b appalachian Hardwood center, West Virginia university, Morgantown, WV 26506-6125. uSa In this study, a method suitable for online rapid classification and separation of red oak and white oak wood species has been developed with the use of near infrared (NIR) spectroscopy and soft independent modeling of class analogies (SIMCA). The spectra of 10 wood specimens of each species were collected over wavelength window of 800-200 nm. The raw spectra and spectra pre-treated by first derivative and standard normal variate (SNV) transformation were used to develop calibration models using the wavelength ranges 800-200 nm, 1100-2200 nm and 1400-1900 nm by the SIMCA method. Principal component analysis (PCA) models were made for each class from the calibration set consisting of 100 specimens of red oak and white oak, respectively, and specimens not present in the calibration set (Testing set) consisting of 100 specimens were tested for classification according to the SIMCA method at the % and 2% significance level. Type I error (rejection of true member) associated with the models developed ranged from 2% to 20% and from 1% to %, respectively, for % and 2% significance level, while type II error (acceptance of a false member) was 0% for all models developed. There was no significant improvement in models developed with spectra pre-treated with first derivative or standard normal variate transformation over models developed with raw spectra. The full NIR spectral region of 800-200 nm provided the most useful information for distinguishing between the two species. The results of this study showed that NIR spectroscopy coupled with the SIMCA method of multivariate data analysis could be used to reliably separate and sort wood of red oak and white oak.

This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.

Alkali-activated binder (AAB) is recently being considered as a sustainable alternative to portland cement (PC) due to its low carbon dioxide emission and diversion of industrial wastes and by-products such as fly ash and slag from landfills. In order to comprehend the behavior of AAB, detailed knowledge on relations between microstructure and mechanical properties are important. To address the issue, a new approach to characterize hardened pastes of AAB containing fly ash as well as those containing fly ash and slag was adopted using scanning electron microscopy (SEM) and energy dispersive X-ray spectra microanalyses. The volume stoichiometries of the alkali activation reactions were used to estimate the quantities of the sodium aluminosilicate (N-A-S-H) and calcium silicate hydrate (CSH) produced by these reactions. The 3D plots of Si/Al, Na/Al and Ca/Si atom ratios given by the microanalyses were compared with the estimated quantities of CSH(S) to successfully determine the unique chemical compositions of the N-A-S-H and CSH(S) for ten different AAB at three different curing temperatures using a constrained nonlinear least squares optimization formulation by general algebraic modeling system. The results show that the theoretical and experimental quantities of N-A-S-H and CSH(S) were in close agreement with each other. The R 2 values were 0.99 for both alkali-activated fly ash and alkali-activated slag binders.

This is the first of a two-paper series that reports on the use of fluorescence spectroscopy coupled with multivariate data analysis as a potential process analytical tool to develop calibration and prediction models for some physical and chemical properties of yellow poplar (Liriodendron tulipifera L.). Waste streams emanating from the processing of this wood species may potentially serve as feedstock for biofuels and biochemicals With the exception of holocellulose content, all the properties considered in the study were predicted with moderate to strong coefficient of determination (R2). Fluorescence spectra-based prediction model for each property considered in this study was compared with near infrared (NIR) spectra-based prediction models of similar properties from a previous study using the same population. The NIR-based prediction models exhibited slightly superior model strength over the fluorescence spectra-based prediction models of similar properties.

Background Established bacterial diagnostic techniques for orthopaedic-related infections rely on a combination of imperfect tests that often can lead to negative culture results. Spectroscopy is a tool that potentially could aid in rapid detection and differentiation of bacteria in implantassociated infections. Questions/purposes We asked: (1) Can principal component analysis explain variation in spectral curves for biofilm obtained from Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa? (2) What is the accuracy of Fourier transformed-near infrared (FT-NIR)/multivariate data analysis in identifying the specific species associated with biofilm? Methods Three clinical isolates, S aureus, S epidermidis, and P aeruginosa were cultured to create biofilm on surgical grade stainless steel. At least 52 samples were analyzed per group using a FT-NIR spectrometer. Multivariate and principal component analyses were performed on the spectral data to allow for modeling and identification of the bacterial species. Results Spectral analysis was able to correctly identify 86% (37/43) of S aureus, 89% (16/18) of S epidermidis, and 70% (28/40) of P aeruginosa samples with minimal error. Overall, models developed using spectral data preprocessed using a combination of standard normal variant and first-derivative transformations performed much better than models developed with the raw spectral data in discriminating between the three classes of bacteria because of its low Type 1 error and large intermodel distinction. Conclusions The use of spectroscopic methods to identify and classify bacterial biofilms on orthopaedic implant material is possible and improves with advanced modeling that can be obtained rapidly with little error. The sensitivity for identification was 97% for S aureus (95% CI, 88-99%), 100% for S epidermidis (95% CI, 95-100%), and 77% for P aeruginosa (95% CI, 65-86%). The specificity of the S aureus was 86% (95% CI, 3-93%), S epidermidis was 89% (95% CI, 67-97%), and P aeruginosa was 70% (95% CI, 55-82%). Clinical Relevance This technique of spectral data acquisition and advanced modeling should continue to be explored as a method for bacterial biofilm identification. A spectral The institution of one or more of the authors (JET) has received, during the study period, funding from