manufacturers. Fourier transform middle-infrared (FTIR) spectra for the paper samples were recorded by using an ATR module, and reflectance near-infrared (NIR) spectra with the aid of a fibre-optic probe. Both techniques are fast and require no sample pretreatment.The primary aim of this work was to develop a new methodology affording the accurate classification and identification of paper finishes in samples other than those used to construct the calibration model. To this end, we used the discriminant chemometric techniques principal component analysis (PCA) and canonical variate analysis (CVA), application of which was followed by that of the k-nearest neighbour algorithm to the samples in the prediction set. This procedure was also used to classify the coated samples into three subgroups. Both FTIR and NIR spectroscopy allowed most of the samples in the prediction sets to be accurately classified and identified.     

Monitoring the polymerization process of polypyrrole films by thermogravimetric and X-ray analysis

Bio-composite fibers were developed from wood pulp and polypropylene (PP) by an extrusion process. The thermo-physical and mechanical properties of wood pulp-PP composite fibers, neat PP and wood pulp were studied using thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and dynamic mechanical analysis (DMA). The thermal stability of bio-composite fibers was found to be significantly higher than pure wood pulp. An understanding into the melting behaviour of the composite system was obtained which would assist in selecting a suitable temperature profile for the extruder during processing. The visco-elastic properties of bio-composite fibers were also revealed from the study. The generated bio-composite fibers were also characterized using Fourier transform infrared spectroscopy (FTIR) to understand the nature of chemical interaction between wood pulp reinforcement and PP matrix. The use of maleated polypropylene (MAPP) as a compatibilizer was investigated in relation to the fiber microstructure. Changes in absorption peaks were observed in FTIR spectra of bio-composite fibers as compared to the pure wood pulp which indicated possible chemical linkages between the fiber and polymer matrix.     

Length determination of vessel elements in tree trunks used for water and nutrient transport by Fourier transform Raman spectroscopy

Length analysis of vessel elements in tree trunks used for water and nutrient transport is a lengthy, multistep procedure although it reflects environmental stresses on a tree. The feasibility of using FT-Raman spectroscopy for rapid determination of vessel element length in a tree was examined using wood powders of two Eucalyptus species, including samples of various ages and colors. The first-derivative transformation followed by the multiplicative scatter correction of Raman spectroscopic data and the partial least-squares regression revealed highly significant correlation between conventionally measured and Raman-predicted vessel element length with correlation coefficients (r) of 0.843 and 0.826, respectively, in the calibration (for known samples, n=186) and in the prediction (for unknown samples, n=40). FT-Raman spectroscopy coupled with multivariate data analysis will contribute to solving the interactions between emerging environmental issues and the anatomical structure of wood, which allow efficient management practices in growing forests to fix atmospheric CO₂ effectively.     

Effect of sol-gel derived nano-silica and organic peroxide on the thermal and mechanical properties of low-density polyethylene/wood flour composites

The influence of nano-silica, synthesized and mixed with low-density polyethylene (LDPE) through a sol-gel process, on the thermal and mechanical properties of LDPE and LDPE/wood flour (WF) composites, prepared in the absence and presence of dicumyl peroxide, was investigated. Scanning electron microscopic (SEM) analyses show a uniform dispersion of silica nano-particles of size 10-50 nm in the matrix, and Fourier-transform infrared (FTIR) spectroscopic results indicated interaction between the nano-silica and the LDPE matrix, which seems to improve for samples prepared in the presence of dicumyl peroxide (DCP). WF and nano-silica, as well as the presence of DCP during sample preparation, substantially improve the thermal stability of the LDPE matrix. The tensile strength of the samples decreased with increasing wood flour content, while the tensile modulus substantially increased. The presence of nano-silica gave rise to lower values for both tensile strength and tensile modulus, while higher tensile strength (and an increase in tensile strength with WF content) is observed for samples prepared in the presence of DCP. The tensile modulus increases with increasing WF content, but is not substantially influenced by the presence of nano-silica or by sample preparation in the presence of DCP. The DMA results were in line with the tensile results.     

Pyrolysis of waste materials using TGA-MS and TGA-FTIR as complementary characterisation techniques

Pyrolysis of waste materials, biomass wood waste, waste tyre, refuse derived fuel (RDF) and waste plastic was performed using two thermogravimetric analysers (TGA). One TGA was coupled to a mass spectrometer (MS) and the other to an infrared spectrometer (FTIR). The kinetic parameters of the pyrolysed waste materials obtained for TGA-MS and TGA-FTIR were compared using a model based on first-order reactions with a distribution of the activation energies. A further comparison of the volatile species evolved by thermal degradation (TGA) and the subsequent characterisation by the MS and FTIR spectra was performed. The first-order reaction pathways and subsequent activation energies calculated from the differential TGA data presented good repeatability between the TGA-MS and TGA-FTIR. The TGA-MS and TGA-FTIR produced a broad spectrum of qualitative data characterising the volatile gaseous fraction of the waste materials pyrolysed. TGA-MS and TGA-FTIR are shown to be valuable techniques in corroborating the respective thermograms and spectrograms of the volatile species evolved during the pyrolysis of waste materials. However both techniques are prone to interference and careful interpretation of the spectra produced is required.     

Thermoplasticization of wood. II. Cyanoethylation

Cyanoethylation was attempted to convert wood into thermoplastic material as a means to utilize low quality wood species as well as wood waste materials. Cyanoethylation reaction was conducted with control of the alkalinity of the reaction media, reaction temperature and time, and wood-to-acrylonitrile ratio. Cyanoethylated wood was purified and its nitrogen content and weight gain were determined. Fourier transform infrared (FTIR) spectroscopy was used to monitor the absorption peak of cyano group. Thermoproperties of cyanoethylated wood were analyzed by means of a differential scanning calorimeter (DSC) and a dynamic mechanical thermal analyzer (DMTA). Reaction mechanisms and chemistry influencing the thermoproperties of cyanoethylated wood were discussed.     

Laser desorption/ionization mass spectrometry fingerprinting of complex hydrocarbon mixtures: application to crude oils using data mining techniques

Crude oil fingerprints were obtained from four crude oils by laser desorption/ionization mass spectrometry (LDI-MS) using a silver nitrate cationization reagent. Replicate analyses produced spectral data with a large number of features for each sample (>11,000 m/z values) which were statistically analyzed to extract useful information for their differentiation. Individual characteristic features from the data set were identified by a false discovery rate based feature selection procedure based on the analysis of variance models. The selected features were, in turn, evaluated using classification models. A substantially reduced set of 23 features was obtained through this procedure. One oil sample containing a high ratio of saturated/aromatic hydrocarbon content was easily distinguished from the others using this reduced set. The other three samples were more difficult to distinguish by LDI-MS using a silver cationization reagent; however, a minimal number of significant features were still identified for this purpose. Focus is placed on presenting this multivariate statistical method as a rapid and simple analytical procedure for classifying and distinguishing complex mixtures.