Online monitoring of the morphology of an industrial sugarcane biofuel yeast strain via in situ microscopy

Belini, Valdinei Luís; Suhr, H.; Wiedemann, Philipp

doi:10.1016/j.mimet.2020.105973

Cited by 9 publications

(5 citation statements)

References 34 publications

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“…Several techniques can be used to compute quantitative and qualitative morphological analysis of yeast's cells enabling the possibility of on-line estimation: current mainstream methods are bright field or phase contrast microscopy (light microscopy) in which cells are stained with a suitable dye to observe the morphology of the cells, flow cytometry to analyze the size and shape of the cells in a population and electron microscopy to reveal cellular structure or surface details as in situ microscopy (ISM). An application of ISM based on an image analysis algorithm that uses detection of regional maxima can be used to determine the average size of single cells enabling on-line size monitoring [6]. With the progress in image processing technology, it turns out to be easier to extract information on several parameters, enabling the use of this technology for the measurement of microbial morphology, in fact image analysis and automated methods can be used to provide a better and detailed understanding of the morphology of Saccharomyces cerevisiae cells; in this context are well suited image-processing techniques to monitor yeasts cultivation directly using high-speed cameras [7], [8] and machine learning approaches [9] using classical segmentation algorithms [10], [11] and ones based on a set of relevant individual cell features based on first and second order histograms and waveletbased texture measurement extracted from the microscope images of the yeast cells to represent the morphological characteristics in a more sophisticated way [12].…”

Section: Related Results In the Literaturementioning

confidence: 99%

Use of Artificial Intelligence in optical microscope imaging

Carratù,

Gallo,

Laino

et al. 2023

Proceedings of the 19th IMEKO TC10 International Conference on Measurement for Diagnostics, Optimization and Control on Measure

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Identification and morphological analysis of microorganisms are of high interest in scientific research, especially in the medical field and food industry. Identification allows rapid functional characterization based on similarities with known related species enabling to confirm the identity of an isolate used, for example, in a trademarked industrial process. Monitoring of microorganisms within a given ecosystem and analysis of the morphological characteristics of the observed species enable quality control of the process under analysis. Such procedures are carried out manually in specialized laboratories by trained personnel using the appropriate optical equipment; therefore, it may be of great interest to use automatic measurement approaches that enable rapid and effective process analysis. Artificial intelligence techniques in computer vision and especially deep learning are well suited for this purpose. This article describes the realization of an automatic measurement system based on deep learning for the identification and measurement of morphological parameters of Saccharomyces cerevisiae microorganisms present in brewer's yeast by returning for each of the objects identified within the image the confidence score, the coordinates, and the dimensions of the corresponding ellipsoid-shaped cell. The metrological characteristics of the system have been defined through a calibration process by comparing measurements with a reference system.

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Section: Related Results In the Literaturementioning

confidence: 99%

Use of Artificial Intelligence in optical microscope imaging

Carratù,

Gallo,

Laino

et al. 2023

Proceedings of the 19th IMEKO TC10 International Conference on Measurement for Diagnostics, Optimization and Control on Measure

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“…Therefore, the cell morphology significantly affects the productivity of fermentation. To monitor fermentation process, an automated online monitoring system for yeast morphology has recently been developed (Belini et al 2020 , Zhuang et al 2021 ). Nonetheless, it was challenging to predict the quantity of fermentation products using time-dependent morphological data of yeast as input data.…”

Section: Application Of Morphology In Prediction Of Fermentation Prod...mentioning

confidence: 99%

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Ohya,

Ghanegolmohammadi,

Itto-Nakama

2024

FEMS Yeast Research

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Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

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“…For instance, with floating cell culture, it is necessary to frequently monitor the optical density of cell suspensions during the exponential growth phase. [27,28] While this review mainly focuses on animal cells, optical density has been used to measure most types of plants, bacteria, and viruses. Recent developments in optical density analysis include sampling methods and direct analysis through in situ microscopic, UV-vis, and near-infrared (NIR) spectroscopic analysis.…”

Section: Cellular Physiologymentioning

confidence: 99%

“…Optical density: Owing to assess cell culture status in terms of proliferation, researchers can conduct one of the simplest and earliest methods to measure the optical density of the cell culture environment. For instance, with floating cell culture, it is necessary to frequently monitor the optical density of cell suspensions during the exponential growth phase [27,28] . While this review mainly focuses on animal cells, optical density has been used to measure most types of plants, bacteria, and viruses.…”

Section: Cellular Physiologymentioning

confidence: 99%

Advances in sensor developments for cell culture monitoring

Kim,

Yeo

2023

BMEMat

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Cell culture encompasses procedures for extracting cells from their natural tissue and cultivating them under controlled artificial conditions. During this process, various factors, including cell physiological/morphological properties, culture environments, metabolites, and contaminants, have to be precisely controlled and monitored for the survival of cells and the pursuit of the desired properties of the cells. This review summarizes recent advances in sensor technologies and manufacturing strategies for various cell culture platforms using traditional plastics, microfluidic chips, and scalable bioreactors. We share the details of newly developed biological sensors, chemical sensors, optical sensors, electronic chip technologies, and material integration methods. The precise control of parameters based on the feedback by these sensors and electronics enhances cell culture quality and throughput.

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Online monitoring of the morphology of an industrial sugarcane biofuel yeast strain via in situ microscopy

Cited by 9 publications

References 34 publications

Use of Artificial Intelligence in optical microscope imaging

Use of Artificial Intelligence in optical microscope imaging

Application of unimodal probability distribution models for morphological phenotyping of budding yeast

Advances in sensor developments for cell culture monitoring

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