Control of a <i>thiobacillus denitrificans</i> bioreactor using machine vision

Camp, Carl E.; Sublette, Kerry L.

doi:10.1002/bit.260390508

Cited by 8 publications

(5 citation statements)

References 17 publications

(4 reference statements)

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“…Many studies used image analysis algorithms to monitor organisms during fermentation processes online, having a potential impact on biotechnological process engineering. [5][6][7] In newer studies image analysis is also used to investigate characteristic properties of microorganisms. 8,9 In most cases the algorithms have been tailored to the individual approach and are somewhat complicated.…”

Section: Introductionmentioning

confidence: 99%

Single-cell bacteria growth monitoring by automated DEP-facilitated image analysis

Peitz

Leeuwen

2010

Lab Chip

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Growth monitoring is the method of choice in many assays measuring the presence or properties of pathogens, e.g. in diagnostics and food quality. Established methods, relying on culturing large numbers of bacteria, are rather time-consuming, while in healthcare time often is crucial. Several new approaches have been published, mostly aiming at assaying growth or other properties of a small number of bacteria. However, no method so far readily achieves single-cell resolution with a convenient and easy to handle setup that offers the possibility for automation and high throughput. We demonstrate these benefits in this study by employing dielectrophoretic capturing of bacteria in microfluidic electrode structures, optical detection and automated bacteria identification and counting with image analysis algorithms. For a proof-of-principle experiment we chose an antibiotic susceptibility test with Escherichia coli and polymyxin B. Growth monitoring is demonstrated on single cells and the impact of the antibiotic on the growth rate is shown. The minimum inhibitory concentration as a standard diagnostic parameter is derived from a dose-response plot. This report is the basis for further integration of image analysis code into device control. Ultimately, an automated and parallelized setup may be created, using an optical microscanner and many of the electrode structures simultaneously. Sufficient data for a sound statistical evaluation and a confirmation of the initial findings can then be generated in a single experiment.

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Section: Introductionmentioning

confidence: 99%

Single-cell bacteria growth monitoring by automated DEP-facilitated image analysis

Peitz

Leeuwen

2010

Lab Chip

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“…Machine vision has been mostly applied to biomass determination, product estimation, and cell count (Camp and Sublette, 1992;Moller et al, 1995;Packer et al, 1992;Ren et al, 1994;Zhang et al, 1995). In all cases, the developed approaches were specific to the problem; therefore, they cannot be directly applied to other cultures without further development.…”

Section: Introductionmentioning

confidence: 99%

A self-tuning vision system for monitoring biotechnological processes: I. Application to production of pullulan by Aureobasidium pullulans

Guterman

Shabtai

2000

Biotechnol. Bioeng.

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A prototype of a self‐tuning vision system (STVS) has been developed to monitor cell population in fermentations. The STVS combines classical image processing techniques, neural networks and fuzzy logic technologies. By combining these technologies the STVS is able to analyze sampled images of the culture. The proposed system can be “tailored” with minimum effort by an expert who can “teach” the system to recognize cells by showing examples of different morphologies. After adaptation, the STVS is able to capture images, isolate the different cells, classify them according to the expert's criteria, and provide the profile of the cell's population. The system was applied to the classification and analysis of Aureobasidium pullulans. The importance of understanding the changes of population distribution during the fermentation and its effect in the production of pullulan are emphasized. The STVS can be used for monitoring and control of the cell population in small research fermentors or in large‐scale production.

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“…In [ 61 ], the mean value of the RGB histogram and the ratio of red to blue intensity components are used to determine the concentration of bacteria biomass. The sum of the mean values of the RGB distributions are linearly related to the total biomass protein concentration.…”

Section: Microorganism Biovolume Measurement Methodsmentioning

confidence: 99%

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

Zhang

Rahaman

et al. 2022

Arch Computat Methods Eng

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With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications.

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Control of a thiobacillus denitrificans bioreactor using machine vision

Cited by 8 publications

References 17 publications

Single-cell bacteria growth monitoring by automated DEP-facilitated image analysis

Single-cell bacteria growth monitoring by automated DEP-facilitated image analysis

A self-tuning vision system for monitoring biotechnological processes: I. Application to production of pullulan by Aureobasidium pullulans

A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

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