Intermediate pyrolysis and product identification by TGA and Py-GC/MS of green microalgae and their extracted protein and lipid components

The thermo-chemical conversion of green microalgae Chlamydomonas reinhardtii wild type (CCAP 11/32C), its cell wall deficient mutant C. reinhardtii CW15 (CCAP 11/32CW15) and Chlorella vulgaris (CCAP 211/11B) as well as their proteins and lipids was studied under conditions of intermediate pyrolysis. The microalgae were characterised for ultimate and gross chemical composition, lipid composition and extracted products were analysed by Thermogravimetric analysis (TG/DTG) and Pyrolysis-gaschromatography/mass-spectrometry (Py-GC/MS). Proteins accounted for almost 50% and lipids 16-22 % of dry weight of cells with little difference in the lipid compositions between the C. reinhardtii wild type and the cell wall mutant. During TGA analysis, each biomass exhibited three stages of decomposition, namely dehydration, devolatilization and decomposition of carbonaceous solids. Py-GC/MS analysis revealed significant protein derived compounds from all algae including toluene, phenol, 4-methylphenol, 1H-indole, 1H-indole-3methyl. Lipid pyrolysis products derived from C. reinhardtii wild type and C. reinhardtii CW15 were almost identical and reflected the close similarity of the fatty acid profiles of both strains. Major products identified were phytol and phytol derivatives formed from the terpenoid chain of chlorophyll, benzoic acid alkyl ester derivative, benzenedicarboxylic acid alkyl ester derivative and squalene. In addition, octadecanoic acid octyl ester, hexadecanoic acid methyl ester and hydrocarbons including heptadecane, 1-nonadecene and heneicosane were detected from C. vulgaris pyrolysed lipids. These results contrast sharply with the types of pyrolytic products obtained from terrestrial lignocellulosic feedstocks and reveal that intermediate pyrolysis of algal biomass generates a range of useful products with wide ranging applications including bio fuels.

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

Teleconsultoria Off-line: uma arquitetura distribuída e compartilhada para sistemas heterogêneos

The Telehealth Brazil Networks Program, created in 2007 with the aim of strengthening primary care and the unified health system (SUS - Sistema Único de Saúde), uses information and communication technologies for distance learning activities related to health. The use of technology enables the interaction between health professionals and / or their patients, furthering the ability of Family Health Teams (FHT). The program is grounded in law, which determines a number of technologies, protocols and processes which guide the work of Telehealth nucleus in the provision of services to the population. Among these services is teleconsulting, which is registered consultation and held between workers, professionals and managers of healthcare through bidirectional telecommunication instruments, in order to answer questions about clinical procedures, health actions and questions on the dossier of work. With the expansion of the program in 2011, was possible to detect problems and challenges that cover virtually all nucleus at different scales for each region. Among these problems can list the heterogeneity of platforms, especially teleconsulting, and low internet coverage in the municipalities, mainly in the interior cities of Brazil. From this perspective, the aim of this paper is to propose a distributed architecture, using mobile computing to enable the sending of teleconsultation. This architecture works offline, so that when internet connection data will be synchronized with the server. This data will travel on compressed to reduce the need for high transmission rates. Any Telehealth Nucleus can use this architecture, through an external service, which will be coupled through a communication interface.

Determination of endocrine disruptor pesticides in river waters by SPME-GC-MS

FCT - PEst-C/EGE/LA0006/2011

Desenvolvimento de método para determinação de espécies de mercúrio empregando MSPD e GC-MS

Neste trabalho é proposto pela primeira vez, o desenvolvimento e validação de um método analítico baseado no emprego da dispersão da matriz em fase sólida (MSPD) modificada, para extração das espécies CH3Hg+ e Hg2+ em amostras de peixe e determinação por cromatografia em fase gasosa acoplada à espectrometria de massas (GC-MS). O método de extração utilizando a MSPD combina o rompimento da estrutura física da amostra, através da maceração e do uso de SiO2 como suporte sólido, com o método da extração ácida, utilizando uma solução de HCl 4,2 mol L-1 e NaCl 0,5 mol L-1. Para otimização da MSPD, foram avaliados parâmetros como massa de amostra, massa de suporte sólido, concentração de HCl, concentração de NaCl, tipo de suporte sólido e o tempo de agitação, com auxílio da metodologia de superfície de resposta. Além disso, a etapa de derivatização e a separação cromatográfica também foram otimizadas na determinação de CH3Hg+ e Hg2+ por GC-MS. O método mostrouse adequado para extração e determinação de espécies de mercúrio através da aplicação em materiais de referência certificados de fígado de peixe (DOLT-3) e músculo de peixe (DORM-2), apresentando boas concordâncias com os valores certificados e desvio padrão relativo inferior a 9,5%. Os limites de detecção foram de 0,06 e 0,12 µg g-1 para CH3Hg+ e Hg2+, respectivamente. Além disso, foi observado um significativo efeito de matriz e, por isso, a calibração foi feita com curvas preparadas com o extrato da MSPD. O método mostrou boa concordância na comparação entre a soma da concentração das espécies e a concentração de mercúrio total determinada por espectrometria de massas com plasma indutivamente acoplado com geração de vapor frio (CVG-ICP-MS), após digestão assistida por micro-ondas (MAD) em peixes do tipo atum (Thunnus thynnus), cação anjo (Squatina squatina) e cação viola (Rhinobatos blochii.).

Desenvolvimento e validação de método para determinação do perfil graxo do biodiesel de Tungue e Blendas com soja empregando GC-MS

No Brasil o biodiesel é utilizado em misturas com óleo diesel em proporções de 5%, sem que haja modificações nos motores. Com o intuito de diversificar a utilização de oleaginosas não comestíveis no ramo dos biocombustíveis, e ainda vincular a produção com agricultura sustentável, uma alternativa para o RS é a utilização do óleo de tungue para a produção de biodiesel. A caracterização e quantificação de ácidos graxos do biodiesel de tungue, torna-se importante devido à seu exclusivo perfil graxo. Neste trabalho, foi estudado o desenvolvimento e validação de método para a determinação do perfil graxo do biodiesel metílico de tungue e blendas com soja utilizando GC-MS. Os parâmetros de validação considerados foram: curva analítica, linearidade, seletividade, limite de detecção e quantificação, robustez, precisão e exatidão. Para determinar as melhores condições cromatográficas, foram testadas diferentes programações de temperatura no forno cromatográfico; fluxo de gás; temperatura do injetor, detector e interface; e modo de injeção. As condições do GCMS após a otimização foram: injeção de 1 µL com injeção em alta pressão (300 kPa), T do injetor: 250 ºC, injeção split 1:30, fluxo de 1 mL min-1, coluna Rtx-5MS com dimensões 30 m x 0,25 mm x 0,25 µm, T forno: isoterma de 2 min a 130 ºC, aumento de 20 ºC/min até 220 ºC, aumento de 0,5ºC/min até 223ºC, aumento de 7 ºC/min até 250 ºC e isoterma em 250 ºC por 3 min, resultando em 20 min de análise. A temperatura da fonte e interface foram de 200 ºC e 250 ºC, respectivamente, com o MS no modo full scan, ionização por impacto eletrônico a 70 eV, e intervalo de massas de 30 a 500 u.m.a. A identificação do α-eleosteárico foi baseada na fragmentação característica do composto, pela comparação com o espectro do ácido linolênico, e ainda pelo tempo de retenção do composto. Na validação, as curvas analíticas apresentaram valores de r maiores que 0,99. O LD e LQ foram adequados, permitindo a quantificação de ésteres na concentração mínima de 0,6%. Os valores de exatidão ficaram entre 86 e 117%, com RSD% menores que 8%. O efeito matriz também foi avaliado, sendo que esse efeito foi considerado médio para a maioria dos compostos, ficando entre ± 20 e 50%. Durante a aplicação do método, o mesmo se mostrou adequado para amostras de biodiesel metílico de tungue e blendas com soja, nas proporções de 15:85, 20:80 e 25:75 (T:S, v/v). A aplicabilidade do método também foi testada para o biodiesel de soja, obtendo resultados satisfatórios, mostrando-se assim, além de tudo, ser um método robusto.

GC-MS Analysis, Antimicrobial and Antioxidant Activities of Extracts of the Aerial Parts of Conyza sumatrensis

Phytochemical analyses as well as antimicrobial and antioxidant activities of the extracts of C. sumatrensis aerial parts were investigated in this study. METHODS: The aerial parts of C. sumatrensis were air dried, weighed and exhaustively extracted with hexane, ethyl acetate and methanol successively. The crude extracts were screened for metabolites. These extracts of the plant were evaluated for antimicrobial and antioxidant activities using agar diffusion and DPPH method respectively. The extracts were also analysed using Gas chromatography – Mass spectrometry, and the chromatogram coupled with mass spectra of the compounds were matched with a standard library. RESULTS: Preliminary phytochemical investigation of crude n-hexane, ethyl acetate and methanol extracts of the aerial parts of Conyza sumatrensis revealed the presence of anthraquinones, flavonoids, terpenoids, phenolics, tannin, glycosides and carbohydrate. All the crude extracts gave a clear zone of inhibition against the growth of the test bacteria ( Staphylococcus aureus , Escherichia coli , Bacillus subtilis , Pseudomona aeruginosa, Salmonella typhi , Klebsiellae pneumonae ) at moderate to high concentrations, as well as test fungi ( Candida albicans , Aspergillus niger , penicillium notatum and Rhizopus stolonifer ) at high concentration. Methanolic extract exhibited significant radical scavenging property with IC50 of 17.08 μg/mL while n-hexane and ethyl acetate extracts showed no significant antioxidant activity. GC-MS of N-hexane extract showed a total number of eleven chemical constituents with α-Farnesene and spathulenol being the most abundance compounds constituting 20.27 and 22.28% of the extract respectively. Ethyl acetate extract revealed thirteen compounds with two most abundant compounds, cis-β-farnesene (16.64 %) and cis-pinane (21.09 %). While methanolic extract affords seventeen compounds with Ephytol being the most abundant compound (19.36 %).

UTILIZING ELECTRONIC NOSE AND GC-MS TO EXAMINE CRITICAL FACTORS INFLUENCING THE FORMATION OF ODOROUS VOC’S IN BIOSOLIDS.

Despite the efforts to better manage biosolids field application programs, biosolids managers still lack of efficient and reliable tools to apply large quantities of material while avoiding odor complaints. Objectives of this research were to determine the capabilities of an electronic nose in supporting process monitoring of biosolids production and, to compare odor characteristics of biosolids produced through thermal-hydrolysis anaerobic digestion (TH-AD) to those of alkaline stabilization in the plant, under storage and in the field. A method to quantify key odorants was developed and full scale sampling and laboratory simulations were performed. The portable electronic nose (PEN3) was tested for its capabilities of distinguishing alkali dosages in the biosolids production process. Frequency of recognition of unknown samples was tested achieving highest accuracy of 81.1%. This work exposed the need for a different and more sensitive electronic nose to assure its applicability at full scale for this process. GC-MS results were consistent with those reported in literature and helped to elucidate the behavior of the pattern recognition of the PEN3. Odor characterization of TH-AD and alkaline stabilized biosolids was achieved using olfactometry measurements and GC-MS. Dilution-to-threshold of TH-AD biosolids increased under storage conditions but no correlation was found with the target compounds. The presence of furan and three methylated homologues in TH-AD biosolids was reported for the first time proposing that these compounds are produced during thermal hydrolysis process however, additional research is needed to fully describe the formation of these compounds and the increase in odors. Alkaline stabilized biosolids reported similar odor concentration but did not increase and the ‘fishy’ odor from trimethylamine emissions resulted in more offensive and unpleasant odors when compared to TH-AD. Alkaline stabilized biosolids showed a spike in sulfur and trimethylamine after 3 days of field application when the alkali addition was not sufficient to meet regulatory standards. Concentrations of target compounds from field application of TH-AD biosolids gradually decreased to below the odor threshold after 3 days. This work increased the scientific understanding on odor characteristics and behavior of two types of biosolids and on the application of electronic noses to the environmental engineering field.

DEVELOPMENT OF AN OFF-LINE RAINFLOW COUNTING ALGORITHM WITH TEMPORAL PARAMETERS AND DATA REDUCTION

Rainflow counting methods convert a complex load time history into a set of load reversals for use in fatigue damage modeling. Rainflow counting methods were originally developed to assess fatigue damage associated with mechanical cycling where creep of the material under load was not considered to be a significant contributor to failure. However, creep is a significant factor in some cyclic loading cases such as solder interconnects under temperature cycling. In this case, fatigue life models require the dwell time to account for stress relaxation and creep. This study develops a new version of the multi-parameter rainflow counting algorithm that provides a range-based dwell time estimation for use with time-dependent fatigue damage models. To show the applicability, the method is used to calculate the life of solder joints under a complex thermal cycling regime and is verified by experimental testing. An additional algorithm is developed in this study to provide data reduction in the results of the rainflow counting. This algorithm uses a damage model and a statistical test to determine which of the resultant cycles are statistically insignificant to a given confidence level. This makes the resulting data file to be smaller, and for a simplified load history to be reconstructed.

Rapid portable GC-MS analysis for on-site screening of phytochemical leaf oils in an Australian Native Botanic Garden.

2016

Análise de hidrocarbonetos policíclicos aromáticos (HPA's) em amostras de lodo de esgoto por GC-MS/MS.

2009

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.