Browsing by Subject "Chemometrics."
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Item Application of chemometric analysis to UV-visible and diffuse near-infrared reflectance spectra.(2007-08-21T16:23:23Z) Davis, Christopher Brent.; Busch, Kenneth W.; Busch, Marianna A.; Chemistry and Biochemistry.; Baylor University. Dept. of Chemistry and Biochemistry.Multivariate analysis of spectroscopic data has become more common place in analytical investigations due to several factors, including diode-array spectrometers, computer-assisted data acquisition systems, and chemometric modeling software. Chemometric regression modeling as well as classification studies were conducted on spectral data obtained with chili peppers and fabrics samples. Multivariate regression models known as partial least squares (PLS-1) were developed from the spectral data of alcoholic extracts of Habanero peppers. The developed regression models were used to predict the total capsaicinoids concentration of a set of unknown samples. The ability of the regression models to correctly predict the total capsaicinoids concentration of unknown samples was evaluated in terms of the root mean square error or prediction (RMSEP). The prediction ability of the models produced was found to be robust and stable over time and in the face of instrumental modifications. A near-infrared spectral database was developed from over 800 textile samples. Principal components analysis (PCA) was performed on the diffuse near-infrared reflectance spectra from these commercially available textiles. The PCA models were combined together into a soft independent modeling of class analogy (SIMCA) in order to classify the samples according to fiber type. The samples in the study had no pretreatments. The discriminating power of these models was tested by creating validation sets within a given fiber type as well as attempting to classify samples into a category that they do not belong to. The apparent sub-class groupings within the same fiber class were investigated as to whether or not they were caused by chemical processing residues, multipurpose finishes, or dyes.Item Chemometric modeling of UV-visible and LC-UV data for prediction of hydrolysate fermentability and identification of inhibitory degradation products.(2011-12-19) Hedayatifar, Negar.; Chambliss, C. Kevin.; Chemistry and Biochemistry.; Baylor University. Dept. of Chemistry and Biochemistry.Production of ethanol from lignocellulosic biomass requires a pretreatment step to liberate fermentable sugars trapped within the plant. During pretreatment, lignin and some sugars undergo degradation to form compounds which have shown inhibitory effects to fermentative microorganisms. Accordingly, development of a rapid and accurate method for assessment of microbial inhibition and identification of inhibitory compounds is essential for gaining a better understanding of pretreatment and its downstream effects on fermentation processes. Traditional methods for identification of inhibitory compounds involve a “bottom-up” approach. Using this approach, one or more known degradation compounds are added to fermentation media and their effects on batch fermentation of ethanol are observed. These methods are extremely time-consuming and labor-intensive which makes them unattractive to researchers. Furthermore, they are carried out on degradation compounds that have already been identified. Given that biomass hydrolysates contain many unidentified constituents, identification of inhibitory compounds by traditional means is unlikely to occur on a timescale that is consistent with current mandates for commercial production of cellulosic ethanol. To address these limitations, we have developed a chemometric model that correlates ultraviolet (UV)-visible spectroscopic data of 21 different biomass hydrolysates with their fermentability (percent inhibition of ethanol production). This novel approach enables rapid prediction of hydrolysate fermentability using UV-visible spectroscopic data alone and offers significant improvements in throughput and labor when compared to traditional batch fermentation methods. The model was subsequently used to predict percent inhibition for five hydrolysate samples, with a root-mean-square error of prediction of 6%. To evaluate the use of chemometric modeling for identification of inhibitory compounds in biomass hydrolysate, a second model was developed to correlate HPLC-UV chromatographic data of the 21 hydrolysates with their percent inhibition. Detection was monitored at four specific wavelengths identified by the UV-visible model as significant spectral regions. Once constructed, the HPLC-UV model was used to identify retention times that had the highest correlation with inhibition. To determine whether better resolution or more universal detectability of sample constituents may lead to identification of additional retention times, a third chemometric model was developed with chromatographic data of hydrolysates obtained via ion chromatography with conductivity detection.Item Multivariate analyses of near-infrared and UV spectral data.(2009-07-01T16:59:19Z) Dogra, Jody A.; Busch, Kenneth W.; Busch, Marianna A.; Chemistry and Biochemistry.; Baylor University. Dept. of Chemistry and Biochemistry.Various chemometric analyses were applied to spectroscopic data with goals to develop alternative methods that could be employed in government or industrial settings. With the concerns of these organizations in mind, the described methods are cost-effective and time-efficient. The first method is aimed at establishing a time of death from skeletal remains—an issue that continues to be difficult for the forensic community. Following death, the skeletal remains undergo changes in chemical composition. This includes the breakdown of protein and the loss of water. Near-infrared spectroscopy is sensitive to vibrations associated with both protein and water. In the described method, near-infrared reflectance measurements of aging porcine skeletal remains were correlated to postmortem interval (PMI). Initial studies were conducted to determine the optimum sampling orientation—cross-sectional or surface. Several chemometric approaches were investigated, but the best results were obtained through a scheme involving classification by partial least-squares discriminant analysis (PLS–DA) followed by segmented partial least-squares regression (PLSR). The method was evaluated through independent test sets. The optimized method was able to predict PMI with an average deviation of six days. A brief field study was also conducted and yielded similar results. The second study relates to a present analytical encumbrance faced by the pharmaceutical industry, namely assuring the enantiomeric purity of chiral active pharmaceutical ingredients (APIs). With the rising number of chiral drugs on the market, the analytical burden continues to increase. Ultraviolet absorption spectral data were correlated to enantiomeric composition by PLSR for solutions containing a chiral analyte and a chiral ionic liquid (IL) as a chiral selector. Test set evaluation gave results of average deviations of ± 4.0–12 units of %D depending on the analyte and chiral IL involved. Finally, a quality control analysis was demonstrated, which follows a classification format where the sample either meets or does not meet the specified requirement regarding enantiomeric purity. Test set evaluation gave results of 97% correct classifications for a threshold of 1% impurity.Item Multivariate analysis of luminescence spectra as a means of determining postmortem interval.(2011-09-14) Diamond, Patricia A.; Busch, Kenneth W.; Busch, Marianna A.; Chemistry and Biochemistry.; Baylor University. Dept. of Chemistry and Biochemistry.Post-mortem interval (PMI) is the time elapsed since a person died. Currently there is no accurate method for determining PMI of skeletal remains. Existing methods are best suited for deciding whether a bone is of forensic interest, meaning less than fifty years old. This is a problem for areas that have extreme climates, specifically those areas that experience high heat and high humidity, which accelerate decomposition. The objective of this study was to develop a method to accurately predict PMI of skeletal remains through luminescence studies of the change in the intensity of the luminol reaction with skeletal remains over time. Previous research in the area demonstrated that a correlation can be found between the PMI and the change in intensity over long periods of time. This research aims to demonstrate a similar correlation with PMI and to correctly predict the PMI of skeletal remains over much shorter age ranges.