Microbial Ethanol Production Experimental Study and Multivariate Evaluation

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Microbial

ethanol

production:

Experimental

study

and

multivariate

evaluation§

Vassiliki A. Boumba a,*, Vangelis Economou b, Nikolaos Kourkoumelis c, Panagiota Gousiab,Chrissanthy Papadopouloub, Theodore Vougiouklakis a

aDepartment of Forensic Medicine & Toxicology, Medical School, University of Ioannina, 45110 Ioaninna, GreecebDepartment of Microbiology, Medical School, University of Ioannina, 45110 Ioaninna, GreececDepartment of Medical Physics, Medical School, University of Ioannina, 45110 Ioaninna, Greece

1. Introduction

The microbial formation of ethanol is a well recognized

complication in the proper interpretation of postmortem ethanol

analysis results [1–3]. Many microorganisms potentially present ina dead body are capable of ethanol production. At least 58 species

of bacteria, 17 species of yeasts and 24 species of molds can

produce ethanol as well as other volatiles through various

biosynthetic pathways [1,4,5].

However, only a limited number of microbial species have been

reported to produce ethanol in experimental studies [6–11]. From

the available relevant literature the microbial species studied so far

are:

Candida

tropicalis in blood [6],

Candida

albicans in blood [6,7]

and in urine [8,9], Candida tropicana in blood [6] and in urine [8],Candida parapsilosis in blood [6] and in urine [9], Corynebacterium

sp. in blood [6], Lactococcus

garviae in blood [10],

Escherichia

coli in

blood [6] and in urine [8,9],

Candida

glabrata in urine [11],

Enterococcus and Klebsiella oxytoca in urine [8], and Klebsiella

pneumoniae and Proteus mirabilis in urine [9].

In particular, the species

C.

albicans,

C.

tropicalis, C.

tropicana,

C.

glabrata,

C.

paralsilosis, E.

coli, K.

oxytoca, Corynebacterium sp.,

Enterococcus and L. garviae have been identified in postmortem

blood and urine [6,8,10,11], while only C. albicans, C. parapsilosis, K.

pneumoniae,

E.

coli, Proteus

mirabilis, and

S.

cerevisiae have been

experimentally inoculated in human blood or urine [7,9,12].

Forensic Science International 215 (2012) 189–198

A R T I C L E I N F O

Article history:

Received 30 September 2010

Received in revised form 3 March 2011

Accepted 7 March 2011

Available online 5 April 2011

Keywords:

Post-mortem blood

Ethanol

Microbial ethanol production

Multivariate statistics

Fermentation

Volatiles

Higher alcohols

Biomarker(s)

A B S T R A C T

Ethanol can be produced from all the postmortem available substrates, though with higher rates andyields from carbohydrates, during the early stages of putrefaction. The so-called higher alcohols (1-

propanol, isobutanol, 2-methyl-1-butanol and 3-methyl-2-butanol) and 1-butanol could be produced,

from all the available postmortem substrates. However, a quantitative relationship between the

produced ethanol and the potentially produced other alcohols is still missing.

Theobjective of thisstudywas thedevelopmentof a simple, mathematicalmodelwhich could be able

to approximate themicrobialproduced ethanolin correlationwithotherproducedalcohols.The selected

bacterial speciesincluded twoGram+ spore-forminganaerobic bacteria andtwo (oneGram+oneGram-)

aerobic/facultative anaerobic bacteria, all being commoncommensalsof thedigestive tract andcommon

colonizersof the corpse.The selectedbacterial strains, Escherichia coli,

Clostridium perfrigens,

Clostridium

sporogenes and Enterococcus faecalis, were cultured separately at 25 8C, for 30days, under controlled

anaerobic conditions. Theproduced ethanoland thepreviouslyreferred alcohols were determinedin the

culture medium in 24h intervals. Using partial least squares (PLS) regression, the estimation of the

relevance score for the available descriptors established the statistical model to assess the ethanol

concentration produced by each studied microbe. E. coli,

C. perfrigens,

and C. sporogenes produced

differentpatterns of ethanolandother alcohols,while E. faecalis produced negligible amountsof ethanol

and higher alcohols. In constructing the mathematical models to predict the produced ethanol, 1-propanol, 1-butanol, and isobutanolwere significant for C. perfrigens and C. sporogenes, while 1-butanol,

1-propanol, andmethyl-butanol were significant forE. coli.

The applicability of these modelswas tested

in microbial, anaerobic cultures of normal human blood and plasma at 25 8C. The results indicate that

factors such as thetype ofmicrobe species, theglucose contentand themediumcomposition apparently

affect the procedure of microbial ethanol, and other alcohols production. However, the models can be

applied with acceptable accuracy and they show potential for application in real postmortem cases.

2011

Elsevier Ireland Ltd. All rights reserved.

§ 48th Annual Meeting of the International Association of Forensic Toxicologists

(TIAFT). Joint meeting with the society of Toxicological and Forensic Chemistry

(GTFCh).

* Corresponding

author.

Tel.:

+30

26510

07724;

fax:

+30

26510

07857.

E-mail addresses: [email protected], [email protected] (V.A. Boumba).

Contents

lists

available

at

ScienceDirect

Forensic Science International

jour nal h o mepage: w ww.elsev ier .co m/loc ate / fo r sc i int

0379-0738/$ – see front matter

2011 Elsevier Ireland Ltd. All rights reserved.

doi:10.1016/j.forsciint.2011.03.003

http://dx.doi.org/10.1016/j.forsciint.2011.03.003











mailto:[email protected]



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penetration depth of 20 mm. After injection syringe was flushed with helium for

1.5

min.

Acquisition

time

was

20

min.

A

standard

GC–FID

chromatogram

is

provided as complimentary data (Fig. 1C).

In 10 mL HS vials containing 0.50 g ammonium sulfate were added 500 mL of the

calibration solution or of the culture medium or of blood culture and 500 mL of the

internal standard solution. The vials were then sealed with metal crimp caps fitted

with silicon septa and were put to the HS auto sampler for analysis. Ammonium

sulfate was added to increase the ionic strength of the solution. Calibration curves

were constructed for each analyte in six concentration levels within the relevant

working concentration range (Table 1).

2.3. Multivariate statistical analysis

Regression analysis was employed to model the correlation between the

microbial produced ethanol and the other higher alcohols. The experimental data

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). Using the model built, we calculated each descriptor’s relevance to the

current model using partial least squares (PLS) regression based on the PLS1

algorithm. Since MLR is very sensitive to outliers, the dimensionality of some

models has been decreased by leaving out certain independent variables. This has

also permitted us to find the simplest acceptable solution in each case.

3.

Results

and

discussion

3.1. Microbial anaerobic cultures in BHI culture medium

C.

perfrigens, C.

sporogenes,

E.

coli and

E.

faecalis (former known

as

Streptococcus

faecalis) have been reported as being the main

colonizers in corpses and in parallel the main ethanol producers

[4]. C. perfrigens and C. sporogenes are Gram-positive, rod-shaped,obligate anaerobic, spore-forming bacteria of the genus Clostridia

widely distributed in nature and also in the intestinal tract of

humans and other vertebrates, insects, and soil. E. coli is a Gram

negative, facultative anaerobic and non-sporulating, rod-shaped

bacterium that is commonly found in the lower intestine of warm-

blooded organisms (endotherms). E.

faecalis is a Gram-positive

commensal bacterium, facultative anaerobic, inhabiting the

gastrointestinal tracts of humans and other mammals [20].

In Fig. 1 the growth curves for each bacterial strain during the

30 days of incubation at 25

8C are presented. By the fifth day of

incubation all strains reached the stationary phase of growth. At

4

8C no growth was observed for all studied bacterial strains, as it

was expected (not shown) [9,21].

3.2.

Method

validation

In this study a HS-GC–FID method has been optimized, in order

to determine simultaneously the concentrations of ethanol, 1-

propanol, 1-butanol, isobutanol, 2-methyl-1-butanol and 3-

methyl-2-butanol, in the culture medium and in human blood

samples. These volatiles were baseline separated from other

common determinants in volatile blood analysis (methanol,

acetaldehyde, 2-propanol, acetone, ethyl acetate, butanone, 2-

butanol). Baseline separation of 2-methyl-1-butanol (active-amylalcohol) and 3-methyl-2-butanol (isoamyl alcohol) is not feasible

on polar columns typically used for separation of alcohols [22].

Therefore, herein they are determined as mixture and referred as

methyl-butanol. The method was evaluated for selectivity, limit of

detection (LOD), limit of quantitation (LOQ), linearity, precision

and accuracy. Selectivity was accomplished by analyzing six

different blank samples and the matrix effect was evaluated. The

LODs and LOQs for each analyte were determined as the lowest

concentration of each analyte yielding a signal to noise ratio of at

least 3:1 and 10:1 respectively. LODs and LOQs are listed in Table 1.

Linearity was expressed by the correlation coefficient (R2) of the

regression line calculated by the method of least-squares with a

weighting factor of 1/ x2. Correlation coefficient exceeded 0.999 for

each analyte (Table 1). Each calibrator was back calculated againstthe total curve. Precision and accuracy of the method were

evaluated by analyzing three QC samples, one at the lower

concentration, one at the higher and one within the working range

of concentration of each analyte. Precision was expressed as the

relative standard deviation (% RSD) and was lower than 6.0% for all

analytes. Accuracy of the method was calculated as the percent

difference from the expected concentration (% Er) and ranged from

1.0% to 2.5%.

The applied methodology was based on previously reported

methods [17–19] and met acceptable analytical criteria for the

quantitative determination of alcohols. Acetonitrile was used as

internal standard since it has similar physicochemical properties

with the determined volatiles; it is neither an ingredient of

alcoholic

beverages

nor

a

putrefactive

product

[5]; and

it

was

alsoused in ethanol analysis in the past [17]. The LODs and LOQs of our

method lie between the values of previous reported methods being

either lower [19] or higher [18,23].

3.3.

Alcohols

determination

Under the applied experimental conditions the clostridia

species and E. coli produced ethanol, 1-butanol and higher alcohols

in variable quantities (Tables 2–4). The higher amounts of ethanol

were produced from

C.

sporogenes, followed by

E.

coli and

C.

perfrigens. The most abundant produced other alcohol was 1-

butanol for C. sporogenes and C. perfringens, followed from 1-

propanol and isobutanol, while for

E.

coli was 1-propanol. The less

abundant

produced

volatile

were

methyl-1-butanol

for

all

the

Table 1

Validation of the HS-GC–FID volatiles determination method.

Volatile LOD (mg/dL) LOQ (mg/dL) Working range (mg/dL) R2

Ethanol 0.01 0.03 0.03–400 0.999

1-Propanol 0.02 0.04 0.04–32.0 0.999

Isobutanol 0.005 0.015 0.02–0.08 0.989

1-Butanol

0.005

0.01

0.01–0.08

0.995

3-Methyl-2-butanol 0.01 0.025 0.04–0.32 0.997

3-Methyl-1-butanol 0.01 0.025 0.04–0.32 0.999

1,E+02

1,E+04

1,E+06

1,E+08

1,E+10

1,E+12

1,E+14

302520151050

Days

B a c t e r i a l c o u n t s ( l o g C F U / m l )

Clostridium sporogenes Clostridium perfringensEnterococcus faecalis Escherichia coli

Fig. 1. Growth curves for each microbe under study showing the number of bacteria

during

the

incubation

period

of

30

days

at

25

8

C

under

anaerobic

conditions.

V.A. Boumba et al. / Forensic Science International 215 (2012) 189–198 191



studied microbes. E.

faecalis produced negligible amounts of

ethanol and other alcohols indicating that under the applied

conditions the branched fermentation pathways producing

alcohols were unfavorable for this microbe. Therefore, the

statistical evaluation of the relevant results was practical only

for the species

C.

perfigens,

C.

sporogenes and

E.

coli and impractical

for E. faecalis.

The higher alcohols are metabolic products of the fermentation

mainly of amino acids which are used by microbes as a nitrogen

source. The higher alcohols, under the applied experimental

Table 2

Volatiles concentration during the fermentation period of C. perfrigens in BHI culture medium at 25 8C.

Days Ethanol (g/L) 1-Propanol (mg/dL) 1-Butanol (mg/dL) Isobutanol (mg/dL) Methyl-butanol (mg/dL)

0 0.01 0 0 0 0

1 0.01 0.02 0.04 0 0

2 0.02 0.04 0.08 0.01 0.02

3

0.06

0.08

0.16

0.01

0

4 0.05 0.15 0.44 0.03 0.03

5 0.08 0.2 0.57 0.04 0.03

6

0.07

0.26

0.78

0.05

0.057 0.11 0.31 0.72 0.07 0.04

8 0.13 0.32 0.99 0.08 0.06

9 0.09 0.39 0.72 0.07 0.04

10 0.12 0.40 0.86 0.07 0.06

11 0.13 0.46 1.08 0.10 0.07

12 0.14 0.50 1.35 0.11 0.07

13

0.12

0.51

1.02

0.10

0.09

14 0.16 0.52 1.51 0.11 0.09

15 0.14 0.48 1.18 0.11 0.07

16

0.13

0.42

1.21

0.11

0.05

17 0.14 0.55 1.26 0.11 0.04

18 0.13 0.51 1.23 0.10 0.07

19 0.14 0.52 1.27 0.09 0.07

20 0.14 0.53 1.31 0.10 0.06

21 0.14 0.51 1.25 0.11 0.06

22 0.15 0.55 1.30 0.11 0.07

23 0.14 0.63 1.20 0.10 0.08

24 0.15 0.65 1.16 0.13 0.07

25 0.14 0.55 0.98 0.13 0.07

26

0.15

0.64

1.52

0.14

0.08

27 0.15 0.60 1.47 0.13 0.09

28 0.15 0.68 1.32 0.12 0.08

29 0.16 0.69 1.90 0.12 0.10

30 0.15 0.70 1.33 0.15 0.08

Table 3

Volatiles concentration during the fermentation period of C. sporogenes in BHI culture medium at 25

8C.

Days Ethanol (g/L) 1-Propanol (mg/dL) 1-Butanol (mg/dL) Isobutanol (mg/dL) Methyl-butanol (mg/dL)

0 0.00

0.04

0

0.00

0

1 0.10 2.42 0.43 0.70 0.07

2 0.73 2.67 0.70 0.72 0.10

3 0.76 4.59 2.07 1.53 0.18

4 0.85 5.07 2.31 1.76 0.22

5 0.87 5.73 3.56 2.58 0.24

6 0.85 5.67 3.25 2.30 0.23

7 0.84 5.82 3.72 2.56 0.25

8 0.78 6.20 3.81 2.40 0.21

9 0.71 5.06 3.22 2.24 0.20

10

0.79

6.55

4.77

3.03

0.34

11 0.81 6.59 4.81 3.12 0.32

12 0.84 6.68 5.19 3.18 0.32

13

0.75

6.67

6.35

3.36

0.39

14 0.77 7.10 6.45 3.51 0.38

15 0.81 8.16 7.59 4.36 0.44

16 0.78 7.50 6.98 4.57 0.36

17 0.81 8.12 8.85 5.07 0.57

18 0.79 7.81 8.25 4.33 0.52

19 0.76 7.62 8.74 3.99 0.53

20

0.77

7.67

8.00

4.10

0.52

21 0.78 8.55 9.20 4.43 0.51

22 0.76 8.21 8.87 4.20 0.55

23

0.66

8.20

9.35

4.35

0.52

24 0.70 8.47 9.70 4.67 0.55

25 0.70 8.64 10.2 5.38 0.55

26 0.73 8.69 10.5 4.82 0.55

27 0.76 8.88 9.57 4.75 0.56

28 0.73 9.36 11.1 4.90 0.55

29 0.67 10.5 10.2 5.50 0.55

30 0.72 10.2 11.9 6.10 0.57

V.A. Boumba et al. / Forensic Science International 215 (2012) 189–198192



conditions, were generated in parallel with ethanol. Furthermore,

the relatively high amounts of 1-butanol produced from the

clostridia species (Tables 2 and 3) indicate that clostridia use the

fermentation pathway for 1-butanol and acetone formation to

ferment carbohydrates to ethanol and to a lesser extend to 1-

butanol as previously has been suggested [5].The amounts of ethanol produced by each microbe were quite

different although the initial glucose content of the medium was

the same (200 mg/dL). The observed differences in microbes’

ability to produce ethanol and higher alcohols during fermentation

probably reflect differences in their metabolic efficiency under

anaerobic conditions in respect to the substrate catabolism.

These results indicate that the glucose content is not the only

determinant of the produced ethanol – although it should have a

significant role in the procedure, and the process is potentially

affected by other factors as well. The most obvious factor is themicrobe type even for species becoming of the same genera (herein

C. pergrigens and sporogenes). The patterns of the higher alcohols

have also shown significant differences between the different

microbes’ cultures indicating the flexibility offered by branched

Table 4

Volatiles concentration during the fermentation period of E. coli in BHI culture medium at 25 8C.

Days Ethanol (g/L) 1-Propanol (mg/dL) Isobutanol (mg/dL) 1-Butanol (mg/dL) Methyl-butanol (mg/dL)

0 0 0 0 0 0

0.5 0.18 0.06 0.01 0.01 0.07

1 0.36 0.09 0.02 0.04 0.06

2

0.42

0.11

0.02

0.07

0.07

3 0.43 0.18 0.02 0.08 0.09

4 0.44 0.24 0.02 0.09 0.09

5

0.46

0.34

0.03

0.09

0.086 0.45 0.37 0.03 0.09 0.09

7 0.44 0.38 0.03 0.10 0.11

8 0.46 0.43 0.03 0.11 0.09

9 0.48 0.46 0.02 0.09 0.09

10 0.47 0.45 0.03 0.10 0.11

11 0.47 0.51 0.02 0.10 0.09

12

0.46

0.54

0.03

0.12

0.11

13 0.50 0.62 0.03 0.09 0.08

14 0.45 0.68 0.02 0.11 0.12

15

0.49

0.66

0.02

0.10

0.08

16 0.49 0.72 0.02 0.12 0.12

17 0.52 0.66 0.03 0.10 0.11

18 0.46 0.69 0.02 0.10 0.10

19 0.46 0.76 0.03 0.10 0.10

20 0.50 0.70 0.04 0.10 0.08

21 0.51 0.79 0.03 0.13 0.08

22 0.56 0.81 0.02 0.16 0.08

23 0.46 1.02 0.08 0.16 0.09

24 0.53 0.92 0.07 0.13 0.10

25

0.49

0.92

0.07

0.11

0.09

26 0.53 0.95 0.05 0.11 0.09

27 0.50 0.91 0.02 0.10 0.09

28 0.52 0.86 0.07 0.10 0.09

29 0.49 0.86 0.09 0.10 0.09

30 0.50 0.88 0.10 0.08 0.09

Fig. 2. (A) Four descriptors (4D) model and; (B) one descriptor (1D) model for ethanol production by C. perfrigens.




fermentation pathways to microbes, so as, to cover their needs for

energy and redox balance [5].

3.4. Models of microbial ethanol production

3.4.1.

Models

for

C.

perfrigens

The initial model for

C.

perfrigens was built using four

independent variables, namely the 1-propanol, 1-butanol, iso-

butanol and methyl-butanol concentrations respectively. Then the

less significant variable (methyl-butanol with descriptor power

1.50) was skipped and a model with three independent variables

(1-propanol, 1-butanol, isobutanol) was built and, so on, iso-

butanol was skipped (with descriptor power 82.94) resulting to the

creation of the model with two independent variables (1-propanol,

1-butanol with descriptor power 100 and 87.17 respectively). The

significance of 1-propanol as a descriptor (descriptor power 100) in

this case is so powerful that a considerable satisfactory model

could be created by using it as the only independent variable.

Thereinafter are given for comparison the models by using four

independent variables (Spearman Rank Correlation, r = 0.95)

(Eq. (1)) and the most powered descriptor variable (Spearman

Rank Correlation, r = 0.95) (Eq. (2)). In Fig. 2 the graphs and the

statistical parameters of the relevant models described by the

Eqs. (1) and (2) are showed:

Ethanol ¼ 0:08 1Propanol þ 0:03 1Butanol þ

0:30 Isobutanol 0:01 Methyl-butanol þ 0:03

(1)

Ethanol ¼ 0:11 þ 0:047 1Propanol (2)

3.4.2. Models for C. sporogenes

The initial model for C. sporogenes was built using four

independent variables, 1-propanol, 1-butanol, isobutanol and

methyl-butanol concentrations, respectively. Then the less signifi-

cant variable (methyl-butanol with descriptor power 21.95) was

skipped and a model with three independent variables (1-

propanol, 1-butanol, isobutanol) was built and, so on, isobutanol

was skipped (descriptor power 30.32) resulting to the develop-

ment of the model with two independent variables (1-propanol, 1-

butanol with descriptor power 100 and 56.01 respectively).


independent variables (Spearman Rank Correlation,

r = 0.55

(Eq. (3)) and the two most powered descriptor variable (Spearman

Rank Correlation, r = 0.39) (Eq. (4)). In Fig. 3 the graphs and the



Ethanol ¼ 0:16 1Propanol 0:07 1Butanol þ

0:61 Methyl-butanol 0:07 Isobutanol þ 0:05

(3)

Ethanol ¼ 0:15 1Propanol 0:14

Isobutanol þ 0:09

(4)

3.4.3.

Models

for

E.

coli

The initial model for E. coli was built using four independent

variables, 1-propanol, 1-butanol, isobutanol and methyl-butanol

concentrations respectively. Then the less significant variable

(isobutanol with descriptor power 7.65) was skipped and the

model with three independent variables (1-propanol, 1-butanol,

methyl-butanol) was built and, so on, 1-propanol with descriptor

power 28.14 was skipped resulting to the development of the

model with two independent variables (1-butanol, methyl-butanol

with descriptor power 100 and 53.68 respectively).


independent variables (Spearman Rank Correlation,

r = 0.70)

(Eq. (5)) and the two most powered descriptors (Spearman Rank

Correlation, r = 0.56) (Eq. (6)). In Fig. 4 the graphs and the



Ethanol ¼ 0:07 1Propanol þ 0:20 Isobutanol þ

1:61 1Butanol þ 1:15 Methyl-butanol þ 0:15

(5)

Ethanol ¼ 2:25 1Butanol þ 0:98

Methyl-butanol þ 0:15

(6)

3.4.4. Aspects of ethanol production modeling

Interestingly the significance of each higher alcohol as a

descriptor in building the relevant model was not in relevance with

its produced amount. The model corresponding to C. perfrigens for

predicting ethanol production (Eq. (1))was highly correlated to the

Fig.

3.

(A)

Four

descriptors

model

and;

(B)

two

descriptor

model

for

ethanol

production

by

C.

sporogenes.




production of 1-propanol (descriptor significance 100) making

feasible the use of 1-propanol alone as a quite satisfactory

descriptor for calculating ethanol (Eq. (1)), although it was the

second most abundant produced higher alcohol. However, the

usage of three-descriptors has improved the model and has

introduced 1-butanol (the most abundant) and even isobutanol

(third in abundance) as relatively important determinants. The

decision on which model could be the most applicable depends on

the experimental error for the determination of each descriptor in

agreement with the general consideration that the simplest the

model the easier its application.

The model corresponding to E. coli for predicting ethanol

production (Eq. (5)) was presumably correlated to the production

of 1-butanol (descriptor significance 100) and of methyl-butanol(descriptor significance 53.68). Even by using these two descrip-

tors the model was quite satisfactory (Eq. (6)). The use of three

descriptors (1-butanol, methyl-butanol and 1-propanol) resulted

practically to the best fit, while the skip of isobutanol as a

descriptor did not affect the results.

Finally, the model corresponding to C. sporogenes appeared to

be the most complicated one. The significant deviation of the

experimental values from the relative theoretical ones indicates

that the system did not follow the linear regression but a more

complex one. Meanwhile the model using three descriptors (1-

propanol, 1-butanol and isobutanol) appeared to be the most

satisfactory.

Our contribution introduces the alcohols 1-butanol, 1-propa-

nol,

isobutanol,

and

methyl-butanol

as

potential

biomarkers

forquantifying microbial ethanol production and not only as

qualitative indicators of the process. This is the main difference

of our study compared to previously reported results by other

groups. The study of Nanikawa and colleagues has correlated the

ratio of 1-propanol to ethanol detected in rat blood and skeletal

muscle after death to the postmortem formed ethanol [13]. Felby

and Nielsen in their study have correlated the blood ethanol and 1-

propanol levels to the state of putrefaction of the body [14], while

the study of Moriya and Hashimoto has correlated ethanol to 1-

propanol in respect to the stage of putrefaction of the brain of

drowned persons [15]. Finally 1-butanol has been suggested by

Gubala as an indicator of how long a dead body has been laid in

water [16]. Our results are in agreement with the aforementioned

studies

in

identifying

1-propanol

and

1-butanol

as

main

indicators

of microbial ethanol production. Furthermore, our results identify

the set of alcohols, 1-propanol, 1-butanol and isobutanol, and the

set of alcohols, 1-butanol, 1-propanol and methyl-butanol, as

significant biomarkers of ethanol production by the clostridial

species (C. perfrigens and C. sporogenes) and E. coli respectively.

3.5.

Applicability

of

the

models

in

human

blood

and

plasma

products

The applicability of the constructed models was tested by

performing anaerobic cultures of normal human blood and plasma

inoculated with each one of the bacterial species at 25

8C. The

alcohols content was determined by HS-GC–FID at days 0, 1, 2, 3, 5,

7, 11 and 15 and the experimental results were compared to the

theoretical results deriving from the relevant models for C. perfigens, C. sporogenes and E. coli, respectively.

The following blood culture systems were tested in parallel:

I. whole human blood with EDTA inoculated with C. perfrigens;

II. whole human blood with citric ions inoculated with C.

perfrigens;

III. human plasma inoculated with (A)

E.

coli; (B)

C.

sporogenes; and

(C) C. perfrigens;

IV. human plasma spiked with glucose so as to achieve 200 mg/dL

initial concentration inoculated with (A)

E.

coli; and (B)

C.

sporogenes.

3.5.1. Alcohols production

In

the

blood

culture

system

‘‘I’’

ethanol

and

1-propanol

wereproduced during the fermentative period, while in the presence of

citric ions (system ‘‘II’’) ethanol was produced after the fifth day of

fermentation, without production of detectable amounts of any

other alcohol.

When human plasma was used as the culture medium and was

inoculated with

E.

coli (IIIA) and

C.

perfrigens (IIIC), ethanol and 1-

propanol were the only detectable alcohols produced during

fermentation. The system ‘‘IVA’’, consisting of human plasma

which was spiked with glucose and inoculated with

E.

coli,

produced higher amounts of ethanol than the system ‘‘IIIA’’

consisting of human plasma inoculated with E. coli without

additional glucose. Interestingly, the ethanol produced in the

system ‘‘IVA’’ was significantly lower than the ethanol amount

produced

in

the

E.

coli

cultures

with

BHI

as

the

culture

medium

Fig. 4. (A) Four descriptors model and; (B) two descriptor model for ethanol production by E. coli.




(Table 4), although the initial glucose concentration in both culture

media was the same (200 mg/dL). The systems ‘‘IIIB’’ and ‘‘IVB’’

consisting of human plasma inoculated with C. sporogenes did not

produce ethanol or other alcohols during the 15 days of incubation,

irrespectively of the additional glucose (system ‘‘IVB’’).

These results emphasize that differences in the composition of

the culture medium (BHI, whole human blood and human plasma)

can affect the amount of the produced ethanol by each microbe and

they are in agreement with our previous observation (Section 3.3).

The cease in the higher alcohols production in

C.

pefrigens human

blood cultures in the presence of citric ions (system ‘‘II’’) may be

attributed to the flexibility offered by the aminoacids fermentation

pathways (which generate the higher alcohols) which can be omitted

under certain conditions, as suggested earlier [5]. The determination

of 1-propanol as the only detectable higher alcohol produced in the

microbial blood and plasma cultures could be attributed to its ability

to be produced from many different substrates and fermentation

pathways, in response to changing environmental conditions, in

agreement with previous suggestion [5].

3.5.2. Testing of the models

The results of the system consisting of whole human blood

inoculated with

C.

perfrigens (system ‘‘I’’) appear to be very

complex compared to the relevant model as this was described by

Eq. (1). The experimental values cannot be predicted successfully

by PLS regression and the most appropriate fitting model is a 2nd

degree polynomial (R2 = 0.79) implying the difficulty in success-

fully predicting its behavior. An alternative methodology might be

to remove the experimental values from the first two days,

although our intension was to approach the volatiles relationship

during the whole experimental time, which will result in a linear

relationship with R2 = 0.80.

In the experiment with human plasma inoculated with C.

perfrigens (IIIC) the concentration of produced 1-propanol remains

almost constant from day one to day fifteen. The application of the

relevant model (Eq. (1)) gives a maximum error of 22% (average

16%). The model also confirms the linear relationship between

ethanol and 1-propanol, at first approximation, showing

R2 = 0.78

when the high experimental value for ethanol measured on the

first day was excluded.

The model of E. coli has a quite low mean square error

(indicating the difference between the true values of the

concentrations and the estimated ones) which is suggestive of a

successful prediction. The results have showed that the relevant

model (Eq. (5)) is applicable for human plasma spiked with glucose

(system ‘‘IVA’’). The maximum error between the calculated and

theoretical values of ethanol concentration is approximately 25%

(average 22%) for ‘‘IVA’’. When the model is applied to the system

Table 5

Results for the calculated ethanol concentrations after applying each model (Eqs. (1)–(6)) in postmortem cases with the standard error (E%) for each case.

# Manner of death Measured ethanol (g/L) C. perfrigens,

Eq. (1)

C. perfrigens,

Eq. (2)

E. coli, Eq. (5) E. coli, Eq. (6) C. sporogenes,

Eq. (3)

C. sporogenes,

Eq. (4)

Ethanol

(g/L)

E% Ethanol

(g/L)

E% Ethanol

(g/L)

E% Ethanol

(g/L)

E% Ethanol

(g/L)

E% Ethanol

(g/L)

E%

1 Violent 0.10 0.13 26 0.05 151 0.24 128 0.15 44 0.24 131 0.27 156

2 Natural 0.16 0.15 8 0.05 129 0.71 344 0.80 399 0.24 53 0.29 82

3 Natural 0.16 0.09 43 0.07 145 0.20 26 0.15 8 0.18 8 0.21 28

4 Violent 0.19 0.22 18 0.00 99 0.32 69 0.15 20 0.43 128 0.44 136

5 Natural 0.20 0.06 70 0.09 146 0.18 12 0.15 25 0.11 45 0.15 27

6 Natural 0.21 0.30 45 0.05 78 0.39 85 0.15 29 0.58 176 0.59 179

7

Undetermined

0.21

0.20

6

0.03

116

0.29

38

0.15

29

0.30

41

0.31

488

Violent

0.22

0.27 23

0.03

87

0.36 64

0.15

31

0.52 139

0.53 144

9 Undetermined 0.25 0.14 43 0.04 117 0.25 1 0.15 41 0.28 11 0.31 21

10 Undetermined 0.29 0.29 1 0.04 85 0.38 32 0.15 48 0.57 97 0.57 100

11 Undetermined 0.35 0.36 2 0.06 82 0.43 23 0.15 57 0.63 81 0.63 80

12 Natural 0.39 0.33 16 0.03 91 0.61 57 0.45 16 0.52 34 0.53 36

13 Natural 0.42 0.44 5 0.06 85 0.49 16 0.15 64 0.61 45 0.59 39

14 Violent 0.42 0.54 28 0.19 56 0.59 41 0.15 64 1.05 151 1.03 145

15 Natural 0.42 0.40 6 0.11 75 0.66 55 0.33 21 0.85 102 0.78 84

16 Undetermined 0.47 0.84 79 0.32 32 0.84 79 0.15 68 1.49 217 1.42 202

17 Natural 0.48 0.72 50 0.29 39 0.75 57 0.15 69 1.42 196 1.37 186

18

Violent

0.52

0.65 26

0.25

51

0.69 34

0.15

71

1.28 149

1.25 142

19 Undetermined 0.58 0.85 47 0.28 52 0.83 44 0.15 74 1.33 129 1.25 115

20 Undetermined 0.59 0.29 52 0.04 93 0.37 37 0.15 75 0.55 6 0.56 5

21

Undetermined

0.60

0.63 6

0.23

61

0.67 13

0.15

75

1.21 104

1.18 97

22 Undetermined 0.62 1.12 80 0.20 67 0.98 59 0.15 76 0.98 58 0.83 33

23 Violent 0.62 1.02 64 0.45 27 1.01 63 0.15 76 1.95 214 1.86 200

24 Undetermined 0.63 1.01 60 0.42 33 0.99 58 0.15 76 1.85 193 1.76 179

25 Undetermined 0.64 0.43 34 0.05 91 1.22 91 0.89 40 0.87 35 0.56 13

26 Undetermined 0.68 0.43 36 0.11 84 0.50 27 0.15 78 0.80 17 0.78 15

27 Undetermined 0.68 0.41 39 0.11 84 1.11 63 1.03 51 0.76 12 0.78 15

28 Violent 0.69 0.42 39 0.08 89 0.48 31 0.15 78 0.67 2 0.66 5

29 Undetermined 0.69 0.25 64 0.02 98 0.34 51 0.15 78 0.48 31 0.49 30

30 Undetermined 0.71 0.29 60 0.04 94 0.37 47 0.15 79 0.56 21 0.57 20

31 Natural

0.72

0.46

35

0.14

81

1.03 43

0.82 13

0.92 28

0.88 23

32 Violent 0.73 0.67 8 0.26 64 0.71 3 0.15 79 1.30 78 1.26 73

33 Undetermined 0.75 1.00 34 0.46 38 1.00 33 0.15 80 2.00 166 1.91 155

34 Undetermined 0.81 0.30 64 0.03 96 0.74 9 0.63 22 0.56 32 0.54 33

35 Undetermined 0.87 0.95 9 0.42 52 1.56 80 0.99 13 1.84 112 1.76 103

36 Undetermined 0.91 0.92 2 0.42 54 0.93 3 0.15 83 1.84 103 1.76 95

37 Undetermined 0.94 1.13 20 0.33 65 1.72 83 0.94 0 1.61 71 1.32 40

38 Natural 0.98 0.43 56 0.10 90 0.49 50 0.15 85 0.74 24 0.73 26

39 Undetermined 1.08 0.29 73 0.04 96 0.37 65 0.15 86 0.56 49 0.56 48

40 Undetermined 2.58 1.08 58 0.49 81 1.06 59 0.15 94 2.08 19 1.99 23

The

original

ethanol

concentrations

measured

for

each

case

are

also

provided

along

with

the

manner

of

death.




of human plasma (IIIA) the model loses its power probably due to

the fact that ethanol and 1-propanol values are strongly correlated

exhibiting a steady proportion of 1:10.

Our results indicate that the constructed models have the

potential to be applied when different culture media are used,

under controlled experimental conditions. Factors such as the type

of microbial species, the glucose content and the medium

composition apparently affect the procedure of microbial ethanol,

1-butanol and higher alcohols production.

3.6.

Use

of

the

models

in

real

postmortem

cases

We retrospectively looked our chromatograms archives of the

last six years and we selected 40 chromatograms corresponding to

postmortem cases, having presence of other alcohols during the

original forensic ethanol analysis. The classification of these cases –

as resulted fromthe retrospective review ofthe forensic pathologist’s

reports – was: 10 casesof natural deaths; 8 cases of violent deaths;

and 22 casesof undetermined cause of death. Furthermore, 39 cases

had markedputrefactive phenomena at autopsy while the other one

had extensive traumatic lesions (Case no 4) – making easier the

invasion of microbes. We calculated for these cases the concentra-

tions of higher alcohols and 1-butanol. In Table 5 the original ethanol

concentrations measured for each case and the calculated ethanolconcentrationsafter applying each model (Eqs. (1)–(6)) are provided

along with the standard error for each case.

The models corresponding to

C.

perfrigens estimated the

microbial produced ethanol with a standard error <40% for 27

out of the 40 cases (68%), 26 of them with marked putrefaction (96%).

Additionally, 21 of these cases (78%) had ethanol lower than 0.7 g/L.

The models corresponding to

E.

coli estimated the microbial

produced ethanol with a standard error <40% for 25 out of the 40

cases (63%), 24 of them with marked putrefaction (96%). Moreover,

18 of these cases (72%) had ethanol lower than 0.7 g/L.

The models corresponding to C. sporogenes estimated the

microbial produced ethanol with an error <40% for 18 out of the 40

cases (45%) all presented with putrefaction at autopsy, while 12 of

these cases (67%) had ethanol lower than 0.7 g/L.It is worth mentioning that 28 out of the 29 cases (97%) having

original ethanol concentration lower than 0.7 g/L succeeded a

standard error

<40% in predicting the microbially produced

ethanol by at least one of the models.

The caseswere selected due to the co-detection of significant

amounts of volatiles along with ethanol during the original

ethanol analysis a factor making the relevant cases suspicious

for microbial ethanol production, as it was suggested [24]. Then

it was proved that the majority of the selected cases had marked

putrefaction at autopsy – another aggravating factor for

microbial activity and ethanol neo-formation [14,15,24,25].

Our results have shown that the models could effectively be

applied in postmortem cases especially when marked putrefac-

tion

is

present. In addition, the models of

C.

perfrigens

showbetter applicability than these of E.

coli and than those by C.

sporogenes. Yet, the models are applied more satisfactory when

ethanol concentrations are lower than 0.7 g/L. It is generally

accepted that ethanol concentrations lower than 0.7 g/L is more

possible to be of microbial origin for the majority of cases with

neo-formation of ethanol [2,3].

However, it has to be underlined that so far the study of real

postmortem cases meets serious limitations. As the ethanol

concentrations increase, it is possible part of the detected ethanol

to be of ante mortem origin making the proper interpretation of

postmortem ethanol analysis difficult. In such a case it is extremely

challenging and premature to suggest that one model is more

suitable than another. On the other hand, other microbes might

generate

different

alcohols

patterns

resulting

to

different

models.

This might to explain the major discrepancies observed between

measured and calculated ethanol concentrations for some of the

presented cases.

Ideally, it should be known which microbial species have

been present or have been activated in each post-mortem case.

Practically, more mathematical models, covering a large

spectrum of bacterial or fungal species should be available.

Moreover, the identification of more influencing factors and

the quantification of their impact on the procedure would

eventually result to the construction of more

accurate models which could be applied more efficiently to

real cases.

Under these views it is naive to suggest that such a complicated

process, with so many potential effectors, could be entirely

covered by the simple models resulting from the study of only

three microbes. However, this study represents only an initial

approach, albeit with some useful outcomes; it is promising that

even sucha simplifiedapproach,highly challengingat the present,

might provide a methodical insight into this huge, long lasting

problem.

4. Concluding remarks

To our knowledge the present contribution is a first approach to

the quantification of microbial ethanol production by demonstrat-

ing a mathematical model of the procedure in cases where other

alcohols are produced simultaneously with ethanol. The microbial

biosynthesis of ethanol in parallel with the biosynthesis of 1-

butanol and the higher alcohols (1-propanol, isobutanol, 2-methyl-

1-butanol and 3-methyl-2-butanol) identifies these alcohols as

biochemical biomarkers suitable for quantifying the microbial

ethanol production.

The correlation between the levels of the alcohols and the

amount of the microbially produced ethanol is expressed by

mathematical models (equations).

The alcohols 1-propanol and 1-butanol appear to have a

preponderant

role

as

descriptors

of

the

relevant

models.The most obvious factor affecting the produced ethanol, even

for species within the same genus (such as C. perfrigens and C.

sporogenes), is the microbe type.

Differences in the composition of the culture medium (despite

the initial glucose concentration) result in the production of

different amounts of ethanol and other alcohols from the same

species.

The overall conclusion of the reported experimental study is

that mathematical modeling of themicrobial ethanol production

is feasible, at least partially. Nevertheless, given the complexity

of the process, extrapolation of the results to everyday practice

requires ongoing work on the identification and

quantification of the factors influencing the postmortem ethanol

production.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in

the online version, at doi:10.1016/j.forsciint.2011.03.003 .

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Microbial Ethanol Production Experimental Study and Multivariate Evaluation

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