Biodetection grinder

Work on a biodetection grinder is summarized. It includes development of the prototype grinder, second generation grinder, and the production version of the grinder. Tests showed the particle size distribution was satisfactory and biological evaluation confirmed the tests

Definition of a near real time microbiological monitor for space vehicles

Efforts to identify the ideal candidate to serve as the biological monitor on the space station Freedom are discussed. The literature review, the evaluation scheme, descriptions of candidate monitors, experimental studies, test beds, and culture techniques are discussed. Particular attention is given to descriptions of five candidate monitors or monitoring techniques: laser light scattering, primary fluorescence, secondary fluorescence, the volatile product detector, and the surface acoustic wave detector

Survey of a wastewater treatment plant microfauna by image analysis

The microfauna present in the activated sludge of a wastewater treatment plant is mainly composed by bacteria, protozoa and metazoa. The protozoan species are quite sensitive to physical, chemical and operational processes making them, thus, precious indicators of the state of the plant. Several authors already established relationships between the predominance of certain species or group and some parameters of the plant, such as the biotic indices namely the Sludge Biotic Index. All the above-mentioned procedures demand the identification, classification and quantification of the different species. Normally this is done manually, which implies both time and expertise. In the present work a semi-automatic protozoan recognition procedure by means of image analysis is attempted. The program built for this purpose (ProtoRec v.3) was also used to study the evolution of the microfauna during transient operation times (stoppage and re-run). The results were rather satisfactory in terms of protozoa recognition and the survey of the transient phase allowed verifying the aging and degradation of the microfauna by means of the different predominant species

Optical Micromanipulation Techniques Combined with Microspectroscopic Methods

Předložená dizertační práce se zabývá kombinací optických mikromanipulací s mikrospektroskopickými metodami. Využili jsme laserovou pinzetu pro transport a třídění živých mikroorganismů, například jednobuněčných řas, či kvasinek. Ramanovskou spektroskopií jsme analyzovali chemické složení jednotlivých buněk a tyto informace jsme využili k automatické selekci buněk s vybranými vlastnostmi. Zkombinovali jsme pulsní amplitudově modulovanou fluorescenční mikrospektroskopii, optické mikromanipulace a jiné techniky ke zmapování stresové odpovědi opticky zachycených buněk při různých časech působení, vlnových délkách a intenzitách chytacího laseru. Vyrobili jsme různé typy mikrofluidních čipů a zkonstruovali jsme Ramanovu pinzetu pro třídění mikro-objektů, především živých buněk, v mikrofluidním prostředí.The subject of the presented Ph.D. thesis is a combination of optical micromanipulation and microspectroscopic methods. We used laser tweezers to transport and sort various living microorganisms, such as microalgal or yeast cells. We employed Raman microspectroscopy to analyze chemical composition of individual cells and we used the information about chemical composition to automatically select the cells of interest. We combined pulsed amplitude modulation fluorescence microspectroscopy, optical micromanipulation and other techniques to map the stress response of cells to various laser wavelengths, intensities and durations of optical trapping. We fabricated microfluidic chips of various designs and we constructed Raman-tweezers sorter of micro-objects such as living cells on a microfluidic platform.

Image Analysis and Multiphase Bioreactors

The applications of visualisation and image analysis to bioreactors can be found in two main areas: the characterisation of biomass (fungi, bacteria, yeasts, animal and plant cells, etc), in terms of size, morphology and physiology, that is the far most developed, and the characterisation of the multiphase behaviour of the reactors (flow patterns, velocity fields, bubble size and shape distribution, foaming), that may require sophisticated visualisation techniques

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis and neural network

A mixed culture of microorganisms is usually present in biological wastewater treatment processes such as the activated sludge system in aeration tanks. These microorganisms are capable of reducing the organic matter and other pollutants in the sewage. Protozoa and metazoa play an important role in this system because they maintain the density of bacterial populations by predation and contribute to the flocculation process, being responsible for an mprovement in the quality of the effluent. Moreover, protozoa and metazoa are considered to be important bioindicators of the activated sludge process due to their association with physical, chemical and operational parameters of the treatment plant. Furthermore, the analysis of the number and classes of the predominant groups of these organisms is used to predict the effectiveness of the aeration, extent of the nitrification process, sludge age and final effluent conditions1,2. Classical microfauna analysis is frequently done by microscopic observation and assessment of the different protozoa and metazoa species present. However, this task is not only timeconsuming and labour intensive but also requires the expertise of a zoologist or protozoologist. Therefore, digital image analysis can be seen as a useful tool to achieve taxonomic classification and organism’s quantification in an automatic, non subjective manner. Some studies have already been carried out using this technique combined with statistic multivariable analysis such as Neural Networks, Discriminant Analysis, and Principal Components Analysis to perform the recognition of protozoa and metazoa commonly present in the aeration tank of wastewater treatment plants activated sludge, including the works of Amaral et al. (2004)3. In this work an image analysis programme was developed in MATLAB code for the semi-automatic recognition of several groups of protozoa and metazoa commonly present in wastewater treatment plants. The protozoa and metazoa were characterized by different morphological parameters of Euclidean and fractal geometry, with or without their external structures (peduncles, cirri, tentacles). Finally, the morphological parameters (around 40) of the above-mentioned geometries were analysed using the multivariable statistical techniques Discriminant Analysis and Neural Network to identify and classify each protozoan or metazoan image. The procedure obtained was adequate for distinguishing between amoebas, sessile ciliates, crawling ciliates, large flagellates and free swimming ciliates in terms of the protozoa classes and also for the metazoa. Furthermore, with the exception of some sessile species, the value of overall species recognition was high. In terms of the wastewater conditions assessment such as aeration, nitrification, sludge age and effluent quality the obtained results were found to be suitable for the prediction of these conditions.ALFA cooperation project; the Biological Engineering Department of Minho University; Chemistry School – Federal University of Rio de Janeiro

Pool-spetsiifiliste BHT biosensorite uurimine biosensor-riviks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Reovee reostuse taset määratakse selle biokeemilise hapnikutarbe alusel (BHT). BHT iseloomustab hapniku hulka, mis on vajalik proovis leiduva orgaanilise aine biokeemiliseks lagundamiseks. Kuigi BHT analüüs ei ole spetsiifiline ühelegi saasteainele, on see siiski väga oluline üldine indikaator aine potentsiaalsest keskkonnaohtlikkusest pinnavetele. Paraku kulub analüüsi tulemuste saamiseks 5 või 7 päeva ning seetõttu on reoveepuhastusseadmete juhtimine selliseid teste kasutades väga keeruline. Antud probleemi lahendamiseks koostati lihtsad ja usaldusväärsed pool-spetsiifilised BHT biosensorid, mis võimaldasid tulemuse saada vähem, kui 30 minutiga. Antud biosensoritega oli võimalik hinnata BHT-d, mis oli põhjustatud raskesti lagundatavatest ühenditest, mille suhtes nad olid pool-spetsiifilised. Samas kui universaalne biosensor ja biosensorid, mis on pool-spetsiifilised mõnele teisele raskesti lagundatavale ühendile, ei määranud seda ja alahindasid proovi BHT7 umbkaudu selle raskesti lagundatava ühendi poolt tekitatud BHT väärtuses, 10-25%. Kuigi biosensorid alahindasid enamike reaalsete tööstuslike reoveeproovide BHT7, võimaldasid pool-spetsiifilised biosensorid siiski saada täpsemaid tulemusi kui universaalne biosensor, mis alahindas proovi BHT7 suuremas ulatuses. Seega on pool-spetsiifilised biosensorid sobivamad BHT mõõtmiseks tööstuslikes reovetes, kui universaalne biosensor, kuid ainult juhul, kui on olemas eelinfo proovi koostise ja päritolu kohta, mis võimaldab valida sobiva pool-spetsiifilise biosensori. Antud probleem lahendati erinevate pool-spetsiifiliste biosensorite ühendamisega sensor-riviks – „bioelektrooniliseks keeleks“. Selle sensor-rivi signaali analüüsiks rakendati mitmemõõtmelise andmete analüüsi meetodeid. Antud meetodite rakendamisel võimaldas PCA eristada proove nende koostise ja BHT7 väärtuse alusel ning PLS võimaldas märgatavalt paremini hinnata proovide BHT7 väärtusi kõigis proovides.Pollution load of wastewaters is determined on the basis of their biochemical oxygen demand (BOD) which measures the oxygen required for the biochemical degradation of organic material. Although the BOD test is not specific to any pollutant, it continues to be one of the important general indicators of the substance potential to be an environmental pollutant for surface waters. However, it takes 5 or 7 days to gain results and management of wastewater treatment facilities can be very difficult using this kind of tests. To address this limitation, simple and reliable semi-specific BOD biosensors were constructed which enabled us to gain results within less than 30 minutes. In addition, these biosensors can measure BOD derived from refractory compounds to which they are semi-specific. Therefore, better estimation of BOD is gained. On the other hand, universal biosensor and biosensors not semi-specific to that certain refractory compound cannot detect it and thus, underestimate the BOD7 of the sample to the extent made up by this compound, 10-25%. Although biosensors underestimated the BOD7 of most real industrial wastewater samples, the semi-specific biosensors still produced better correlation of sensor-BOD and BOD7 in real samples than universal biosensor which underestimated the BOD7 of samples to a greater extent. Therefore, semi–specific biosensors are more appropriate for measuring BOD in specific industrial wastewaters than universal biosensor. However, it is vital to have a prior knowledge about samples composition and origin to select the suitable sensor. This problem was overcome by using different biosensors as an array – bioelectronic tongue - and application of multivariate data analysis. Qualitative information was extracted by using PCA, which enabled us to distinguish different samples by their composition and BOD7 values. In addition, PLS was used for quantitative analysis which resulted in good correlation of sensor-BOD and BOD7 in all samples

Semi-automatic model to colony forming units counting

Colony forming units counting is a conventional process carry out in bacteriological laboratories, and it is used to follow the behavior of bacteria in different conditions. Currently exist different systems, automatic or semi-automatic, to counting colony forming units exits, but, in general, many laboratories continue using manual counting, which consumes considerable time and effort from researchers and laboratory employees. This paper presents a mathematical model carry out to segment the colony forming units and, in this way, counting them from a digital image of the sample. The method uses the color space information of some points in the image and shows good behavior for images with many or few colony forming units in the sample, according to manual counting. The results show efficiencies close to 98% with MacConkey agar