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Habich T, Beutel S. Digitalization concepts in academic bioprocess development. Eng Life Sci 2024; 24:2300238. [PMID: 38584688 PMCID: PMC10991719 DOI: 10.1002/elsc.202300238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 01/17/2024] [Accepted: 01/30/2024] [Indexed: 04/09/2024] Open
Abstract
Digitalization with integrated devices, digital and physical assistants, automation, and simulation is setting a new direction for laboratory work. Even with complex research workflows, high staff turnover, and a limited budget some laboratories have already shown that digitalization is indeed possible. However, academic bioprocess laboratories often struggle to follow the trend of digitalization. Due to their diverse research circumstances, high variety of team composition, goals, and limitations the concepts are substantially different. Here, we will provide an overview on different aspects of digitalization and describe how academic laboratories successfully digitalized their working environment. The key aspect is the collaboration and communication between IT-experts and scientific staff. The developed digital infrastructure is only useful if it supports the laboratory worker and does not complicate their work. Thereby, laboratory researchers have to collaborate closely with IT-experts in order for a well-developed and maintainable digitalization concept that fits their individual needs and level of complexity. This review may serve as a starting point or a collection of ideas for the transformation toward a digitalized laboratory.
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Affiliation(s)
- Tessa Habich
- Institute of Technical ChemistryLeibniz University HannoverHannoverGermany
| | - Sascha Beutel
- Institute of Technical ChemistryLeibniz University HannoverHannoverGermany
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2
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Arora P, Tewary S, Krishnamurthi S, Kumari N. An experimental setup and segmentation method for CFU counting on agar plate for the assessment of drinking water. J Microbiol Methods 2023; 214:106829. [PMID: 37797659 DOI: 10.1016/j.mimet.2023.106829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 10/07/2023]
Abstract
Quantification of bacterial colonies on an agar plate is a daily routine for a microbiologist to determine the number of viable microorganisms in the sample. In general, microbiologists perform a visual assessment of bacterial colonies which is time-consuming (takes 2 min per plate), tedious, and subjective. Some automatic counting algorithms are developed that save labour and time, but their results are affected by the non-illumination on an agar plate. To improve this, the present manuscript aims to develop an inexpensive and efficient device to acquire S.aureus images via an automatic counting method using image processing techniques under real laboratory conditions. The proposed method (P_ColonyCount) includes the region of interest extraction and color space transformation followed by filtering, thresholding, morphological operation, distance transform, and watershed technique for the quantification of isolated and overlapping colonies. The present work also shows a comparative study on grayscale, K, and green channels by applying different filter and thresholding techniques on 42 images. The results of all channels were compared with the score provided by the expert (manual count). Out of all the proposed method (P_ColonyCount), the K channel gives the best outcome in comparison with the other two channels (grayscale and green) in terms of precision, recall, and F-measure which are 0.99, 0.99, and 0.99 (2 h), 0.98, 0.99, and 0.98 (4 h), and 0.98, 0.98, 0.98 (6 h) respectively. The execution time of the manual and the proposed method (P_ColonyCount) for 42 images ranges from 19 to 113 s and 15 to 31 s respectively. Apart from this, a user-friendly graphical user interface is also developed for the convenient enumeration of colonies without any expert knowledge/training. The developed imaging device will be useful for researchers and teaching lab settings.
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Affiliation(s)
- Prachi Arora
- Thin Film Coating Facility/Materials Science and Sensor Applications, CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30-C, Chandigarh 160030, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Suman Tewary
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Advanced Materials and Processes, CSIR-National Metallurgical Laboratory (CSIR-NML), Jamshedpur 831007, India
| | - Srinivasan Krishnamurthi
- MTCC-Gene bank, CSIR-Institute of Microbial Technology (CSIR-IMTECH), Sector 39-A, Chandigarh 160039, India
| | - Neelam Kumari
- Thin Film Coating Facility/Materials Science and Sensor Applications, CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Sector 30-C, Chandigarh 160030, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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Heuser E, Becker K, Idelevich EA. Evaluation of an Automated System for the Counting of Microbial Colonies. Microbiol Spectr 2023; 11:e0067323. [PMID: 37395656 PMCID: PMC10433998 DOI: 10.1128/spectrum.00673-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 06/08/2023] [Indexed: 07/04/2023] Open
Abstract
Counting of microbial colonies is a common technique employed in research and diagnostics. To simplify this tedious and time-consuming process, automated systems have been proposed. This study aimed to elucidate the reliability of automated colony counting. We evaluated a commercially available instrument (UVP ColonyDoc-It Imaging Station) in regard to its accuracy and potential time savings. Suspensions of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Enterococcus faecium, and Candida albicans (n = 20 each) were adjusted to achieve growth of approximately 1,000, 100, 10, and 1 colony per plate, respectively, after overnight incubation on different solid media. Compared with manual counting, each plate was automatically counted by the UVP ColonyDoc-It with and without visual adjustment on a computer display. For all bacterial species and concentrations automatically counted without visual correction, an overall mean difference from manual counts of 59.7%, a proportion of isolates with overestimation/underestimation of colony numbers of 29%/45%, respectively, and only a moderate relationship (R2 = 0.77) with the manual counting were shown. Applying visual correction, the overall mean difference from manual counts was 1.8%, the proportion of isolates with overestimation/underestimation of colony numbers amounted to 2%/42%, respectively, and a strong relationship (R2 = 0.99) with the manual counting was observed. The mean time needed for manual counting compared with automated counting without and with visual correction was 70 s, 30 s, and 104 s, respectively, for bacterial colonies through all concentrations tested. Generally, similar performance regarding accuracy and counting time was observed with C. albicans. In conclusion, fully automatic counting showed low accuracy, especially for plates with very high or very low colony numbers. After visual correction of the automatically generated results, the concordance with manual counts was high; however, there was no advantage in reading time. IMPORTANCE Colony counting is a widely utilized technique in the field of microbiology. The accuracy and convenience of automated colony counters are essential for research and diagnostics. However, there is only sparse evidence on performance and usefulness of such instruments. This study examined the current state of reliability and practicality of the automated colony counting with an advanced modern system. For this, we thoroughly evaluated a commercially available instrument in terms of its accuracy and counting time required. Our findings indicate that fully automatic counting resulted in low accuracy, particularly for plates with very high or very low colony numbers. Visual correction of the automated results on a computer screen improved concordance with manual counts, but there was no benefit in counting time.
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Affiliation(s)
- Elisa Heuser
- Friedrich Loeffler-Institute of Medical Microbiology, University Medicine Greifswald, Greifswald, Germany
| | - Karsten Becker
- Friedrich Loeffler-Institute of Medical Microbiology, University Medicine Greifswald, Greifswald, Germany
| | - Evgeny A. Idelevich
- Friedrich Loeffler-Institute of Medical Microbiology, University Medicine Greifswald, Greifswald, Germany
- Institute of Medical Microbiology, University Hospital Münster, Münster, Germany
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An Application of Pixel Interval Down-Sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147314] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny object (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end convolutional neural network (CNN) model with an encoder–decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. This addresses the limitation of contour conglutination of dense objects while counting. The evaluation was conducted using classical segmentation metrics (the Dice, Jaccard and Hausdorff distance) as well as counting metrics. The experimental results show that the proposed PID-Net had the best performance and potential for dense tiny object counting tasks, which achieved 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches, such as Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting.
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Marro FC, Laurent F, Josse J, Blocker AJ. Methods to monitor bacterial growth and replicative rates at the single-cell level. FEMS Microbiol Rev 2022; 46:6623663. [PMID: 35772001 PMCID: PMC9629498 DOI: 10.1093/femsre/fuac030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 06/01/2022] [Accepted: 06/28/2022] [Indexed: 01/09/2023] Open
Abstract
The heterogeneity of bacterial growth and replicative rates within a population was proposed a century ago notably to explain the presence of bacterial persisters. The term "growth rate" at the single-cell level corresponds to the increase in size or mass of an individual bacterium while the "replicative rate" refers to its division capacity within a defined temporality. After a decades long hiatus, recent technical innovative approaches allow population growth and replicative rates heterogeneity monitoring at the single-cell level resuming in earnest. Among these techniques, the oldest and widely used is time-lapse microscopy, most recently combined with microfluidics. We also discuss recent fluorescence dilution methods informing only on replicative rates and best suited. Some new elegant single cell methods so far only sporadically used such as buoyant mass measurement and stable isotope probing have emerged. Overall, such tools are widely used to investigate and compare the growth and replicative rates of bacteria displaying drug-persistent behaviors to that of bacteria growing in specific ecological niches or collected from patients. In this review, we describe the current methods available, discussing both the type of queries these have been used to answer and the specific strengths and limitations of each method.
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Affiliation(s)
- Florian C Marro
- Evotec ID Lyon, In Vitro Biology, Infectious Diseases and Antibacterials Unit, Gerland, 69007 Lyon, France,CIRI – Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Univ Lyon, F-69007 Lyon, France
| | - Frédéric Laurent
- CIRI – Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Univ Lyon, F-69007 Lyon, France,Institut des Sciences Pharmaceutiques et Biologiques (ISPB), Université Claude Bernard Lyon 1, Lyon, France,Centre de Référence pour la prise en charge des Infections ostéo-articulaires complexes (CRIOAc Lyon; www.crioac-lyon.fr), Hospices Civils de Lyon, Lyon, France,Laboratoire de bactériologie, Institut des Agents Infectieux, French National Reference Center for Staphylococci, Hospices Civils de Lyon, Lyon, France
| | - Jérôme Josse
- CIRI – Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, Ecole Normale Supérieure de Lyon, Univ Lyon, F-69007 Lyon, France,Institut des Sciences Pharmaceutiques et Biologiques (ISPB), Université Claude Bernard Lyon 1, Lyon, France,Centre de Référence pour la prise en charge des Infections ostéo-articulaires complexes (CRIOAc Lyon; www.crioac-lyon.fr), Hospices Civils de Lyon, Lyon, France
| | - Ariel J Blocker
- Corresponding author. Evotec ID Lyon, In Vitro Biology, Infectious Diseases and Antibacterials Unit, France. E-mail:
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Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting. Bioengineering (Basel) 2022; 9:bioengineering9070271. [PMID: 35877322 PMCID: PMC9312004 DOI: 10.3390/bioengineering9070271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in microbiology settings in a wide range of fields. The enumeration process of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular cases require an urgent and accurate response for effective processing. Methods: To reduce time limitations and discrepancies, this work introduces an image processing method capable of semi-automatically quantifying the number of colony forming units (CFUs). This rapid enumeration technique enables the technician to provide an expeditious assessment of the microbial load of a given sample. To test and validate the system, three bacterial species were cultured, and a labeled database was created, with subsequent image acquisition. Results: The system demonstrated acceptable classification measures; the mean values of Accuracy, Recall and F-measure were: (1) 95%, 95% and 0.95 for E. coli; (2) 91%, 91% and 0.90 for P. aeruginosa; and (3) 84%, 86% and 0.85 for S. aureus. Conclusions: Evidence related to the time-saving potential of the system was achieved; the time spent on quantification tasks of plates with a high number of colonies might be reduced to a half and occasionally to a third.
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Performance of four bacterial cell counting apps for smartphones. J Microbiol Methods 2022; 199:106508. [PMID: 35691441 DOI: 10.1016/j.mimet.2022.106508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/05/2022] [Accepted: 06/05/2022] [Indexed: 12/27/2022]
Abstract
Quantifying bacterial colony forming units is important in microbiological diagnostics. Recent progress in imaging technology allows automation of this tedious and error-prone task. We compared the accuracy of four smartphone colony counter applications conducting standardized measurements, using a self-built apparatus. One app showed high accuracy at lower colony counts.
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Development and validation of software that quantifies the larval mortality of Rhipicephalus (Boophilus) microplus cattle tick. Ticks Tick Borne Dis 2022; 13:101930. [DOI: 10.1016/j.ttbdis.2022.101930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 02/18/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022]
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9
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Human–Device Interaction in the Life Science Laboratory. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2022; 182:83-113. [DOI: 10.1007/10_2021_183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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10
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A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Artif Intell Rev 2021; 55:2875-2944. [PMID: 34602697 PMCID: PMC8478609 DOI: 10.1007/s10462-021-10082-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper.
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Marquard D, Porr M, Lange F, Austerjost J, Beutel S. Smartglasses im Labor. CHEM UNSERER ZEIT 2020. [DOI: 10.1002/ciuz.202000022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
| | | | | | | | - Sascha Beutel
- Leibniz‐Universität Hannover Institut für Technische Chemie Callinstraße 5 D‐30167 Hannover
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12
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Bär J, Boumasmoud M, Kouyos RD, Zinkernagel AS, Vulin C. Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application. Sci Rep 2020; 10:16084. [PMID: 32999342 PMCID: PMC7528005 DOI: 10.1038/s41598-020-72979-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/08/2020] [Indexed: 12/14/2022] Open
Abstract
Populations of genetically identical bacteria are phenotypically heterogeneous, giving rise to population functionalities that would not be possible in homogeneous populations. For instance, a proportion of non-dividing bacteria could persist through antibiotic challenges and secure population survival. This heterogeneity can be studied in complex environmental or clinical samples by spreading the bacteria on agar plates and monitoring time to growth resumption in order to infer their metabolic state distribution. We present ColTapp, the Colony Time-lapse application for bacterial colony growth quantification. Its intuitive graphical user interface allows users to analyze time-lapse images of agar plates to monitor size, color and morphology of colonies. Additionally, images at isolated timepoints can be used to estimate lag time. Using ColTapp, we analyze a dataset of Staphylococcus aureus time-lapse images including populations with heterogeneous lag time. Colonies on dense plates reach saturation early, leading to overestimation of lag time from isolated images. We show that this bias can be corrected by taking into account the area available to each colony on the plate. We envision that in clinical settings, improved analysis of colony growth dynamics may help treatment decisions oriented towards personalized antibiotic therapies.
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Affiliation(s)
- Julian Bär
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mathilde Boumasmoud
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roger D Kouyos
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Institute of Medical Virology, University of Zurich, Zurich, Switzerland
| | - Annelies S Zinkernagel
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Clément Vulin
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. .,Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092, Zurich, Switzerland. .,Department of Environmental Microbiology, 8600, Eawag, Dubendorf, Switzerland.
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Porr M, Marquard D, Stanislawski N, Austerjost J, Russo M, Bungers S, Klimmt C, Scheper T, Beutel S, Lindner P. smartLAB - Interaktives Arbeiten in digitalisierter Laborumgebung. CHEM-ING-TECH 2019. [DOI: 10.1002/cite.201800090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Marc Porr
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
| | - Daniel Marquard
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
| | - Nils Stanislawski
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
| | - Jonas Austerjost
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
| | - Mario Russo
- Labfolder GmbH; Elsenstraße 106 12435 Berlin Deutschland
| | - Simon Bungers
- Labfolder GmbH; Elsenstraße 106 12435 Berlin Deutschland
| | - Christoph Klimmt
- Hochschule für Musik, Theater und Medien Hannover; Institut für Journalistik und Kommunikationsforschung; Expo Plaza 12 30539 Hannover Deutschland
| | - Thomas Scheper
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
| | - Sascha Beutel
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
| | - Patrick Lindner
- Leibniz Universität Hannover; Institut für Technische Chemie; Callinstraße 5 30167 Hannover Deutschland
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Austerjost J, Porr M, Riedel N, Geier D, Becker T, Scheper T, Marquard D, Lindner P, Beutel S. Introducing a Virtual Assistant to the Lab: A Voice User Interface for the Intuitive Control of Laboratory Instruments. SLAS Technol 2018; 23:476-482. [PMID: 30021077 DOI: 10.1177/2472630318788040] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The introduction of smart virtual assistants (VAs) and corresponding smart devices brought a new degree of freedom to our everyday lives. Voice-controlled and Internet-connected devices allow intuitive device controlling and monitoring from all around the globe and define a new era of human-machine interaction. Although VAs are especially successful in home automation, they also show great potential as artificial intelligence-driven laboratory assistants. Possible applications include stepwise reading of standard operating procedures (SOPs) and recipes, recitation of chemical substance or reaction parameters to a control, and readout of laboratory devices and sensors. In this study, we present a retrofitting approach to make standard laboratory instruments part of the Internet of Things (IoT). We established a voice user interface (VUI) for controlling those devices and reading out specific device data. A benchmark of the established infrastructure showed a high mean accuracy (95% ± 3.62) of speech command recognition and reveals high potential for future applications of a VUI within the laboratory. Our approach shows the general applicability of commercially available VAs as laboratory assistants and might be of special interest to researchers with physical impairments or low vision. The developed solution enables a hands-free device control, which is a crucial advantage within the daily laboratory routine.
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Affiliation(s)
- Jonas Austerjost
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany.,2 Institute of Brewing and Beverage Technology, Forschungszentrum Weihenstephan, Technische Universität München, Germany
| | - Marc Porr
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Noah Riedel
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Dominik Geier
- 2 Institute of Brewing and Beverage Technology, Forschungszentrum Weihenstephan, Technische Universität München, Germany
| | - Thomas Becker
- 2 Institute of Brewing and Beverage Technology, Forschungszentrum Weihenstephan, Technische Universität München, Germany
| | - Thomas Scheper
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Daniel Marquard
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Patrick Lindner
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
| | - Sascha Beutel
- 1 Institute of Technical Chemistry, Leibniz University Hannover, Hannover, Germany
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