Advances in Microbiology, 2013, 3, 403-411
http://dx.doi.org/10.4236/aim.2013.35055 Published Online September 2013 (http://www.scirp.org/journal/aim)
Quantification of Zoonotic Bacterial Pathogens within
Commercial Poultry Processing Water Samples
Using Droplet Digital PCR
Michael J. Rothrock Jr.1*, Kelli L. Hiett2, Brian H. Kiepper3, Kim Ingram1, Arthur Hinton1
1Poultry Processing and Swine Physiology Research Unit, USDA-Agricultural Research Service, Athens, Georgia
2Poultry Microbiological Safety Research Unit, USDA-Agricultural Research Service, Athens, Georgia
3Department of Poultry Science, University of Georgia, Athens, Georgia
Received June 21, 2013; revised July 20, 2013; accepted July 31, 2013
Copyright © 2013 Michael J. Rothrock Jr. et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
Raw poultry and poultry products are a significant source of zoonotic bacterial pathogen transmission; thus the sensitive
detection of major zoonotic pathogens (Salmonella spp., Campylobacter jejuni, and Listeria monocytogenes) is a vital
food safety issue. Recently, third generation PCR technology, known as droplet digital PCR (ddPCR) has been devel-
oped to be more accurate and sensitive to detect genetic targets than current quantification methods, but this technology
has not been tested within an industrial setting. There is an on-going study within our laboratory is investigating the effects
of sampling times and sampling methods on the cultural and molecular (via qPCR) quantification of dominant zoonotic
pathogens within a poultry processing facility. This presents a unique opportunity to compare the quantification resulted
from this emerging, third generation technology to traditional quantification methods currently employed by the poultry
industry. The results show that ddPCR detected pathogen-specific genes from more pathogen:sampling time combina-
tions than either the qPCR or culturing methods from the final scalder and chiller tanks at three stages of processing
(Start, Mid, and End). In fact, both ddPCR and qPCR substantially outperformed culture methods commonly used in
poultry processing food safety-related studies, with Salmonella recovered only from the Mid and End sampling times
from the scalder tank. While neither C. jejuni nor L. monocytogenes were recovered culturally, ddPCR was able to detect
their respective genes commonly throughout the processing day in both the scalder and chiller water samples. Addition-
ally, the use of unfiltered processing water provided significantly greater detection of bacterial and pathogen-specific gene
abundances than did an analysis of larger volumes of filtered water. Considering the ddPCR-derived concentrations of
the bacterial pathogens were consistent with what was previously found culturally in commercial poultry processing
operations, ddPCR represented a significant advancement in poultry processing zoonotic pathogen quantification.
Keywords: ddPCR; Poultry Processing; Zoonotic Pathogens; qPCR
1. Introduction
The handling and consumption of poultry or poultry
products have been repeatedly associated with the trans-
mission of bacterial pathogens to the human population.
Incidences of zoonoses originating from poultry products
and processing environments have been reported for
Salmonella spp. [1-3], Campylobacter jejuni [4,5], and
Listeria monocytogenes [6,7]. Considering the high en-
vironmental, economic, and public health costs of these
zoonoses, a comprehensive understanding of the poultry
production and processing parameters that allow for the
survival/transmission of these bacterial pathogens is es-
sential.
To assess pathogen survival and transmission, accurate,
sensitive, and highly specific quantification methods are
needed. Historically, the quantification of foodborne pa-
thogens in food production systems was based either on
cultures or quantitative PCR (qPCR). While these me-
thods have been used successfully, both come with cave-
ats; either being time consuming and too ineffectively
selective (culture-based) or dependent upon the proper
standards and assay efficiencies (qPCR-based). To cir-
cumvent these issues, third generation PCR technology,
known as droplet digital PCR (ddPCR) was introduced
*Corresponding author.
C
opyright © 2013 SciRes. AiM

M. J. ROTHROCK JR. ET AL.
404
to provide absolute quantification of target genes and the
pathogens to possess those genes [8,9]. The advantages
of ddPCR over qPCR-based assays are threefold: ddPCR
is based on endpoint PCR (efficiency of primer/probe
annealing is minimized); ddPCR does not require the use
of standards for accurate quantification; and most impor-
tantly, ddPCR is a high throughput (15,000 - 20,000 PCR
reactions per well) assay.
While ddPCR represents the newest quantification
technology, only relatively pure bacterial or cell culture
samples have been analyzed [9,10]. To our knowledge,
this emerging third generation PCR technology has not
been applied to complex environmental samples contain-
ing mixed microbial populations, as well as organic/in-
organic particulates/contaminants. Considering zoonotic
foodborne pathogens can be detected in poultry carcasses
but many times not within the limited sample volumes
from the high capacity processing water tanks, the goal
of this study is to determine the utility of ddPCR for the
detection of Salmonella spp., C. jejuni, and L. monocy-
togenes from the commercial poultry processing tank
waters, and compare these results to common pathogen
detection assays (cultural, qPCR). Water from the final
scalder and chiller tanks in a commercial poultry proc-
essing facility was sampled at three time points during
the processing day (prior to the introduction of the first
carcasses, halfway through the day, and after the last
carcasses leave the tank) over three consecutive days.
Additionally, the effect of water sampling method (raw
water versus filtered samples) on-molecular-based detec-
tion efficacy was also determined.
2. Materials and Methods
2.1. Sample Collection
Processing water samples were collected from a com-
mercial broiler processing facility. Small (~4.40 pounds)
Cobb® broilers were processed at an average line speed
of 364 birds per minute for 18 hr each day. For scalder
water samples, 3 sterile 1-L plastic Nalgene® bottles
(Fisher Scientific, Pittsburgh, PA) were used to collect 3
L of water from ~5 cm below the surface at the turn-
around (midpoint) of the final scalder tank of a triple
tank counterflow system. For chiller water samples, ster-
ile 1-L Tri-pour beakers (Fisher Sci.) were placed in the
basket end of a metal pole to retrieve 4 L of water from
the posterior end of the counterflow chiller tank. In total,
3 sterile 1-L plastic Nalgene® bottles and 1 sterile 1-L
glass Mason jar (for Oil and Grease content analysis; see
below) were used to collect these samples. Samples were
collected from these two tanks at three times during the
processing shift: 1) prior to the first birds entering the
cleaned and disinfected tanks (Start); 2) after 9 hours
(~half of the processing day) of processing (Mid); and, 3)
after the last birds left the tank and the waters were con-
sidered “dirtiest” (End). Samples were taken from these
three time points on three successive days, and were
placed on ice for transport back to the laboratory for fur-
ther sample processing and preparation.
2.2. Cultural Procedures
Nalgene® bottles were vigorously shaken to homogenize
samples, and 2 - 100 mL subsamples were placed into
sterile 250-mL Corning Media Bottles (Fisher Sci.). The
enumeration and isolation protocols were performed as
previously described for each foodborne pathogen of
interest: most probable number (MPN) analysis for Sal-
monella spp. (Cason and Hinton, 2006); direct plate
counting for C. jejuni using the CEFEX [11] and Campy-
Check (Lastovica and Le Roux, 2001) methods; MPN
and direct plate counting for L. monocytogenes (Donnelly
et al., 1992).
2.3. DNA Extraction and qPCR
Molecular quantification was applied to raw water taken
directly from the initial water samples and from the fil-
trate recovered from the filtration of the processing water
samples. For the raw samples, DNA was extracted from
two 0.5 mL aliquots using the FastDNA Spin Kit for Fe-
ces according to manufacturer’s specifications (MP Bio,
Solon, OH). For the filtrate samples, sterile pre-mois-
tened (in 1X PBS) cheesecloth was used to initially filter
100 mL of the homogenized processing water samples
into a fresh 1-L Tri-pour beaker. The cheesecloth was
rinsed in 20 mL of 1X PBS and the resultant filtrate was
divided into 3 - 40 mL subsamples and filtered simulta-
neously through 3 separate 0.8 μm Nalgene filter units
(Fisher Sci.). The three filtrate samples were combined in
1 sterile 250-mL centrifuge bottle (Beckman Coulter),
and the cells were pelleted at 10000 rpm for 20 min at
4˚C. The pellet was re-suspended in 2 mL of 1X PBS and
DNA was extracted from four 0.5 mL aliquots using the
FastDNA Spin Kit for Feces (MPBio). For both the raw
and filtrate samples, all individual DNA extracts were
dry-pelleted using a VacufugeTM Plus (Eppendorf, Haup-
page NY), and all extracts coming from a single sample
were combined in 100 μL sterile molecular grade water.
The DNA concentration in each sample was determined
spectrophotometrically using the Take3® plate with the
Synergy H4 multimode plate reader (BioTek, Winooski,
VT).
All DNA extracts were diluted in sterile molecular
biology grade water (5 Prime, Inc., Gaithersburg, MD) so
that 10 - 15 ng of genomic extract DNA was added to
each qPCR reaction. All qPCRs were performed on the
RealPlex 4S system (Eppendorf) in a total volume of 25
μL using the PerfeCta® qPCR Supermix (Quanta Bio-

M. J. ROTHROCK JR. ET AL.
405
sciences, Gaithersburg, MD) following the previously
published thermocycling conditions and final primer/
probe concentrations (Table 1).
2.4. ddPCR DNA Amplification and
Quantification
Droplet digital PCR was performed as previously de-
scribed [10] using the Bio-Rad QX100 system (Bio-Rad,
Hercules, CA). In short, 1:10 dilutions of the DNA ex-
tracts were used as templates for general or pathogen-
specific PCR assays using primer/probe sets listed in
Table 1. TaqMan-based PCR reaction mixtures (com-
posed of 2X ddPCR MasterMix (Bio-Rad), 900 nM
primers, 250 nM probe, 10 - 15 ng template DNA in a
final volume of 20 μL) were mixed with droplet genera-
tion oil (Bio-Rad) and loaded into an 8-channel dispos-
able droplet generator cartridge (Bio-Rad). The cartridge
was placed into the droplet generator (Bio-Rad) to create
the ~20,000 droplets, which were collected from the
droplet well of the cartridge and manually transferred to
a 96-well PCR plate. The plate, after heat-sealing, was
placed on a conventional thermal cycler (S1000; Bio-Rad)
and amplified to end-point (40 cycles for all reactions).
Upon completion, the 96-well plate was transferred to the
droplet reader (Bio-Rad), and the droplets were auto-
matically scanned from each well at a rate of ~32
wells/hr. Analysis of the ddPCR data was performed with
the QuantaSoft analysis software package (Bio-Rad).
2.5. Processing Water Analyses
Poultry processing water samples were analyzed using
the appropriate Standard Method [12] for COD (chemical
oxygen demand method 5220D), O & G (oil and grease
method 5520D), TS (total solids method 2540B), TSS
(total suspended solids method 2540D), and TKN (total
Kjeldahl nitrogen method 4500-Norg C and 4500-NH3C).
The concentration (mg/L) results from the final scalder
and chiller tank processing water samples from all time
points throughout the study are shown in Tables 2 and 3,
respectively.
2.6. Statistical Analyses
Prior to analysis, all quantification (cultural and mole-
cular) data was log10-transformed to ensure the data was
normally distributed. Prism 6.0b (GraphPad Software
Inc., La Jolla, CA) was used to perform all regres-
sion analyses, means comparisons (t-tests), and ANOVAs
on the microbiological data. For one-way ANOVAs,
Tukey’s post-tests were used to determine significant
differences between pair-wise combinations. An alpha
level of 0.05 was used to determine significance in all
analyses.
Table 1. ddPCR and qPCR primers and probe inform ation for this study.
Target Oligo
Final PCR mastermix
concentration (nM) Tm (˚C) Reference
Group Gene Name Sequence (5’-3’) qPCR ddPCR qPCR ddPCR
1055F ATG GCT GTC GTC AGC T 600 500 58 60
1392R ACG GGC GGT GTG TAC 600 500 All Bacteria 16S
16STaq1115-BHQ FAM-CAA CGA GCG-ZEN-CAA CCC-3IABkFQ200 250
(Harms
et al. 2003)
[28]
Sal TTR-6-F CTC ACC AGG AGA TTA CAA CAT GG 400 500 65 60
Sal TTR-4-R AGC TCA GAC CAA AAG TGA CCA TC 400 500
Salmonella spp. ttr
Sal TTR-5 ZEN FAM-CAC CGA CGG-ZEN-CGA GAC CGA CTT T
-3IABkFQ 250 250
(Malorny
et al. 2004)
[29]
hipO-Cj-F TCC AAA ATC CTC ACT TGC CAT T 500 500 60 60
hipO-Cj-R TGC ACC AGT GAC TAT GAA TAA CGA 500 500
Campylobacter
jejuni hipO
hipO-Cj-P FAM-TTG CAA CCT CAC TAG CAA AAT CCA
CAG CT-BHQ-1 250 250
(He et al.
2010) [30]
hlyA-LisM-F ACT GAA GCA AAG GAT GCA TCT G 600 500 60 60
hlyA-LisM-R TTT TCG ATT GGC GTC TTA GGA 600 500
Listeria
monocytogenes hlyA
hlyA-LisM-P FAM-CAC CAC CAG CAT CTC CGC CTG C
-BHQ-1 200 250
(Suo et al.
2010) [31]

M. J. ROTHROCK JR. ET AL.
406
Table 2. Final scalder tank processing wa ter analyses at 3 times (Start, Mid, and End) during the processing shift 1.
COD (mg/L) BOD (mg/L)2 TS (mg/L) TSS (mg/L)
Start Mid End p-value Start MidEndp-value Start MidEndp-value Start Mid Endp-value
Day 1 0 1723 2546 0 10141498 17714432176 2 775 1150
Day 2 7 1364 1375 4 802809 89 10131000 17 640 640
Day 3 3 2457 1833 2 14451078 11619461559 4 1090 780
Mean3 3b 1848a 1918a 0.0039 2b 1087a1128a0.0039 127b1467a1578a0.0113 8b 835a 857a0.0033
1COD = Chemical Oxygen Demand, BOD = Biological Oxygen Demand, TS = Total Solids, TSS = Total Suspended Solids; 2BOD was estimated from calcu-
lated COD values using a 1.7:1.0 COD:BOD ratio that is consistent for the commercial processing plant from which these samples were collected (according to
plant’s environmental quality manager); 3Superscript letters (a, b, c) indicate group mean separation from one-way ANOVA, based on the Tukey’s post-test.
Table 3. Chiller tank processing water analyses at 3 times (Start, Mid, and End) during the pr ocessing shift1.
COD (mg/L) BOD (mg/L)2 TS (mg/L)
Start Mid End p-value StartMid End p-value StartMid End p-value
Day1 0 1478 2609 0 869 1535 195 646 1440
Day2 82 1207 2144 48 710 1261 133 1294 1949
Day3 60 1346 2367 35 792 1392 348 1433 2176
Mean3 47c 1344b 2373a <0.0001 28b 790a 1396a <0.0001 225b 1124a 1855a 0.0029
TSS (mg/L) TKN (mg/L) Oil & Grease (mg/L)1
Start Mid End p-value StartMid End p-value StartMid End p-value
Day1 2 350 550 0 114 97.2 0 76.2 119
Day2 18 275 595 1.0181 114 14 66.4 125
Day3 16 268 478 1.2791.8 137 7 96.2 192
Mean3 12c 298b 541a 0.0001 0.76b95.6a 116.1a 0.0002 7.0c 79.6b 145.3a 0.0016
1COD = Chemical Oxygen Demand, BOD = Biological Oxygen Demand, TS = Total Solids, TSS = Total Suspended Solids, TKN = Total Kjeldahl Nitrogen;
2BOD was estimated from calculated COD values using a 1.7:1.0 COD:BOD ratio that is consistent for the commercial processing plant from which these
samples were collected (according to plant’s environmental quality manager); 3Superscript letters (a, b, c) indicate group mean separation from one-way
ANOVA, based on the Tukey’s post-test.
3. Results and Discussion
3.1. Commercial Poultry Processing Water
Analyses
All processing water characteristics significantly in-
creased after the beginning of the processing day for the
final scalder (Table 2) and chiller (Table 3) tanks. This
was expected since a variety of organics/particulates are
introduced into these water tanks throughout the proc-
essing day from the carcasses. All measured final scalder
water samples reached a plateau value by the Mid sam-
pling time that did not significantly change by the End
sampling (Table 2). Conversely, only 2 of the tested va-
riables in the chiller tank (TS and TKN) reached this
plateau, with all other variables significantly increasing
throughout the sampling day (Ta ble 3). These values are
consistent with previous scalder and chiller tank assess-
ments from within commercial processing facilities [13,
14], indicating that this study was run under normal in-
dustry conditions.
3.2. Comparison of Processing Water Sampling
Techniques
Considering commercial poultry processing waters con-
tain a variety of organic particulates (e.g. blood, feathers,
oils/fats), two different water sampling methods were
assessed for molecular analyses: 1) analysis of 1 mL of
raw water sample, or 2) filtering 100 mL of processing
water through a 0.8 μM filter (to remove particulates)
and analyzing that cells in the filtrate. When looking at
16S rRNA gene copies (a molecular estimate of total
bacteria) in these processing water samples, significantly
higher total bacterial gene copies were recovered from
both scalder (Figure 1(a)) and chiller (Figure 1(b)) wa-
ters when using the 1 mL raw samples for both ddPCR
(closed bars) and qPCR (open bars) as compared to the
100 mL filtrate samples. Additionally, 16S rRNA gene

M. J. ROTHROCK JR. ET AL. 407
0
2
4
6
8
10
log
10
16S rDNA copie s
mL
-1
scalder water
S M ES MES M ES M E
A
ccc
ccc
b
b
aa
aa
(a)
0
2
4
6
8
log
10
16S rDNA copies
mL
-1
chiller water
qPCR Raw qPCR Filtered
ddPCR RawddPCR Filtered
S M ES M ES M ES M E
bbb
aaa
bbb
aaa
B
(b)
Figure 1. Comparison of total bacteria recovered molecu-
larly (based on 16S rDNA gene copies) between sampling 1
mL of raw processing water or 100 mL of filtrate from the
final scalder (a) and chiller (b) processing water samples.
Samples were taken at three time points during the proc-
essing day (S = Start, M = Mid, E = End) and pathogen
concentrations were log10-transformed. Molecular quanti-
fication was performed using ddPCR (black) and qPCR
(light gray) on both raw samples (solid bars) and filtrate
samples (open bars). Bars represent the mean value for
three consecutive sampling days, and the error bars repre-
sent the standard deviation, while the letters above the bars
represent groups with significantly different mean pathogen
concentrations (according to one-way ANOVA using Tukey’s
post-test).
copies significantly increased in the Mid and End sam-
pling times in the scalder water for both the ddPCR and
qPCR methods when using the 1 mL raw sample (Figure
1(a)). This gene abundance change within the scalder
water was not observed in the 100 mL filtrate samples.
It has been shown that scald water, although continu-
ally recharged with fresh water, can build up a microbi-
ota associated with carcass-associated organic contami-
nants (e.g. feathers, fecal material) [15], and this bacte-
rial load can become intimately associated with this or-
ganic material. The removal of this bacterial-rich organic
material during filtration could explain the significant
difference in the bacterial populations between the fil-
trate and raw samples at the Mid and End sampling times
(Figure 1). As observed in the raw scalder water samples,
carcasses sampled from the middle to end of commercial
poultry processing runs have shown significantly greater
bacterial contamination [16,17], thus supporting the use
of this non-filtration based sampling technique for these
processing water samples. While the qPCR data showed
a much greater increase (~4 log) as compared to the
ddPCR method (~1 log), the dynamic range of the qPCR
method (up to ~9 log) is much higher than that of ddPCR
(~6 log) [8-10]. Therefore, more dilute DNA extracts
need to be used for highly concentrated processing water
samples (>107 cells·mL−1) when using ddPCR quantifica-
tion.
In terms of zoonotic bacterial pathogens, higher gene
abundances were consistently detected from the 1 mL
raw samples as compared to the 100 mL filtrate samples
using both ddPCR and qPCR (Table 4), with raw sample
values being significantly higher in 33% of the possible
scalder water combinations for both ddPCR and qPCR.
High levels of organic material in scalder tanks, like
those observed during the Mid and End sampling times
(Table 2), have allowed for the persistence and cross
contamination of Salmonella and other bacterial patho-
gens during the scalding process [1,18,19]. The removal
of this organic material via filtration, and the bacteria
associated with this material, could explain the signifi-
cantly higher bacterial pathogen detection within the raw
scalder samples. In only four possible pathogen:sampling
time combinations for the chiller tank water samples
were the 100 mL filtrate values higher than the 1 mL raw
water samples, but none were significantly higher. Con-
sidering this demonstrated enhanced detection for both
total bacteria and the zoonotic bacterial pathogens, all
results discussed below represent analyses of the 1 mL
raw water samples.
3.3. Bacterial Pathogen Detection in Commercial
Poultry Processing Water Tanks
In the final scalder tank, detectable levels of pathogen-
specific genes were found using ddPCR during all three
sampling times on at least one of the sampling days
(Figure 2), with most being detected at least twice. Only
for Salmonella spp. did the other quantification methods
perform as well as ddPCR, although the cultural numbers
were significantly lower than those found by both the
ddPCR and qPCR methods (Figure 2(a)). The samples
(prior to any carcasses being introduced into the tanks)
may be due to the ability of bacterial pathogens to sur-
vive these disinfection procedures. Previous research has
demonstrated that C. jejuni can survive on these tank
surfaces after cleaning and disinfection [20].
While the use of qPCR produced statistically similar
values to the ddPCR method for the scalder tank water
samples (Figure 2), there were three instances (C. je-
juni-Start; L. monocytogenes-Start and End) where only
ddPCR detected pathogen-specific genes. Cultural quan-
tification only detected Salmonella spp. in the scalder

M. J. ROTHROCK JR. ET AL.
408
Table 4. Comparison of 1 mL raw and 100 mL filtrate processing water-sampling techniques on recovered ddPCR- and
qPCR-derived log10-transformed zoonotic pathogen concentrations1.
ddPCR qPCR
Final Scalder Tank Water Chiller Tank Water Final Scalder Tank Water Chiller Tank Water
Target2 Sspp Cj Lm Sspp Cj Lm Sspp Cj Lm Sspp Cj Lm
Raw 0.924 1.638 0.808 0.0000.8240.000 0.866 0.000 0.000 0.632 0.3940.373
Filtrate 0.261 0.000 0.000 0.6360.0000.000 0.002 0.000 0.000 0.158 0.0000.085 Start
p-value3 0.4928 0.0452 0.5135 0.34690.3366>0.9990.0216 >0.999>0.999 0.4658 0.12850.4722
Raw 2.032 2.731 0.830 0.0000.8090.823 2.400 2.639 0.688 0.038 0.0000.689
Filtrate 0.317 0.147 0.000 0.0000.3011.508 0.143 0.000 0.000 0.000 0.1380.000Mid
p-value 0.0222 0.00970.5018 >0.9990.5487 0.5461<0.00010.00200.1136 0.9528 0.57730.1014
Raw 1.092 1.165 0.846 1.0800.8101.660 0.675 1.148 0.000 0.883 0.0000.428
Filtrate 0.157 0.000 1.503 0.0000.1420.000 0.072 0.000 0.118 0.000 0.0190.000 End
p-value 0.3379 0.19100.5937 0.12250.4328 0.15780.0905 0.11410.7738 0.1857 0.93890.2922
1Values represent log10-transformed means of zoonotic pathogen-specific gene abundances from three consecutive sampling days within the commercial poultry
processing plant, and the p-values result from one-tailed t-tests; 2Pathogen abbreviations are: Sspp = Salmonella spp.; Cj = Campylobacter jejuni; Lm = Listeria
monocytogenes; 3Bolded values indicate significant differences between the raw and filtered samples at the α = 0.05 level.
log
10
Salmonella (copies or MPN)
mL
-1
scalder water
Start
Mid End
-2
0
2
4
6
Cul t ural
qPCR
ddPCR
A
Start
Mid End
0
1
2
3
4
5
l
og
10
C
.
j
e
j
uni (
c
op
i
es or
M
P
N)
mL
-1
scald er water
Cultu
r
al
qPC R
dd
P
C
R
B
S
tart Mid End
0
1
2
3
4
log
10
L. mono
c
yto
g
ene
s
(
c
opies or
M
P
N
)
mL
-1
scalder water
Cultural
qPCR
ddPC R
C
(a) (b) (c)
Figure 2. Quantification of zoonotic pathogens found within the final scalder tank of a commer cial poultry proce ssing faci lity.
Samples were taken at three time points during the processing day (Star t, Mid, End) and log10-transformed pathogen quanti-
fications were performed using ddPCR (checkered bars), qPCR (solid bars), and cultural methods (open bars). Bars repre-
sent the mean value for three consecutive sampling days, and the error bars represent the standard deviation. Zoonotic
pathogens tested for were (a) Salmonella spp.; (b) C. jejuni; and (c) L. monocytogenes.
cial poultry processing plant [7]. The consistency of
these ddPCR results with cultural results from previous
commercial processing investigations, in addition to the
significantly higher detection rate compared to cultural
data from this current study, indicates the efficacy of
ddPCR for bacterial pathogen detection within these
samples.
tank in two of the three sampling days, failing to detect
and recover any of the other pathogens during this study.
While these pathogens have been isolated from commer-
cial scalder tanks previously [1,3,21,22], their recovery
was significantly higher in the first two tanks of similar
commercial triple scalder tank systems [19,23]; whereas
the final scalder tank was sampled in the current study.
While there are obvious arguments about the use of
cultural versus molecular detection techniques (e.g. iso-
lating a living organism versus the DNA signature of
one), the ddPCR values for Salmonella were consistent
with those found culturally in previous commercial scal-
der tank studies [19,23]. Additionally, the ddPCR-based
prevalence of C. jejuni and L. monocytogenes within the
scalder water samples from this study were consistent
with one of the few other studies to molecularly detect
and culturally confirm these pathogens within a commer-
In the chiller water samples, Salmonella spp., C. jejuni,
and L. monocytogenes specific genes were detected in the
End samples using ddPCR, and all but Salmonella spp.
specific genes were present in the Mid samples (Figure
3). As was observed with the scalder water samples,
ddPCR was able to detect pathogen-specific genes in
more possible pathogen:sampling time combinations than
either comparative quantification method. Pathogen de-
tection in these chiller water samples was more sporadic
than in the scalder tank samples, with ddPCR detecting

M. J. ROTHROCK JR. ET AL. 409
Start
Mid End
0
2
4
6
l
o
g
10
Sa
l
mone
l
l
a (cop
i
es or MPN)
mL
-1
chiller water
Cultural
qPCR
ddPCR
A
(a)
S
tart Mid End
0
1
2
3
4
5
log
10
C. jejuni (copies or CFU)
mL
-1
chiller wate r
Cultural
qPCR
ddPCR
B
(b)
log
10
L. monocytogenes (copies or MPN)
mL
-1
chiller water
Start
Mid End
0
1
2
3
4
Cultural
qPCR
ddPCR
C
(c)
Figure 3. Quantification of zoonotic pathoge ns found within
the chiller tank of a commercial poultry processing facility.
Samples were taken at three time points during the proc-
essing day (Start, Mid, End) and log10-transformed patho-
gen quantifications were performed using ddPCR (check-
ered bars) and qPCR (solid bars). None of the pathogens
were recovered culturally from the chiller water Bars rep-
resent the mean value for three consecutive sampling days,
and the error bars represent the standard deviation. Zoono-
tic pathogens tested for were (a) Salmonella spp., (b) C.
jejuni, and (c) L. monocytogenes.
these pathogens on only one of the three sampling days
in most samples. Considering one of the main objectives
is disinfection via chlorination in the chiller tank to re-
duce bacterial loads [2,3,24], the lower and less consis-
tent detection of pathogen-specific genes in this tank
versus the scalder tank samples was expected. In com-
parison to the qPCR-based method, ddPCR more consis-
tently detected C. jejuni (Figure 3(b)) whereas qPCR
more consistently detected Salmonella spp. (Figure 3( a)).
The presence of each of these bacterial pathogens at
some point during the processing run was surprising, but
high organic matter loads (Table 3; COD, BOD) is
known to reduce the bactericidal efficacy of chlorine
within chiller tanks [1,25]
None of the bacterial pathogens were detected cultur-
ally from any of the chiller water samples. While Salmo-
nella, C. jejuni, and L. monocytogenes have been previ-
ously recovered culturally from commercial chiller tanks
[1,2,7,21,26], chiller samples for this current study were
retrieved from the distal end of the counterflow chiller
tank (carcasses leave the tank and clean water enters the
tank). This is important to note because ria, specifically
Salmonella, cannot be detected using traditional culturing
methods at the endpoint of the chiller tank, even if found
in water samples from the proximal end of the tank [27].
It is also possible that the chlorine contained within these
chiller samples confounded the cultural recovery, since
its bactericidal effects potentially extended onto the se-
lective media plates within the aliquot that was incubated.
Considering the DNA extraction process claims to re-
move contaminants such as chlorine, and ddPCR was the
most robust pathogen quantification technique in the
chiller water samples, ddPCR represents a powerful new
tool effectively detect and quantify zoonotic pathogens
within chiller water.
4. Conclusion
These findings represent the first report of the use of
third generation PCR technology to detect zoonotic bac-
terial pathogen signatures in environmental samples
along the poultry production continuum. While more
validation of this ddPCR method needs to be performed
on more poultry-related environmental sample types, the
results of this study highlight the advantages of ddPCR
and the potential for the integration of this highly sensi-
tive and specific method into future poultry food safety
research. Given the much higher throughput and absolute
quantification of ddPCR while producing statistically
similar results to qPCR in this study, this third generation
technology represents a significant improvement in the
molecular detection and quantification of zoonotic patho-
gens in commercial industry environments. Obtaining
cultural isolates is still essential within the regulatory
framework of food safety research, but ddPCR represents
a significant improvement in the ability to determine the
presence and possible transmission of pathogen-specific
genes within the poultry production environment, espe-
cially given the low infectious dose of some of these
zoonotic bacterial pathogens.
5. Acknowledgements
The authors would like to acknowledge the expert tech-
nical assistance of Latoya Wiggins, Nicole Bartenfeld,
and Kathy Tate for their assistance in sampling and cul-

M. J. ROTHROCK JR. ET AL.
410
tural work, as well as John Gamble and Laura Lee Ruth-
erford for their assistance in sampling and molecular
analyses. These investigations were supported equally by
the Agricultural Research Service, USDA CRIS Projects
“Pathogen Reduction and Processing Parameters in Poul-
try Processing Systems” #6612-41420-017-00 and “Mo-
lecular Approaches for the Characterization of Food-
borne Pathogens in Poultry” #6612-32000-059-00.
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