COMPARISON OF LAG TIMES OBTAINED FROM OPTICAL DENSITY AND VIABLE COUNT DATA FOR A STRAIN OF <i>PSEUDOMONAS FRAGI</i>

Hudson, John; Mott, S.J.

doi:10.1111/j.1745-4565.1994.tb00604.x

Cited by 17 publications

(15 citation statements)

References 14 publications

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“…For this reason, the TTD method is not able to distinguish separately. The increase of around the growth boundary becomes significantly longer than under optimal conditions, as has been reported for Listeria monocytogenes (31) and Pseudomonas fragi (13). The increase of closer to the growth boundary is also seen for B. cereus (Fig.…”

Section: Discussionsupporting

confidence: 59%

Comparison of Two Optical-Density-Based Methods and a Plate Count Method for Estimation of Growth Parameters of Bacillus cereus

Biesta-Peters

Reij

Joosten

et al. 2010

Appl Environ Microbiol

100

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Quantitative microbiological models predicting proliferation of microorganisms relevant for food safety and/or food stability are useful tools to limit the need for generation of biological data through challenge testing and shelf-life testing. The use of these models requires quick and reliable methods for the generation of growth data and estimation of growth parameters. Growth parameter estimation can be achieved using methods based on plate counting and methods based on measuring the optical density. This research compares the plate count method with two optical density methods, namely, the 2-fold dilution (2FD) method and the relative rate to detection (RRD) method. For model organism Bacillus cereus F4810/72, the plate count method and both optical density methods gave comparable estimates for key growth parameters. Values for the maximum specific growth rate ( max ) derived by the 2FD method and by the RRD method were of the same order of magnitude, but some marked differences between the two approaches were apparent. Whereas the 2FD method allowed the derivation of values for lag time () from the data, this was not possible with the RRD method. However, the RRD method gave many more data points per experiment and also gave more data points close to the growth boundary. This research shows that all three proposed methods can be used for parameter estimation but that the choice of method depends on the objectives of the research.

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Section: Discussionsupporting

confidence: 59%

Comparison of Two Optical-Density-Based Methods and a Plate Count Method for Estimation of Growth Parameters of Bacillus cereus

Biesta-Peters

Reij

Joosten

et al. 2010

Appl Environ Microbiol

100

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“…The it is due to an increase in mean cell volume, the data may suggest that cells need to reach an appropriate volume prior to initiating their first division (14). On the other hand, if the increase in OD is due to an increase in mean cell refraction, the data may suggest that cells need to accumulate intracellular substances necessary for growth (5) and tend to absorb light more intensively.…”

Section: Discussionmentioning

confidence: 84%

“…However, (14) found that estimates based on OD data were systematically smaller than estimates based on VCE data. These authors explained their results by the existence of cell inflation during the lag phase.…”

Section: Individual Fits Of the Exponential Model And The Ed Modelmentioning

confidence: 84%

“…This problem was solved by proposing a linear calibration function. With regard to the max , Hudson and Mott (14) found that estimates derived from OD and VCE data were very similar, whereas Dalgaard et al (12) showed that OD-based estimates of max are systematically lower than VCE-based estimates and that the discrepancy differs from one model to another. In fact, the accuracy of estimates of max and closely depends on the model chosen to fit data (11).…”

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confidence: 99%

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Modeling the Lag Time of Listeria monocytogenes from Viable Count Enumeration and Optical Density Data

Baty

Flandrois

Delignette-Muller

2002

Appl Environ Microbiol

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The following two factors significantly influence estimates of the maximum specific growth rate ( max ) and the lag-phase duration (): (i) the technique used to monitor bacterial growth and (ii) the model fitted to estimate parameters. In this study, nine strains of Listeria monocytogenes were monitored simultaneously by optical density (OD) analysis and by viable count enumeration (VCE) analysis. Four usual growth models were fitted to our data, and estimates of growth parameters were compared from one model to another and from one monitoring technique to another. Our results show that growth parameter estimates depended on the model used to fit data, whereas there were no systematic variations in the estimates of max and when the estimates were based on OD data instead of VCE data. By studying the evolution of OD and VCE simultaneously, we found that while log OD/VCE remained constant for some of our experiments, a visible linear increase occurred during the lag phase for other experiments. We developed a global model that fits both OD and VCE data. This model enabled us to detect for some of our strains an increase in OD during the lag phase. If not taken into account, this phenomenon may lead to an underestimate of .The following two methods are commonly used to monitor growth of bacteria: viable count enumeration (VCE) and absorbance measurement. Monitoring bacterial growth by VCE is time-consuming and rather expensive, but it remains the method of reference. Methods based on absorbance measurements constitute a second family of methods based on the direct proportionality between the optical density (OD) of a liquid medium and the concentration of bacteria. OD techniques are rapid, convenient, and inexpensive. However, many drawbacks are inherent to these techniques. The main problem encountered is the limited range of validity since the detection threshold typically corresponds to a bacterial concentration greater than 10 6 bacteria/ml (8). From these two kinds of data, characteristic growth parameters, mainly the lag-phase duration () and the maximum specific growth rate ( max ), can be assessed. The use of mathematical growth models allows accurate and objective estimation of these parameters. In the field of predictive microbiology, numerous models have been developed. Mechanistic models are especially interesting as they provide both a method of estimating and max and a means of understanding bacterial growth.In fact, the following two potential sources of bias influence estimation of growth parameters: the type of data (OD or VCE) and the model used to fit data. Because of the high detection threshold of OD techniques, the initial inoculum must be large enough to allow reliable measurements, and the question which has arisen is whether the estimates of max at high concentrations are not systematically lower than the actual max because of possible end-of-growth inhibition. Nevertheless, this phenomenon seems to have no effect on the estimates of max (9). Hudson and Mott (14) fitted the modified Gompert...

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“…). Alternatively, a logarithmic transformation to normalize the variance has been reported, also resulting in a linear relationship between ln(OD) and ln(N) (Hudson and Mott ; Chorin et al . ; Francois et al .…”

Section: Resultsmentioning

confidence: 99%

Critical Assessment of the Time‐to‐Detection Method for Accurate Estimation of Microbial Growth Parameters

Baka

Noriega

Stamati

et al. 2014

Journal of Food Safety

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The time-to-detection (TTD) method is a rapid and high throughput approach for the estimation of microbial growth parameters (maximum specific growth rate μmax and lag phase duration λ), which relies on optical density (OD) measurements. The performance of this method depends on several factors that are often selected in an arbitrary way. In this work, a sensitivity analysis was performed to assess the effect of several key factors on the resulting output data of this method with Listeria monocytogenes. The factors showing higher influence on the results include (1) the calibration curve relating viable plate counts and OD data; (2) the approach to estimate TTD values; (3) the detection limit of OD measurements; and (4) the range of the initial cell concentrations considered (Ni). In general, lag phase (λ) estimates were more sensitive than maximum specific growth rate (μmax) estimates. The approach to estimate TTD values and the OD detection limit was the most influential factors for the μmax and λ estimation. This work has illustrated that, despite all the advantages of the TTD method, there are crucial steps in experimental design and data processing that significantly influence its output in terms of lag phase duration and maximum specific growth rate. PRACTICAL APPLICATIONSThis study contributes to understanding which are the most influential factors on the output of time-to-detection (TTD) method. These factors can be considered throughout different steps of the method, experimental design, data processing and growth parameter estimation. Guidelines are provided about how the selection of the factors assessed should be made and which variables -i.e., research scope, materials and equipment available for experimental and analytical stepsshould be considered. In addition, some limitations of the method regarding the range and quality of the experimental data when implementing an automated global fitting are described. Overall, the performance of the TTD method and the accuracy of microbial growth parameters estimation will be improved.

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COMPARISON OF LAG TIMES OBTAINED FROM OPTICAL DENSITY AND VIABLE COUNT DATA FOR A STRAIN OF PSEUDOMONAS FRAGI

Cited by 17 publications

References 14 publications

Comparison of Two Optical-Density-Based Methods and a Plate Count Method for Estimation of Growth Parameters of Bacillus cereus

Comparison of Two Optical-Density-Based Methods and a Plate Count Method for Estimation of Growth Parameters of Bacillus cereus

Modeling the Lag Time of Listeria monocytogenes from Viable Count Enumeration and Optical Density Data

Critical Assessment of the Time‐to‐Detection Method for Accurate Estimation of Microbial Growth Parameters

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