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Applied and Environmental Microbiology, January 2005, p. 392-399, Vol. 71, No. 1
0099-2240/05/$08.00+0 doi:10.1128/AEM.71.1.392-399.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.
Institute of Technology of Agricultural Products, National Agricultural Research Foundation, Lycovrissi,1 Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Technology, Agricultural University of Athens, Athens, Greece2
Received 17 May 2004/ Accepted 10 August 2004
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The use of gradient plates to determine the response of bacteria to two different environmental conditions was first described by Caldwell and Hirsch (2) and was refined by Wimpenny and Waters (24). Since then, gradient plates have been used to evaluate the combined effects of simultaneously varying physiological parameters, such as temperature, NaCl, pH, and preservatives, on bacterial growth either singly or in competition with other species (14, 17, 20, 21, 25). This technique has been applied primarily to food-borne bacteria, while there is little information available on filamentous fungi. In particular, in the existing literature either the focus is on yeasts rather than filamentous fungi (9, 26, 27) or the gradient is established with highly specific controlling factors (sanitizers and antibiotics) (4, 9). Moreover, this technique can yield quantitative data on microbial growth. The time to visible growth and the increase in the amount of biomass provide data on the kinetics and the growth yield, respectively. The accumulation of values for combinations of gradient factors around the area of growth form the growth/no-growth interface. Application of the logistic regression (16) permits the identification of these boundaries and can be used to assess the potential for microbial growth in response to changes in several experimental factors.
In the present work, two-dimensional gradient plates were used for the first time to rapidly screen the combined effects of temperature, NaCl concentration, and pH on the growth of M. ruber, the major spoilage microorganism during storage of processed table olives. The use of this technique was expanded to determine the growth/no-growth boundaries of fungus, providing useful information about the "habitat domain" of the fungus as established by Wimpenny (23), i.e., the physicochemical environmental conditions under which microorganisms can grow.
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Preparation of two-dimensional gradient plates.
Two-dimensional pH-NaCl gradient plates were prepared in 12- by 12-cm disposable plastic petri dishes with four 20-ml layers of malt extract agar (Merck, Darmstandt, Germany) as the growth medium. The medium was made in 200-ml volumes. The first two volumes were adjusted with 6 ml of 5 N HCl and 6 ml of 1 M NaOH for the acid and alkali layers, respectively. One side of the plate was raised with a 3-mm-diameter rod, and the first layer (acid) was poured. After the medium solidified, the plates were leveled and the second layer (alkali) was added. When the pH gradient was set, the plates were turned 90o and the NaCl gradient was formed. The plates were again raised at one end by 3 mm, and a third layer of medium containing 35% (wt/vol) NaCl was added. Finally, the plates were leveled again and a final layer of malt extract agar was poured. The plates equilibrated for 24 h at room temperature prior to gradient measurement and inoculation. For each temperature, duplicate plates were prepared.
Gradient measurement.
The pH gradient was determined after equilibration at 2-cm intervals across the plates. For this purpose, 2-mm core samples of medium were removed and melted with 2 ml of deionized water. The pH was determined with an EA 940 digital pH meter (Orion Research Inc., Boston, Mass.). The NaCl gradient was determined by taking 2-mm core samples of medium, melting them in 2 ml of deionized water, and measuring the conductivity with a Wheatstone bridge. A calibration curve was constructed with malt extract agar containing known amounts of NaCl in order to convert conductivity readings to NaCl concentrations (percent, weight/volume). The pH gradient varied from 1.9 to 6.8, and the NaCl gradient varied from 3 to 9% (wt/vol). Prior to experimentation, one uninoculated plate was incubated at each temperature to check the stability of gradients. It was found that gradients were stable for no longer than 11 days, regardless of temperature.
Inoculation of gradient plates.
A suspension of ascospores (4 ml) was poured over the surface of the gradient plate, and the excess amount was removed after 1 min with a sterile Pasteur pipette to avoid interference with the NaCl gradient. The plates were allowed to stand for 15 min until the surface was dry and then incubated at 25, 30, and 35°C for up to 260 h.
Mapping of fungal growth.
Images were captured with a high-sensitivity SSC-DC50AP (Sony Corp., Tokyo, Japan) digital camera and processed with Image Pro Plus version 2.5 (Media Cybernetics) image analysis software. All plates were photographed at 24, 68, 92, 118, 188, and 260 h. Each plate was corrected for background by subtracting the image of an uninoculated (blank) plate incubated together with the test plates. Images were 512 by 512 pixels, each with a grayness (brightness) level from 0 (black) to 255 (white). To avoid edge effects, only the central 11-by-11 area was analyzed. Visible growth is expressed in optical density (OD) units based on the equation (13) OD = 0.4343 ln (255/pixel value). As the gray values of a malt extract agar plate immediately after inoculation were equal to or higher than 200, all pixels with gray values exceeding 200 were automatically set to 255 so that the equation above gave OD values equal to 0 (no visible growth). Results are presented in three-dimensional graphs of pH, NaCl, and optical density values.
Determination of growth/no-growth boundaries.
Based on the determination of NaCl concentrations (percent) and pH values at 1.2- and 0.8-cm intervals along the two gradients, respectively, a grid in the two-dimensional space of pH and NaCl was created to account for pH and NaCl values (14 x 10 = 140) at and within these intervals. The optical density data were converted into probabilities of growth by assigning 1 to areas of the grid where visible fungal growth was evident (OD values greater than 0) and 0 to areas where no visible growth was detected (OD values equal to 0). Probability data were modeled by using linear logistic regression to determine the growth/no-growth boundaries of the fungus at each temperature. The following quadratic equation (6, 7) was fitted to the data:
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Although more cardinal expressions have been employed in growth/no-growth studies (15, 22), the use of quadratic expressions in logistic regression has been proven to be a simple and successful means of modeling the probability of microbial growth or no growth (6, 7). Equation 1 was fitted by using the SAS Proc Logistic procedure (19). The automatic variable selection option with a stepwise selection method was used to choose the most significant effects (P < 0.05). The predicted growth/no-growth interfaces for P = 0.1, 0.5, and 0.9 were calculated by using Microsoft Excel Solver.
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FIG. 1. Wire frame representation of M. ruber visible growth on malt extract agar pH-NaCl gradient plates at 25°C.
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FIG. 2. Wire frame representation of M. ruber visible growth on malt extract agar pH-NaCl gradient plates at 30°C.
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FIG. 3. Wire frame representation of M. ruber visible growth on malt extract agar pH/NaCl gradient plates at 35°C.
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When the growth/no-growth surface model at 260 h of incubation was obtained by applying linear logistic regression, it was evident that pH, temperature, and NaCl, as well as their interactions, were significant (Table 1). Plots of growth/no-growth interfaces for P values of 0.1, 0.5, and 0.9 are given in Fig. 4, 5, and 6. It seems that predicted interfaces describe the results satisfactorily at 25 and 35°C, whereas at 30°C the model predicts lower minimum pH values for growth in the range of 7 to 10% NaCl than those observed on gradient plates. With regard to the goodness of fit of the growth/no-growth model, logistic regression resulted in 99.3% concordance and a maximum rescaled r2 value of 0.844, whereas the Hosmer-Lemeshow statistic was 16.125 (X2, 8 df; P = 0.041). On the other hand, the 0.6% discordance was attributed to six false-negative predictions (growth was detected, while the model predicted a probability of growth of less than 0.5) and eight false-positive predictions (no growth was detected, with the model predicting a probability of growth higher than 0.5), from a total of 420 cases (140 cases for each of the three temperatures), corresponding to 1.4 and 1.9%, respectively. The majority of false-negative predictions occurred for NaCl concentrations of 4 to 7% and pHs of 2.00 to 3.75. The majority of false-positive predictions were associated with either 3 to 4% or 6 to 8% NaCl and the same pH range as for false-negative predictions. Higher number of false predictions occurred at 30°C, followed by 35 and 25°C (Fig. 4 to 6).
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TABLE 1. Fitted values for the significant (P < 0.05) coefficients of the visible growth/no-growth surface model for M. ruber obtained by logistic regression with the maximum-likelihood method
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FIG. 4. Plot of predicted visible growth/no-growth interface at P values of 0.9 (- · · -), 0.5 (__), and 0.1 (...) and observed growth () and no-growth ( ) responses of M. ruber growing on malt extract agar gradient plates at 25°C for 10 days.
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FIG. 5. Plot of predicted visible growth/no-growth interface at P values of 0.9 (- · · -), 0.5 (__), and 0.1 (...) and observed growth () and no-growth ( ) responses of M. ruber growing on malt extract agar gradient plates at 30°C for 10 days.
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FIG. 6. Plot of predicted visible growth/no-growth interface at P values of 0.9 (- · · -), 0.5 (__), and 0.1 (...) and observed growth () and no-growth ( ) responses of M. ruber growing on malt extract agar gradient plates at 35°C for 10 days.
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The highest OD values recorded were in the pH region from 3.7 to 6.3. In this area, fungal growth extended over more of the NaCl concentration gradient regardless of temperature. Thus, under normal brine conditions (pH 3.8 to 4.5), the fungus is very salt tolerant and can grow on nearly 9% (wt/vol) NaCl. Consequently, the combined effect of low pH and high salt concentration may not suffice for table olive preservation, and further treatment is necessary to ensure product stability during storage. This conclusion is consistent with previous work by Balatsouras and Vaughn (1), who isolated various fungal species from olive brines and evaluated their tolerance to pH and NaCl. They reported that all isolates could grow at NaCl concentrations of up to 8% (wt/vol) and at pH values of as low as 3.5. However, the species reported in that work were isolated from fermented brines without any thermal treatment, and in addition, no information was provided about M. ruber, since the fungus was only recently isolated from thermally processed olives (11).
The type of growth stage may also interfere with absorbance values, since areas in the reproductive growth stage are likely to have higher OD values than those where simply vegetative growth occurs. This is due to the presence of cleistothecia and not necessarily due to the increase of mycelial density itself, and it may be considered a potential limitation in the application of the method, especially in the case of modeling. Although we have no evidence of whether the fungus is homothallic or heterothallic, i.e., whether the ascospores have the same genetic potential or not, differentiation in growth may be attributed to the effects of different levels of pH and NaCl across the surface of the plates. The reason for this is that the plates were inoculated with a relatively large inoculum of ascospores (106 spores/ml), corresponding to approximately 104 spores per cm2, based on the total area of the plate (144 cm2). Therefore, there is a high number of spores located within an area where the pH and NaCl concentration are slightly changed by dpH and dNaCl intervals. Consequently, the appearance of mycelium in every spot on the plate is based on the assumption that visible growth derives from those spores that have the highest potential to initiate vegetative growth under the conditions of a specific biothesis.
Another advantage of the gradient plate technique is that collective data could be used as a basis for determination of the growth/no-growth interface on a solid substrate, simulating the microbial contamination of food surfaces. The first approach to monitor growth on gradient plates and to determine growth/no-growth responses in liquid culture was performed with Listeria monocytogenes by McClure et al. (8). Efforts on modeling growth/no-growth boundaries were recently refined for Escherichia coli (15), Salmonella enterica serovar Typhimurium (6), and L. monocytogenes (22), as well as for spoilage microorganisms such as Brochothrix thermosphacta (7). In the present work, an attempt was made to monitor fungal growth and model growth/no-growth boundaries with data collected from a single plate. The model for the growth/no-growth boundaries of M. ruber indicated good performance, judging by the statistical evaluation (concordance rate, maximum rescaled r2, and Hosmer-Lemeshow statistic), as well as by the graphical comparison of the predicted interface with actual data (Fig. 4 to 6). However, as noticed in relevant studies, the experimental cases close to the point where growth ceases are those that likely account for the discordance of the respective models. Indeed, under conditions approaching the growth limits, the shifts in growth responses become abrupt and hence more difficult to describe. For instance, Salter et al. (18) observed false predictions by the growth/no-growth model for E. coli at temperatures of below 20°C (especially close to 10°C) and water activities (aws) of below 0.977. Similarly, in the present study, the discordance of the model derived mainly from combinations of low pH with medium to high NaCl concentrations. However, the discordance may also be associated with the type and the intervals of a factor(s) under investigation. This is probably the reason why in the study of Presser et al. (15), the growth/no-growth interface of E. coli was narrow at pHs of around 4.0 to 4.5 and the optimum aw, while as the aw dropped below 0.97, the predicted interface became wider even at pHs close to neutral. Conversely, in the same study, the narrow intervals of pH adjusted by the combined addition of HCl and lactic acid allowed for a more accurate determination of the E. coli growth/no-growth interface in response to lactic acid concentration and pH. In our case, the gradient plates offer a more detailed description (in terms of intervals) of pH and NaCl ranges, thus increasing the robustness of the developed model for the growth/no-growth interface of M. ruber.
The need for research in the area of growth/no-growth boundaries becomes more intense because of the acid tolerance and osmotolerance of the fungus, raising concerns for many products commonly preserved by a combination of low aw, pH, and acidic preservatives (e.g., bakery products) (10). Moreover, growth/no-growth data should be expanded to include not only the effects of aw and pH but also the effects of the size of the inoculum, which has been found to be of great importance also (7).
Overall, the gradient plate technique can be advantageous in two ways: (i) in combination with image analysis, it is a convenient way of obtaining kinetic growth data, through changes in the OD of biomass over time, and (ii) in combination with logistic regression, it can be used for modeling of growth/no-growth boundaries and determination of conditions that form the habitat domain of a microorganism, i.e., the limiting growth conditions under which the likelihood of growth varies between 10 and 90%. This work can be extended to other spoilage fungi, taking into account other variables such as preservatives, essential oils, and natural antimicrobial substances, in order to determine their habitat domains and increase the effectiveness of food preservation technologies.
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