Modelling the Effect of Temperature on the Initial Decline during the Lag Phase of Geotrichum candidum

Abstract

The study of lag phase provides essential knowledge for food quality control. With respect to significance of Geotrichum candidum in the food context, the aim of this study was to quantitatively characterize the relationship between temperature (6–25 °C) and initial decline period during G. candidum lag phase. The decrease in G. candidum cells in the lag phase was primary modelled by Weibull’s model to define the first-decimal reduction time (δ). Subsequently, the lag death rate (LDR) values were recalculated from δ and further modelled by using Arrhenius equations, as well as a square root model, and the models’ suitability was proven by selected statistical indices. The square root model with the estimated parameters b = 0.016 °C−1 h−0.5 and Tmin = −0.72 °C showed better indices relating to goodness of fit based on a low root mean sum of square error (RMSE = 0.028 log CFU mL−1), a higher coefficient of determination (R2 = 0.978), and the lowest value of AIC (AIC = −38.65). The present study provides a solution to the possible application of secondary predictive models to the death rate dependence on temperature during the microbial lag phase. Despite limited practical importance, under specific conditions, it is possible to consider its use, for example, in exposure assessment.

How to determine microbial lag phase duration?

“…2021 [24] PARAMETER FITTING TO THE BARANYI MODEL Using parameter fitting procedures in order to simultaneously find all Baranyi & Roberts model parameters that provide the best fit to the entire growth curve. Then the lag is defined as ln(1+ 1/𝑞 0 )/r where r is the maximal growth rate and 𝑞 0 represents the physiological state of the inoculum.…”

“…In such cases, some assumptions are needed to calculate the lag phase duration with sufficient accuracy. If one assumes that there is no population growth during the lag phase and then cells start synchronically dividing at a constant growth rate, the end of the lag phase can be calculated as the intersection between the tangent line to the point of maximum growth rate and the y = log(N0) line, where N0 is the inoculation density (hereinafter “tangent method” [6]; see for example: [12,15,24]). This is in fact the most frequently used method of calculating the lag duration.…”

“…Viability counts (CFU) provide precise estimates of cell abundance even in a small population, however, this technique is time-consuming, requires previously determined culture dilutions, and is difficult to automatize and scale up. Nevertheless, due to its high sensitivity, it's broadly used in food safety control [24,25]. Flow cytometry has a broad detection spectrum and it enables simultaneous measurements of various cell properties e.g.…”

Microbial lag calculator: A shiny‐based application and an R package for calculating the duration of microbial lag phase

“…If one assumes that there is no population growth during the lag phase and then cells start synchronically dividing at a constant growth rate, the end of the lag phase can be calculated as the intersection between the tangent line to the point of maximum growth rate and the y = log(N0) line, where N0 is the inoculation density (hereinafter ‘ tangent method ’ (Bertrand, 2019); e.g. Cerulus et al., 2018; Jomdecha & Prateepasen, 2011; Valík et al., 2021). This is in fact the most frequently used method of calculating the lag duration.…”

“…Scale) The population size does not increase its size during the lag (or it increases very slowly) and then starts growing exponentially with a constant growth rateIf the growth rate varies, it may be challenging to find the 'real' maximal growth rate. Moreover, the initial population size needs to be determined with high confidenceValík et al (2021)…”