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

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

Detection and Identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via Machine Learning Based FTIR Spectroscopy

The Bacillus cereus group comprises genetical closely related species with variable toxigenic characteristics. However, detection and differentiation of the B. cereus group species in routine diagnostics can be difficult, expensive and laborious since current species designation is linked to specific phenotypic characteristic or the presence of species-specific genes. Especially the differentiation of Bacillus cereus and Bacillus thuringiensis, the identification of psychrotolerant Bacillus mycoides and Bacillus weihenstephanensis, as well as the identification of emetic B. cereus and Bacillus cytotoxicus, which are both producing highly potent toxins, is of high importance in food microbiology. Thus, we investigated the use of a machine learning approach, based on artificial neural network (ANN) assisted Fourier transform infrared (FTIR) spectroscopy, for discrimination of B. cereus group members. The deep learning tool box of Matlab was employed to construct a one-level ANN, allowing the discrimination of the aforementioned B. cereus group members. This model resulted in 100% correct identification for the training set and 99.5% correct identification overall. The established ANN was applied to investigate the composition of B. cereus group members in soil, as a natural habitat of B. cereus, and in food samples originating from foodborne outbreaks. These analyses revealed a high complexity of B. cereus group populations, not only in soil samples but also in the samples from the foodborne outbreaks, highlighting the importance of taking multiple isolates from samples implicated in food poisonings. Notable, in contrast to the soil samples, no bacteria belonging to the psychrotolerant B. cereus group members were detected in the food samples linked to foodborne outbreaks, while the overall abundancy of B. thuringiensis did not significantly differ between the sample categories. None of the isolates was classified as B. cytotoxicus, fostering the hypothesis that the latter species is linked to very specific ecological niches. Overall, our work shows that machine learning assisted (FTIR) spectroscopy is suitable for identification of B. cereus group members in routine diagnostics and outbreak investigations. In addition, it is a promising tool to explore the natural habitats of B. cereus group, such as soil

The CFA Franc: French monetary imperialism in Africa

Ndongo Samba Sylla argues that the CFA franc - officially created on 26 December 1945 by a decree of General de Gaulle - used across much of Africa today is a colonial relic. For those hoping to export competitive products, obtain affordable credit, work for the integration of continental trade, or fight for an Africa free from imperialist control, the CFA franc is an anachronism demanding orderly and methodical eliminatio

Ultrastructural Analysis of Urinary Stones by Microfocus Computed Tomography and Comparison with Chemical Analysis

Objective: To investigate the ultra-structure of urinary system stones using micro-focus computed tomography (MCT), which makes non-destructive analysis and to compare with wet chemical analysis. Methods: This study was carried out at the Ankara Train­ing and Research hospital. Renal stones, removed from 30 patients during percutaneous nephrolithotomy (PNL) surgery, were included in the study. The stones were blindly evaluated by the specialists with MCT and chemi­cal analysis. Results: The comparison of the stone components be­tween chemical analysis and MCT, showed that the rate of consistence was very low (p0.05). It was also seen that there was no significant relation between its 3D structure being heterogeneous or homogenous. Conclusion: The stone analysis with MCT is a time con­suming and costly method. This method is useful to un­derstand the mechanisms of stone formation and an im­portant guide to develop the future treatment modalities

Flerskala analyser for vibrasjonsspektroskopi av pollen

Pollen has a vital role in the reproduction of flowering plants by producing genetically diverse offspring via pollination. Environmental effects have strong influence on reproductive structures of plants, including on pollen. Phenotypic plasticity allows plants to adjust trait values to suit specific environmental conditions, and to persist in variable conditions. Plant phenotyping is the comprehensive assessment of complex plant traits related to their morphological, biochemical and physiological features. In order to determine the climate related plant adaptation and acclimation, it is imperative to improve plant phenotyping and monitoring of plant communities. Plant products are at the center of challenges posed by increasing requirements for food, feed and raw materials. Integrating new solutions across all scales, from molecular to field research, is necessary to develop sustainable plant production with higher yield by using limited resources. Therefore, plant phenotyping with a cost efficient and rapid methodology is highly desirable. The main aim of this study was to develop a novel methodology for plant phenotyping by employing vibrational (infrared and Raman) spectroscopic techniques in combination with chemometrics. More specifically, the goal was to correlate pollen biochemical data, obtained by vibrational spectroscopy, with phylogenetic and environmental data by multivariate analyses.Pollen spiller en viktig rolle i reproduksjonen av blomstrende planter ved å produsere avkom via pollinering som er genetisk forskjellige. Det er kjent at miljøeffekter har en sterk innflytelse på reproduktive strukturer hos planter, inkludert pollen. Plantene er i stand til å tilpasse seg spesifikke miljøforhold og for å overleve i varierende forhold, ved å justere egenskapene. Evnen til planter til å tilpasse egenskapene til spesifikke miljøforhold kalles fenotypisk plastisitet. Komplekse planteegenskaper som morfologiske, biokjemiske og fysiologiske funksjoner vurderes i biologi gjennom plantefenotyping. For å bestemme den klimarelaterte tilpasningen og akklimatiseringen hos planter er det derfor viktig å forbedre fenotypingen og overvåkingen av ulike plantesamfunn. Produkter fra planter er av hovedutfordringene knyttet til økt etterspørsel etter mat, fr og råvarer. å integrere nye løsninger på tvers av flere felt, fra molekylre metoder til feltarbeid, er nødvendig for å utvikle en mer brekraftig planteproduksjon med høyere avkastning ved bruk av begrensede ressurser. Derfor er plantefenotyping med en kostnadseffektiv og hurtig metode meget ønskelig. Hovedformålet med denne studien var å utvikle en ny metode for plantefenotyping ved å bruke vibrasjonsspektroskopiske teknikker (infrarød og Raman) i kombinasjon med kjemometri. Mer spesifikt var målet å korrelere biokjemiske data av pollen, innhentet ved vibrasjonsspektroskopi, med fylogenetiske data og miljødata ved hjelp av multivariabel analyse

Multiscale vibrational spectroscopy of pollen

Pollen has a vital role in the reproduction of flowering plants by producing genetically diverse offspring via pollination. Environmental effects have strong influence on reproductive structures of plants, including on pollen. Phenotypic plasticity allows plants to adjust trait values to suit specific environmental conditions, and to persist in variable conditions. Plant phenotyping is the comprehensive assessment of complex plant traits related to their morphological, biochemical and physiological features. In order to determine the climate related plant adaptation and acclimation, it is imperative to improve plant phenotyping and monitoring of plant communities. Plant products are at the center of challenges posed by increasing requirements for food, feed and raw materials. Integrating new solutions across all scales, from molecular to field research, is necessary to develop sustainable plant production with higher yield by using limited resources. Therefore, plant phenotyping with a cost efficient and rapid methodology is highly desirable. The main aim of this study was to develop a novel methodology for plant phenotyping by employing vibrational (infrared and Raman) spectroscopic techniques in combination with chemometrics. More specifically, the goal was to correlate pollen biochemical data, obtained by vibrational spectroscopy, with phylogenetic and environmental data by multivariate analyses

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

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

Data from: A multiscale vibrational spectroscopic approach for identification and biochemical characterization of pollen

Background: Analysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques. Methodology: Pollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques. Results: The vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data