spectra. The efficiency of this method has been demonstrated for several bacterial species such as *Staphylococcus*, *Streptococcus*, *Clostridium* and *Legionella* (Helm et al., 1991), among others. Thi et al. (2003) assessed an improvement of this technique by coupling the FT-IR spectrometer with a light microscope and obtain microscopic spectra that provide information on the number, size and shape of the micro-colony cells. Furthermore, this technique was extended in Thi and Naumann (2007) to study the spatial heterogeneity of cell growth within macro-colonies. Cells were grown in agar under optimal conditions in order to get macro-colonies with a diameter of about 3–5 mm. These macro-colonies were excised from the agar, frozen at –70°C and cryosectioned in slices of 20 µm for further FT-IR mapping measurements. Obtained spectra revealed a substantial spatial heterogeneity of cell composition among the microorganisms studied (*Legionella bozemanii*, *Bacillus megaterium* and *Candida albicans*).

Invasive techniques have clearly contributed to increase the knowledge about bacterial colonies behavior; however, their invasive character, the subsequent sample discard and the lack of individual information gathered at the colony level are disadvantages to be overcome. In order to collect more accurate information and fundamental knowledge, non-invasive analytical techniques focused on mesoscopic and microscopic assessments of colony growth, are replacing the traditional ones (Mertens et al., 2012).

## NON-INVASIVE AND IN SITU TECHNIQUES

As previously described, most studies involving bacterial colonies are based on colony estimation of overall cell population dynamics in solid systems; however, an individual approach is required for a deeper insight in single colony behavior. Latest trends are moving toward the development of non-invasive techniques that enable to monitor bacterial colonies without altering their environment or subsequent sample discard. These techniques offer the advantage of obtaining quantitative data, faster and more accurately. Hereunder some of these novel techniques are grouped by the applied principle, which to date are mainly imaging techniques, optical density (OD) and microcalorimetric techniques. Nevertheless, the transition from traditional to novel techniques is gradually occurring since the latter also present some limitations. Under this situation, it can be observed that in many of the works here reviewed, novel techniques are either combined or compared with traditional ones, such as viable plate count.

### Imaging techniques

The use of microscopy-based methods enables to monitor growth of bacterial colonies in real time and to estimate different parameters related to colony evolution. This can be obtained through a further analysis of the obtained images. These methods are gaining interest in the field of food microbiology, since image analysis programs and automation software tools make data processing more feasible (Skandamis et al., 2007). Hereunder are some of the most applied techniques in this field detailed.

**Light microscopy and digital camera coupling.** Direct colony observation through a light microscope provides a useful tool to monitor certain colony parameters such as the colony radius and their area evolution on the surface. The combination of a

light microscope with a digital camera offers the possibility to immortalize the visualized image for further process and study. Several studies based on these techniques have been conducted to estimate colony dynamics, i.e., growth rate and lag phase, which are generally the most representative parameters to be identified (Guillier et al., 2006; Rasch et al., 2007; Skandamis et al., 2007; Koutsoumanis and Lianou, 2013). With this aim, Guillier et al. (2006) introduced a new method to estimate and compare individual lag times of either stressed or exponentially growing colonies of *L. monocytogenes*. Obtained results were afterwards compared with the individual lag time distributions observed in broth. In this work, the setup for surface colony development was agar plates, on which the colonies were monitored with a digital camera PixelFly (Photon Lines) and an automatic image processing system. Initially, suspensions of *L. monocytogenes* cells were disposed with a spiral plater on plates of tryptone soya agar enriched with yeast extract (TSAYE). During incubation, images were taken at constant time intervals of 10 min. The imaging set-up for this study consisted of a circular motorized platform OptoSigma (Photon Lines, Marly-le-Roy, France), that was coupled with the digital camera and enabled to study four plates simultaneously. While the colonies were growing, the intensity of the pixels in the obtained images was also increasing and this increment was then related to the lag phase. In this context, the time required for a colony to reach a surface threshold is considered proportional to the lag time of the cell initiating the colony, as also assumed for turbidity-based methods. This work presents a valuable method for studying colony growth dynamics since results showed means and standard deviation values similar to those obtained in broth, as well as no clear difference between liquid and solid conditions.

One of the main systems used for obtaining and visualizing submerged and surface colonies, is the gel-cassette. The gel cassette set-up was already widely used to estimate population growth dynamics in solid systems and it was at the beginning of the 21st century when its specific configuration with two transparent windows, inspired the coupling with microscopy techniques, enabling thus *in vivo* colony visualization. Skandamis et al. (2007) applied this combination of devices, a light microscope coupled with a digital camera and the gel-cassette to determine the effect of temperature and pH on the growth rate of *E. coli* O157:H7 colonies. The gel-cassette system was aseptically filled in with brain heart infusion agar (BHIA) adjusted at different pH values and then inoculated. Afterwards, it was coupled to a microscope with a high-sensitivity SSC-DC50AP digital camera (Sony Corp, Tokyo, Japan) that allowed to capture images of the growing colonies. The evolution of the colony surface was monitored with 40× objective lens from a light microscope and expressed in $log_{10}$ transformed pixels. Surface area data were used to estimate maximum growth rates ($\mu_{max}$)$_A$ by applying the model of Baranyi and Roberts (1994). The observed ($\mu_{max}$)$_A$ values were fitted to a quadratic secondary model to express the natural logarithm of the ($\mu_{max}$)$_A$ as a function of temperature and pH. Based on a correlation between the observed ($\mu_{max}$)$_A$ and the observed viable count-based $\mu_{max}$ ($\mu_{max}$)$_{VC}$, the predicted ($\mu_{max}$)$_A$ values were transformed into predicted ($\mu_{max}$)$_{VC}$. This model was validated by graphical comparison of the predicted
($\mu_{max}$)$_{VC}$ with published ($\mu_{max}$)$_{VC}$ values from *E. coli* O157:H7 in broth and different foods, as well as by mathematical means, i.e., the bias and accuracy performance indices (Ross, 1996). These data were gathered from ComBase database. Results showed that the model performed well with published responses of *E. coli* O157:H7 in broth, although a poorer agreement of the model was obtained from the correspondent responses of *E. coli* O157:H7 in food. It can be concluded that the use of ($\mu_{max}$)$_{A}$ is an adequate alternative in the development of predictive models to describe the ($\mu_{max}$)$_{VC}$ and a potential alternative to traditional viable plate count.

A further step in surface colony image monitoring with a light microscope was presented by Koutsoumanis and Lianou (2013). This research evaluated image series obtained at short time intervals, sequentially compiled and transformed into a video. The aim of this study was to assess the heterogeneity in the growth dynamics of micro-colonies emerging from single cells of *S. Typhimurium* investigation. Bacterial colonies from individual cells were grown on TSA disposed on a glass slide, which was covered with a coverslip. This set-up allowed to directly observe under the z-motorized microscope (Olympus BX61; Olympus, Tokyo, Japan), equipped with a high resolution device camera. Images were obtained with phase-contrast time-lapse microscopy and compiled to create the video. This video was a useful tool to characterize the cells in terms of division times, cell length and growth kinetics. Results showed a high heterogeneity in the growth behavior if individual cells are compared. This heterogeneity became higher for those cells that gave rise to small colonies. Generally, the experimental error from the application of monitoring methods, remains as the main constrain at describing colonial behavior. Time-lapsed microscopy method described in this paper, eliminated this limitation since it allowed to count each cell with time in each micro-colony.

**Fluorescence and confocal laser scanning microscopy.** Fluorescence microscopy technique is based on detecting the emitted fluorescence by fluorescent molecules. The excitation of these molecules is followed by an emission of a band of light with a specific wavelength, being the excitation-emission spectra specific for each molecule. The use of fluorophores allows to stain target components such as, proteins, oil droplets, carbohydrates, etc., and thus, to visualize them. Several works have been presented to study bacterial colonies by applying this principle.

Malakar et al. (2000) presented one of the first works involving gel-cassette with a fluorescence microscope to monitor bacterial colonies and experimentally confirming the presence of pH microgradients within and around them. In this study, colonies of *Lactobacillus curvatus* were grown in De Man, Rogosa, Sharpe (MRS) broth with bovine gelatine as gelling agent and supplemented with Oregon Green™, a fluorescent dye which intensity was linearly related to a pH range; once prepared the mixture, it was aseptically introduced in the gel-cassette system for further analysis. Subsequently, a fluorescence microscope (Nikon, Surrey, UK) with the following filters, excitation (440 ± 10 nm, 510 ± 11.5 nm), emission (535 ± 11.5 nm) and a dichroic mirror (540 nm, long pass) was set up with the gel-cassette system and serial measurements of fluorescence intensity in the inoculated

media were taken. These images were obtained after focusing on a defined plane between the bottom and top of each cassette-window and exciting this area with defined light and wavelengths. Furthermore, light transmission images were taken in order to determine the colony radius, once they were visible and put into focus. By representing the measured radius and pH values, 2D pH maps were elaborated. Finally, the superimposition of the pH maps with the transmission images, helped to reveal the pH profiles in and around the colony. As the authors stated, this technique was the first approach that allowed to monitor colony growth non-invasively, as well as to reinforce the concept of pH gradients in solid systems, being more representative of what happens in a colony in real time.

CLSM works on the same principle as fluorescence microscopy although it presents certain advantages, e.g., better resolution images due to the pinhole that eliminates out-of-focus light and the possibility to observe thicker samples, making optical sections at different depths that enables to reconstruct a 3D-image. This device, coupled with the gel-cassette was applied by Jeanson et al. (2011) to study colony size and distribution as a function of the inoculum level in a model cheese. In literature, has often been object of study the effect of the initial microbial load on colony size and distribution. High initial inoculum levels promote small colonies closely located; in contrast, low initial cell densities lead to large colonies separated by long distances (Malakar et al., 2002, 2003; Theys et al., 2009). As a consequence, inter- and intra-colony interactions appear, affecting bacterial behavior. Inter-colony interactions are typically found between small colonies separated by short distances in which the fast diffusive transport has been described as comparable phenomena to convection in broth (Malakar et al., 2002, 2003). In contrast, intra-colony interactions are dominant in large colonies formed from a low inoculation levels. In this scenario, diffusion becomes a constraint since it is not able to cope with the demands of the cells situated in the center of the colony; as a consequence, this delayed molecular transport within the colony triggers the interaction between the inner cells (Malakar et al., 2002, 2003). In this context, the work of Jeanson et al. (2011) aimed at describing this phenomenon in a cheese matrix with immobilized bacteria whose metabolism is the main responsible for its ripening. The model cheese matrix was developed at laboratory level with the retentate from milk ultrafiltration (UF) and coagulant agent Maxiren 180 (DSM Food Specialities, France). This model system was afterwards introduced in a gel cassette and inoculated with different levels of *Lactococcus lactis*, in order to observe the bacterial distribution under the CLSM. The selected strain produces a green fluorescent protein (GFP) that can be linked to the metabolically active state of the cells and therefore, observed under the microscope. Several CLSM photographs of the fluorescent colonies were taken aiming at characterizing their spatial distribution in the model cheese. This distribution resulted to be random in the whole structure, independently of the inoculum level. Nevertheless, the initial density was found to determine the spatial separation between the colonies, as previously reported by Malakar et al. (2003). Traditional viable plate counting was also carried out, confirming that independently of the initial inoculation level, the final stationary population remains equal. Besides colony
size and distribution, Floury et al. (2013) used CLSM coupled with the gel-cassette system to study the potential porosity of the *L. lactis* colonies to certain compounds in a cheese model system. As above described, cheese ripening depends on the metabolism of immobilized bacteria. Thus, cheese ripening can be affected by diffusional limitations of nutrients and metabolites within and around the colonies. For this case study, colony porosity to different sizes of dextran molecules (from 4.4 to 155 kDa) was assessed in the model cheese system and in M17-agar. Both media were aseptically prepared, inoculated and disposed in mini gel-cassette systems for directly observation under CLSM. For the porosity observation, the chosen dextrans were fluorescently labeled with Rhodamine B isothiocyanate (Sigma Aldrich, Saint-Quentin Falavier, France) and viable and total bacterial counts were performed with the LIVE/DEAD® Bacterial Viability Kit (BacLight™, Molecular Probes, Invitrogen). Obtained images were also used to study the different morphologies of the colonies depending on the media; while in the model cheese they were spherical, colonies grown in M17-agar showed under the same growing conditions, a lenticular shape.

Besides the gel-cassette, other setups like chambered cover glass are applied for CLSM studies. Jeanson et al. (2013) used CLMS with CoverWell imaging chambers (Sigma-Aldrich, Quentin Fallavier, France) to assess the pH around colonies of *L. lactis* inoculated in the cheese model system with different inoculum levels. Bacterial colonies were visualized with the fluorescent dye SYTO-9 (Molecular Probes, Invitrogen, Villebon-sur-Yvette, France), which was excited at 488 nm and detected at 515 ± 15 nm. This visualization confirmed that the low inoculum levels gave rise to bigger colonies. A pH sensitive probe C-SNARF-4F (Molecular Probes, Invitrogen, Villebon-sur-Yvette, France) was used to monitor micro-pH around the colonies. This C-SNARF-4F was excited at 543 nm and detected at 590 ± 15 nm and over 650 nm, enabling afterwards to calculate the ratio (R) from these two peaks. Measurements were taken along 100 µm from the edge of the colony at 10 µm steps from opposite sides of the colony. In order to correlate R values with pH, macro-pH measurements were performed with a pH meter (Inlab pH level 1; WTW, Germany) and pH electrode (Sebtix 41; WTW, Germany) in flasks containing the inoculated model cheese and incubated under the same conditions as the imaging chambers. Results showed that lower inoculum levels yielded to lower acidification kinetics; however, from the micro-pH measurements around the colonies, no microgradients were identified independently of the size of the studied colonies. The absence of these microgradients reveals, for the first time, that the pH perceived by the cells in a model cheese while acidification, is actually the macro-pH. Boons et al. (2013b) also used CLSM with imaging well chambers (Nunc Lab-Tek (USA) chambered borosilicate cover-glass system) to determine the effect of the media composition and microstructure. This paper describes for the first time the behavior of *E. coli* in a heterogeneous protein-polysaccharide solid media with CLSM. The main novelty in this work was to use a gelatine-dextran mixture at different concentration ratios, which led to different microstructures characterized by a phase-separation, affecting thus bacterial behavior. This phenomenon was observed in the system thanks to the Rhodamine B, which

enabled gelatine phase visualization. Initially, increasing gelatine concentrations promoted the transition from a homogeneous to a heterogeneous structure in which dextran spheres appeared in the gelatine matrix. However, if the gelatine phase concentration was also increased, a phase inversion occurred and gelatine spheres appeared in the dextran matrix. This phase inversion induced *E. coli* colonies to grow as diffuse strings instead as defined spheres. This morphological characterization was observed thanks to in the Venus fluorescent protein included in the *E. coli* strain, which expression enabled to visualize the bacterial cells with CLMS. In spite of these morphological changes, colony development occurred exclusively in the dextran phase. The same imaging technique was applied in Boons et al. (2014), although viable plate counting was also performed to estimate the microbial dynamics of *E. coli* in the heterogeneous microstructure, in order to decipher relation between micro- and macroscale results.

**Imaging techniques for identification.** Besides monitoring microbial growth, imaging techniques are also a useful tool to study specific microbial colony characteristic, such as strain identification. In this context, label-free bacterial colony phenotyping technology, also called bacterial rapid detection using optical scattering technology (BARDOT), has shown promising results in bacterial identification and classification. Kim et al. (2014) presented a theoretical model in order to explain the underlying mechanism of speckle formation by the colonies from *Bacillus*. Laser-based optical sensor interrogated the entire volume of the colonies in order to gather information that could be encoded in the far-field scatter patterns. This technique was also assessed by Bae et al. (2010) on bacterial colonies of *S. Montevideo*, *L. monocytogenes* and *E. coli*, obtaining results that correlate colony morphology and growing characteristics with characteristic diffraction patterns.

It can be concluded that a wide range of applications are available for studying bacterial colonies with non-invasive imaging methods, although many authors still include traditional invasive techniques in their studies, like viable plate count, to enumerate total cell population. Experimental setups based on microscopy devices coupled with high resolution cameras and, e.g., the gel-cassette system, provide a valuable and promising tool to monitor individual cell colonies. However, their routine application in food microbiology remains a challenge since they also have some limitations.

For instance, the use of the gel-cassette limits the number of experimental conditions that can be assessed simultaneously (Mertens et al., 2012), as well as the tedious required work for the data analysis. These limitations point out the need for further studies to optimize available non-invasive methods to monitor colony behavior.

## Alternative methods

Optical density measurements have been traditionally applied as an indirect method to monitor individual cell growth, which is related to the media absorbance evolution. Based on Beer's law, OD is proportional to microbial concentration, as described by Koch (1981). In diluted suspensions, most bacteria have nearly
the same absorbance per unit of dry weight concentration, inde-
pendently of cell size. This method appeared as an alternative to
more laborious and time consuming techniques such as viable
plate count. OD-based methods represent a useful tool for growth
parameter estimation due to its non-destructive character and
its ability to fast provide high-throughput data; furthermore,
it is not expensive and relatively easy to automate in compar-
ison to other classical methods (Mertens et al., 2012). Several
authors have applied this method to address microbial growth
of freely suspended cells (McClure et al., 1993; Dalgaard et al.,
1994; Nerbrink et al., 1999; Dalgaard and Koutsoumanis, 2000;
Métris et al., 2006). Traditionally, measurements were taken one
by one in spectrophotometers, requiring hard work and limited
number of samples; however, this technique has evolved and more
advanced devices, such as the Bioscreen C®, Versamax®, etc., have
appeared in the market. These latest set-ups are provided with
reading chambers for microtiter plates that contain numerous
wells; moreover, temperature can be programmed as well as the
number of measurements and the interval time. These charac-
teristics enable to simultaneously study different environmental
conditions due to the multiple and independent wells per plate
and a high-throughput non-invasively data collection. For a direct
data processing, several software packages have been developed
that can be coupled with the mentioned experimental devices.

OD-based techniques have been recently applied for colony
growth characterization. Mertens et al. (2011) used the Spec-
traMax M2e microplate reader to assess the effect of a solid
environment on the growth/no growth (G/NG) boundaries of
Zygosaccharomyces bailii, in comparison to liquid media. This
work supposed the first approach of an OD method to study
microbial colonies. In this work, the structured model system was
based on Carbopol and xanthan gum as gelling agents, which
were independently tested and, in the case of Carbopol, prepared
at different concentration ratios. Rheological measurements were
applied to characterize the different media based on the Carbopol
concentration. From this measurements it was shown that higher
concentration levels gave rise to highly structured systems and
those with intermediate levels of Carbopol induced low structured
systems. After preparation, the different media were inoculated
and distributed in the 48-well microtiter plates. Prior to inocula-
tion, a preculture of Z. bailii was obtained under optimal growing
conditions and serially diluted to reach an inoculation level of
5 × 10⁴ CFU/mL. Due to the planktonic character of the cells
in the preculture, the last dilution before inoculation was carried
out in the same structured model system used afterwards. This
specific model system, developed by Mertens et al. (2009), aims
at obtaining similar conditions to those found in acidic sauces
like ketchup, which is commonly spoiled by Z. bailii. Including
these compounds as gelling agents supposed a novelty in the area,
as well as the use of Z. bailii in solids, since the majority of
studies to date have focused on agar or gelatine and on pathogenic
bacteria, respectively. Once the microtiter plates were filled in
with the inoculated media and the equipment conditions settled,
measurements were programmed to be taken at regular time
intervals. For the experiments with planktonic cells, OD was
determined at one single point of each well in the microtiter plate;
however, in the case of the solid structure, the scan method was

performed by measuring every well at nine different positions.
Afterwards, obtained values were used to plot OD curves. Results
showed that yeast colonies development depended on the struc-
tured degree of the media. In one hand, intermediately structured
Carbopol media led to numerous and homogeneously distributed
colonies, easily identified with a shift in the OD-based growth
curves. On the other hand, highly structured Carbopol media
limited colony formation to certain environmental conditions.
For environmental conditions close to the G/NG boundaries,
between none and small few colonies appeared and remained
mainly undetectable. This supposes a limitation of the method
since just one single cell can yield a colony, risking food safety
and quality. Authors concluded that it would be more realis-
tic to independently consider the nine measurements taken at
different positions of each well, instead of as mean values, for
more accurate identification and definition of colonial behavior.
This method was improved in Mertens et al. (2012) to study the
colony growth of E. coli at optimal temperature (30°C). As in the
previous study, the area scan method was applied by measuring
OD at nine different points of each well; however, a much higher
resolution was applied this time in order to characterize colony
dynamics and measuring points were individually considered.
This study was performed on submerged and surface individual
colonies after inoculating the wells with an initial level of 1-2
cells, which was achieved after performing the adequate decimal
dilutions from the preculture. To obtain surface colonies, the spot
inoculation method was carried out by pipetting a single drop
from the preculture on the agar surface. OD measurements were
performed with a FilterMax™ F5 microplate reader with Multi-
Mode Analysis software at a wave length of 590 nm. Results
provided the necessary information to calculate the colony area
by establishing 100 pixels as the minimum number to consider
an object a colony. Authors reported a correspondence of this
value with approximately 6 log (CFU/colony). Through the scan
method the evolution of the area of colonies was followed, which
was linearly expanding with time and showed a similar trend
between the independent colonies at early stages. At an individual
level, an OD increment at the center of the colonies showed an
increase in their height. The length of the experiments lasted for
23 h, although this time is not the absolute limit of growth for a
colony, the limited dimensions of the individual wells determined
the experiments duration. The authors concluded that besides
the limitations of the method, OD is an easy-to-use and flexible
technique for monitoring bacterial colonies.

OD-based techniques provide many advantages for studying
bacterial colonies, but there are still some limitations that it is
necessary to overcome such us, the extended time required for the
adequate data analysis, the uncertainty of the measured cell state
(live, dead, sublethally injured, etc.), as well as some discrepancies
between OD and viable count methods.

Another novel non-invasive technique to monitor bacterial
colonies is based on microcalorimetric measurements. As defined
by Braissant et al. (2010), isothermal calorimetry measures
the heat flow of biological processes, which is proportional to
the rate at which a chemical or physical process takes place.
Microcalorimeters are the specific devices used in this field that
measure heat flow of less than a micro-watt. The high sensitivity
of the microcalorimeters favors their application in microbiology, since they can determine the heat released by bacterial cells while growing. This heat is represented as a function of time and the obtained curves are called calorimetric thermograms or power-time curves. Information gathered from this curves has been quantitatively correlated with biomass generation, changes in the number of cells, uptake oxygen, or substrates, etc., (Birou et al., 1987). This method presents several advantages for microbial studies, like the little preparation of the samples, non-invasive character (Braissant et al., 2010) and a continuous signal in real time. As a consequence, the applications of this method have been widely spread in different fields, more specifically for clinical (Trampuz et al., 2007), environmental and food microbiology (Gram and Sögaard, 1985; Alklint et al., 2005).

In the last years, microcalorimetric methods are drawing attention for studying bacterial colonies. The application of this technique to monitor and describe the growth of bacterial colonies in solid model systems, was presented for the first time by Kabanova et al. (2012). The main objective of this work was to assess the growth of individual colonies of *L. lactis* of different sizes. To obtain colonies of different sizes, serial inoculation levels were prepared from 10⁰ to 10⁶ CFU/mL by serially diluting the corresponding preculture. Colonies were grown in carbohydrate restricted medium (CMR) agar, which was initially inoculated and afterwards aseptically introduced in the specific ampoules for the microcalorimetric analysis. This analysis was carried out in a multi-channel thermal activity monitor TAM III, in which the heat is measured with a heat conduction calorimeter. From the resulting power-time curves different growth phases were identified, i.e., lag, exponential and deceleration or stationary phase (Kabanova et al., 2012, 2013). Maximum growth rate showed a dependency on the inoculum level, i.e., low inoculum levels led to higher values than those obtained from high inoculum levels. Although methods based on calorimetry allowed to study bacterial colonies non-invasively, the estimated growth dynamics gave information of the overall population. Thus, in Kabanova et al. (2012) the dimensions of the colonies were measured with a microscope Zeiss Axiovert 200 M coupled with AxioCam MRc5 camera and Zeiss AxioVs40 V45.0.0 software. Colonies up to 10⁴ CFU/mL showed a lenticular shape. This morphology was related to the agar gel structure and the size of the pores in which colonies grow. Once the colony grew over the pore size, the bonds between chains of the gel agar were broken, triggering a lenticular colony formation. Furthermore, this microscopically analysis revealed a homogeneous distribution of bacterial colonies in the media with the same characteristics as reported by Jeanson et al. (2011).

It can be concluded that microcalorimetry is a powerful technique to collect information of cell growth in solid (food) systems. Moreover, Kabanova et al. (2012) showed that in combination with other methods like the imaging-based ones a complete description of microbial behavior in solid (food) systems can be obtained.

## CONCLUSION

Bacteria in solid (food) systems are known to grow forming colonies. This structured environment constraints cell mobility as well as nutrient and oxygen availability, giving rise to pH and

oxygen gradients, due to diffusion limitations inside and around the colonies. Since studies of bacterial behavior have been mainly performed in liquid systems with planktonic cells, it comes of outmost importance to study colony growth dynamics in solid (food) systems, for an effective design of food safety assurance systems. Traditional techniques like viable plate counting, have widely contributed to increase the knowledge about bacterial growth in solid systems. However, these techniques provide information of the overall population but not of individual colonies and additionally, their invasive character requires the consequent sample discard. In order to overcome these disadvantages, new non-invasive techniques are being developed. Most of these novel techniques are based on processing microscopic images obtained from monitoring bacterial colony growth. From image processing, information regarding, e.g., morphology, the colony radius and colony area is gathered and related to bacterial growth. Other techniques based on OD measurements and microcalorimetry, provide information of colony appearance and evolution. Additionally, microcalorimetry has been shown to provide similar information to viable plate counting, thus, power-time curves can be used together with other measurements. Nevertheless, these techniques also show some disadvantages, i.e., the extended time required for data analysis with OD-based techniques and the limited number of experimental conditions that can be assessed with the gel-cassette, etc. In order to overcome these disadvantages, novel techniques are usually combined with viable plate counting. Further research is required to get more accurate knowledge about colony growth dynamics.

## ACKNOWLEDGMENTS

This work was supported by project PFV/10/002 (Center of Excellence OPTEC-Optimization in Engineering) of the KU Leuven Research Council FWO-1518913N (KaN Multi-Objective Dyn. Opt.) project G.0930.13 of the Fund for Scientific Research-Flanders, and the Belgian Program on Interuniversity Poles of Attraction, initiated by the Belgian Federal Science Policy Office (IAP Phase VII-19 DYSCO).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 30 September 2014; accepted: 09 February 2015; published online: 06 March 2015.

Citation: Lobete MM, Noriega Fernandez E and Van Impe JFM (2015) Recent trends in non-invasive in situ techniques to monitor bacterial colonies in solid (model) food. *Front. Microbiol.* 6:148. doi: 10.3389/fmicb.2015.00148

This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology.

Copyright © 2015 Lobete, Noriega Fernandez and Van Impe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_21.png" /></figure>

# Growth and location of bacterial colonies within dairy foods using microscopy techniques: a review

Cian D. Hickey<fnref n="1" />,<fnref n="2" />, Jeremiah J. Sheehan<fnref n="1" />, Martin G. Wilkinson<fnref n="2" /> and Mark A. E. Auty<fnref n="1" />*

**Edited by:**

Sophie Jeanson, Institut National de la Recherche Agronomique, France

**Reviewed by:**

Louis Coroller, Université de Brest, France

Ilkin Yucel Sengun, Ege University, Turkey

**\*Correspondence:**

Mark A. E. Auty, Teagasc Food Research Centre, Fermoy, Ireland
e-mail: mark.auty@teagasc.ie

The growth, location, and distribution of bacterial colonies in dairy products are important factors for the ripening and flavor development of cheeses, yogurts, and soured creams. Starter, non-starter, spoilage, and pathogenic bacteria all become entrapped in the developing casein matrix of dairy foods. In order to visualize these bacterial colonies and the environments surrounding them, microscopy techniques are used. The use of various microscopy methods allow for the rapid detection, enumeration, and distribution of starter, non-starter and pathogenic bacteria in dairy foods. Confocal laser scanning microscopy is extensively utilized to identify bacteria location via the use of fluorescent dyes. Further study is needed in relation to the development of micro-gradients and localized ripening parameters in dairy products due to the location of bacteria at the protein-fat interface. Development in the area of bacterial discrimination using microscopy techniques and fluorescent dyes/tags is needed as the benefits of rapidly identifying spoilage/pathogenic bacteria early in product manufacture would be of huge benefit in relation to both safety and financial concerns.

**Keywords:** lactic acid bacteria, milk fermentation, bacterial location, cheese, microscopy, fat-protein interface

## BACTERIA WITHIN DAIRY PRODUCTS

Bacteria are naturally present and are used extensively across all areas of dairy and food fermentation, either as natural microflora, or as starter cultures added under controlled conditions (Yang et al., 2012). Their fermentative ability, especially that of lactic acid bacteria (LAB) is based on the creation of an acidic environment through the breakdown of carbohydrates such as lactose, maltose, lactulose and sucrose thereby ensuring preservation of food stuffs. Fermented dairy products are often not manufactured under sterile conditions or with sterile milk (unpasteurized) and this can allow non-starter LAB as well as spoilage or pathogenic bacteria access to the fermenting food system (Montville and Matthews, 2005). LAB's commonly found in dairy products include strains of *Streptococcus*, *Lactococcus*, *Lactobacilli*, *Bifidobacteria*, *Enterococcus*, and *Pediococci*. Within these species there are numerous strain types which can be used in fermentation processes to give specific acidification and flavor profiles to the final product.

Bacteria associated with dairy fermentations can grow over a wide temperature range from 4 to 50°C. Mesophilic bacteria have an optimum growth range of 25–35°C, while thermophilic species have an optimum range of 37–45°C (Johnson and Steele, 2013). The growth of bacterial cells within dairy foods is heavily influenced by parameters such as pH, water activity and salt-in-moisture levels as well as temperature.

The use of starter bacteria is needed in order to acidify the cheese milk before and during dairy food production. These starter bacteria are inoculated into the milk at their optimum growth temperature (described above) and then stored post manufacture at temperatures ranging from 4 to 12°C (depending on the

type of product) in order to slow the growth and acidification of these bacteria. Adjunct cultures such as *Propionibacterium* become active via exposure to warmer temperature ranging from 20 to 25°C for a set period of time and are directly involved in the metabolism of lactate to propionic and acetic acid, water, and CO₂ (Choisy et al., 2000; Hayaloglu and McSweeney, 2014).

## LACTIC ACID BACTERIA

Lactic acid bacteria are the most common and important starter cultures used in fermented dairy products and may originate from the microflora of raw milks (e.g., bovine, ovine, caprine) but more frequently are inoculated intentionally during product manufacture. The initial role of LAB is to control pH of ripening milk and subsequent dairy products via the conversion of naturally occurring lactose found in milk to lactic acid (glycolysis). The rapid reduction (4–8 h) of pH to below 5.3 in cheese or 4.6 in fermented milk products allows for the control of non-starter microflora as only acid-tolerant bacteria can survive in those conditions (Johnson and Steele, 2013). The secondary function of LAB's in dairy fermentations is flavor development. Intracellular enzymes released by starter and non-starter bacteria during manufacture and ripening are the main contributors to flavor development via the three main biochemical pathways (glycolysis, lipolysis, and proteolysis). The breakdown of caseins is the most important pathway for flavor development in hard and semi-hard type cheese, which LAB contribute heavily to with the formation of small peptides and free amino acids which can then be further converted to form various alcohols, aldehydes, acids, and esters (Smit et al., 2005). Examples of dairy foods produced through LAB fermentations include cheeses, yogurts and sour creams such

<footnote n="1">¹Teagasc Food Research Centre, Fermoy, Ireland</footnote>

<footnote n="2">²University of Limerick, Limerick, Ireland</footnote>
as crème fraîche. Examples of common starter LAB used in the dairy industry include *Lactococcus lactis* spp. *lactis*, *L. lactis* spp. *cremoris* (Cheddar), *Lactobacillus helveticus*, *Lb. delbruecki* spp. *bulgaricus* and *Lb. casei* (Swiss-/Italian-type cheese), *Streptococcus thermophilus* (Swiss-type cheese/yogurts) and *Lb. acidophilus* (yogurts, soured creams; Leroy and De Vuyst, 2004). These various starter cultures can often be used on their own or as part of a culture mix, incorporating positive aspects from various bacterial strains. Swiss type cheeses display the symbiotic role of mixed starter cultures. Lactose is converted to galactose and L- lactic acid by *S. thermophilus* and *L. helveticus* metabolizes this galactose to L- and D- lactic acid as *S. thermophilus* is incapable of doing so (Fox et al., 1990).

Due to food safety concerns, commercially produced dairy products such as cheeses and yogurts are increasingly manufactured from pasteurized milk. Pasteurization inactivates pathogenic bacteria, but also results in a significant reduction, or inactivation of, naturally occurring microflora populations. Further control is achieved through competitive inhibition. Non-starter lactic acid bacteria (NSLAB) contribute toward flavor development in dairy foods. They are described as adventitious species which, in the case of dairy products, can originate from the factory environment or from the raw milk where they are present as adventitious contaminants (Crow et al., 2001). The most common NSLAB species found in the dairy industry are variants of Lactobacilli (*Lactobacillus casei*, *L. paracasei*, *L. plantarum*, *L. curvatus*, *L. brevis*, and *L. fermentum*) and common non-*Lactobacillus* species include *Pediococcus acidilactici*, *P. pentosaceus*, *Enterococcus durans*, *E. faecalis*, and *E. faecium* (Settanni and Moschetti, 2010).

While LAB account for the majority of bacteria present in dairy foods, either naturally occurring or through deliberate inoculation, other types of bacteria are also used within the dairy industry. These include adjunct cultures, defined as those added to fermented products for reasons other than acid formation (Giraffa, 2003), such as *Staphylococci*, *Micrococci*, *Enterococci*, and *Propionibacterium* (Chamba and Irlinger, 2004). The latter are used extensively in the manufacture of Swiss- type cheeses in order to produce the distinctive nutty flavor and metabolize lactate to acetate and carbon dioxide (CO₂) creating the characteristic eyes associated with these types of cheeses. *Propionibacterium* are responsible for high levels of lipolysis associated with Swiss-type cheese and have been shown to have up to 100 times more lipolytic activity than LAB's (Chamba and Perreard, 2002; Chamba and Irlinger, 2004). Other adjunct culture types are not directly involved in cheese manufacture but become active only during ripening and include yeasts (*Geotrichum candidum*, *Saccharomyces cerevisiae*) and molds (*Penicillium camemberti* and *P. roqueforti*; Chamba and Irlinger, 2004).

## PATHOGENIC AND CONTAMINANT BACTERIA

Bacteria may also be present in dairy foods as undesirable food spoilage or pathogenic agents. These undesirable bacteria may include psychrotrophic bacteria (*Pseudomonas fluorescens* and *P. putrefaciens*) *Listeria monocytogenes*, *Salmonella* spp., *Escherichia coli*, *Staphylococcus aureus*, *Clostridium botulinum*, *C. perfringens*, *C. tyrobutyricum*, and *Vibrio cholera* (Giraffa, 2003; Oliver et al.,

2005; Gálvez et al., 2008; Machado et al., 2013). Proteases and lipases released by psychrotrophic bacteria in milk such as *Pseudomonas fluorescens* and *P. putrefaciens* can cause bitterness and off flavors in dairy products. These enzymes are heat stable and therefore unaffected by standard pasteurization temperatures (72-74°C for 15-30 s), allowing for the development of off flavors in fermented dairy products (Sheehan, 2013).

The main pathogenic bacteria of concern in the dairy industry are those which are capable of surviving the manufacturing process of cheeses, yogurts and soured creams. *L. monocytogenes* is a gram positive bacterium responsible for causing gastroenteritis along with listeriosis, which in turn can cause serious illness through sepsis or meningitis. The fatality rate from extreme listeriosis ranges from 20 to 30% (WHO, 2004; Carpentier and Cerf, 2011). *E. coli* is a gram negative bacterium which occurs naturally in the lower intestine but certain strains can cause gastroenteritis and urinary tract infections. *E. coli* in dairy products such as yogurts results from post pasteurization contamination. *E. coli* O157 has been shown to be able to survive the acidic conditions associated with yogurt manufacture thus causing serious health risks to consumers (Cirone et al., 2013). *Salmonella* spp. consists of several gram negative species each capable of causing food borne illness. Two of the most common found in dairy foods are *S. typhimurium* and *S. enteritidis* (Leyer and Johnson, 1992). *S. aureus* is a gram positive bacterium and one of the world's leading causes of food borne illness, such as gastroenteritis which is caused by the ingestion of enterotoxins produced by the bacteria. Therefore, even if the bacteria are killed via heat or pressure treatment, the heat resistant enterotoxin remains (Gálvez et al., 2008; Fleurot et al., 2014). The acidic environment created by the fermentation of dairy products, high levels of cleanliness and hygiene practice throughout manufacturing plants and correct storage of dairy products help prevent the growth and contamination of products with these undesirable pathogenic and food spoilage bacteria.

## BACTERIOCINS AND PROBIOTICS

Bacteriocins are described as low-molecular mass proteins or peptides produced via bacterial ribosomal synthesis. They have an antimicrobial mode of action usually restricted to related Gram positive bacteria (Caplice and Fitzgerald, 1999; Leroy and De Vuyst, 2004). Bacteriocins are used as a mode of biopreservation. This is the extension of a food's storage life and increased food safety due the antibacterial products of natural or controlled microflora (Stiles, 1996; Gálvez et al., 2008). Many LAB are bacteriocin producers along with certain species of *Enterococci* (*E. faecalis* and *E. faecium*) which produce enteriocins (Giraffa, 2003). Examples of two commercially produced bacteriocins are nisin (Nisaplin in commercial form) which is produced by fermentation of milk based substrates by strains of *Lactococcus lactis* and Pediocin PA-1/AcH (ALTA 2341 in commercial form) which is produced from *Pediococcus acidilactici* (Gálvez et al., 2008). Nisin is used extensively throughout the dairy industry to prevent gas blowing in semi-hard and hard type cheeses caused by *C. tyrobutyricum* and as an antimicrobial agent in clotted and heat treated creams preventing the growth of *Bacillus cereus* even at low concentrations and in sliced cheeses it reduces the populations of
*Listeria innocula* and *S. aureus* (Scannell et al., 2000; Gálvez et al., 2008).

Probiotics improve the nutritional value of a food and when administered in certain quantities provide health benefits to the host (Granato et al., 2010). Many LAB's display probiotic characteristics. The most common certified probiotic bacteria strains used in foods today consist of Gram-positive *Lactobacillus* and *Bifidobacterium* species, both of which are found heavily within the human gastrointestinal tract. The afore mentioned bacteria are linked to health benefits such as reduced lactose intolerance, relief from some diarrhoeas, lower blood cholesterol, increased immune response and preventions of certain cancers (Savadogo et al., 2006). The numbers of live bacteria reaching the gut is critical for probiotic efficacy and for this reason, quantifying and visualizing the bacterial colonies in a food prior to ingestion is very important.

## LOCATION OF BACTERIA

The location of bacteria within dairy food matrices is of interest due to the possible effects these bacteria and their enzymes may have on ripening, particularly at the microscopic level in cheese and yogurts. The effect of bacterial colony size, distribution, and where colonies locate may all have an effect on the rate of enzyme release and localized ripening. In order to investigate these possibilities we need to visualize the behavior of developing bacterial colonies using microscopy techniques as discussed below. Bacterial distribution is not homogenous throughout dairy products as all types of bacterial cells (starter, non-starter, spoilage, pathogenic) become entrapped in the developing protein matrix resulting in random distribution of bacterial colonies (Fitzsimons et al., 2001). The immobilization of bacteria therefore requires the diffusion of substrates to the bacterial colony location and resulting metabolites must also diffuse through the protein matrix, resulting in the potential developments of micro-gradients in pH and water activity in and around the bacterial colonies (Floury et al., 2010). Initial studies suggest an interaction between milk fat globules and starter bacterial cells/colonies due to the regular location of bacterial colonies in close proximity or in direct contact with milk fat globules and their membranes (Laloy et al., 1996). Bacteria have since been shown to preferentially locate at the fat-protein interface and sometimes within whey pockets in dairy products.

## IMPORTANCE OF BACTERIA IN DAIRY FERMENTATIONS

The growth of bacteria within fermenting food products is of extreme importance as this growth rate determines final cell numbers, acidification rates and thus the intensity of the fermentation process. Starter bacteria are also responsible for releasing intracellular enzymes upon death and subsequent lysis of the cell membrane. These enzymes catalyze a wide range of metabolic pathways (lipolysis, proteolysis, and glycolysis) which result in the formation of flavor compounds adding to flavor development in many cases of fermented foods (Wilkinson et al., 1994; Wilkinson and Kilcawley, 2005; Steele et al., 2013).

Each bacterial cell is believed to grow and form a colony within the food matrix after inoculation and in certain cases immobilization within the matrix (Jeanson et al., 2011). It is the activity,

location, and environment of these colonies which are of interest in this review. To date a lot of research has been undertaken with relation to bacterial growth in various conditions in dairy foods. This review will focus on colony growth, location, and influence on the surrounding environment of starter, non-starter LAB, spoilage and pathogenic bacteria in dairy products such as cheeses, sour creams and yogurts using microscopy to accurately quantify and visualize the bacteria in the food matrices.

## ANALYTICAL TECHNIQUES

Methods for analysis of food can be separated into separate categories such as microbiological, microscopy, sensory, physical, and physico-chemical. These methods can be destructive or non-destructive in their procedure. For this review we will focus on the microscopy methods associated with dairy food analysis. Examples of non-destructive microscopic methods include confocal laser scanning microscopy (CLSM), cryo- and regular scanning electron microscopy (SEM), and transmission electron microscopy (TEM) which have helped to map the location and distribution of bacterial colonies in dairy foods.

Other rapid and reliable methods for enumerating and identifying bacterial cells which include polymerase chain reaction (PCR) amplification and its variants including PCR-denaturing gradient gel electrophoresis (PCR-DGGE) and PCR-temporal

<figure><img src="image_22.png" /><figcaption>**FIGURE 1** | Transmission electron microscopy (TEM) images (ultrathin sections) of starter bacteria (*Lactococcus lactis* subsp. *cremoris* KB) location in full-fat Cheddar cheese in image 1 (left). Image 2 (right) shows the starter strain KB locating in close proximity to the MFGM (arrow) together with proteolysed casein protein (PC). The Bar represents 400 nm. Reprinted with permission from Laloy et al. (1996) and Elsevier. Copyright (1996) Elsevier.</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>**FIGURE 2** | Scanning electron microscopy images of naturally occurring microflora (arrows) and strains of *Lb. rhamnosus* [+H12 (left) and +D44 (right)] (open arrows) on the surface of cheese after stomach treatment. Reprinted with permission from Pitino et al. (2012). Copyright (2012) Elsevier.</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>**FIGURE 3** | Scanning electron microscopy (left) image of low-fat cheddar cheese matrix showing colonies of starter cultures (*L. lactis*) (black arrows) and areas previously occupied by fat globules (white arrows);</figcaption></figure>

scale bar = 20 µm. CLSM (right) image of cheese matrix with protein in gray, fat coded in green and bacterial colonies in blue; scale bar = 10 µm. Reprinted with permission from Romeih et al. (2012). Copyright (2012) Elsevier.

<figure><img src="image_25.png" /><figcaption>**FIGURE 4** | (A) Cryo-SEM image of a Cheddar-type cheese showing the location of the starter bacteria (*S. thermophilus*) (white arrow) and fat globule (F), including fractured MFGM (black arrow), within the protein network (P) on day 12 of ripening; scale bar = 1 µm. (B) Cryo-SEM image</figcaption></figure>

of starter bacteria in yogurt showing both *S. thermophilus* (open arrow) and *Lb. delbruecki* subsp. *bulgaricus* (closed arrow) entrapped within the acidified casein-based matrix (CM); scale bar = 2 µm. Auty, unpublished results.

temperature gradient gel electrophoresis (PCR-TTGE; Ndoye et al., 2011; Postollec et al., 2011; O'Sullivan et al., 2013). PCR targets a specific sequence such as the 16S rRNA gene in conjunction with genera/species specific primers. The 16S rRNA gene is universal amongst bacteria and large databases exist for specific species of most food related bacteria. They have been used to determine the presence and quantity of undesirable bacteria such as *C. tyrobutyricum* and certain *Lactobacillus* communities in cheese. However, the issue with these methods of bacterial detection is the lack of information we obtain relating to bacterial colony location and distribution within dairy foods. For these reasons, this review focuses on microscopy methods allowing for the visualization of bacterial colony location and distribution.

## CONFOCAL LASER SCANNING MICROSCOPY

The use of CLSM in food analysis has been at the forefront in recent years due to its ability to image individual components within a food matrix via the use of various fluorescent dyes (Auty et al., 2001, 2013; Romeih et al., 2012). This method is favored as

a result of its ability to visualize thin optical sections below the surface of a sample due to the laser scanning function. CLSM uses argon and/or helium-neon lasers to individually obtain images from thin sections of sample which can then be stacked together in order to create a three dimensional (3D) image of a without disturbing the internal structure (De Freitas et al., 2007; Ong et al., 2011b; El-Bakry and Sheehan, 2014). Another advantage is its ability to analyze various components simultaneously via the use of fluorescent labels and stains, allowing for fat, protein, and bacterial colony location to be identified from one sample (Ong et al., 2010, 2011a, 2012, 2013b; Abhyankar et al., 2011). Commonly used fluorescent dyes in relation to dairy products and their components are fast green and rhodamine for the protein fractions, nile red for fats, and Oregon green 488/514 for determining localized pH. Bacterial viability is commonly determined using a LIVE/DEAD BacLight viability kit consisting of two fluorescent nucleic acid stains SYTO9 (green) and propidium iodide (PI; red). SYTO9 permeates both viable and non-viable cell membranes, while PI only permeates damaged cell membranes which in turn negate the SYTO9 fluorescence. Thus, viable bacterial cells fluoresce green
<figure><img src="image_26.png" /></figure>

**FIGURE 5** | Confocal laser scanning microscopy image of probiotic cheddar cheese showing star shaped clusters of live (bright green), presumptive Bifidobacteria, (large arrow), and dead (red) bacterial cells at fat (black)/protein (green) interface and presumptive NSLAB bacteria (small arrow). Scale bar = 25 µm. Reprinted with permission from Auty et al. (2001) and American Society for Microbiology (ASM).

<figure><img src="image_27.png" /></figure>

**FIGURE 6** | Confocal laser scanning microscopy images of bacteria in Emmenal cheese after 1 day of ripening, showing location of bacterial colonies (light color) in whey pockets (A, black areas) and at the interface (B) between fat (red) and protein (gray). Adapted from Lopez et al. (2006) with permission from the authors.

and those with a damaged or non-viable membrane fluoresce red (Auty et al., 2001). Bacterial location alone can be measured using Acridine orange which is a fluorescent dye which stains the DNA of bacteria (Lopez et al., 2006). This methodology has also been utilized recently to identify and track pathogenic bacterial growth in dairy foods (Fleurot et al., 2014).

## SCANNING ELECTRON MICROSCOPY

Conventional SEM involves the generation of an electron beam which interacts with a given sample resulting in the emission of multiple secondary electrons. The image obtained is based on the electrons which scatter back when the electron beam strikes the surface of the sample (McMullan, 2006). In order to obtain a high number of secondary electrons and therefore give a clear

image, a conducting layer is often placed over the sample surface to prevent charging (El-Bakry and Sheehan, 2014). The dehydration of the sample using a series of ethanol concentrations is necessary prior to examination, which must also be carried out under vacuum. This method has been used to study food microstructure for many years, offering a clear concise image of a samples surface showing fat, protein, and bacterial location (Pitino et al., 2012). The limitations associated with this methodology are the labor intensive sample preparation and the ability to only view the topographical area of a sample in addition to artifacts (Tunick et al., 2002).

Cryo-SEM comprises conventional SEM with a cryo-chamber attached allowing for the microscopic examination of dairy foods high in moisture, fat or air, i.e., cheese, yogurts, and soured creams. Operating at temperatures below –80°C, it utilizes liquid nitrogen in order to ultra-freeze samples. The advantages of this method over conventional SEM include a substantially reduced sample preparation time and a greater ability to view fat components which can become distorted due to the dehydration and defatting steps associated with conventional SEM. The use of this method has increased in recent years, in conjunction with techniques such as CLSM, for the study of dairy food microstructure and microbial population (Hassan et al., 2003; Romeih et al., 2012; Martinovic et al., 2013; Ong et al., 2013a).

## TRANSMISSION ELECTRON MICROSCOPY

Transmission electron microscopy is similar to its scanning counterpart in that a beam of electrons are used but in this case the electrons pass through (transmit) the sample and the image generated is based on the scatter of these electrons, therefore samples need to be very thin (0.1–0.2 µm). In relation to dairy analysis, replica type TEM is most commonly used. This involves either resin embedding and ultrathin sectioning or freeze-fractured replicas (Kaláb et al., 1995). Advantages include the best resolution of all electron based microscopy techniques allowing for greater examination of a samples ultra-structure (Laloy et al., 1996) and was first used on cheese by Green et al. (1981). Disadvantages consist of high cost, labor intensive sample preparation and possible presence of artifacts due to the use of osmium tetraoxide, which can cause fat and proteins to be misinterpreted due to inadequate fixing of the structures during sample preparation (Reis and Malcata, 2011; Auty et al., 2013; El-Bakry and Sheehan, 2014).

The use of these microscopy techniques in relation to bacterial location, survival and distribution in cheeses, yogurts and soured creams are discussed below in relation to the various studies which have been conducted on this important topic.

## LOCATION OF BACTERIAL COLONIES IN DAIRY PRODUCTS

Location and distribution of various types of starter bacteria, NSLAB, contamination bacteria, and spoilage bacterial strains in a number of cheese varieties has been widely studied but rarely visualized using microscopic methods. (Hannon et al., 2006; Lopez et al., 2006, 2007; Jeanson et al., 2011). The ability to visualize the location of these bacteria within the developing protein matrix is of huge importance in relation to food quality, consistency and safety.
<figure><img src="image_28.png" /><figcaption>**FIGURE 7** | Confocal laser scanning microscopy images of dairy starter bacteria (Blue), synthetic green fluorescent protein (sGFP) (Green), Staphylococcal enterotoxin D (SED) (Red) and *S. aureus* (magenta) on the cheese surface 14 days into</figcaption></figure>

ripening. The cheese sample structure is visualized by the reflection of the 405-nm laser diode in a grayscale image. Reprinted (adapted) with permission from Fleurot et al. (2014). Copyright (2014) ASM.

Jeanson et al. (2011) studied bacterial distribution in a real food matrix (cheese) showing the spatial distribution of bacterial colonies at various levels of inoculation using CLSM. Bacterial colonies were shown to be randomly distributed which fit the proposed Poisson model. Their results supported the theory that increased inoculation levels (10⁷ CFU/g) resulted in smaller colonies and displayed a sevenfold increase in the interfacial area of exchange with the cheese matrix compared to colonies formed at lower inoculation levels (10⁴ CFU/g) as shown previously by McKay et al. (1997). Colonies can consist of bacterial cells in various physiological states of growth and McKay et al. (1997) have previously shown bacterial cells which are in the exponential phase of growth to be located on the colony exterior touching the matrix, most likely resulting in a high level of metabolic activity. This hypothesis allows for the assumption that the larger the interfacial area, the greater the bacterial activity on the food matrix which will in turn influence ripening. At higher levels of inoculation (10⁶ and 10⁷ cfu/g) colonies were located extremely close together with mean distances of 25–30 µm between colonies (Jeanson et al., 2011).

Studies have been conducted into the possible location of bacterial micro-colonies within the cheese matrix. Laloy et al. (1996) used TEM to observe the location and distribution of starter bacteria in fat-free, 50% reduced fat and full fat Cheddar cheese (Figure 1). They found bacterial populations to be directly related to the fat content of the cheese. Compared to fat-free cheese, starter populations were 30–100% and 4–10 fold higher in 50% reduced fat and full fat cheese, respectively. Bacteria were found to be located in direct contact with the milk fat globular membrane (MFGM) or located at the casein-fat interface. As ripening

progressed (>1–2 months) bacteria seemed to become imbedded within or located inside the MFGM itself.

Pitino et al. (2012) utilized both SEM and CLSM in order to track the survival of *Lb. rhamnosus* inoculated in cheese during simulated human digestion. They showed the interaction between *Lb. rhamnosus* and the cheese starter bacteria both of which appear to form colonies at the fat-protein interface or in contact with whey pockets (Figure 2).

Romeih et al. (2012) observed the location of *L. lactis* ssp. *lactis* ML8 using SEM in conjunction with CLSM (Figure 3), while Ong et al. (2013a) used cryo-SEM to detect the location of a mixture of mesophilic starter bacteria in full-fat Cheddar cheese. Auty (unpublished results) also used the same technique to show exopolysaccharide-producing lactic acid bacteria in Cheddar cheese and starter bacteria in yogurt, respectively, (Figure 4). As in the case of many others, the images appear to show preferential location of bacteria at the protein-fat interface. Images produced of yogurt show the entrapment of bacterial cells amongst the acidified casein-based matrix.

Auty et al. (2001), Hannon et al. (2006), and Lopez et al. (2006, 2007) used CLSM to visualize bacterial colonies in various cheeses such as Cheddar- type and Swiss- type cheeses made with ultrafiltered milk, respectively. In each case bacterial colonies were observed at the fat-protein interface also (Figures 5 and 6, respectively).

Fleurot et al. (2014) employed CLSM in order to locate the food borne pathogenic bacteria *S. aureus* in a cheese system. They found *S. aureus* formed colonies on the exterior surface of the cheese with colonies in the core a rarity, leading to the conclusion that *S. aureus* entrapped in the interior of the cheese did not multiply,
while those on the aerated surface continued to multiply and form large colonies (Figure 7).

The application of microscopic techniques to investigate bacteria localization in dairy foods has been limited and in cases where it has been used, bacterial location is often observed as a result of investigating components such as fat content. Studies rarely focus on bacterial location and distribution specifically (Lopez et al., 2006, 2007; Romeih et al., 2012; Ong et al., 2013a). Despite many of the aforementioned studies confirming that bacteria in dairy foods invariably locate on or in close proximity to the fat-protein interface or in contact with whey pockets, the effect this localization has on micro-gradients (pH, water activity), flavor development and overall product quality is still relatively unknown. Fleurot et al. (2014) is, to date, the only published work which uses CLSM in conjunction with fluorescent stains in order to discriminate between starter and pathogenic bacteria.

## CONCLUSION

This review provides an overview on current information regarding use of microscopic techniques to investigate the growth and localization of bacteria within dairy based fermented foods. Such studies on bacterial colony location and distribution have not yet addressed the relationship between colony location and on product quality, consistency and on ripening parameters. However, the use of microscopy has made the visualization of bacteria in food matrices possible and allows for the enumeration, location, and distribution of starter LAB, NSLAB, spoilage, and pathogenic bacteria via non-destructive methods. Microscopy methods such as CLSM and cry-SEM allow for rapid sample analysis and CLSM allows for the detection of spoilage and pathogenic bacteria via the use of specific fluorescent dyes. Further study is greatly needed in this area regarding the influence bacterial location at the fat-protein interface has on localized micro-gradients and ripening parameters in a wide variety of dairy products. Microscopy allows for bacteria to be visualized within a solid food system and this tool is key to understanding bacterial behavior and influences on ripening fermented products. The discrimination of minority spoilage or pathogenic bacterial populations from the highly dense starter and non-starter dairy populations is an area which requires immediate attention as the benefits of possible rapid analysis and discrimination would be of great benefit to the entire food industry. Recent developments in super-resolution optical microscopy and confocal Raman microscopy could be used in future to further characterize the microflora of solid foods and the localized biochemical transformations they influence.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 08 October 2014; accepted: 26 January 2015; published online: 18 February 2015.*

*Citation: Hickey CD, Sheehan JJ, Wilkinson MG and Auty MAE (2015) Growth and location of bacterial colonies within dairy foods using microscopy techniques: a review. *Front. Microbiol.* 6:99. doi: 10.3389/fmicb.2015.00099*

*This article was submitted to Food Microbiology, a section of the journal *Frontiers in Microbiology*.

Copyright © 2015 Hickey, Sheehan, Wilkinson and Auty. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_29.png" /></figure>

# *In situ* examination of *Lactobacillus brevis* after exposure to an oxidizing disinfectant

Yu Zhao, Susanne Knøchel and Henrik Siegumfeldt*

Food Microbiology, Department of Food Science, Faculty of Science, University of Copenhagen, Frederiksberg C, Denmark

**Edited by:**
Sophie Jeanson, Institut National de la Recherche Agronomique, France

**Reviewed by:**
Carmen Wacher, Universidad Nacional Autónoma de México, Mexico
Laurent Guillier, Agence Nationale de Sécurité Sanitaire de l'Alimentation, de l'Environnement et du Travail, France

**\*Correspondence:**
Henrik Siegumfeldt, Food Microbiology, Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958
Frederiksberg C, Denmark
e-mail: siegum@food.ku.dk

Beer is a hostile environment for most microorganisms, but some lactic acid bacteria can grow in this environment. This is primarily because these organisms have developed the ability to grow in the presence of hops. It has been speculated that hop resistance is inversely correlated to resistance against oxidation, and this would have great impact on the use of various disinfectants in the brewing industry. In this study, we cultivated bacteria under aerobic and anaerobic conditions, and then investigated the *in situ* outgrowth of individual cells into microcolonies on de Man Rogosa Sharpe (MRS) agar after exposure to the oxidizing agent peracetic acid (PAA). An automated microscope stage allowed us to analyse a much larger number of cells over extended periods of incubation. After PAA treatment, the lag time increased markedly, and extensive variation in morphology, $\mu_{max}$ as well as stress resistance was observed between and within the tested *Lactobacillus brevis* strains. The results suggest that aerobic cultivation increased the oxidative stress tolerance in *Lactobacillus brevis*. The results also show that dead cells are randomly distributed in a microcolony and the majority of non-growing individual cells do not stain with a membrane impermanent dye (Propidium iodide), which indicates that PAA may not destroy the plasma membrane. In conclusion, the developed microscopic analysis of individual cells on MRS agar can provides faster results and more details of cell physiology compared to the traditional CFU method.

**Keywords:** *Lactobacillus brevis*, aerobic cultivation, anaerobic cultivation, peracetic acid, solid surface, microscopic method, heterogeneity

## INTRODUCTION

Lactic acid bacteria (LAB) are fermentative organisms which have been generally regarded as anaerobic bacteria, but most of them can grow under aerobic conditions (Sakamoto and Komagata, 1996). Beer is a relatively hostile medium for most microorganisms. The low pH will prevent most Gram-negative bacteria from growing, and the addition of hops will usually prevent LAB from spoiling the beer (Vaughan et al., 2005; Suzuki et al., 2006; Menz et al., 2010; Suzuki, 2011). However, some LAB possess a level of hop resistance, and therefore also possess the ability to spoil beer. 60–90% of bacteria isolated from spoiled beer are LAB (Suzuki, 2011). Among those LAB, *Lactobacillus brevis* is the most common bacteria and frequently detected in breweries (Hollerová and Kubizniaková, 2001; Suzuki et al., 2008a; Menz et al., 2010).

Recently, it has been suggested that hop resistance in LAB is inversely correlated to resistance toward oxidative compounds (Behr and Vogel, 2010). Consequently, it would be interesting to investigate the response of beer spoilage isolates toward oxidative compounds, as some of these are utilized as sanitizers in the food industry (Rossoni and Gaylarde, 2000; Kitis, 2004).

Another potential challenge is the relatively slow growth of the beer spoilage organisms. This means that often they are not detected readily in various culture media (Suzuki et al., 2008b). One way of facilitating the detection would be to use detection

of growth of individual cells into micro-colonies, as the formations of macrocolonies require a longer incubation period in traditional CFU method. Recently, some studies have developed bioimaging methods for detecting the growth of individual cells in/on a solid matrix. Elfwing et al. (2004) designed a flow chamber microscopic method to observe growth and proliferation of single cells of *Escherichia coli* and *Listeria innocua*. Niven et al. (2006) developed a phase-contrast microscopy method to determine the first division time and individual lag times on agar media. Mertens et al. (2012) studied the colony growth dynamics based on optical density measurements on solid medium in microtiter plates. Koutsoumanis and Lianou (2013) used time-lapse microscopy videos to count the cells and to observe the division of *Salmonella* single cells directly on agar media. Ryssel et al. (2013) developed a microscopy method to monitor growth and death of individual *Lactococcus lactis* cells based on staining with propidium iodide (PI) in the agar media. In addition, another advantage of investigating individual cells growing on a solid substrate is the ability to analyze the heterogeneity of a given population, as each individual cell gives rise to a unique microcolony.

The current study therefore investigates the impact of oxidizing substances on the survival of beer spoilage LAB. The study describes an automated image-acquisition microscopic method that enables the analysis of growth as well as the death of individual cells while growing on the surface of a semisolid substrate.
# MATERIALS AND METHODS

## BACTERIAL STRAINS AND GROWTH CONDITIONS

The strains of *Lactobacillus brevis* used in this study are listed in **Table 1**. All experiments were initiated by inoculating 10 ml of Man Rogosa Sharpe (MRS) broth (Merck, pH 5.7) from a frozen stock culture, followed by incubation at 30°C overnight. Subsequently, 100 µl culture was subcultured into 10 ml fresh MRS broth. For aerobic cultivation, the tubes were shaken around 300 rpm at 30°C. For anaerobic cultivation, the tubes were incubated at 30°C in an anaerobic jar, and incubated until an approximate OD₆₀₀ value of 1.5. The cultures were subsequently exposed to oxidizing agents as described below.

## TREATMENTS OF LAB WITH OXIDIZING AGENTS

Two kinds of disinfectants were used in this study: peracetic acid (PAA, Sigma, 101272695) and sodium hypochlorite (NaClO, Sigma, 101292621). The final concentration of PAA solution during exposure was 0.0014%, and the final concentration of NaClO solution was 0.0021%. 0.5 ml of the aerobic cultures or anaerobic cultures were added into three 15 mm × 18 cm glass tubes containing either 12 ml saline (control), 12 ml PAA solution or 12 ml NaClO solution, mixed with a whirlmixer for 20 s and leave them for 10 min at 23°C. Subsequently, 0.5 ml of each cell suspension was diluted into 4.5 ml saline and mixed as described above in order to rapidly reduce the toxicity of oxidizing agents greatly (Grönholm et al., 1999). Subsequently, the surviving cells were enumerated by CFU.

## DETERMINATION OF CFU

Cell suspensions were serially diluted in saline (0.9%, pH 5.8) and transferred to MRS agar plates (Merck), then incubated at 30°C for 5 days, with analysis on day 3–5.

## MICROSCOPIC METHOD

Peracetic acid was chosen as the oxidizing agent for microscopic analysis, but in order to reduce the number of killed cells, the concentration of PAA was decreased to 0.001%. The treatment was otherwise the same as previously described.

<table><caption>**Table 1 | Overview of the strains used in this study.**</caption><thead><tr><th>Abbreviation</th><th>Strain</th><th>origin</th><th>Type</th></tr></thead><tbody><tr><td>JK09</td><td><i>Lactobacillus brevis</i><br>JK09</td><td>Danish craft beer</td><td>Wild type</td></tr><tr><td>JK09-horA</td><td><i>Lactobacillus brevis</i><br>JK09-horA*</td><td>Danish craft beer</td><td>Plasmid cured</td></tr><tr><td>MI2158</td><td><i>Lactobacillus brevis</i><br>MI2158</td><td>DSM20054T</td><td>Wild type</td></tr><tr><td>HF01</td><td><i>Lactobacillus brevis</i><br>HF01</td><td>Danish craft beer</td><td>Wild type</td></tr><tr><td>HF02</td><td><i>Lactobacillus brevis</i><br>HF02</td><td>Danish craft beer</td><td>Wild type</td></tr></tbody></table>

The microscope set-up was the same as described by Ryssel et al. (2013). After the treatment with PAA, 5 µl of the cell suspension was transferred to the bottom of a well in an Ibi di µ-Slide 8-well chamber (hydrophobic, uncoated, sterile, ibidi GmbH, München, Germany). The dead cell impermanent dye PI (Molecular Probes, Invitrogen, Oregon) was previously added to molten MRS agar to a final concentration of 2.0 µg/ml at 45°C, and 300 µl of the molten MRS-PI agar medium was added slowly to the well (to prevent the cells from leaving the bottom surface). The addition of MRS-PI agar constituted time zero for the experiment. After the agar solidified, the chamber was placed in the automated microscope stage, and a random spot was chosen in each well as the starting position. Subsequently, a total of 49 positions were recorded in a 7 × 7 grid, which was programmed into the software. The pre-programmed grid was used to avoid user bias when selecting appropriate spots in the specimen. Furthermore, the 49 positions enabled us to obtain more valid information about the heterogeneity of the specimen. The recording of all 49 positions were then repeated throughout the experiment. Brightfield images and PI fluorescent images were captured using MetaMorph 7.0 software package (Molecular Devices Inc., Silicon Valley, CA, USA). The chamber was left on the microscope stage at 23°C during the entire experiment.

## DATA ANALYSIS

### Calculation of log reduction

Log reduction was defined as the difference between the log CFU count of saline treatment (control) and disinfectant treatment.

### Calculation of survival in percentage

In order to compare the microscopic analysis and the CFU, the survival percentage was calculated as follows.

$$ \text{Survival}_{\text{CFU}} = \frac{\text{CFU}_{\text{Treatment}}}{\text{CFU}_{\text{Control}}} \left( \text{Treatment is control treatment or PAA treatment} \right) $$

$$ \text{Survival}_{\text{microscope}} = \frac{\text{Num}_{\text{dividing cells}}}{\text{Num}_{\text{Total cells at time zero}}} $$

### Cell size analysis

The brightfield images were analyzed with the free image analysis software Image J [version 1.48; National Institutes of Health (NIH), Bethesda, MD, USA<fnref n="1" />]. Before cell division occurs, the size of individual cells is measured directly by pixels, but after cell division, the areas of the growing microcolonies were measured.

### Calculation of lag time and $\mu_{max}$

Growth data (time and cell size) were analyzed using the DMFit software available on the Combase website<fnref n="2" />. Growth data were fitted to the model proposed by Baranyi and Roberts (1994) for estimation of lag time ($\lambda$, hour) and maximum specific growth rates ($\mu_{max}$, Ln pixels/hour) of each growth curve.

# RESULTS

## EFFECT OF OXIDIZING AGENTS ON LAB

**Figure 1** shows the log reduction after treatment with PAA and NaClO on five *Lactobacillus brevis* strains. The initial log CFU of

<footnote>*This strain was cured from the horA plasmid.</footnote>

<footnote n="1">¹http://imagej.nih.gov/ij/</footnote>

<footnote n="2">²http://www.combase.cc/index.php/en/</footnote>
<figure><img src="image_30.png" /><figcaption>**FIGURE 1** | Log reduction of five *Lactobacillus brevis* strains after exposure to 0.0014% peracetic acid (PAA; gray bars) and 0.0021% NaClO (black bars). The smaller the log reduction, the bigger the tolerance. The error bars indicate the SD.</figcaption></figure>

all the strains after anaerobic cultivation was 8.72 ± 0.10 (mean value ± SD). The two disinfectants exhibited different effectiveness against the different strains. After PAA treatment, JK09-hor A, which is a plasmid-cured strain, was the most sensitive strain, while the non-beer associated bacteria MI12158 was the most tolerant one; for NaClO treatment, JK09-hor A was still the most sensitive and HF02 was the most tolerant.

The two strains HF01 and JK09 were subsequently chosen for further experiments in a microscopic set-up, because they are both beer spoilage bacteria, and exhibited varying levels of sensitivity toward PAA, which was selected as the oxidizing agent for the microscopic analysis. The isolate HF02 was very tolerant toward the oxidizing agents, which would impede the microscopic analysis, and on the other hand, NaClO did not produce a pronounced difference between HF01 and JK09.

## GROWTH UNDER AEROBIC AND ANAEROBIC CULTIVATIONS

Both HF01 and JK09 grew better under aerobic cultivation. For HF01, the aerobic culture reached OD₆₀₀ of 1.5 after 16h and the anaerobic culture after 21h. For JK09, the time was 14h and 18h under aerobic and anaerobic cultivations, respectively. In addition, the pH at OD₆₀₀ of 1.5 under aerobic and anaerobic cultivations were 4.91 and 4.94 for HF01, whereas the pH of JK09 were 4.85 and 4.93, respectively.

## COMPARISON BETWEEN CFU AND MICROCOLONY FORMATION

For all treatments, colony forming units were detected up to 5 days by traditional CFU method, and microcolonies were observed for up to 2 days in the microscopic method. In the control experiments of HF01 and JK09, the size of the individual (macro)colonies on the plates was comparatively large and very similar and the number of colonies would not increase after 3 days of incubation. In contrast, the number of colonies increased for up to 5 days after exposure to PAA, and the size of colonies were heterogenous, since some colonies were as large as in the control, and other

<figure><img src="image_31.png" /><figcaption>**FIGURE 2** | Colony morphology of HF01 and JK09 on MRS agar after anaerobic cultivation. The colonies were photographed after 5 days of incubation. HF01 control (A), JK09 control (B), HF01 exposed to PAA (C), JK09 exposed to PAA (D). Strain HF01 grows as rough colonies while JK09 grows as smooth colonies. For both HF01 and JK09, the colonies without treatment were uniform in size, in contrast, the colonies after treatment with PAA were varying in size. (A) and (B) were illuminated from above to highlight the surface structure; (C) and (D) were illuminated from below to visualize the size difference of the colonies.</figcaption></figure>

colonies were still much smaller on day 5. **Figure 2** is an example of colony morphologies after anaerobic cultivation, the colony morphologies were similar after aerobic cultivation (results not shown).

A clear difference in colony morphology between HF01 and JK09 could be observed both on the plates (Figures 2A,B) and in the microscope (Figure 3). For HF01, the surface of (macro)colonies was rough and the edge appeared fluffy (Figure 2A). In the microscope, the microcolonies did not develop in all directions equally, but in a more random fashion and there were sometimes empty space within a microcolony (Figure 3E). For JK09, the surface of the (macro)colonies was smoother, and the edge was rounder (Figure 2B), with the growth of the microcolony expanding more equally in all directions (Figure 3H).

Although we conventionally assume that all untreated cells (i.e., control) would grow and form colonies, it was observed that some individual cells never started to divide under the microscope (Figure 6), and therefore the calculation of the survival in the microscopic method will not reach 100%.

**Table 2** shows that there is good reproducibility of both methods, although the variation between repetitions of CFU could be up to 17.4%, where the variation between repetitions of the microscopic method was up to 12.8%. In general, for the control, the survival of the CFU method is slightly higher than that of the microscopic method, from 1.9% to 7.1%. But in most cases, after PAA, the survival of the microscopic method is higher, from -0.7% to 25.9%. We can also
<figure><img src="image_32.png" /><figcaption>**FIGURE 3** | Development of individual cells for HF01 control and JK09 control on MRS agar after anaerobic cultivation. (A), (B), (F), (G) have the same magnification; (C), (D), (H), (I) have the same magnification and (E), (J) have the same magnification.</figcaption></figure>

see from Table 2 that after exposure to PAA, the survival rate of HF01 was always higher than that of JK09 regardless of cultivation, and the survival rates of both HF01 and JK09 after aerobic cultivation were significantly higher than after anaerobic cultivation.

<table><caption>**Table 2** | Comparison of survival rate measured by CFU and microscopic method.</caption><thead><tr><th>Strain</th><th>Cultivation</th><th>repetition</th><th>Treatment</th><th>CFU method %</th><th>Microscopic method %</th></tr></thead><tbody><tr><td rowspan="6">HF01</td><td rowspan="2">Anaerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>93.6</td></tr><tr><td>PAA</td><td>39.7</td><td>38.6</td></tr><tr><td rowspan="2">Aerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>92.9</td></tr><tr><td>PAA</td><td>47.4</td><td>51.4</td></tr><tr><td rowspan="6">JK09</td><td rowspan="2">Anaerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>97.8</td></tr><tr><td>PAA</td><td>72.4</td><td>94.2</td></tr><tr><td rowspan="2">Aerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>97.1</td></tr><tr><td>PAA</td><td>85.7</td><td>96.5</td></tr><tr><td rowspan="6">JK09</td><td rowspan="2">Anaerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>97.5</td></tr><tr><td>PAA</td><td>1.9</td><td>1.3</td></tr><tr><td rowspan="2">Aerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>98.1</td></tr><tr><td>PAA</td><td>1.0</td><td>0.3</td></tr><tr><td rowspan="6">JK09</td><td rowspan="2">Anaerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>97.8</td></tr><tr><td>PAA</td><td>32.1</td><td>58.0</td></tr><tr><td rowspan="2">Aerobic</td><td rowspan="2">1</td><td>Control</td><td>100.0</td><td>98.6</td></tr><tr><td>PAA</td><td>49.5</td><td>55.6</td></tr></tbody></table>

## MICROSCOPIC METHOD

In Figure 3, we show the universal behavior of individual cells in the control experiments after anaerobic cultivation. Usually the cells elongate to two or three times the initial length, where after we observe the division into two or three cells. The cells continue to multiply, and eventually form a microcolony. We also observed a few cells that increased in cell length up to six times the initial length before division, while very few other cells prolonged a little but never started dividing (Figure 6).

Staining with PI did not by itself affect viability of *Lactobacillus brevis* (results not shown). Examples of corresponding brightfield and PI images of JK09 after anaerobic cultivation followed by PAA treatment are shown in Figure 4. At 44 h, there are some red cells randomly distributed inside the microcolony (Figure 4B). But only two cells out of the initial seven non-growing cells were red. After 4 h, the number of red cells increased within the extending microcolonies, and one additional individual cell turned red (Figure 4D).

The number of individual cells at time zero was from 0 to 14 in each image, and the total individual cell number in 49 images was between 200 and 500. The growth curves of 50 dividing cells for each treatment are shown in Figures 5A–D (except for JK09 after anaerobic cultivation after PAA treatment, where only four cells divided). The experiments were repeated, and both strains showed good repeatability (results not shown). The experiment was stopped when the dividing microcolonies merged. It can be seen that for treatment with saline (control, blue lines), the growth of HF01 was similar to that of JK09 after the same cultivation, but with a certain variation in growth of individual cells. After

<footnote>*Experiments which were used to draw the growth curves of individual cells in Figure 5.</footnote>
<figure><img src="image_33.png" /><figcaption>**FIGURE 4** | Images of brightfield and PI fluorescent of JK09 after anaerobic cultivation followed by exposure to PAA. Brightfield at 44 h (A), propidium iodide (PI) fluorescent at 44 h (B), brightfield at 48 h (C), PI fluorescent at 48 h (D).</figcaption></figure>

treatment with PAA (red lines), the individual growth curves were quite diverse for both strains.

The distributions of λ and μmax values, estimated by the Baranyi and Roberts (1994) primary model for 340 microcolonies (14 microcolonies could not fit the model) originating from individual cells, are shown in **Figures 5a–d**. After saline treatments (control, blue dots), the median λ values of JK09 were a little bit smaller than that of HF01 regardless of cultivation. The median λ values for both strains were also slightly smaller after aerobic conditions. The median μmax were almost similar for all control experiments. After PAA treatments (red dots), the median λ values were distinctly bigger and the median μmax were clearly lower compared with the same strain and incubation in the control experiment. For HF01, the aerobic incubation gave smaller median λ values and higher median μmax than the anaerobic incubation. It is impossible to draw conclusions about the influence of incubation on JK09, due to the limited number of dividing cells after anaerobic cultivation. We can also see that both λ and μmax exhibit a significant variability for each experiment, especially after exposure to PAA. However, we could not find a clear correlation between λ and μmax. As an example, we show the calculated λ and μmax from three neighbor colonies (**Figure 6**). The cells have almost similar distance to the other two cells, but with a distinct difference in λ and μmax.

DISCUSSION

As previously mentioned, there may be an inverse correlation between hop resistance and oxidation resistance. However, in this study a plasmid cured strain lacking the hop resistance gene horA

had a slightly higher sensitivity toward oxidizing compounds compared to the wild type JK09 (Figure 1). This does not support the idea of an inverse correlation, but the plasmid cured strain also exhibited similar hop resistance to the wild type (results not shown), which suggests that hor A is neither important for hop resistance nor oxidation resistance.

It was previously found that *Lactobacillus brevis* ATCC 14869 exhibit Smooth(S)-type colonies when grown under anaerobic conditions, whereas the majority of colonies exhibit a Rough(R)-type morphology under aerobic conditions (Jakava-Viljanen et al., 2002). In our study, the two *Lactobacillus brevis* strains HF01 and JK09 had distinctly different morphologies as JK09 exhibited S-type morphology and HF01 exhibited R-type morphology (Figure 2). However, their morphologies appeared to be less variable, as they retained the same morphology after PAA treatment, and the morphology was the same after aerobic and anaerobic cultivation. The study of Jakava-Viljanen et al. (2002) indicated that an oxidative environment promotes the formation of R-type colonies, which could suggest that strains with R-type morphology has an increased survival after exposure to oxidative compounds. This is consistent with our findings, where HF01 (R-phenotype) was more tolerant toward PAA.

In addition, our microscopic results suggest that already when microcolonies are formed, a distinct difference in colony morphology can be observed, which may predict the resulting morphology of macrocolonies (Figures 2A,B and 3).

It is clear that the microscopic method is more rapid than the CFU method for detection of dividing cells, (Asano et al., 2009). The traditional approach required at least 2 days before visible colonies (consisting of millions of cells) could be detected. In the present study, small colonies continued to appear until five days after the PAA treatment. The origin of these colonies is cells that can be considered ‘hard-to-culture’ (Suzuki, 2011), but our microscopic results suggests that this phenomenon can be attributed to the large variation in lag time of individual cells, after PAA treatment. This could be the reason why some survival rates using the CFU method were 20% lower than that using the microscopic method and with similar big differences between repetitions in the CFU method (Table 2). It is possible that some individual cells started division so late that the (macro)colonies were too small to be observed on the last day of the experiment. However, the microscopic method could observe cell elongation and division down to few hours after the beginning of the experiment. The microscopic method also has the potential to provide more details of the growth of individual cells into microcolonies. In our study, we can clearly see the growth dynamics of individual cells (Figure 3), where cells divided and eventually formed a microcolony. We also observed in our experiments that a few cells elongated, but then stopped dividing (Figure 6). This type of sub-population cannot be observed with a CFU method, although the cells may possess some amount of biochemical activity. This might be another reason why the survival rates using the CFU method in some cases were 20% lower than the microscopic method.

Several studies have shown that individual cells exhibit heterogeneity in how they deal with stress in the same environment (Lianou et al., 2006; Métris et al., 2008; Muñoz-Cuevas et al., 2013). We observed that there are relatively small differences in the growth
<figure><img src="image_34.png" /></figure>

HF01-Anaerobic

<figure><img src="image_35.png" /></figure>

HF01-Anaerobic

<figure><img src="image_36.png" /></figure>

JK09-Anaerobic

<figure><img src="image_37.png" /></figure>

JK09-Anaerobic

<figure><img src="image_38.png" /></figure>

HF01-Aerobic

<figure><img src="image_39.png" /></figure>

HF01-Aerobic

<figure><img src="image_40.png" /></figure>

JK09-Aerobic

<figure><img src="image_41.png" /></figure>

JK09-Aerobic

**FIGURE 5** | Growth curves, lag times and μmax of individual cells of HF01 and JK09 after different treatments. Blue: after saline treatment (control); red: after PAA treatment. The dotted red lines indicate that the two fastest growing microcolonies reached approximately 31000 pixels at 35 h. The center of the cross is the median of lag time and μmax (a–d).

curves and lag time of both strains in the control experiments (Figure 5), but extensive variation in growth behavior, survival, lag time and maximum growth rate was observed between the two strains after treatment with PAA (Table 2, Figure 5). This biological variability may be due to the genetic diversity between the strains, but the large variations in the resulting λ and μmax of HF01 and JK09, especially after PAA treatments suggest that there is a phenotypic diversity that cannot be fully explained by presence

of genes, as all of the individual cells of a strain can be considered clonal. After exposure to PAA, the lag time of the dividing cells of both strains increased pronouncedly. Interestingly, after the prolonged lag time, some cells of both strains exhibited the same μmax as in the control experiments, whereas other cells grow at a much slower rate. This is interesting, because it suggests that even after repair mechanisms have enabled the individual cells to divide, the resulting daughter cells in a microcolony continue to divide at a
<figure><img src="image_42.png" /><figcaption>FIGURE 6 | Example of neighboring cells of JK09 in the control experiment after anaerobic incubation at *T* = 0 (A) and *T* = 12 h (B).</figcaption></figure>

rate that is predicted by the initial divisions, even though PAA has been removed. This result is inconsistent with the results from Kutalik et al. (2005), which points out that the extent of the lag phase only influence the first cell cycle and the subsequent division is uncorrelated to the cell history. In addition, we found that the lag time was significantly shorter and the μmax was distinctly higher after the aerobic cultivation for both strains (Figure 5). This result indicates that aerobic cultivation makes *Lactobacillus brevis* more tolerant to PAA. It may be because the bacteria have already built up some kind of defense mechanism or repair mechanism in order to protect them against oxygen during the aerobic cultivation. For example, high NADH oxidase activity and NADH peroxidase activity were found in *Lactobacillus brevis* after aerobic cultivation (Sakamoto and Komagata, 1996; Hummel and Riebel, 2003). In addition, there are marked differences in λ and μmax of neighboring microcolonies (Figure 6). The differences are not caused by the proximity between the resulting microcolonies, because even after 12 h and several divisions, the resulting microcolonies still have similar distances to the neighboring colonies. It is likely that there would be an interaction between adjacent colonies when they are so close that they must share nutrients (propinquity effect), but it is unlikely that this is the case within the timeframe of our experiments.

The microscopic method used in this study can therefore provide quantitative data suitable for analysis of growth of individual cells. It should be noted that we only quantified the amount of pixels that are covered by microcolonies in two dimensions. When the microcolonies become very big (several hundred cells), the microcolony started to take on the traditional three-dimensional structure (Figures 4A,C), where a simple measure of area is no longer adequate. However, during many divisions, the cells are primarily growing on the surface of the agar, as our cells are enclosed between the coverslip that constitutes the bottom of the well and the agar. Additionally, if the microcolonies expand in less coordinated fashion such as HF01, where there can be visible holes inside the microcolony, it is important to subtract the area of the holes to obtain a valid estimate of cell growth.

Propidium iodide is a popular red-fluorescent DNA counterstain for estimating the amount of dead cells in a bacterial population (Bunthof et al., 2001; Rault et al., 2007). Due to its molecular weight and charge, it only penetrates cells with a damaged plasma membrane. As PI does not fluoresce without the

presence of DNA, we incorporated PI into our growth matrix, in order to determine when and how, individual cells begin to die. Surprisingly, the large proportion of cells that failed to divide after treatment with PAA, did not exhibit red fluorescence (Figures 4B,D). In JK09, the survival after anaerobic cultivation after PAA treatment was around 1%, so we would expect to observe 99% red cells. This observation suggests that PAA does not compromise the membrane integrity of cells that fail to divide, and PI would therefore be a poor indicator of the efficacy of PAA. To rule out potential artifacts in our experiments, we determined that PAA treated cells that were afterward exposed to 70% EtOH (which destroys the membrane) all stained red in our setup (results not shown). As microcolonies started to grow, we observed that a few individual cells turned red, which indicates that these cells have lost their membrane integrity (Figure 4). The cells appeared randomly within the microcolony, which does not suggest that they died from nutrition depletion or accumulation of toxic substances. If the cells should die from nutrient depletion or accumulation of toxic substances, it would be expected that the majority of the dead cells would be located in the center of the microcolony, this observation was previously reported by Ryssel et al. (2013).

In conclusion, the investigated beer spoilage LAB after different cultivations exhibit different sensitivity toward PAA, but there is no indication that the tolerance toward PAA is inversely correlated to the potential to spoil beer. On the other hand, the present study demonstrates a novel approach to investigate the formation of microcolonies as an indicator of physiological fitness. The method provides results faster than CFU determination, but seems to correlate very well with CFU. Furthermore, the method provides a tool to investigate the phenotypic heterogeneity of a clonal population, which can be expanded to many interesting aspects.

## ACKNOWLEDGMENT

The authors are grateful to China Scholarship Council for financial support.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 29 August 2014; accepted: 31 October 2014; published online: 26 November 2014.

Citation: Zhao Y, Knøchel S and Siegumfeldt H (2014) In situ examination of *Lactobacillus brevis* after exposure to an oxidizing disinfectant. *Front. Microbiol.* 5:623. doi: 10.3389/fmicb.2014.00623

This article was submitted to Food Microbiology, a section of the journal *Frontiers in Microbiology*.

Copyright © 2014 Zhao, Knøchel and Siegumfeldt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_43.png" /></figure>

# Laser-induced speckle scatter patterns in *Bacillus* colonies

Huisung Kim<fnref n="1" />, Atul K. Singh<fnref n="2" />, Arun K. Bhunia<fnref n="2" /> and Euiwon Bae<fnref n="1" />*

**Edited by:**

Sophie Jeanson, Institut National de la Recherche Agronomique - INRA, France

**Reviewed by:**

Folarin Anthony Oguntoyinbo, University of Lagos, Nigeria
Stella Maris Reginensi Rivera, Universidad de la República Oriental del Uruguay, Uruguay

**\*Correspondence:**

Euiwon Bae, School of Mechanical Engineering, Purdue University, 585 Purdue Mall, West Lafayette, IN 47907, USA
e-mail: ebae@purdue.edu

Label-free bacterial colony phenotyping technology called BARDOT (Bacterial Rapid Detection using Optical scattering Technology) provided successful classification of several different bacteria at the genus, species, and serovar level. Recent experiments with colonies of *Bacillus* species provided strikingly different characteristics of elastic light scatter (ELS) patterns, which were comprised of random speckles compared to other bacteria, which are dominated by concentric rings and spokes. Since this laser-based optical sensor interrogates the whole volume of the colony, 3-D information of micro- and macro-structures are all encoded in the far-field scatter patterns. Here, we present a theoretical model explaining the underlying mechanism of the speckle formation by the colonies from *Bacillus* species. Except for *Bacillus polymyxa*, all *Bacillus* spp. produced random bright spots on the imaging plane, which presumably dependent on the cellular and molecular organization and content within the colony. Our scatter model-based analysis revealed that colony spread resulting in variable surface roughness can modify the wavefront of the scatter field. As the center diameter of the *Bacillus* spp. colony grew from 500 to 900 µm, average speckles area decreased two-fold and the number of small speckles increased seven-fold. In conclusion, as *Bacillus* colony grows, the average speckle size in the scatter pattern decreases and the number of smaller speckle increases due to the swarming growth characteristics of bacteria within the colony.

**Keywords:** bacterial colony, diffraction, speckle, phase modulation, *Bacillus*

## INTRODUCTION

Bacterial colonies consist of millions of individual cells interacting with each other in complex ways (Shapiro, 1992; Wimpenny, 1992; Shimada et al., 1995; Ben-Jacob et al., 1998). It is important to understand the mechanism of colony formation and the resulting morphology as it is a key to understand organizations and interactions among bacterial cells within a colony. The colony morphology is highly influenced by the shape of individual cells, cell wall components (peptidoglycan, teichoic acid, lipopolysaccharide), the extracellular components and appendages (flagella, fimbriae, curli), and cellular response to the environmental cues including nutrient availability, oxygen and other gases, salt, acidity, alkalinity, temperature, etc. (Nagai et al., 1971; Granek and Magwene, 2010). Therefore, the colony morphology of different bacteria could be used as a foundation for differential characterization using an interrogating tool.

Historically, bacterial colony pattern formation has drawn significant interest among diverse research fields. Microbiologists were more interested in determining how bacterial response to the environmental factors such as nutrient and agar hardness affect colony formation, while the physicist were aiming to recreate the colony formation scenario by using theoretical models. Shapiro and Wimpenny first observed bacterial colony formation in time and space (Shapiro, 1992; Wimpenny, 1992) and since then, many other researchers employed both experimental (Shimada et al., 1995; Ben-Jacob et al., 1998; Bees et al., 2000; Stecchini et al., 2001; Kaito and Sekimizu, 2007; Pipe and Grimson, 2008) and

theoretical models (Kawasaki et al., 1997; Cohen et al., 1999; Kozlovsky et al., 1999; Lega and Passot, 2003) to explain such phenomenon. Among the various organisms tested, a majority of the study used *Bacillus* spp. as a model due to their swarming growth characteristics. Swarming colonies can generate diverse spatio-temporal patterns due to their reproduction and spreading mechanisms (Harshey, 2003). Therefore, we also studied *Bacillus* as a model organism to correlate the swarming colony morphology to the optical light scattering and speckle effect.

Since the first reports (Bae et al., 2007; Banada et al., 2007), optical light scattering of colony have expanded to differentiate diverse bacterial genera. The remarkable resolving power of the BARDOT originates from the accumulation and amplification of both microscopic structural and biochemical differences that exist among different bacterial colonies through an interrogating laser beam. When the laser beam passes through the bacterial colony, both 3D morphological and optical characteristics are integrated into 2D outgoing wave and encoding it on the coherent optical wavefront. The wavefront then propagates through near- and far-fields governed by the diffraction integral to form distinctive forward scattering pattern which serves as an optical “fingerprint.”

Elastic light scatter (ELS) has been used by our group for bacterial colony differentiation and identification (Bae et al., 2007, 2011; Banada et al., 2009; Huff et al., 2012; Singh et al., 2014). Recently we have used this technology for differentiating *Bacillus cereus* (an important food poisoning species) and compared

<footnote n="1">¹ Applied Optics Laboratory, School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA</footnote>

<footnote n="2">² Molecular Food Microbiology Laboratory, Department of Food Science, Purdue University, West Lafayette, IN, USA</footnote>
its colony scattering pattern with another ubiquitous species, *Escherichia coli* (Singh et al., submitted). *Bacillus* is highly motile and considered a swarming bacterium, which shows greater mobility on solid agar. Among the *Bacillus* spp., surprisingly, only *B. polymyxa* expressed typical ELS patterns of concentric rings and spokes consistent with other bacterial genera: *Listeria*, *Salmonella*, *Vibrio*, *Escherichia*, and *Staphylococcus* (Bae et al., 2007; Banada et al., 2009; Huff et al., 2012; Singh et al., 2014). While all other *Bacillus* species tested showed random speckles overlaid with some circular ring patterns (Singh et al., submitted). The scattering patterns of *B. subtilis* and *B. polymyxa* have shown significant differentiating characteristics. In this paper, we investigated the different optical properties of these two *Bacillus* species; (i) to understand how *B. subtilis* and *B. polymyxa* construct their colony using recently developed Integrated Colony Morphology Analyzer (ICMA) (Kim et al., 2013); (ii) to compare the theoretical prediction of ELS patterns with experimental data; and (iii) to calculate the speckle statistics to quantitatively correlate the optical phase modulation into the structure of the bacterial colony.

# MATERIALS AND METHODS

## SAMPLE PREPARATION

*Bacillus subtilis* ATCC 6633 (*B. subtilis*), *Bacillus polymyxa* B719W (*B. polymyxa*), *Bacillus cereus* ATCC 14579 (*B. cereus*), and *Bacillus thuringiensis* DUP6044 (*B. thuringiensis*) cultures were used in this study. Cultures were inoculated in brain heart infusion (BHI) broth (Difco, MD, USA) and incubated at 37°C for overnight (16 h) with 130 rpm shaker, 10-fold serially diluted in 0.2 mM phosphate buffered saline, pH 7.0 (PBS) and diluents were plated on BHI and phenol red mannitol (PRM) agar plates (Becton Dickinson, NJ, USA) to obtain 50–100 colonies. Optical scatter patterns of the colonies were captured using the BARDOT instrument at 6–8 h that corresponds to colony diameter of about 550, 750, and 900 µm, respectively.

## FORWARD SCATTEROMETER

Scatterometer (Figure 1A), consists of laser diode (LD) with 635 nm wavelength that was installed as a light source, and the light source directly illuminates a single bacterial colony grown on semi-solid agar plate. The diffracted light is captured using detector (CMOS camera, PL-B741, ON, Canada) with 1280(H) × 1024(V) pixels, 6.7 × 6.7 µm pixel pitch which is located after the semi-solid agar plate with distance z. Motorized 2 axis lateral stage was integrated to the system, and the semi-solid agar plate was put on the 2 axis lateral stage for a full automatic measurement of optical scatter pattern. The *Bacillus* colonies were then picked from the plate, grown briefly (4 h) in BHI broth at 37°C and analyzed by multiplex PCR (mPCR) assay.

## DNA EXTRACTION AND mPCR

Total DNA was extracted by boiling cultures as described earlier (Ngamwongsatit et al., 2008). mPCR was performed using *gyrB* gene-specific primers (BcF: 5' GTTTCTGGTGGT TTACATGG3'; BcR: 5'TTTTGAGCGATTTAAATGC 3') and *cry* gene-specific primers (K5F: 5'AGG ACCAGGATTTACAGGAGG 3'; K3R: 5' GCTGTGACACGAAGGATATAGCCAC 3') (Kuo and Chak, 1996;

<figure><img src="image_44.png" /></figure>

Manzano et al., 2003). The mPCR reaction mixture contained 200 µM of each dNTP, 2.5 mM of MgCl2, 0.8X GoTaq Flexi buffer, 1U of GoTaq Flexi DNA polymerase (Promega), 0.2–0.3 µM of primers, 60–90 ng of template DNA and the reaction condition was optimized (Henegariu et al., 1997; Lorenz, 2012). Ultra-pure sterile water was used as negative control. PCR reaction was only considered valid when control reaction was either positive or negative as appropriate and 16S rRNA gene was positively amplified with all DNA templates. All amplicons were analyzed on 1.2% agarose gel.

## INTEGRATED COLONY MORPHOLOGY ANALYZER (ICMA)

As shown in Figure 1B, the laser confocal displacement meter (CDM) (Keyence, LT9010M) is located at the top and attached to a linear horizontal translation stage (Edmund Optics, NJ, USA) in order to position the probing laser beam (λ = 670 nm) along the vertical z axis and to focus it on the surface of colony. A motorized 2D lateral stage translated the Petri dish in the x-y plane to align the diagnostic laser beam with each selected bacterial colony to obtain their 3-D profiles. Two linear motors (850G-HS) connected to the ESP 300 multi-axis closed-loop controller (Newport, NY, USA) with the specification of a 42 mm maximum stroke and a 0.1 mm minimum step size control the movement of the x-y stage. This instrument implements a high accuracy surface scanning method by using a laser light source with a Gaussian beam spot of approximately 2 µm. The position of the doublet lenses is controlled by a tuning fork moving up and down at high frequency. When the beam was focused on the sample (colony) surface, the reflected light traveled back along the original optical path and was directed toward the pinhole to produce the highest level of intensity on the light receiving element. If the laser is unfocused, some of the reflected light would be occluded at the pinhole. Therefore, the relative height of the measured surface was registered by the sensor positioned at the bottom of the tuning fork, and the values are calculated and converted to an absolute height value (Keyence-Corporation, 2006).

A micro-objective lens (Bausch & Lomb, 20x, N.A. 0.4) is placed just below the Petri dish to focus the transmitted light
onto the active area of the photodiode (Hamamatsu, S1087) (Figure 1B). This photodiode offered a low background noise (10 pA) with a wide dynamic range and provided a spectral response from 320 to 730 nm with the peak sensitivity at 560 nm. The active area for this photodiode is 1.3 × 1.3 mm², providing a photo sensitivity of 0.19 A/W for the incoming laser from the CDM sensor head on which a custom built active components driven I-V converter and preamp were installed at a preamp section. A custom built micro controller unit (MCU) (AVR128, Atmel) was used as a data acquisition unit. Through use of the MCU's internal 10 bit A/D conversion, all signals from the photodiode circuit and CDM were captured and transferred simultaneously to the PC by means of serial communication. All sequences were controlled by a custom built graphic user interface (GUI) at the PC, which was developed using Microsoft Visual Studio 2008 and both were analyzed and visualized in MATLAB.

## MODELING SPECKLE EFFECT FROM ELASTIC LIGHT SCATTERING

The bacterial colony and a semi-solid media are positioned at the aperture plane, and the forward scattering pattern is captured at the image plane, defined as $(x_a, y_a)$ and $(x_i, y_i)$, respectively. A bacterial colony is modeled as a bell curve shape with tailing edge (Gaussian-like profile) where colony center height and radius is defined as $H_0$ and $r_c$, respectively. Based on Rayleigh and Sommerfeld formation of diffraction, and Fresnel diffraction approximation (Bae et al., 2007, 2010), intensity of electric field at aperture and image plane is derived as Equations (1) and (2), respectively, where distance between aperture plane to a point at image plane, $r_{ai}$ is assumed as Equation (3).

$$E_a(x_a, y_a, z) = E_0 \exp \left[ -\frac{(x_a^2 + y_a^2)}{\omega^2(z)} \right] \exp(ikz) \\ \exp \left[ ik \frac{(x_a^2 + y_a^2)}{2R(z)} \right] \qquad (1)$$

$$E_i(x_i, y_i) = \frac{1}{i\lambda} \iint t(x_a, y_a) E_a(x_a, y_a) \exp[ik\Phi(x_a, y_a)] \frac{\exp[ikr_{ai}]}{r_{ai}} \cos\theta dx_a dy_a \qquad (2)$$

$$r_{ai} = [z_i^2 + (x_a - x_i)^2 + (y_a - y_i)^2]^{\frac{1}{2}} \\ \approx z_i \left[ 1 + \frac{1}{2} \left( \frac{x_a - x_i}{z_i} \right)^2 + \frac{1}{2} \left( \frac{y_a - y_i}{z_i} \right)^2 \right] \qquad (3)$$

Since previous researchers focused on structural morphology of bacterial colony such as elevation and colony diameter, the colony was modeled as smooth curve with Gaussian profile function without considering surface roughness. In reality, a cross section of bacterial colony showed the accumulation of densely packed multiple layers of bacterial cells (Banada et al., 2009; Bae et al., 2010; Suchwalko et al., 2013; Marcoux et al., 2014). Furthermore, *Bacillus* colonies also showed some degree of surface roughness (Figures 2, 3). To simplify the modeling, surface roughness is generated by random signal with colony area, and superposed to

<figure><img src="image_45.png" /><figcaption>FIGURE 2 | Phase contrast images of the *B. subtilis* on (A) BHI and (B) PRM where colony shows swarming growth characteristics. For comparison, *B. polymyxa* on (C) BHI and (D) PRM are shown where colony forms a circular shape.</figcaption></figure>

the colony morphology. Considering the surface roughness of the colony, intensity of electric field at image plane is simplified as Equation (4).

$$E_i(x_i, y_i) = C \iint T(x_a, y_a) \exp[i\Phi_r] \exp[i\Phi_c] \exp[i(\Phi_g + \Phi_s)] \\ \exp[-2\pi i(f_x x_a + f_y y_a)] dx_a dy_a \qquad (4)$$

where T is amplitude modulator; $f_x$ and $f_y$ is defined as $xi/(\lambda z_2)$ and $yi/(\lambda z_2)$, known as a spatial Fourier frequency; $\Phi_r$, $\Phi_q$, $\Phi_g$, and $\Phi_s$ is radial, quadratic, Gaussian, and surface roughness phase component respectively, and defined as:

$$\Phi_r(x_a, y_a) = \frac{k(x_a^2 + y_a^2)}{2R} \qquad (5)$$

$$\Phi_q(x_a, y_a) = \frac{k(x_a^2 + y_a^2)}{2z_i} \qquad (6)$$

$$\Phi_g(x_a, y_a) = k(n_{bac} - 1)H_0 \exp \left[ - \frac{(x_a^2 + y_a^2)}{r_c^2} \right] \qquad (7)$$

$$\Phi_s(x_a, y_a) = k(\text{random}(x_a, y_a)) \text{ where } (x_a^2 + y_a^2 < r_c^2) \qquad (8)$$

The summation of four phase components, $\Phi_{overall}$ is working as phase modulator for the propagating light. The amplitude modulator T is derived as:

$$T(x_a, y_a) = \exp \left[ -\frac{(x_a^2 + y_a^2)}{\omega^2(z)} \right] \frac{E_{out}}{E_0} \\ = \exp \left[ -\frac{(x_a^2 + y_a^2)}{\omega^2(z)} \right] (1 - r_{air-bac}) (1 - r_k)^{2l} \\ (1 - r_{bac-agar})$$

$$\Phi_{overall} = \Phi_r + \Phi_q + (\Phi_g + \Phi_s) \qquad (10)$$
<figure><img src="image_46.png" /><figcaption>FIGURE 3 | Profile and OD map acquired from ICMA. Top row displays the profile map of (A) B. subtilis ATCC 6633 colony and (B) B. polymyxa B719W colony, and (C) profile cross section of both</figcaption></figure>

species. Bottom row shows the OD map of (D) B. subtilis ATCC 6633 colony and (E) B. polymyxa B719W colony, and (F) OD cross section of both species on PRM.

Coefficient of Equation (4), C is derived as:

$$C = \frac{E_0 \exp(ikn_{agar} \Delta_{agar}) \exp(ikH_0) \exp[ik(z + z_i)] \exp[ik(x_i^2 + y_i^2)/2z_i]}{i\lambda z_i} \quad (11)$$

where $\Delta_{agar}$ and $n_{agar}$ is defined as thickness of agar and refractive index of agar respectively.

## SPECKLE ANALYSIS

To provide quantitative speckle analysis, a MATLAB code was constructed utilizing an image processing toolbox V2.6 (Vliet, 2014). A radial Gaussian blur was applied to the raw image to apply a low pass filter that eliminates the objects while keeps the slow change in the background, and the blurred image was subtracted from the raw image to remove the background noise. The subtracted image was binarized with locally adaptive threshold using approximation of gradient of background intensity to eliminate the external lighting and local CMOS sensor sensitivity characteristics effect on the image. Then, each speckle was segmented, grouped, and labeled. Number of pixels (size of the speckle) and feret diameter (Merkus, 2009) of each segmented group was computed, and analyzed.

## RESULTS

Here, we report how colony morphology correlates with its speckle patterns using both theoretical and experimental approaches. Thus, each colony was first interrogated by ICMA and phase contrast microscopy to record height, transmittance and morphology.

## COLONY DEVELOPMENT PROFILES

The phase contrast microscopic (PCM) images of colonies of B. subtilis and B. polymyxa on both BHI and PRM were contrasting (Figure 2). B. subtilis colony has an irregular boundary with bumpy and uneven surface structure on both media (Figures 2A,B). Due to the limitation of phase contrast microscopic setup, the 3-D elevation cannot be interpreted with current image but B. subtilis shows a representative swarming colony characteristics, where spatially localized variations of bacterial cell densities are observed. Meanwhile, B. polymyxa had symmetrical circular boundary with relatively smooth surface curvature (Figures 2C,D). As the PCM result showed, the colony forming characteristics of B. polymyxa was drastically different from the majority of other Bacillus species including B. subtilis, B. cereus, and B. thuringiensis.
## COMPARISON OF TRANSMITTANCE AND ELEVATION PROFILE

To compensate the phase contrast microscope image, ICAM was utilized to acquire spatially resolved optical characteristics from a single colony. **Figures 3A,B** show the morphology map of *B. subtilis* and *B. polymyxa* colony on PRM, respectively from morphology channel of ICMA; whereas panel (d) and (e) show their corresponding spatially resolved transmittance in optical density (OD) units with 670 nm light source. Colonies of both species on PRM agar were captured after 7 h of growth, when the center diameter of *B. polymyxa* colony reached to approximately 800 µm. Similar to the PCM result, morphology map by ICMA revealed that boundary of *B. subtilis* colony was irregular, while, *B. polymyxa* colony had near circular symmetrical boundary (**Figures 3A,B**). Comparison of morphology cross section for the both species at their center area showed that *B. subtilis* colony had random bumpy profile with rough surface, while *B. polymyxa* colony had bell curve-like profile with relatively smooth surface (**Figure 3C**). *B. subtilis* colony had approximate 890 µm cross sectional diameter and 57 µm of center peak height, while, *B. polymyxa* colony had 814 µm diameter and 66 µm of colony center height. Considering the colony aspect ratio (colony center height to diameter ratio) of each species, *B. subtilis* (1:15.6) colony had more flat cross sectional profile than *B. polymyxa* (1:12.4) at the same diameter. Furthermore, the 2-D transmittance map (cross-sectional OD profile) of both species shows more distinctive characteristics, which can be attributed to colony morphology and opacity (**Figures 3C,F**). Since the OD of BHI agar plate alone at 670 nm was approximately 0.2, cross section of the OD result had 0.2 offset. Cross section of *B. subtilis* colony OD fluctuates from 0.24 to 0.41, while *B. polymyxa* colony had OD with maxima 0.29. Assuming that each species of bacterial cell had identical optical characteristic, this irregularity and randomness of OD can be ascribed to different spatial cell density that is translated into variation in light absorption. In addition, surface roughness of the *B. subtilis* colony can be modeled as variation in reflected and transmitted light intensity (**Figure 3**).

## ELS MEASUREMENT

In previous studies, optical forward scattering pattern or ELS of *Salmonella*, *E. coli*, and *Staphylococcus* showed characteristics symmetric or concentric circle with some radial spokes (Banada et al., 2009). However, forward scattering pattern of *Bacillus* species (except *B. polymyxa*) showed speckle pattern (**Figures 4A, 5**). Diffraction pattern of *B. subtilis* colony is consisted of only small sized random speckle, while, *B. polymyxa* shows concentric circular pattern (**Figure 4**). To verify the time resolved speckle development of *Bacillus* species on their forward scattering pattern, *B. subtilis*, *B. cereus*, and *B. thuringiensis* were selected and their diffraction patterns were measured after 6–8 h of growth using BARDOT (**Figure 5**). As the incubation time increased from 6 to 8 h, the diffraction patterns also evolved to fully developed speckles, i.e., structured patterns progressively became unstructured random speckles. All three *Bacillus* species displayed similar trend as the incubation time increased where average speckle numbers decreased.

<figure><img src="image_47.png" /></figure>

**FIGURE 4** | Elastic light scatter (ELS) patterns on PRM agar for (A) *B. subtilis* ATCC 6633, and (B) *B. polymyxa* B719W; (C) Multiplex PCR (mPCR) for differentiation of *Bacillus cereus*-group strains (Lane 1–5) from other *Bacillus* species (Lane 6–11). mPCR resulted a 374 bp PCR product for all *Bacillus cereus*-group strains and a 1.6 kb size PCR product only for *B. thuringiensis*. Lane 12 and 13 represents single gene specific amplification for *gyraseB* gene in *Bacillus cereus* and *cry* gene in *Bacillus thuringiensis*. mPCR was performed with *gyrase B* gene-specific and *cry* protein gene-specific primers.

## COMPARATIVE ANALYSIS OF EXPERIMENT WITH SIMULATION

To investigate the effect of surface roughness on forward scattering pattern, other components of models are kept constant for Equations (5)–(11). The effect of random surface phase component ($\Phi_s$) on forward scattering pattern was determined (**Figure 6**). **Figure 6A** shows the forward scattering pattern prediction when the surface phase component was equal to zero which means, the colony surface was devoid of any random structures. The predicted scatter image contained concentric ring patterns with small bright ring at the center and the thickness of rings increased as they moved outwardly. To mimic the random surface roughness effect, Equation (8) was adopted which is a random function multiplied by the wave number $k$, and is only influenced by bacterial colony region. The maximum amplitude of the random function for Equation (8) was chosen as 1/150, 1/100, and 1/80 of colony center height for fine, medium, and coarse surface roughness, respectively. The surface roughness component ($\Phi_s$) is added to Gaussian phase component ($\Phi_g$), and worked as a part of phase modulator, which can deform the incident wave front. Predicted forward scatter patterns are shown for fine (**Figure 6B**), medium (**Figure 6C**), and coarse surface roughness (**Figure 6D**). For a diffraction pattern prediction with fine surface roughness effect, relatively larger size speckles and less
<figure><img src="image_48.png" /><figcaption>**FIGURE 5** | Time-resolve ELS patterns for three different *Bacillus* species: *B. subtilis*, *B. cereus*, and *B. thuringiensis* on PRM agar. As the incubation time increases from 6 to 8 h, ELS pattern evolves to a fully developed speckle when an average speckle diameter decreases.</figcaption></figure>

number of speckles were generated, and still had concentric circle pattern. As the surface roughness increased, the size of the speckle decreased and higher number of speckles were generated, and formed a random speckle pattern rather than concentric circle pattern as predicted.

**Figure 7** shows the comparison of theoretical speckle development on diffraction pattern by surface roughness and that from experiment. The results were plotted for speckle area vs. the number of speckles. For the prediction, the maximum amplitude of the random function for $\Phi_s$ were set as 1/200, 1/150, and 1/100 of *B. cereus* colony center height for fine, medium, and coarse roughness, respectively. As the theoretical model moved from coarse (□) to fine (▶) surface roughness, the model predicts decreasing average speckle size with increasing numbers. Similar trend was observed in experiment with *B. cereus* ATCC14579 incubated from 6 h (□) to 8 h (△) (**Figure 7A**). For *B. thuringiensis* DUP6044, $\Phi_s$ was changed to 1/180, 1/130, and 1/100 of colony center height and an excellent agreement between theory and experiment was evident (**Figure 7B**). For example, coarse surface roughness (□) and 9 h incubation (△) showed almost similar trend in average speckle size and the numbers.

DISCUSSION

Understanding bacterial growth on a solid surface is important for biophysicist, microbial ecologist, and food microbiologist for better insight of colony development and its ecological

and virulence properties. In context to virulence attributes of colony, small colony variants (SCVs) of different bacteria are often considered more virulent facilitating persistence and recurrent infections (Proctor et al., 2006). Harshey (2003) wrote an excellent review on how bacteria form colony in four different ways (swarming, gliding, twitching, and sliding). Our motivation to this study was to correlate the unique growth characteristics of *Bacillus* species to the optical speckle theory and help understand colony growth dynamics. *Bacillus* has long been used as a model to study the environment dependent variations in growth patterns (Shimada et al., 1995; Ben-Jacob et al., 1998; Bees et al., 2000; Stecchini et al., 2001; Kaito and Sekimizu, 2007; Pipe and Grimson, 2008). However, recent application of optical scattering-based bacterial colony interrogation requires different perspective for fundamental understanding of scatter pattern generation and colony morphology.

Previous studies have shown that availability of nutrients and agar concentration affect the distribution/propagation of cells within a colony (Bae et al., 2011). However, the published studies on colony scatter pattern did not report theoretical interpretation of speckle pattern formation by a colony. To the best of our knowledge, this is the first report of theoretical interpretation of speckle pattern formation by *Bacillus* colony, which is largely attributed to the roughness of bacterial colony. The surface roughness is believed to be arisen from the swarming behavior of the *Bacillus* cells during colony formation. ICMA and PCM measurements
revealed that *B. subtilis* generates more rough surface morphology than the *B. polymyxa* (Figures 2, 3).

For theoretical comparison, ICMA measurement was critical since the scalar diffraction theory required accurate description of the amplitude and phase modulation component of Equations (1)–(11). Both qualitative trend and quantitative measurement showed good agreement (Figure 7). Both theoretical model and the experimental evidence indicate that as bacteria grow, the speckle size decreases and the number increases. Furthermore, the quantitative comparison also revealed that as surface roughness changed from fine to coarse, the number of speckle increased 7

(B. cereus) to 10 (B. thuringiensis) -fold. Similar quantitative trend was observed for two other bacterial species: B. cereus and B. thuringiensis. In future, this information could be used to provide fundamental understanding of inverse scattering method for analyzing bacterial colony formation since size and the number of speckle correlate with incubation time and surface roughness.

The light scattering sensor discussed in this study was successfully used for detection of bacterial pathogens including *Listeria monocytogenes* (Banada et al., 2007, 2009), *Vibrio* spp. (Huff et al., 2012), *Salmonella* serovars (Singh et al., 2014), *E. coli* O157:H7 (Tang et al., 2014), and *Bacillus* spp. (Singh et al., submitted). However, these studies did not investigate if mathematical modeling could be used to explain resulting scatter patterns of colonies. Here we focused on developing and comparing the computational model for *B. subtilis* ATCC 6633 and *B. polymyxa* B719W for speckle formation and its resultant effect on the experimental scatter pattern. Such in silico study would be of great significance for extrapolating the colonial properties of highly virulent *Bacillus* species such as *Bacillus anthracis*, a causative agent of anthrax that requires a high containment facility for handling.

Rather than utilizing specific labeling reagents, proposed method adopted the combination of a laser light and a 2D CMOS sensor to transform the both 3-D macroscopic and microscopic structures (morphology) and material characteristics (refractive indices) effect into a single 2-D scatter image which can be further analyzed by chemometric methods. The proposed method does not disrupt the colony structure while capturing the scatter pattern, thus can be further used in biochemical, molecular, immunological and mass-spectroscopic methods for confirmation of the cultures.

As the modeling of forward scattering pattern for the bacteria colony shows, the morphology and optical characteristics play major roles into formation of diffraction patterns. Previous studies have shown that availability of nutrients and agar concentration (Bae et al., 2011), and storage condition of the agar plate (Mialon et al., 2012) possibly affect the distribution/propagation of cells within a colony and the resulting diffraction pattern. Furthermore, previous studies (Bae et al., 2007, 2010; Kim et al., 2013) indicate that the forward scattering patterns produced by

<figure><img src="image_49.png" /></figure>

**FIGURE 6** | Theoretical analysis of speckle development based on the random phase shift ($\phi_s$) of scalar diffraction theory. Phase shift was modified as (A) 1/200, (B) 1/150, (C) 1/100, and (D) 1/80 and their simulated scatter patterns show similar trend with the experiment.

<figure><img src="image_50.png" /></figure>

**FIGURE 7** | Comparison of theoretical speckle development on diffraction pattern by surface roughness and that of experimental time resolved result for *B. cereus* and *B. thuringiensis*. For prediction, the

maximum amplitude of random function of surface roughness was adopted as (A) 1/200, 1/150, and 1/100 (B) 1/180, 1/130, and 1/100 of colony center height for fine, medium, and coarse surface roughness, respectively.
BARDOT are circular symmetric or concentric circle with some radial spokes for most of the microorganisms (*E. coli*, *Listeria*, *Salmonella*, and *Staphylococcus*) studied.

The outcome of this study would be helpful in analyzing the growth characteristic and understanding of the colonial behavior on solid agar surface using visual, physical, mathematical and empirical models. The results will also help in development of better scatter signatures based classification algorithm for microbial detection, especially the pathogens and spoilage microbes relevant to food safety, food quality and food security.

## CONCLUSION

Here we report a theoretical modeling and experimental verification of the swarming growth characteristics of *Bacillus* species using optical scattering technology. To quantify the growth characteristics, PCM and ICMA along with BARDOT were utilized. Scalar diffraction theory coupled with new random phase component provided good agreement with experimental speckle pattern formation. The results indicate that as bacteria grow, their average speckle size decrease and the number of smaller speckle increases due to the swarming growth characteristics of bacteria within the colony.

## ACKNOWLEDGMENT

This research was supported through a cooperative agreement with the Agricultural Research Service of the US Department of Agriculture project number 1935-42000-035 and the Center for Food Safety Engineering at Purdue University.

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**Conflict of Interest Statement:** Euiwon Bae and Arun K. Bhunia are inventors of "System and method for rapid detection and characterization of bacterial colonies using forward light scattering" (US Patent No. 7465560), designated BARDOT, described in this review. The authors declare that the research was conducted in

the absence of any commercial or financial relationships that could be construed
as a potential conflict of interest.

Received: 02 August 2014; paper pending published: 01 September 2014; accepted: 26 September 2014; published online: 14 October 2014.

Citation: Kim H, Singh AK, Bhunia AK and Bae E (2014) Laser-induced speckle scatter patterns in Bacillus colonies. *Front. Microbiol.* 5:537. doi: 10.3389/fmicb. 2014.00537

This article was submitted to Food Microbiology, a section of the journal Frontiers in Microbiology.

Copyright © 2014 Kim, Singh, Bhunia and Bae. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
<figure><img src="image_51.png" /></figure>

# Microcalorimetric study of the growth of *Streptococcus thermophilus* in renneted milk

Irina Stulova<fnref n="1" />,<fnref n="2" />*, Natalja Kabanova<fnref n="2" />, Tiina Kriščiunaite<fnref n="2" />, Kaarel Adamberg<fnref n="1" />,<fnref n="2" />, Tiiu-Maie Laht<fnref n="1" /> and Raivo Vilu<fnref n="1" />,<fnref n="2" />

**Edited by:**

Michael Gänzle, Alberta Veterinary
Research Institute, Canada

**Reviewed by:**

Louis Coroller, Université de Brest,
France

Laurent Guillier, Agence Nationale
de Sécurité Sanitaire de
l'Alimentation, de l'Environnement
et du Travail, France

*Correspondence:

Irina Stulova, Department of
Chemistry, Tallinn University of
Technology, Akadeemia Tee 15,
Tallinn 12618, Estonia

e-mail: irina.stulova@tftak.eu

The growth of *Streptococcus thermophilus* ST12 (ST12) in liquid milk, reconstituted from low-heat skim milk powder reconstituted skim milk (RSM) and in RSM with rennet addition (r-RSM) at 40°C was monitored by microcalorimetry. It was shown that the growth rate of bacteria decreased in renneted samples in comparison with liquid RSM starting from certain sizes of the colonies ("deviation moments"), which depended on the inoculation rates. The hydrolysis of lactose was delayed for about 1 h in the r-RSM in comparison with RSM but otherwise the metabolism of carbohydrates in the renneted and non-renneted milks was similar. The total free amino acids (TFAA) content by the end of fermentations was higher in r-RSM than in RSM presumably due to the enzymatic hydrolytic activity of rennet. The quantitatively dominating amino acids were remarkably different in the r-RSM and RSM indicating that the hydrolysis cascade of caseins and/or metabolism of amino acids by the bacteria functioned differently in the two cases. The data obtained showed potential of microcalorimetry to characterize quantitative differences of growth and metabolism of the bacteria in renneted and liquid samples of milk.

**Keywords:** *Streptococcus thermophilus*, growth and metabolism of the bacteria, milk, renneted milk, microcalorimetry

## INTRODUCTION

Studies of growth of LAB in the liquid milk (Favrot and Maubois, 1994, 1996; Letort et al., 2002) and solid cheese are complicated because traditional microbiological methods are not well suited for the enumeration of bacteria in opaque and especially in solid media. Peculiarities of colonial growth of bacteria in solid transparent matrices (agar, gelatine) have been described, however, in several papers (Brocklehurst et al., 1995; McKay et al., 1997; Malakar et al., 2002; Wilson et al., 2002; Kabanova et al., 2009, 2012; Velliou et al., 2013). Studies of growth of LAB in solid coagulated milk samples are clearly less numerous (Favrot and Maubois, 1996; Floury et al., 2010, 2013; Jeanson et al., 2011). One of the reasons of the noted scarcity is the lack of suitable methods for the studies.

The most noticeable advantage of the calorimetric method in comparison with the other mostly invasive techniques is the possibility to follow the bacterial growth in opaque and/or solid environments like various food matrices monitoring the heat evolution without destroying or perturbing samples studied (Lobete et al., in press). The calorimetric technique was first used in dairy research in seventies for milk grading (Berridge et al., 1974) and growth studies of pure LAB cultures under limiting substrate conditions (Fujita et al., 1978). The interactions of *Streptococcus thermophilus* and *Lactobacillus bulgaricus* were studied in milk using calorimetry in 1979 (Monk, 1979). Riva et al. (1997) investigated shelf life of fresh milk using isothermal calorimetry for continuous monitoring of

microbial growth. Gardea et al. (2002) compared heat evolution by bacteria with the results of traditional plate counts assessing microbiological quality of milk undergone different treatments. Wadsö and Galindo (2009) compared the thermal power-time profiles of the fermentation of milk at two incubation temperatures using two different buttermilk cultures. Kriščiunaite et al. (2011) applied microcalorimetry to study the influence of H₂O₂ on the growth of thermophilic starter bacteria in UHT milk.

A considerable methodical improvement in use of microcalorimetry was introduced in Kabanova et al. (2009) and Kabanova et al. (2012). Two major advancements introduced were as follows: (a) a method of serial dilution was developed, which means that inoculation rates of the bacteria in the samples were changed from 10⁰ to 10⁶ CFU mL⁻<fnref n="1" /> with the precisely ten-fold increment increment. This allows to study the growth of the populations and colonies of different numbers of cells, and (b) a number of additional samples prepared in parallel with the microcalorimetric samples are incubated at the same temperature, and they are used for the measurements of sugars, organic and amino acids etc. The data obtained from the study of these parallel samples is used together with the microcalorimetric data. The approach was applied for the characterization of peculiarities of the growth of *Streptococcus thermophilus* ST12 in differently pretreated milk samples (Stulova et al., 2011), and in milk reconstituted from non-irradiated and irradiated at 10 kGy milk powder (Stulova et al., 2013).

<footnote n="1">¹ Department of Chemistry, Tallinn University of Technology, Tallinn, Estonia</footnote>

<footnote n="2">² Competence Centre of Food and Fermentation Technologies, Tallinn, Estonia</footnote>
The approach developed was used also in this paper. The specific aim of the present study was to investigate the growth of *Streptococcus thermophilus* ST12 in renneted reconstituted skim milk (RSM) (in milk gel) and compare the peculiarities of the growth with those in liquid milk samples.

# MATERIALS AND METHODS

## PREPARATION OF RECONSTITUTED SKIM MILK (RSM)

Low heat skim milk powder (LHSMP) was obtained from Valio Ltd. (Helsinki, Finland). LHSMP was suspended in distilled water to yield a final concentration of 10% (w v⁻¹) milk solids, with addition of CaCl₂ in final concentration of 10 mM. The samples with LHSMP were mixed thoroughly for 1 h at room temperature, heated at 90°C for 30 min and cooled to 30°C immediately before the experiments and used as liquid RSM samples.

## BACTERIAL CULTURES AND PREPARATION OF INOCULUM

The strain of *Streptococcus thermophilus* ST12 (further *St. thermophilus* ST12) was kindly provided by Chr. Hansen (Hørsholm, Denmark). Deep-frozen cultures of *St. thermophilus* ST12 were thawed and pre-grown on M17 agar (LAB M, Lancashire, UK) for 24 h at 40°C. One colony from a M17 agar plate was inoculated into 10 mL of RSM and left at 40°C until coagulation (16 h). One per cent of the culture was used for inoculation of the 10 mL of RSM samples, left until coagulation (12 h) and further used for inoculation of the microcalorimetry samples. The number of viable cells in the inocula was determined by plating on M17 agar followed by incubation for 72 h at 40°C to be (1.15 ± 0.19) × 10⁹ CFU mL⁻¹.

## PREPARATION OF SAMPLES FOR GROWTH EXPERIMENTS

RSM as growth media were prepared in 50 mL Erlenmeyer flasks. Milk samples were inoculated with 1% (v v⁻¹) of inoculum and stirred thoroughly. The concentrations of inocula in milk samples for microcalorimetry were varied from 10¹ to 10⁶ CFU mL⁻¹ with the 10-fold increment. Milk gels were prepared by addition of 100 µl of a 10 g L⁻¹ chymosin (CHY-MAX Powder Extra, Hørsholm, Denmark) aqueous solution to 10 mL of RSM. The enzyme (chymosin) was added immediately after the inoculation. Milk samples for the microcalorimetric experiments, 2 mL samples of RSM and r-RSM were transferred into the autoclaved microcalorimetric ampoules, inserted into microcalorimeter and maintained at 40°C. Monitoring of the growth of microorganisms was started in microcalorimeter about 1 h after the inoculation, after the temperature of the samples was in perfect equilibrium with the set temperature of the thermostat (40°C). Taking into account that the coagulation of the liquid milk samples was over in 40 min after the inoculation, the growth of the bacteria in case of the r-RSM samples was studied in practice in solid gel matrices from the beginning of the microcalorimetric monitoring.

The remaining after the preparing of the microcalorimetric samples inoculated and renneted milks were divided into 1 mL aliquots and incubated at 40°C in parallel with the samples in microcalorimeter and used for the determination of pH and the concentrations of carbohydrates, lactic acid and amino acids at appropriate time points, determined based on the peculiarities of power-time curves.

# MICROCALORIMETRY

A 24 channel isothermal batch microcalorimeter TAM III Thermal Activity Monitor (TA Instruments, New Castle, DE, USA) was used for the monitoring of the growth of thermophilic starter *St. thermophilus* ST12. Data acquisition and analysis was carried out using TAM Assistant Program (v 0.9.1012.40, SciTech Software AB, Thermometric AB).

## Analysis of calorimetric power-time curves and calculation of growth characteristics

The power-time curves, the output of microcalorimeter describe the heat evolution during the processes studied. A calorimetric

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

**FIGURE 1** | The explanation of the processing of calorimetric power-time curves. (A) The division of calorimetric power-time curves into five phases; (B) Determination of the maximal calorimetric growth rates during the first (μmax1, Wh⁻¹) and the second exponential phase (μmax2, Wh⁻¹), and the duration of the lag-phase (λ, h); (C) Determination of the heat evolution during combined exponential phases (Qexp), and during the growth of the cultures (Qtor).
power-time curve of the growth of *St. thermophilus* ST12 in milk measured by us (Stulova et al., 2013) was divided into five different growth phases as shown in **Figure 1A**.

The pregrowth phase after the inoculation in the beginning of the graph is characterized as the lag-phase (I) during which bacterial cells adapt to the new environment. The length of the lag-phase was determined as shown on **Figure 1B**, where the power-time curve is presented in semilog scale. It is important to note that in measuring of the length of the lag-phase on the basis of calorimetric curves the sensitivity of the instrument TAM III (7 × 10⁻⁴ J/0.5 µW) as well as the time during which heat produced by the cells exceeds the level of sensitivity of the instrument should be taken into account (see Kabanova et al., 2009, 2012). The second phase of the power-time curves following the lag-phase corresponds to the first exponential growth phase (II) during which the bacteria grow at the maximal calorimetric growth rate μmax possible in the experimental conditions studied. During the intermediate non-exponential growth phase (III) lactic acid bacteria presumably synthesize proteinases and metabolism of this bacteria switch from growth on free amino acids (FAA) present in milk to growth on amino acids and peptides released by the enzymes synthesized (Letort et al., 2002). The ends of the first as well as the second exponential phases (IV) of bacterial growth were defined by the corresponding peaks of the power-time curves. In case of analysis of certain metabolic characteristics of the bacteria during the growth (consumption patterns of amino acids etc.) the two exponential phases were considered as one—see below. After the second exponential growth phase the deceleration phase (V) which combines the stationary phase of the growth together with the deceleration of the metabolism of lactic acid bacteria growing in milk was taking place.

Maximum specific growth rate (μmax), which could be considered also as maximal calorimetric growth rate during the first and second exponential growth phases was measured as shown in **Figure 1B**. Taking into account that during the exponential growth phase the relationship between biomass concentration

(X) and specific growth rate (μ) may be described by the first order kinetics:

$$ \frac{dX}{dt} = \mu X \qquad (1) $$

and assuming that the rate of biomass formation (dX/dt) was proportional to the rate of heat production (dQ/dt), the maximum specific growth rate (μmax) was calculated from the power-time curves as a slope of ln dQ/dt over time (t), as shown in Maskow and Babel (2003):

$$ \ln \frac{dQ}{dt_t} = \frac{dQ}{dt_0} + \mu_{max}t \qquad (2) $$

The heat produced during the exponential growth phase Qexp (area between the power-time curve and the vertical line that goes through the major peak of the power-time curve (Pmax value) and the baseline—see **Figure 1C** and the total heat produced during the whole process of growth Qtot (area between the power-time curve and baseline) were determined using the TAM Assistant program (v 0.9.1012.40, SciTech Software AB, Thermometric AB, Järfälla, Sweden) and Microsoft Excel. The recording of the heat evolution was taking place with the frequency 1 s⁻¹.

<figure><img src="image_55.png" /><figcaption>**FIGURE 2 |** Calorimetric power-time curves of the growth of *Streptococcus thermophilus* ST12 and pH changes (circles) in liquid (dashed) and renneted (black) reconstituted skim milk at inoculation rate 10⁵ CFU mL⁻¹. Arrow is indicating the deviation moment where the growth rate in r-RSM is decreasing in comparison with the RSM.</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>**FIGURE 3 |** (A) Calorimetric power-time curves describing growth of *Streptococcus thermophilus* ST12 in liquid (dashed line) and renneted (bold line) reconstituted skim milk at different inoculation rates (CFU mL⁻¹): (a) 10⁶; (b) 10⁵; (c) 10⁴; (d) 10³; (e) 10²; (f) 10¹. The arrow and the red circles mark the "deviation moments." (B) pH at the end of fermentation (at 22 h) at all inoculation rates (10⁶–10¹ CFU mL⁻¹) in RSM (white circles) and in r-RSM (black circles). Initial pH = 6.47 was the same in the samples.</figcaption></figure>