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The present contribution describes the development of simple,
albeit accurate, mathematical models which could quantify the
microbial produced ethanol in correlation with other alcohols in E.
coli cultures under initially aerobic and then anaerobic, controlled
laboratory conditions. The applicability of the constructed models
was tested in real postmortem cases that had positive BACs
(BAC > 0.10 g/L) and co-detection of higher alcohols and 1-butanol
during the original ethanol analysis. The applicability of the
models was also tested to microbial cultures of blood culture
media inoculated with postmortem blood from autopsy cases
under aerobic/anaerobic controlled experimental conditions.
2. Materials and methods
2.1. Microbial cultures
2.1.1. Bacterial strain
The bacterial strain used in this study was E. coli ATCC 35218. The E. coli strain
was stored at 80 8C (Mikrobank beads – Prolab Diagnostics, Canada) and prior to
use it was revived by transferring 3–5 beads into 10 mLof Brain Heart Infusion (BHI)
(CM0225, Oxoid) and was incubated at 37
8C for 18–24 h. The bacterial inoculum
was adjusted to a working concentration of 106 cells/mL using spectrophotometry.
In brief, a standard solution of 108 cells/mL was prepared from an initial culture
after overnight incubation at 37
8C aerobically and the concentration was
determined using the spectrophotometer (Model 6305, Jenway, England) at
600 nm. In order to obtain a final concentration of 106 cells/mL, 100
mL of the
standard
inoculum
were
added
in
9.9
mL
of
BHI
in
sterile
tubes.
The
tubes
wereincubated aerobically for the first 5 days. After this period 1 mLof sterile parafine oil
wasadded in each tube as an overlay and the tubes were placed into GENbag anaer
incubation systems (45534, bioMerieux, France) so as to provide anaerobic
conditions from day 6 to day 30. The incubation, both anaerobic and aerobic, was
performed at 25 8C for 30 days.
2.1.2. Bacterial enumeration
The bacterial concentration was determined by the pour plate count technique
[16]. Decimal dilutions were performed in tubes with 9 mL of Maximal Recovery
Diluent
(MRD,
02-510,
Scharlau
Chemie,
Spain).
From
each
tube
an
inoculum
of
1 mLwas transferred into 2 petri dishes and 15 mL of Plate Count Agar (1.05463,
Merck KGaA, Germany) melted at approximately 45 8C were added. After
solidification
all
plates
were
incubated
aerobically
at
37
8C
for
48
h.
Colonies
were
counted under a colony counter (Stuart SC6, Barloworld Scientific Ltd., UK).
2.2. Postmortem blood derived microbial cultures
2.2.1. Identification of microbes
Seven postmortem blood samples were examined for the presence of E. coli,
Staphylococcus aureus, Enterococcus spp., and Clostridium spp. Ten
mL of aseptically
collected blood was added into 1 mL of Buffered Peptone Water – BPW (CM1049,
Oxoid, UK). E. coli was isolated by plating a loopful of BPW on Violet Red Bile agar
(4021852, Biolife, Italy) and Chromocult Coliform Agar (1.10426.0500, Merck KGaA,
Germany).
The
inoculated
media
were
incubated
aerobically
at
37
8C
for
24
h.
Characteristic colonies were selected, pure cultured and identified to the genus
level using the API20E system (bioMerieux, France). For the isolation of Gram
positive
strains
(S. aureus
and Enterococcus
spp.),
Manitol
Salt
agar
(CM0085,
Oxoid,
UK) and D-Coccosel Bile Esculin Agar (51025, bioMerieux, France) were used,
respectively. The plates were incubated at 37 8C aerobically. Characteristic colonies
were selected, pure cultured and identified to the genus level by biochemical tests.
For Staphylococci the catalase test, the coagulase test (Staphytect Plus, Oxoid, UK)
and the API STAPH system (bioMerieux, France) were used. Biochemical
identification of the Enterococci was performed using the catalase test and the
API 20 STREP system (bioMerieux, France). For the isolation of Clostridium species a
loopful was streaked onto blood agar plates, the inoculated plates were incubated
anaerobically using the GENbag anaer incubation systems (bioMerieux, France) for
48
h
at
37
8C
and
were
examined
for
b-haemolysis.
Identification
was
performed
microscopically after Gram stain and with the use of the API 20A system
(bioMerieux, France).
2.2.2. Normal human blood culture media inoculated with postmortem blood
(postmortem blood derived microbial cultures)
The postmortem blood derived cultures were performed by a procedure based to
that described by Appenzeller and colleagues [15] modified to fit the conditions of
microbial cultures used herein. Specifically, ethanol free whole human blood from
healthy individuals was inoculated with postmortem blood (100
mL/mL of normal
human blood). Postmortem blood samples were collected into sterilized collection
tubes
containing
anticoagulant,
during
autopsy
from
cases
with
putrefaction
and
also were subjected to the procedure of microbial identification (described in
Section 2.2.1). The blood samples were kept at 4 8C until further use. One mL
portions
of
the
inoculated
blood
were
transferred
into
40
sterilized
blood
tubes
(Vacuette EDTAK3, 3 mL). Twenty tubes were spiked with the appropriate volume
of a concentrated glucose solution (sterile dextrose 5% (w/v), pyrogen free solution
for intravenous infusion, DEMO Ltd., Greece) so as to obtain 200 mg/dL final glucose
concentration. The other tubes were incubated without additional glucose. All the
tubes were capped and incubated at 25
8C. On the seventh day a sterile parafin oil
overlay
was
added
into
each
tube
and
the
tubes
were
placed
into
the
GENbag
anaer
incubation system. At days 0, 1, 2, 3, 5, 7, 9, 11, 15 and 20 two tubes were removed
from the incubation system and alcohols concentrations were determined by HS-
GC–FID.
2.3.
Determination
of
volatiles
The alcohols concentration in the culture medium and the blood samples were
determined by the previously described procedure [16].
2.4. Statistical analysis
Regression analysis was employed to model the correlation between the
microbial produced ethanol and the other higher alcohols. The experimental data
were consistent with a Gaussian distribution (normality test) and were analyzed
with multiple linear regression (MLR) methods to model the relationship between
ethanol concentrations as the dependent variable being in linear correlation with
the concentrations of the other alcohols (independent variables). Each descriptor’s
relevance
to
the
current
model
was
assessed
by
partial
least
squares
(PLS)
regression utilizing the PLS1 algorithm. During the analysis, one outlier value was
removed from the dataset.
3.
Results
and
discussion
3.1.
Experimental
determination
of
alcohols
produced
in
pure E. coli
cultures
E. coli is a Gram negative, aerobic and facultative anaerobic, rod-
shaped bacterium that is commonly found in the lower intestine of
warm-blooded organisms. It is considered one of the main
colonizers in corpses and of the main ethanol producers [17].
The growth curve of E. coli during the 30 days incubation at 25
8C is
presented in Fig. 1. During the first 5 days of incubation the
conditions were aerobic. On the fifth day up to the end of the
incubation the conditions of microbial growth changed to
anaerobic. By the fifth day of incubation the bacterial population
reached the stationary phase of growth. The change from aerobic to
anaerobic conditions was selected in order to approximate the
usual conditions where a postmortem blood evolves from death to
autopsy (initially air is available, which gradually decreases and
finally depletes) [1,19] and the duration of changing was selected
in order to obtain the optimal microbial growth. At 4
8C no growth
was observed as expected (not shown) [15,16,18].
The amounts of ethanol, and other alcohols produced under the
applied experimental conditions were variable (Table 1). Ethanol
1,0E+06
1,0E+07
1,0E+08
1,0E+09
1,0E+10
302520151050
Days
B a c t e r i a l c o u n t ( L o g 1 0 C F U
/ m L
Fig. 1. Growth curve of E. coli showing the number of bacteria during the incubation
period
of
30
days
at
25
8C. The
time
of
change
between
aerobic
and
anaerobic
incubation is shown by the dotted line at 5 days.
V.A. Boumba et al. / Forensic Science International 232 (2013) 191–198192
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was produced in higher concentrations compared to the other
alcohols. Considering the ethanol concentration at days 0 and 5
(0 g/L and 0.55 g/L, respectively), it is concluded that the mean rate
of ethanol increase is 0.11 g/L/day (or 11 mg/dL/day) during the
first five-days period of incubation. After switching to anaerobic
conditions, from day six to day nine, the rate of ethanol production
decreased to a mean rate of 0.017 g/L/day (or 1.7 mg/dL/day).
Afterwards, from day 10 to the end of the experiment, ethanol kept
a plateau concentration of about 0.62 g/L.
The most abundantly produced alcohol (except ethanol) was 1-
propanol which was continuously increasing during the first 17
days. Interestingly, the mean rate of 1-propanol’s increase was
0.42 mg/dL/day during the first five days, while for the next twelve
days the increase rate declined, and afterwards, from day 18 to the
end, 1-propanol showed no considerable change. The third in
abundance alcohol was 1-butanol which increased the first five
days with a rate approximately 10 times slower (0.042 mg/dL)
than the respective increase rate of 1-propanol, while afterwards it
remained practically stable. Finally, isobutanol and methyl-
butanol increased slowly for twelve days and then remained
stable.
Our results show that 1-propanol and 1-butanol are the main
alcohols that are produced simultaneously with ethanol by
microbes under the applied experimental conditions. The amountsof ethanol and other alcohols produced by E. coli in BHI cultures
under combined aerobical/anaerobical conditions were higher
compared to those produced by
E.
coli in BHI cultures under strict
anaerobic conditions [16]. The observed discrepancies are
attributed to the presence/absence of air. Therefore it is observed
that higher amounts of alcohols are produced by certain microbes
(here
E.
coli) under aerobic conditions.
It is of importance to underline that 1-butanol was produced
during the first five days of incubation, in the presence of air, while
during this period both ethanol and 1-propanol had the higher
production rates; ethanol and 1-propanol continued to accumu-
late, with a decreased production rate, for further 4 and 12 days,
respectively, under anaerobic conditions. To explain these results
the following aspects should be considered: (i) ethanol can be
produced from all the available postmortem substrates but with
higher rates and yields from carbohydrates; (ii) 1-propanol and 1-
butanol (and the other higher alcohols) could be produced from all
the available postmortem substrates through branched biochemi-
cal pathways that depend on glucose metabolism; and (iii) 1-
propanol, isobutanol and methyl-butanols are produced using
amino acids as substrates [20]. Therefore, it should be concluded
that carbohydrates (as the preferable substrate) were consumed by
the microbes during the aerobic phase of growth, generating
mainly ethanol, 1-propanol and 1-butanol. As carbohydrates were
being depleted from the medium, other available substrates were
consumed continuing to generate higher alcohols (1-propanol,
isobutanol and methyl-alcohols) and ethanol in line with previous
suggestion [20].
3.2. Formulation of models for ethanol production by E. coli
3.2.1.
Statistical
evaluation
of
volatiles
All volatiles during the first 5 days follow a linear approxima-
tion (r 2 > 0.9, not shown here) with the exception of ethanol. Forthe next days (6–30), MLR is not able to successfully fit a similar
linear model (smaller
r 2 value) regarding all variables with the
notable exception of 1-propanol. Probably, 1-propanol plays a
significant role in the successful model prediction while all data is
better to be treated simultaneously since for the first 5 days, data is
ambiguous regarding the variance. By performing a principal
component analysis (PCA) for the whole data to determine how
original variables are projected to a smaller set of underlying
variables, it becomes evident that ethanol explains 99% of the total
variance while the remaining 1% is explained by 1-propanol.
Table 1
Alcohols concentration produced during the 30 days incubation period of E. coli in BHI culture medium at 25
8C under aerobic (days 1–5) and anaerobic (days 6–30)
conditions.
Day Ethanol (g/L) 1-Propanol (mg/dL) Isobutanol (mg/dL) 1-Butanol (mg/dL) Methyl-butanol (mg/dL)
0
0
0
0
0
0
1 0.49 1.12 0.01 0.1 0.03
2 0.48 1.33 0.01 0.15 0.03
3 0.50 1.50 0.01 0.18 0.04
4 0.52 1.81 0.01 0.19 0.04
5 0.55 2.08 0.02 0.21 0.05
6 0.53 2.05 0.02 0.21 0.05
7 0.56 2.18 0.02 0.22 0.06
8 0.59 2.30 0.02 0.23 0.04
9 0.61 2.33 0.02 0.23 0.05
10
0.62
2.85
0.02
0.24
0.06
11 0.58 2.67 0.02 0.23 0.06
12 0.60 2.87 0.03 0.24 0.08
13 0.63 3.18 0.03 0.24 0.08
14 0.60 2.63 0.03 0.23 0.08
15 0.58 2.78 0.02 0.23 0.07
16 0.61 3.04 0.03 0.23 0.11
17 0.61 3.22 0.03 0.23 0.07
18 0.62 3.01 0.03 0.23 0.07
19 0.62 3.15 0.03 0.23 0.07
20
0.63
3.47
0.03
0.24
0.08
21 0.62 3.23 0.03 0.24 0.08
22 0.62 3.30 0.03 0.22 0.06
23
0.61
3.18
0.03
0.24
0.07
24 0.61 3.07 0.03 0.24 0.08
25 0.61 3.08 0.03 0.23 0.07
26 0.61 3.16 0.03 0.23 0.08
27 0.61 3.56 0.03 0.23 0.09
28 0.60 3.13 0.03 0.23 0.07
29 0.61 3.20 0.03 0.24 0.08
30 0.62 3.63 0.03 0.24 0.09
V.A. Boumba et al. / Forensic Science International 232 (2013) 191–198 193
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Therefore, it is shown again that the most important variations
arise from ethanol and 1-propanol. Finally, by subtracting one by
one variable, it is concluded that isobutanol and methyl-butanol
are the variables that do not significantly contribute to the total
variation.
3.2.2.
Modeling
whole
data
By performing a PLS model the descriptors relevance becomes
also evident as it is shown in Fig. 2A. The significance of each
alcohol in building the mathematical models is in relevance with
its produced amount.
The corresponding model (r 2 = 0.90) is described by Eq. (1A):
Ethanol ¼ 0:05 1-propanol þ 1:13 isobutanol þ 0:95
1-butanol 1:60 methyl-butanol þ 0:31 (1A)
By modeling the data with only the most significant variables
(1-propanol and 1-butanol) the prediction shows a considerably
satisfactory linear correlation (r 2 = 0.75), indicating the power of
these two alcohols as descriptors of the process, as it is shown in
Fig. 2B. Therefore we obtain the following model:
Ethanol ¼ 0:05 1-propanol þ 0:53 1-butanol þ 0:32 (1B)
3.2.3.
Modeling
data
from
day
1
to
5
If we analyze the data separately for the first 5 days of aerobical
incubation, we see that the ethanol concentration is heavily
dependent on 1-propanol, followed by isobutanol, and then by 1-
butanol concentration (Fig. 3). The variance of methyl-butanol is of
least significance. The relevant PLS model is described by the Eq. (2):
Ethanol ¼ 0:07 1-propanol þ 3:77 isobutanol 0:26
1-butanol þ 0:07 methyl-butanol þ 0:39 (2)
3.2.4. Modeling data from day 6 to 30
Using similar methodology and the data for the anaerobic
cultivation period from day 6 to day 30 the PLS model is as
described by Eq. (3):
Ethanol ¼ 0:05 1-propanol þ 1:06 isobutanol þ 1:76
1-butanol 1:62 methyl-butanol þ 0:14 (3)
The descriptors relevance shows again the significance of 1-
propanol and 1-butanol in describing the process which is very
similar with the whole dataset (Fig. 4).
3.2.5. Validation modeling with PLS
Validation modeling was performed by creating a small dataset
of 13 randomly selected values of measurements made every 3
days (validation dataset). The rest of the data values were used as
the training dataset. The resulted PLS model is described by the
following equation:
Ethanol ¼ 0:06 1-propanol þ 1:34 isobutanol þ 1:00
1-butanol 1:74 methyl-butanol þ 0:29
This equation was applied to the validation dataset with
r 2 = 0.93 showing that the model for the whole dataset can indeed
successfully describe the ethanol production from E. coli under the
applied experimental conditions confirming our results.
3.2.6. Aspects of microbial ethanol production modeling
The models for estimating ethanol production by E. coli under a
combination of aerobic/anaerobic conditions were presumably
correlated to 1-propanol and 1-butanol, as the relevant descrip-
tor’s significance revealed. This result is in discordance with the
models constructed previously for E. coli under strict anaerobic
conditions, where the main descriptors of ethanol modeling were
1-butanol followed by methyl-butanol. On the other hand, theresult is in concordance with the models constructed previously
for C. perfringens and C. sporogenes [16].
It is worth mentioning that 1-propanol [6,8–10] and 1-butanol
[7] have been recognized, in previous studies by other authors to
be correlated with the postmortem ethanol production basically in
qualitative terms. Our results confirm and enhance the significance
of the presence of 1-propanol and 1-butanol as main indicators of
microbial ethanol production and further, recognize them as the
main descriptors of the quantification process. The relevance of
both these alcohols as descriptors of ethanol modeling is so
powerful that the model constructed by using only these two
variants is quite satisfactory and in agreement with the general
consideration that the simplest the model the easier its applica-
tion.
3.3. Applicability of the models to death-related forensic cases
3.3.1. Real postmortem cases
The applicability of the models presented in this report was
tested in 60 postmortem cases. The cases were selected by
reviewing retrospectively our chromatogram archives and select-
Fig. 2. (A) Four descriptors (4D) model and (B) two descriptors (2D) model for ethanol production by E. coli by analyzing the whole dataset for the 30 days of incubation.
V.A. Boumba et al. / Forensic Science International 232 (2013) 191–198194
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ing those that had positive blood alcohol concentrations
(BAC 0.10 g/L) and co-detection of higher alcohols and 1-butanolduring the original ethanol analysis. The concentrations of 1-
butanol and higher alcohols were calculated for the selected cases.
Cases were classified according to the manner of death – as
resulted from the retrospective review of the forensic pathologist’s
report – as natural (11 cases, 18.3%), violent (14 cases, 23.3%) or
undetermined (35 cases, 58.3%) cases. Also, it was revealed that 58
out of the 60 samples were from corpses with putrefaction, one
from a corpse with extended trauma and one from a totally
carbonized body. In Table 2 are presented the measured BACs, the
estimated ethanol concentrations after applying each model
(Eqs. (1A), (1B), (2) and (3)) and the relative standard error for
each one of the selected cases. The higher acceptable standard
error (in order to consider successful the application of the models)
in the estimation of the produced ethanol when compared to themeasured original BACs, was selected to be 40%, the same as in our
previous relevant study [16].
The model described by Eq. (1A) estimated successfully the
microbial produced ethanol in 24 out of the 60 cases (38%), the
model described by Eq. (2) estimated successfully the microbial
produced ethanol in 16 out of the 60 cases (25%), and the model
described by Eq. (3) estimated successfully the microbial producedethanol in 22 out of the 60 cases (35%). However, the simplest
model, described by Eq. (1B) that was constructed by only two
descriptors 1-propanol and 1-butanol was applied successfully in
the majority of cases (26 out of 60 cases, 42%).
The overall picture was that at least one of the current models
(Eqs. (1A), (1B), (2) and (3)) were applied successfully in: 41 out of
the 60 cases (68%), 39 of them with putrefaction (39/41, 95%); in
28/43 cases (65%) with BAC lower than 0.7 g/L; and, in 13 out of 17
cases (76%) with BAC higher than 0.7 g/L. These results are
consistent with the general expressed consideration that decom-
position is an aggravating factor for postmortem ethanol produc-
tion and, in line with the statement that concentrations of ethanol
lower than 0.7 g/L are most probably due to microbial ethanol neo-
formation [2,21,22].
3.3.2. Postmortem blood-derived microbial cultures
Post-mortem blood samples from seven autopsy cases were
used to inoculate normal human blood, with and without
additional glucose, as described in Section 2.2. The initial glucose
Fig. 3. Four descriptors (4D) model for ethanol production by E. coli by analyzing the data for the first five days of incubation under aerobic conditions.
Fig. 4. Four descriptors (4D) model for ethanol production by E. coli by analyzing the data from day 6 to day 30 of incubation under anaerobic conditions.
V.A. Boumba et al. / Forensic Science International 232 (2013) 191–198 195
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concentration of the spiked blood samples was chosen to be
200 mg/dL in order to be the same with the glucose content of the
BHI culture medium. The selection of cases was randomized
amongst routinely autopsied corpses with obvious signs of
putrefaction at autopsy. The characteristics of the selected cases
(microbes detected, original BACs, estimated BACs and relative
standard error ranges after applying the models to the autopsy
samples) and, the results after applying the models described by
Eqs.
(1B)
and
(2)
to
the
inoculated
samples
for
each
case
(mean
estimated BACs and standard deviations, relative standard error
ranges) are presented in Table 3. Five out of the seven blood
samples carried microbial strains (E. coli, Enterococcus faecalis,
Klebsiella species) commonly found in human corpses [1,23].
Clostridium species were not identified in blood samples from cases
1, 5 and 6, while for the cases 2, 3, 4 and 7 the results were
inconclusive. Four out of the seven blood samples were positive for
ethanol and other alcohols during the original ethanol analysis
(these
were
presented
as
cases
15,
37,
50
and
59
in
Table
2).
When
Table 2
Application of each model constructed for E. coli (Eqs. (1A), (1B), (2) and (3)) in postmortem cases to calculate the microbially produced ethanol. The original ethanol
concentration measured for each case along with the manner of death are also provided.
Case no. Manner of death Measured ethanol (g/L) Estimated ethanol (g/L) Relative standard error (E %)
Eq. (1A) Eq. (1B) Eq. (2) Eq. (3) Eq. (1A) Eq. (1B) Eq. (2) Eq. (3)
1 Undetermined 0.10 0.35 0.37 0.45 0.17 244 259 337 68
2 Undetermined 0.10 0.61 0.63 0.77 0.41 489 504 637 294
3 Natural 0.16 0.59 0.45 1.01 0.25 268 184 530 58
4 Natural 0.16 0.35 0.36 0.44 0.17 113 123 171 3
5 Violent 0.19 0.44 0.45 0.58 0.26 135 138 208 366 Natural 0.20 0.33 0.34 0.42 0.15 64 70 111 25
7 Violent 0.20 0.60 0.60 0.77 0.39 198 201 283 97
8 Natural 0.21 0.51 0.50 0.71 0.32 145 138 236 52
9 Undetermined 0.21 0.54 0.41 1.00 0.35 158 94 376 66
10 Natural 0.22 0.47 0.48 0.60 0.28 116 121 178 29
11 Undetermined 0.25 0.38 0.40 0.48 0.20 52 57 92 21
12 Undetermined 0.27 0.31 0.32 0.39 0.13 13 19 44 52
13 Violent 0.28 0.68 0.67 0.79 0.53 148 142 187 92
14 Undetermined 0.29 0.49 0.49 0.63 0.30 70 72 121 3
15 Violent 0.33 1.00 0.81 0.55 1.20 200 141 63 261
16 Undetermined 0.33 1.34 0.97 0.72 1.76 304 193 117 433
17 Violent 0.34 0.66 0.62 0.64 0.57 92 81 86 66
18 Undetermined 0.35 0.62 0.52 1.03 0.42 78 49 194 20
19 Natural 0.38 0.31 0.32 0.42 0.14 17 15 10 63
20 Natural 0.39 0.77 0.56 1.34 0.23 98 43 245 42
21 Undetermined 0.39 0.40 0.42 0.51 0.22 2 6 29 45
22 Undetermined
0.42
0.84
0.85
1.05
0.61
101
104
151
4723 Violent 0.42 0.67 0.66 0.88 0.46 59 57 110 9
24 Natural 0.42 0.50 0.52 0.63 0.31 20 23 51 26
25 Natural 0.42 0.38 0.58 0.70 0.52 9 38 67 23
26 Undetermined 0.47 1.11 0.81 2.03 0.87 137 73 333 85
27 Violent
0.52
0.74
0.74
0.95
0.52 43 43 84 1
28 Undetermined 0.54 0.31 0.32 0.39 0.13 44 41 28 76
29 Undetermined 0.58 1.36 0.76 2.97 1.10 135 32 414 90
30 Undetermined
0.59
0.49
0.49
0.64
0.29
18
17 8
50
31 Undetermined 0.60 0.76 0.71 1.07 0.54 27 20 79 9
32 Undetermined 0.60 0.36 0.38 0.52 0.18 40 37 14 70
33 Undetermined 0.61 0.79 0.81 0.98 0.57 29 32 61 7
34 Violent 0.62 1.09 0.96 1.65 0.84 76 55 166 36
35 Undetermined 0.62 0.66 0.68 0.83 0.46 7 9 34 27
36 Undetermined 0.63 1.19 0.93 2.06 0.94 89 48 226 49
37 Undetermined 0.64 0.63 0.61 0.72 0.48 2 5 13 26
38 Violent 0.65 1.02 0.96 1.03 0.95 57 48 58 45
39 Violent 0.66 0.51 0.40 0.91 0.32 22 39 38 51
40
Undetermined
0.66
0.28
0.57
2.15
1.35
57
14 224 104
41 Undetermined 0.68 0.93 0.78 1.20 0.27 36 14 76 139
42 Undetermined 0.69 0.48 0.46 0.68 0.29 30 33 1 58
43 Violent 0.69 0.79 0.54 1.55 0.57 12 24 119 19
44 Undetermined 0.71 0.66 0.57 1.04 0.45 7 19 46 36
45 Undetermined 0.71 0.48 0.49 0.62 0.29 31 32 12 59
46 Natural 0.72 0.75 0.75 1.11 0.09 4 4 54 88
47 Undetermined 0.73 0.79 0.74 1.10 0.57 7 2 50 22
48 Undetermined 0.75 0.96 0.98 1.19 0.73 28 30 59 3
49 Undetermined 0.81 0.63 0.59 1.02 0.12 23 28 25 85
50
Violent
0.84
0.88
0.87
1.15
0.65 5 4 37
21
51 Undetermined 0.87 1.23 1.11 1.76 0.25 41 28 102 72
52 Undetermined 0.91 0.91 0.92 1.14 0.68 1 2 26 25
53 Undetermined
0.94
2.13
0.82
5.42
1.81 127
13 477 93
54 Undetermined 0.98 0.73 0.56 1.30 0.52 26 43 32 47
55 Violent 1.06 0.42 0.44 0.55 0.25 60 59 48 77
56 Undetermined 1.08 0.49 0.49 0.65 0.30 55 55 40 72
57 Violent 2.15 0.98 0.95 1.05 0.85 55 56 51 6158 Undetermined 2.58 1.10 1.00 1.59 0.85 57 61 38 67
59 Undetermined 2.94 0.83 0.80 0.97 0.70 72 73 67 76
60 Natural 3.56 0.78 0.78 1.01 0.56 78 78 72 84
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ethanol and other alcohols were produced during the incubation of the inoculated samples the procedure was considered positive and
the applicability of the models was tested. Therefore the procedure
was positive only for two out of the seven tested postmortem
bloods (cases 1 and 2 from Table 3).
The application of the current models for the original autopsy
blood of the first case, where
E.
coli was identified, gave acceptable
standard errors (selected to be
<40%). In the inoculated blood
samples in the presence of additional glucose higher amounts of
ethanol were produced and, the models gave better scores when
applied to these samples compared to the inoculated blood
samples without additional glucose (Table 3). These results show
that the current models could be used to estimate neo-formed
ethanol by
E.
coli either under unspecified environmental
conditions or in laboratory conditions. The observed discrepanciesshould be apparently attributed to the differences in the glucose
content of the medium and possibly to differences in the
succession of microbes in the inoculated blood samples compared
to the original blood.
In the second case the models estimated that only a portion of
the detected ethanol could be of microbial origin. Since E. faecalis is
not an ethanol producing microorganism [16] we should assume
that another microbe (not identified) was activated in the original
blood, from death to autopsy, and then during incubation of the
inoculated samples. In the inoculated blood samples with
additional glucose, more ethanol was produced compared to the
samples without additional glucose. Interestingly, in this latter
case, the application of the models succeeded better scores than
those
obtained
for
the
inoculated
samples
with
glucose.
Theseresults show that the current models could be used successfully to
estimate the neo-formed ethanol in cases where ‘‘ethanol
producing’’ microbe(s) (different than E. coli) had grown either
under unspecified environmental conditions or under laboratory
conditions.
In the autopsy blood samples from cases 3 and 4,
Klebsiella
species had produced the alcohols to each autopsy blood sample.
Previously, it was reported that Klebsiella pneumoniae was capable
of producing ethanol in urine spiked with glucose, by fermentation
at 35
8C [24]. However, the application of the current models was
within acceptable standard error range only in case 4 (Table 3). The
results suggest that in cases where Klebsiella species were
activated, occasionally, the models could be used effectively.
Alternatively,
we
cannot
exclude
the
possibility
that
more
microbe(s) could have grown to each original blood, and hadproduced the alcohols. Finally, the inoculation procedure was
negative for cases 3 and 4, probably indicating that the ‘‘ethanol
producing’’ microbes that generated the alcohols in the original
blood (Klebsiella species or other) could not been regenerated to
the inoculated samples under the applied experimental conditions.
Postmortem blood samples that were negative for ethanol in the
original volatile analysis (cases 5–7) did not produce alcohols during
incubation of the inoculated samples, irrespectively whether
microbes had been detected (case 5) or not (cases 6 and 7). For
these last three cases it can be concluded that there were not present
‘‘ethanol producing’’ microbes in the autopsy bloods and, conse-
quently, there was not alcohols production during the incubation
period of the postmortem blood derived cultures.
In general, these results indicate that whenever a blood sampleis contaminated with microbe species that are ‘‘ethanol produ-
cers’’, a profile of ethanol and other alcohols would be generated if
the conditions are favorable for microbial growth. In such a case
the current models show a potential for application, irrespectively
of the activated microbe(s). Influencing factors appear to be the
culture composition and more specifically the glucose content, the
conditions of microbial growth and probably, the succession of
microbes.
3.3.3. Aspects, limitations and outcomes of the application of models
The working hypothesis for our study is that although the
postmortem or putrefactive conditions could be extremely
different and variable among different cases, the patterns of other
alcohols
would
follow
more
or
less,
in
qualitative
and
quantitativeterms, the ethanol production, since the biochemical pathways of
their microbial production are interactive [20]. The patterns of
produced ethanol and other alcohols, under controlled experi-
mental conditions, could be described by mathematical equations
(models); the patterns of ethanol and alcohols produced under
different, real conditions, could be approximated by the models,
and this is why ethanol estimation should be made within an
acceptable standard error.
Ideally, a model should exist for every postmortem case
(predisposing that it was constructed under the same conditions)
which is impossible, since the conditions could not be defined
accurately. In our view, each model represents a ‘‘tool’’ which could
be used as an alternate choice to estimate the microbially produced
ethanol
in
real
cases.
In
every
given
case,
the
effectiveness
of
each
Table 3
Characteristics of the autopsy bloods used to inoculate normal human blood (original BACs, estimated BACs and relative standard error ranges (jE %j)) and results for the
inoculated samples with and without additional glucose in each case (produced ethanol concentration, relative standard error ranges after applying the models described by
Eqs. (1B) and (2), respectively).
Case Microbes detected BAC (g/L) Estimated
BAC range (g/L)
Range
of jE %j
Fermentation experiment
Blood without glucose Blood with glucose
Produced
ethanol
(g/L),
mean
SD
Range of jE %j
(Eqs.
(1B)
and
(2))
Produced ethanol
(g/L),
mean
SD
Range of jE %j
(Eqs.
(1B)
and
(2))
1 E. coli, E. faecalis 0.64 0.48–0.72 2–26 0.17
0.06 26a 0.69
0.10 27–40b
2 E. faecalis 2.94 0.71–0.97 67–76 0.76
0.11 0–40, 1–27 1.05
0.12 31–40, 3–35
3 K. pneumoniaea,
E. faecalis
0.33 0.55–1.20 63–261 0 – 0 –
4 Klebsiella spp.,
E. faecalis
0.84 0.65–1.15 4–37 0 – 0 –
5 E. faecalis 0 0 – 0 – 0 –
6 ND 0 0 – 0 – 0 –
7 ND 0 0 – 0 – 0 –
Abbreviation: ND, none (microbe) detected.a This value represents the best score achieved by applying the simplest model described by Eq. (1B).b Values resulted for the simplest model described by Eq. (1B).
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model in achieving the goal is different (due to the different
postmortem conditions, resulting in different alcohol patterns);
thus, one ‘‘tool’’ could be more effective and accurate than the
others for each case. Incidentally, all the proposed so far models,
these presented herein, as well as, those presented in our previous
relevant study [16], were applied successfully for 57 out of 60 cases
presented in Table 2. Nevertheless, lack of adequate evidence in
respect to the origin of ethanol for these cases does not allow a
definite conclusion, although putrefaction and extensive trauma of
the body are recognized aggravating factors for ethanol production
[3,4,21,25,26].
4. Concluding remarks
The present approach, approximates the problem of microbial
ethanol production with a novel, simple methodology. The models
are suggested as tools which could make feasible the quantification
of the microbial neo-formed ethanol in postmortem cases. The
alcohols 1-propanol and 1-butanol are proven consistent with
microbial production of ethanol and the two most significant
alcohols in the modeling process. Nevertheless, given the
complexity of the human body decomposition and the co-existing
microbial
activity,
our
results
point
out
that
the
more
models
areavailable the more possible is one of them to estimate adequately
the postmortem ethanol in a given case. Of course, since the extent
of ethanol production in each case cannot be defined, we cannot
conclude, yet, which is the most suitable model for each case. Thus,
ongoing work should be conducted before suggesting the adequate
and reliable application of any model to real cases.
Acknowledgment
We thank Professor Stathis Frilligos for critical reading of the
manuscript.
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