7,418 research outputs found

    Analysis of Multivariate Sensor Data for Monitoring of Cultivations

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    Development of chemometric multivariate calibration models for spectroscopic quality analysis of biodiesel blends

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    Thesis (Master)--İzmir Institute of Technology, Chemistry, İzmir, 2011Includes bibliographical references (leaves: 128-132)Text in English; Abstract: Turkish and Englishxiii, 132 leavesThe fact that the biodiesel is produced from renewable resources and environmentally friendly when compared to the fossil-based petroleum diesel, biodiesel has gained an increasing interest. It is mainly produced from a variety of different animal fat and vegetable oil combined with an alcohol in the presence of a homogeneous catalyst and the determination of the quality of the produced biodiesel is as important as its production. Industrial scale biodiesel production plants have been adopted the chromatographic analysis protocols some of which are standard reference methods proposed by official bodies of the governments and international organizations. However, analysis of multi component mixtures by chromatographic procedures can become time consuming and may require a lot of chemical consumption. For this reason, as an alternative, spectroscopic methods combined with chemometrics offer several advantages over classical chromatographic procedures in terms of time and chemical consumption. With the immense development of computer technology and reliable fast spectrometers, new chemometric methods have been developed and opened up a new era for processing of complex spectral data. In this study, laboratory scale produced biodiesel was mixed with methanol, commercial diesel and several different vegetable oils that are used to prepare biodiesels and then several different ternary mixture systems such as diesel-vegetable oil-biodiesel and methanol-vegetable oil-biodiesel were prepared and gas chromatographic analysis of these samples were performed. Then, near infrared (NIR) and mid infrared (FTIR) spectra of the same samples were collected and multivariate calibration models were constructed for each component for all the infrared spectroscopic techniques. Chemometric multivariate calibration models were proposed as genetic inverse least square (GILS) and artificial neural networks (ANN). The results indicate that determination of biodiesel blends quality with respect to chemometric modeling gives reasonable consequences when combined with infrared spectroscopic techniques

    Prediction of extractives and lignin contents of Anatolian black pine (Pinus nigra Arnold. var pallasiana) and Turkish pine (Pnus brutia Ten.) trees using infrared spectroscopy and multivariate calibration

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    Thesis (Master)--İzmir Institute of Technology, Chemistry, İzmir, 2008Includes bibliographical references (leaves: 61-64)Text in English; Abstract: Turkish and Englishxi, 64 leavesDetermination of quality parameters such as extractives and lignin contents of wood by wet chemistry analyses takes long time. Near-infrared (NIR) and mid-infrared (MIR) spectroscopy coupled with multivariate calibration offer fast and nondestructive alternative to obtain reliable results. However, due to complexity of multi-wavelength spectra, wavelength selection is generally required. Turkish pine and Anatolian black pine are the most growing pine species in Turkey. Forest products industry has widely accepted use of these trees because of their ability to grow on a wide range of sites and their suitability to produce desirable products. Determination of extractives and lignin contents of wood provides information to tree breeders when to cut and on how much chemical is needed in pulping and bleaching process. In this study, 58 samples of Turkish pine and 51 samples of Anatolian black pine were collected to investigate the correlation between NIR and MIR spectra of these samples and their extractives and lignin contents which were determined with reference methods. Genetic inverse least squares (GILS) was used for multivariate calibration. Standard error of calibration (SEC) values were less than 1.86% (w/w) for lignin and 1.19% (w/w) for extractives whereas standard error of prediction (SEP) values were less than 3.81% (w/w) for lignin and 2.04% (w/w) for extractives. Resulting R2 values for calibrations were larger than 0.8. Classification for Turkish pine and Anatolian black pine samples was performed by genetic algorithm based principal component analysis (GAPCA) and these two pine species were classified by using NIR and MIR spectra

    Improvement of seed germination of Fagus orientalis Lipsky

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    This thesis was seeking two main approaches for improvement of seed germination of oriental beech, a timber producing species in Hyrcanian forests in northern Iran. Germination behavior of beechnuts was enhanced either by decreasing the dormancy breakage period, or by increasing seed lot quality. A simple grading based on the weight of beechnuts, before exposing the dormant nuts to dormancy breaking conditions, significantly increased germination capacity of heavy class beechnuts, and reduced the period of dormancy breakage. Almost the same results were obtained by removing the endocarp. Applying alternative chilling temperatures, during dormancy breakage had positive effects on speed of dormancy release. These simple methods can be used with little equipment in forest nurseries and are suggested to be accompanied with more advanced techniques, like restricting moisture content during moist cold stratification period to gain maximum benefit. Previous reports from European beech and the results from the effect of endocarp removal suggest a possible role of other agents in dormancy in oriental beechnuts. Water soluble phenolics extracts from the seed coats, significantly suppress the germination of radish seed. The endocarp may act as a barrier against exudation of these germination inhibitors. The deep embryo dormancy presents problems when assessing the viability of oriental beech nuts. It is therefore possible to test germination performance in semi-dormant nuts to predict the nut viability in this species. A dormant seedlot was stored in sub-chilling conditions for 15 months and a series of germination tests were conducted during the dormancy breakage period of stored and fresh nuts. The results showed that mean germination times for both nut groups were almost the same, but germination capacity was statistically different only for semi-dormant nuts. Non-dormant stored and fresh nuts showed no significant differences, which indicate the complexity of dormancy release in oriental beech nuts. Abscisic acid (ABA) contents of embryonic axes of stored and fresh nuts were measured during the dormancy breakage period, and results indicated a close correlation between ABA levels and increment in germination capacity as dormancy was released. Near infrared spectroscopy (NIRS) combined with partial least squares regression (PLS) were used as rapid and non-destructive methods for discrimination of sound and deteriorated single beechnuts. NIRS-PLS is a promising method for quality improvement of nearly all agricultural products and in this study showed 100% accuracy in separation of viable and non-viable nuts

    Materials surface contamination analysis

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    The original research objective was to demonstrate the ability of optical fiber spectrometry to determine contamination levels on solid rocket motor cases in order to identify surface conditions which may result in poor bonds during production. The capability of using the spectral features to identify contaminants with other sensors which might only indicate a potential contamination level provides a real enhancement to current inspection systems such as Optical Stimulated Electron Emission (OSEE). The optical fiber probe can easily fit into the same scanning fixtures as the OSEE. The initial data obtained using the Guided Wave Model 260 spectrophotometer was primarily focused on determining spectra of potential contaminants such as HD2 grease, silicones, etc. However, once we began taking data and applying multivariate analysis techniques, using a program that can handle very large data sets, i.e., Unscrambler 2, it became apparent that the techniques also might provide a nice scientific tool for determining oxidation and chemisorption rates under controlled conditions. As the ultimate power of the technique became recognized, considering that the chemical system which was most frequently studied in this work is water + D6AC steel, we became very interested in trying the spectroscopic techniques to solve a broad range of problems. The complexity of the observed spectra for the D6AC + water system is due to overlaps between the water peaks, the resulting chemisorbed species, and products of reaction which also contain OH stretching bands. Unscrambling these spectral features, without knowledge of the specific species involved, has proven to be a formidable task

    Applied Fourier Transform Near-infrared Techniques for Biomass Compositional Analysis

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    A new method for rapid chemical analysis of lignocellulosic biomass was developed using Fourier transform near-infrared (FT-NIR) spectroscopic techniques. The new method is less time-consuming and expensive than traditional wet chemistry. A mathematical model correlated FT-NIR spectra with concentrations determined by wet chemistry. Chemical compositions of corn stover and switchgrass were evaluated in terms of glucose, xylose, galactose, arabinose, mannose, lignin, and ash. Model development evaluated multivariate regressions, spectral transform algorithms, and spectral pretreatments and selected partial least squares regression, log(1/R), and extended multiplicative signal correction, respectively. Chemical composition results indicated greater variability in corn stover than switchgrass, especially among botanic parts. Also, glucose percentage was higher in internodes (\u3e40%) than nodes or leaves (~30- 40%). Leaves had the highest percentage of lignin (~23-25%) and ash (~4-9%). Husk had the highest total sugar percentage (~77%). Individual FT-NIR predictive models were developed with good accuracy for corn stover and switchgrass. Root mean square errors for prediction (RMSEPs) from crossvalidation for glucose, xylose, galactose, arabinose, mannose, lignin and ash were 0.633, 0.620, 0.235, 0.374, 0.203, 0.458 and 0.266 (%w/w), respectively for switchgrass, and 1.407, 1.346, 0.201, 0.341, 0.321, 1.087 and 0.700 (%w/w), respectively for corn stover. A unique general model for corn stover and switchgrass was developed and validated for general biomass using a combination of independent samples of corn stover, switchgrass and wheat straw. RMSEPs of this general model using cross-validation were 1.153, 1.208, 0.425, 0.578, 0.282, 1.347 and 0.530 %w/w for glucose, xylose, galactose, arabinose, mannose, lignin and ash, respectively. RMSEPs for independent validation were less than those obtained by cross-validation. Prediction of major constituents satisfied standardized quality control criteria established by the American Association of Cereal Chemists. Also, FT-NIR analysis predicted higher heating value (HHV) with a RMSEP of 53.231 J/g and correlation of 0.971. An application of the developed method is the rapid analysis of the chemical composition of biomass feedstocks to enable improved targeting of plant botanic components to conversion processes including, but not limited to, fermentation and gasification

    Applying Mechanistic Understanding of Optical Absorption and Scattering Phenomena to Enhance the Spectroscopic Analyses of Pharmaceutical Materials

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    The dissertation uses spatially-resolved spectroscopy to separate absorption and scattering behaviors when NIR light interacts with pharmaceutical materials. The separated absorption and scattering were utilized to enhance mechanistic understanding of NIR diffuse reflectance spectroscopy and improve practical spectroscopic analysis in pharmaceutical applications. Near-Infrared (NIR) chemical imaging based spatially-resolved spectroscopy was used to measure radially-diffused reflectance on pharmaceutical materials. A Monte Carlo simulation based partial least square (PLS) model was constructed to determine the absorption and reduced scattering coefficients in pharmaceutical samples from the measured radially-diffused reflectance. The separated absorption and reduced scattering coefficients were combined with Monte Carlo simulation to provide understanding of the effects of physical properties (e.g., particle size and tablet density) on NIR spectral responses, including absorption and depth of penetration profiles. It was discovered that absorption and reduced scattering coefficients are the dominant factors in determining NIR absorbance and depth of penetration profiles, respectively. Both empirical measurements and Monte Carlo simulation were used to explore the photon radial movements in a chemical imaging setting. It is well understood that radial photon movements among adjacent pixels leaded to blurred 2-D chemical images. A Monte Carlo simulation based deconvolution filter was developed to sharpen a blurred feature in a 2-D image while maintaining the original chemical content of that feature. A new scattering correction method via the reduced scattering coefficient was proposed to specifically reduce physical interference with predictions of chemical properties. The wavelength- and absorption- dependent properties of the reduced scattering coefficient were found to allow specific suppression of physical interference and maintain the original chemical information. Combing the separated optical coefficients with contemporary efficient calibration approaches was found to simplify multivariate model calibration using a reduced calibration dataset, allowing parsimonious multivariate models, and reaching the same or even lower prediction error. To the author\u27s best knowledge, this work is the first example of the application of spatially-resolved spectroscopy to the pharmaceutical field. The enhanced understanding and improved spectroscopic analysis demonstrated in this dissertation is expected to provide groundwork for a wide variety of applications of spatially-resolved spectroscopy in pharmaceutical analyses

    Rapid characterization of biomass: The use of near infrared and fluorescence spectroscopy as process analytical technology (PAT) method

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    The heterogeneous property of biomass (wood) affects its potential of being converted into any form of fuel in different ways (both positive and negative effects). Therefore in other to efficiently utilized biomass as a raw material for conversion into any form of clean alternative fuel to displace some of the fossil fuel we consume in the United State on a commercial scale basis, a quick, robust, non destructive on/in/at-line method of characterizing the physical and chemical properties of biomass that are relevant to the bio-refinery industry is imperative.;This study discusses the potential of using near infrared spectroscopy (NIRS) and fluorescence spectroscopy (FS) coupled with multivariate data analysis (MVDA) as a robust and rapid process analytical technology (PAT) to characterize the physical and chemical properties of two potential biomass feedstock (yellow-poplar and northern red oak) in its solid state. This study is aimed at rapidly detecting the properties of potential biomass feedstock to be used in the bio-refinery online before any conversion process is begun. This will reduce cost of manufacturing bio-fuels, provide real time results of biomass characteristics reduce waste and produce a much consistent product. The potential utilization of fluorescence spectrometer which is much cheaper, rapid and sensitive spectrometer with equal model performance as the NIR spectrometer models will reduce the cost of PAT even further.;Generally, the results of this study showed that both NIR and FS can be used as rapid PAT method to characterize the physical and chemical properties of northern red oak and yellow-poplar with moderate to high prediction performance. The NIR prediction models generally exhibited slightly higher prediction model performance as compared to similar models of the same response variable developed with the fluorescence spectra data
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