Surface-enhanced Raman spectroscopy introduced into the International Standard Organization (ISO) regulations as an alternative method for detection and identification of pathogens in the food industry
Surface-enhanced Raman spectroscopy introduced into the International Standard Organization (ISO) regulations as an alternative method for detection and identification of pathogens in the food industry
Evelin Witkowska 0 1 2
Dorota Korsak 0 1 2
Aneta Kowalska 0 1 2
Monika Księżopolska-Gocalska 0 1 2
Joanna Niedziółka-Jönsson 0 1 2
Ewa Roźniecka 0 1 2
Weronika Michałowicz 0 1 2
Paweł Albrycht 0 1 2
Marta Podrażka 0 1 2
Robert Hołyst 0 1 2
Jacek Waluk 0 1 2
Agnieszka Kamińska 0 1 2
0 Faculty of Mathematics and Natural Sciences, College of Science, Cardinal Stefan Wyszyński University , Dewajtis 5, 01-815 Warsaw , Poland
1 Faculty of Biology, Institute of Microbiology, Applied Microbiology, University of Warsaw , Miecznikowa 1, 02-096 Warsaw , Poland
2 Institute of Physical Chemistry, Polish Academy of Sciences , Kasprzaka 44/52, 01-224 Warsaw , Poland
We show that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast, reliable, and easy method for detection and identification of food-borne bacteria, namely Salmonella spp., Listeria monocytogenes, and Cronobacter spp., in different types of food matrices (salmon, eggs, powdered infant formula milk, mixed herbs, respectively). The main aim of this work was to introduce the SERS technique into three ISO (6579:2002; 11290-1:1996/A1:2004; 22964:2006) standard procedures required for detection of these bacteria in food. Our study demonstrates that the SERS technique is effective in distinguishing very closely related bacteria within a genus grown on solid and liquid media. The advantages of the proposed ISO-SERS method for bacteria identification include simplicity and reduced time of analysis, from almost 144 h required by standard methods to 48 h for the SERS-based approach. Additionally, PCA allows one to perform statistical
Salmonella Typhimurium; SERS; ISO methods; Food; Bacteria detection; PCA
classification of studied bacteria and to identify the spectrum
of an unknown sample. Calculated first and second principal
components (PC-1, PC-2) account for 96, 98, and 90% of total
variance in the spectra and enable one to identify the
Salmonella spp., L. monocytogenes, and Cronobacter spp.,
respectively. Moreover, the presented study demonstrates the
excellent possibility for simultaneous detection of analyzed
foodborne bacteria in one sample test (98% of PC-1 and PC-2) with
a goal of splitting the data set into three separated clusters
corresponding to the three studied bacteria species. The studies
described in this paper suggest that SERS represents an
alternative to standard microorganism diagnostic procedures.
Many methods have been developed and applied in the
detection and identification of bacteria species utilizing
biochemical, immunological, and nucleic acid-based approaches .
However, these methods are time-consuming (at least 24 h
to even 2 weeks), expensive because of the use of a variety
of microbiological media, and require qualified personnel.
Recently, real-time PCR assays for the detection of bacterial
meningitis pathogens have been developed [2–4] and
multiplex detection of several target DNAs is realizable .
Vibrational spectroscopy and fluorescence have also been
employed for bacteria spore identification [6–8]. However,
all these methods have some limitations, e.g., in the PCR
technique the commonly used targets are unspecific and may
cause false results, the fluorescence spectroscopic technique
lacks specificity of the chemical information of analyzed
samples, and IR spectroscopy is not suited for measurements in
aqueous solutions. Therefore, there is an urgent need to
develop a rapid, sensitive, simple, and reliable method for
identification of pathogens.
The surface-enhanced Raman spectroscopy (SERS) is an
optical method that can be used in testing of chemical and
biochemical samples with high sensitivity and specificity.
The enhanced signal is explained by the combination of
electromagnetic (EM enhancement) and chemical (CT)
mechanisms. The latter is related to charge transfer between a
substrate and an adsorbed molecule . The electromagnetic
enhancement results from the resonance of the applied field with
surface plasmon oscillations of the metallic nanostructures.
Theoretically, the EM enhancement can reach factors of
103–1011, whilst the CT enhancement factors have been
calculated to be up to 103 [10, 11]. This huge enhancement of
Raman scattering (even single molecules can be observed
) ensures that SERS is very promising for biomedical
and analytical studies. Moreover, this technique offers
nondestructive, reliable, and fast detection, which leads to various
practical applications in studying, for example, nucleic acids
and proteins , therapeutic agents , drugs and trace
materials , microorganisms , and cells . Other
important benefits of SERS include the quenching of the
fluorescence background and improvement of the signal to noise
ratio . In particular, the development of SERS for the
detection and identification of bacterial pathogens has
attracted recent research efforts [19–25]. Rapid and early
detection is potentially useful in clinical diagnosis, the food
industry, or forensics.
In a pioneering study, Efrima and Bronk  presented the
SERS spectra of Escherichia coli mixed with silver colloid
and found that the recorded spectra are dominated by flavin
vibrations. Flavins are important coenzymes present in the
inner site of the bacteria cell wall . The authors explained
the specificity of SERS owing to enhanced binding affinities
of silver nanoparticles to flavins via the isoalloxazine
fusedring moiety, which additionally works as a nucleation center
for these nanoparticles. SERS spectra have also been reported
for bacteria placed on electrochemically roughened metal
surfaces [20, 21], bacteria coated by silver metal deposits [24,
25], or bacteria co-deposited with metal nanoparticles on a
glass surface . Spectral analysis allows one to study the
bacteria structure, thus enabling the detection, diagnosis, and
differentiation among bacteria species. Additionally, it may
provide a considerable amount of detailed information, which
is important for understanding the biological and chemical
structure of the organisms.
An interesting issue is the detection of food-borne bacteria.
Salmonella enterica, common bacteria found in rotten or
unwashed food, is one of the most important food-borne
pathogens worldwide and the second most frequently reported
zoonotic agent in the European Union (EU) after
thermotolerant Campylobacter. In 2014, a total of 88,238
confirmed salmonellosis cases were reported by 27 member states
(MS) of the European Union, resulting in a notification rate of
23.4 cases per 100,000 population [29, 30]. Therefore the fast
and simple detection of Salmonella in food is needed. Assaf
et al.  demonstrated in 2014 the possibility of using
normal Raman spectroscopy coupled with ISO standards for the
detection of Salmonella spp. in selected food samples.
However, principal component analysis (PCA) of Raman
spectra reveals only 51% of total variance between bacteria
species isolated from food industry samples. Another serious
infection is listeriosis usually caused by eating food
contaminated with L. monocytogenes. Listeriosis represents a serious
public health problem since it has been fatal in around 20% of
cases during the last two decades . Infections caused by
Cronobacter sakazakii, formerly Enterobacter sakazakii, are
also dangerous, especially for older people and babies .
The Electronic Supplementary Material (ESM) presents the
characteristics of these three bacteria species in more detail.
The identification methods of food-borne bacteria are
standardized at the international level by the International
Organization for Standardization (ISO) and mostly based on
conventional microbiology. In this paper we show a new
approach of using SERS technique instead of current
identification process standards to detect food-borne bacteria, namely
Salmonella spp., Listeria monocytogenes, and C. sakazakii
(biochemical methods do not allow the identification of the
species within the genus Cronobacter) in different types of
food matrices (milk powder/infant formula, salmon, ham,
eggs, mixed herbs) using Ag@FTO SERS substrates (Polish
Patent Application P-408785).
We compare the SERS experiment with detection steps
requested in ISO 6579:2002  (horizontal method for the
detection of Salmonella spp.), ISO 11290–1:1996/A1:2004
 (horizontal method for the detection and enumeration
of L. monocytogenes), and ISO/TS 22964:2006  (IDF/
RM 210:2006) (a method for the detection of E. sakazakii
in milk powder and powdered infant formula) standards. In
the mentioned standards, the used methods allow one to
detect one Salmonella spp. cell in 25 g of a food sample, one
L. monocytogenes cell in 25 g of ready-to-eat foods intended
for infants and for special medical purposes or in ready-to-eat
foods able to support the growth of L. monocytogenes, and
one C. sakazakii cell in 10 g of dried infant formula and dried
dietary foods for special medical purposes intended for
infants below 6 months of age. The mentioned ISO standards
are adapted to Commission Regulation (EC) No. 2073/2005
of 15 November 2005 on microbiological criteria for
The proposed SERS-based method of bacteria
identification challenges the standard biochemical methods in terms of
simplicity, specificity, and rapidity (the time of the whole
analysis is reduced to 48 h from a total of about 140 h required
by ISO standards). Additionally, the procedure presented in
this study combines the SERS technique with multivariate
statistical methods. To show the significant differences among
SERS spectral features PCA as one of the most robust
statistical methods is applied to (i) extract the biochemical
information from bacteria spectra, (ii) perform the statistical
classification of microorganisms, and finally (iii) identify the
spectrum of an unknown sample by comparing it to the library of
spectra from known bacteria.
Materials and methods
Bacteria strains and growth media
The following bacteria strains were used in this study:
L. monocytogenes ATCC 13932, L. ivanovii PZH 7/04,
S. enterica subspecies I, serovar Typhimurium 2021,
C. sakazakii ATCC 29544. L. ivanovii PZH 7/04, and
Salmonella Typhimurium 2021 were obtained from the
collections of the National Institute of Public Health - National
Institute of Hygiene (Warsaw, Poland).
Cultures were maintained in trypticase soy yeast extract
agar (TSYEA) (Oxoid, Basingstoke, Hampshire, UK) at
4 °C throughout the study period and stored at −80 °C in brain
heart infusion broth (BHI) supplemented with 20% glycerol.
Half Fraser broth, Fraser broth, Chromogenic Listeria
LabAgar acc. to ISO 11290 (Chrom Lis) and Palcam Listeria
Lab-Agar (Palcam) were used to detect L. monocytogenes.
Muller–Kauffmann tetrathionate novobiocine broth
(MKTTn), Rappaport–Vassiliadis soya broth (RVS), xylose
lysine deoxycholate agar (XLD), and Chromogenic
Salmonella Lab-Agar (Chrom Sal) were used to detect
Salmonella spp. Buffered peptone water (BPW), modified
laurylsulfate-tryptose vancomycin broth (mLST), and ESIA
Lab-Agar (ESIA) were used to detect C. sakazakii (formerly
E. sakazakii). All media were purchased from Biocorp
Five food matrices including smoked salmon (for detection of
L. monocytogenes and Salmonella spp.), ham (for detection of
L. monocytogenes), eggs (for detection of Salmonella spp.),
powdered infant formula and mixed herbs (for detection of
Cronobacter spp.) were analyzed. The food samples came
from retail stores and were transported to the laboratory inside
portable insulated cold boxes (except milk infant powder and
spices). These transport conditions guarantee the chemical and
biological stability of samples over time. The samples were
immediately subjected to microbiological analysis.
For preparation of inoculum, colonies from 24-h cultures of
L. monocytogens ATCC 13932, S. Typhimurium 2021, and
C. sakazakii ATCC 29544 were resuspended in sterile saline
solution to a turbidity of 0.5 McFarland units (approximate cell
count density 1.5 × 108 cfu) using densitometer (Densilameter
II, Pliva-Lachema Diagnostika, Czech Republic). Bacterial
suspensions were diluted in saline solution to 1 × 10−6 using
a 10-fold serial dilution protocol.
Food samples of 25 g (smoked salmon, ham, and eggs) or
10 g (powdered infant formula and mixed herbs) were taken in
an aseptic manner and homogenized in 225 ml and 90 ml,
respectively, of pre-enrichment broth.
Each sample was performed in triplicate: (i) as a control,
sample containing the food matrix and appropriate
preenrichment medium. For positive samples, (ii) 0.1 ml of
1 × 10−5 and (iii) 1 × 10−6 dilutions of bacterial suspension
were added to the pre-enrichment medium mixed with food
The pre-enrichment, enrichment, and selective isolation
steps were performed according to the standard procedures,
ISO 11290–1 (L. monocytogenes), ISO 6579:2002
(Salmonella spp.), and ISO/TS 22964:2006 (Cronobacter spp.).
ISO standards versus SERS-based methodology
The SERS-based methodology for bacteria identification in
respect to ISO standards is presented in Fig. 1 and Fig. S1
Salmonella spp. detection
Path I (biochemical) For Salmonella detection (ISO
6579:2002) the number of bacterial cells was increased in
BPW mixed with 25 g of food sample. After that the
simultaneous two enrichment steps in RVS (which is recommended
as a selective enrichment medium for the isolation of
Salmonella from food and environmental specimens) and
MKTTn (which selectively promotes the growth of
Salmonella and inhibits Gram-positive bacteria) were
completed. These selective media reveal the presence of
Salmonella colonies in food samples. Ten microliters of each
selective medium is cultured on (1) XLD agar and on (2)
Chrom Sal agar. Then, five suspect colonies from XLD and
chromogenic Salmonella agar are incubated on nutrient agar
(NA). After 15 h of incubation the colonies are identified
within the next few days by biochemical methods to confirm
or deny the presence of Salmonella in the analyzed sample
Path II (ISO-SERS) The shortest variant of Salmonella
detection procedure from food samples provides the
identification within only 48 h. Here 10 μl of the liquid part of BPW and
food sample mixture was streaked directly onto the surface of
Fig. 1 Scheme representing the different paths applied for a Salmonella spp., b L. monocytogenes, and c C. sakazakii detection in food samples
XLD and Chrom Sal agar and was identified by the proposed
SERS-based method (Fig. 1a).
Reference path (ISO-SERS) After the pre-enrichment in
BPW and selective enrichment in RVS and MKTTn, 10 μl
of each selective media was rinsed and cultured on XLD agar
and on Chrom Sal agar. This level ends the SERS-based
method for Salmonella identification (Fig. 1a) and cuts the total
time required by ISO to 72 h.
Reference from precultures The bacterial stocks stored at
−80 °C in brain heart infusion broth (BHI) supplemented
with 20% glycerol were freshly cultured on appropriate
media (S. Typhimurium on XLD and Chrom Sal media,
L. monocytogenes and L. ivanovii on Chrom Lis and
Palcam media, and C. sakazakii on ESIA).
Figure 1 presents in detail these three paths used for each
bacteria identification. The procedures applied for
Salmonella identification can be divided into several paths:
(i) ISO recommended methods (144 h, path I), (ii) analysis
of bacteria colonies from XLD and Chrom Sal media after
pre-enrichment in BPW and selective enrichment in RVS
and MKTTn media (72 h, Ref. path), and (iii) direct analysis
of bacterial colonies from XLD and Chrom Sal media after
pre-enrichment in BPW (48 h, path II). For all paths we grow
Salmonella onto broths recommended by ISO standards.
L. monocytogenes detection
The procedure applied for L. monocytogenes identification
can be divided into several similar paths as in the case of
Salmonella spp.: (i) ISO recommended methods (120–
168 h), (ii) direct analysis of bacterial colonies from Palcam
and Chrom Lis media after selective enrichment in Half Fraser
medium (48 h) and analysis of bacteria colonies from Palcam
and Chrom Lis media after selective enrichment in Half Fraser
and Fraser media (Ref. path 72 h) (Fig. 1b). For all paths we
grow L. monocytogenes onto broths recommended by ISO
standards. Half Fraser and Fraser media increase the number
of Listeria spp. cells in samples. The whole procedure
(according to path I) of L. monocytogenes detection and
identification takes up to 7 days. Two other paths, i.e., path II and the
Ref. path (novelty introduced in the SERS-based procedure),
simplify the identification processes to 2 and 3 days,
In practice, for L. monocytogenes detection firstly the
number of bacteria in Half Fraser broth mixed with 25 g of food
sample was increased, and, the next day, 100 μl of this
medium was transferred to Fraser broth and incubated in two
variants: for 24 h and 48 h. Then, in each variant, bacteria were
cultured on (i) Palcam with supplements (usually used as a
selective and differential medium for the detection and
isolation of L. monocytogenes from foods and environmental
samples) and on (ii) Chrom Lis (medium for isolation,
enumeration, and presumptive identification of Listeria species and
L. monocytogenes from food samples). After 24 h the SERS
spectra were collected for these two media.
Listeria is a genus of bacteria which encompasses several
species, but only L. monocytogenes is regulated by
Commission Regulation (EC) No. 2073/2005  and should
not be present in food samples. The detection system of
Chrom Lis (ISO standard) is based on
5-bromo-4-chloro-3indolyl-β-D-glucopyranoside, which can be cleaved by
β-Dglucosidase produced by all Listeria spp. The second most
typical pathogenic bacterium is L. ivanovii, but this is so far
not listed in the aforementioned Commission Regulation.
However, the information about bacteria species present in
the sample is crucial. The two pathogenic species,
L. monocytogenes and L. ivanovii, can be distinguished from
non-pathogenic Listeria spp. by their
phosphatidylinositolspecific phospholipase C (PI-PLC) activity . The typical
colony morphology of Listeria spp. is reported to be turquoise
blue. Pathogenic Listeriaceae are additionally surrounded by
a translucent halo .
Following the ISO/TS 22964:2006 (IDF/RM 210:2006)
standard, five colonies from ESIA medium are cultured on tryptic
soy agar (TSA) for 24 h and then identified by biochemical
methods (Fig. 1c, path I, 144 h). In the proposed here
SERSbased method (path II) 10 μl of the mixture of BPW and food
sample was streaked directly onto the surface of ESIA plate
and identified by SERS (Fig. 1c, path II). This path cuts the
total time of the experiment to 48 h. In practice, for
C. sakazakii identification, these bacteria were multiplied in
the mixture prepared by dissolving 10 g of milk powder in
BPW. The next day 100 μl of the obtained liquid was cultured
in mLST medium. After 1 day of culturing, by using a 10-μl
loop, the mixture was streaked onto the surface of the ESIA
agar and incubated for one more day. This step ends the
reference path of C. sakazakii identification (Fig. 1c, Ref.
path) at 78 h.
PCA was performed on the preprocessed SERS spectra. PCA
is a data reduction technique in which the new variables,
called principal components (PC), are calculated from original
variables. The first principal component (PC-1) accounts for
the greatest variance in the data. The method of PCA is based
on a model assuming X = TPT + E, where the X matrix is
decomposed by PCA into two smaller matrices, one of scores
(T) and another of loadings (P) , and E is the error matrix.
PC scores are related to a linear combination of the original
variables and describe the differences or similarities in the
samples. PCA provides insight into the percentage of variance
explained by each PC and shows how many PCs should be
kept to maintain the maximum information from the original
data without adding noise to the current information. Loadings
describe the data structure in terms of variable correlation and
reflect how well one PC takes into account the variation of that
variable. By analyzing the plot of PC loadings as a function of
the variables (i.e., Raman shifts) one can indicate the most
important diagnostic variables or regions related to the
differences found in the data set. In this study we applied
PCA to all collected spectra of bacteria, namely S. enterica,
L. monocytogenes, and C. sakazakii. This analysis enables one
to investigate the spectral variations and to find the most
significant modes contributing to the variance explained
by these PCs. PCA was performed on the preprocessed
Raman spectra to (a) evaluate the spectral differences among
the bacteria species grown on XLD agar and on Chrom Sal
agar (Salmonella spp.), (b) identify Listeria species
(L. monocytogenes and L. ivanovii), and (c) identify
C. sakazakii from among bacteria species grown on ESIA
agar, and finally to (d) develop a model for detection
o f f oo d - b o r n e b a c t e r i a , n a m e l y Sa l m o ne l l a s p p . ,
L. monocytogenes, and Cronobacter spp.
Bacteria sample preparation for SERS measurements
Single typical S. Typhimurium colonies on XLD and Chrom
Sal agar, C. sakazakii on ESIA, and L. monocytogenes and
L. ivanovii on Chrom Lis agar were collected and the bacteria
were resuspended in a sterile saline solution and centrifuged
for 5 min at 1200 × g in order not to destroy the cell
membrane. Finally, the supernatant liquid was discarded and the
bacterial cells were redispersed in 0.9% NaCl water. The
centrifugation process was repeated three times to obtain a
solution of clean bacterial cells. About 10 μl of aqueous bacterial
solution was applied to the SERS substrate.
Silver nitrate (AgNO3) and trisodium citrate dihydrate were
purchased from Sigma–Aldrich; acetone, isopropanol, and
methanol were purchased from Avantor Performance
Materials Poland (POCH S.A., Poland). FTO-coated glass
was from Delta Technologies. Water was purified with an
ELIX system (Millipore, Merck, Germany). All reagents were
used as received without further purification.
Preparation of SERS platform SERS substrates were
produced using a three-electrode electrochemical process with
constant potential of −1.0 Vapplied for 15 min. Silver nanoparticles
(AgNPs) were deposited on an FTO electrode from aqueous
solution of 0.3 mM AgNO3 and 2.6 mM trisodium citrate
dihydrate under controlled conditions of temperature and
stirring. After electrodeposition, the electrodes with AgNPs were
rinsed with deionized water and dried under a stream of air. The
SERS spectra were collected from 40 different points for each
sample in mapping mode (20 × 40 μm).
Raman spectroscopy and SERS Measurements were carried
out using a Renishaw inVia Raman system equipped with a
785-nm diode laser. The light from the laser passed a line filter
and was focused on a sample mounted on an X–Y–Z
translation stage with a ×50 microscope objective, NA = 0.25. The
beam diameter was approximately 2.5 μm. The laser power at
the sample was 5 mW or less. The microscope was equipped
with 1200 grooves per mm grating, cutoff optical filters, and a
1024 × 256 pixel Peltier-cooled RenCam CCD detector,
which allowed registering the Stokes part of Raman spectra
with 5–6 cm−1 spectral resolution and 2 cm−1 wavenumber
accuracy. The experiments were performed at ambient
conditions using a back-scattering geometry.
The recording of the spectra was started immediately after
placing the analyzed sample onto a SERS-active surface.
During a period of about 30 min, SERS spectra were
repeatedly recorded, while at the same time, the focus of the laser
beam was readjusted. The time required for completing a
single SERS spectrum was about 60 s. The obtained spectra were
processed with the Wire3 software provided by Renishaw.
PCA spectral data analysis SERS spectra were prepared for
PCA using a two-step approach. First, using built-in OPUS
software (Bruker Optic GmbH 2012 version) the spectra were
smoothed with a Savitzky–Golay filter, the background was
removed using baseline correction (concave rubberband
correction; no. of iterations 10, no. of baseline points 64), and
then the spectra were normalized using a Min–Max
normalization. All the data were transferred to the Unscrambler
software (CAMO software AS, version 10.3, Norway), where
PCA was performed.
In this study the SERS technique was introduced into the
ISO standards for identification of pathogenic bacteria in food,
namely Salmonella spp., L. monocytogenes, and Cronobacter
spp., in respect to the methodology presented in Fig. 1.
According to Commission Regulation (EC) No. 2073/2005
Salmonella spp. should not be present in food samples in
any amount, and L. monocytogenes or Cronobacter spp.
should not be detected in selected food products. The
identification procedures requested by ISO norms are complex and
time-consuming (up to 6 days, see path I in Fig. 1a–c). As
mentioned above, SERS has been used for fast identification
of pathogens in the selected food samples (according to path II
in Fig. 1a–c). In this analysis the long, time-consuming
incubation is omitted. The direct SERS analysis (48 h) of bacteria
colonies inoculated on agar with selective media
(characteristic for incubated bacteria, see Fig. 1) was performed.
The longer path (72 h), named the Ref. path in Fig. 1, was
applied to identify these three bacteria in respect to ISO
standards and to validate the results obtained in path II. In the
reference path the selective media along with selective
enrichment allow one to grow only the colonies of the analyzed
bacteria. The results from this step were used as a proof of
identification made in path II. The data obtained in both the
reference path and path II were additionally compared with
the reference SERS spectra of all analyzed food-borne bacteria
( S . Ty p h i m u r i u m , L . m o n o c y t o g e n e s , L . i v a n o v i i ,
C. sakazakii) collected from precultures (data not shown).
All the obtained spectra (from reference path, path II, and
precultures) allow identification of bacteria species (positive
control) in the analyzed food samples using the SERS
technique (ESM Fig. S4).
According to Fig. 1a, the biochemical path (path I) of
Salmonella spp. detection and identification takes 6 days.
The novelty introduced in the ISO procedure by adding
SERS (path II) reduces the identification process to 2 days.
In path II, after culturing food samples (from salmon and eggs)
contaminated with Salmonella cells on XLD and Chrom Sal
agar, not only Salmonella colonies but also colonies of other
bacteria species were obtained. On XLD the Salmonella
colonies have a characteristic black color, while interfering
Enterobacteriaceae strains are yellow. In the case of Chrom
Sal agar Salmonella may also grow with two other interfering
Enterobacteriaceae species which are colorless and blue,
while Salmonella colonies are purple. SERS analyses of black
or purple colonies of Salmonella and co-existing species were
performed using Ag@FTO SERS-active substrates. Figure 2a
presents the SERS spectra collected from Salmonella and
other bacteria species grown on both these broths.
For Salmonella grown on both XLD and Chrom Sal media,
several characteristic bands at 649, 723, 958, 1030, 1095, ca.
1220, and ca. 1467 cm−1 are observed. These bands are
detected also in SERS spectra of many Gram-positive and
Gramnegative bacteria species like E. coli or S. epidermidis [40, 41]
and are assigned as follows: 649 cm−1 (guanine and tyrosine);
723 cm−1 (C–N stretching mode of the adenine part of flavin
adenine dinucleotide, FAD); 958 cm−1 (C=C deformation or
C–N stretching); 1030 cm−1 (C–C stretching); 1095 cm−1 (O–
P–O stretching in DNA); 1220 cm−1 (amide III); 1467 cm−1
(CH2 deformation) . The differences between these two
a Salmonella spp.
b Listeria spp.
c Cronobacter spp.
Fig. 2 SERS spectra of S. Typhimurium cells and other bacteria species
grown on XLD and chromogenic agars (a), L. monocytogenes and
L. ivanovii detected in milk powder (infant formula), salmon, ham, and
spectra, especially in the ratio of intensities of some bands,
e.g., 649, 723, and 1030 cm−1, originate from bacteria
responding to environmental changes (XLD or Chrom Sal)
by changing their metabolic profiles and composition of the
cell walls .
Most of these bands appear also in the SERS spectra of
bacteria co-existing with Salmonella (labeled yellow and blue
Enterobacteriaceae in Fig. 2a). All bacteria species reveal
their own individual spectral characteristics, which aids in
the whole organism fingerprint analysis. For example, the
band at 1030 cm−1 can be seen in Salmonella, but not in
interfering Enterobacteriacae species. To distinguish
Salmonella from these co-existing bacteria, the ratio of
eggs (b), and C. sakazakii growing with Enterobacteriaceae (c)
according to path II, see Fig. 1
intensities of the bands at ca. 650 cm−1 and ca. 730 cm−1
can be used. Table 1 contains the assignments of all observed
bands for Salmonella, L. monocytogenes, and C. sakazakii
L. monocytogenes and L. ivanovii spectra (both species
grown on ALOA agar from salmon and ham) presented in
Fig. 2b are very similar; however, one can observe two main
differences. In the case of L. ivanovii one can see an additional
ba nd at 62 6 cm−1 wh ich is a bs en t in the c as e of
L. monocytogenes. Moreover, the intensity ratio of bands
734 and 650 cm−1 is higher in the L. ivanovii spectrum. Both
spectra show also a common band at about 790 cm−1
(cytosine, uracil), 960 cm−1 (C=C deformation), and 1330 cm−1
Main bands observed in Salmonella spp., L. monocytogenes, and Cronobacter spp. spectra and their assignments [28, 44–47]
(adenine, guanine, CH deformation). The detailed assignment
of all the observed bands is presented in Table 1.
Subsequently culturing food samples contaminated with
C. sakazakii cells on ESIA from powdered infant formula
and mixed herbs, we obtained not only blue colonies of
C. sakazakii but also white colonies of other bacterium species
from the Enterobacteriaceae family (in case of mixed herbs).
Figure 2c presents the differences between the SERS spectrum
of Cronobacter cells and that other bacteria species grown on
ESIA medium. One can notice, in both spectra, the presence
of bands, characteristic for all bacteria species, at 732, 960,
1003, 1032, 1377, and 1455 cm−1 (CH2 deformation), but also
additional bands present only in the case of C. sakazakii:
802 cm−1 (O–P–O in RNA) and 1337 cm−1.
The reproducibility of the recorded bacterial SERS signals
is a crucial parameter for analytical and biomedical
applications of this technique. Figure S7 (see ESM) shows an
example of C. sakazakii SERS spectra recorded from different spots
within the same sample. To obtain statistically valid results,
the strong signal at 732 cm−1 was chosen to calculate the
average standard deviation (AvSTD) and equals 15%
based on the 30 SERS spectra recorded for the same platform.
The average standard deviation of the SERS signals of
S. Typhimurium and L. monocytogenes has also been
calculated and is presented in the Table S1 (see ESM).
The SERS data were hereafter analyzed by chemometric
methods to improve the accuracy of discrimination between
these two very similar spectra.
PCA is used to build a model for classification of the closely
related bacteria species. Initially, the analysis was performed
over the whole spectral region between 500 and 1650 cm−1. In
a Salmonella spp.
b Listeria spp.
c Cronobacter spp.
Fig. 3 Scoreplots of PC-1 versus PC-2 component for a Salmonella
Typhimurium (red circle), b L. monocytogenes (green circle) and
L. ivanovii (blue circle), and c C. sakazakii (navy blue circle). Asterisks
represent the scores calculated for test samples (smoked salmon—
Salmonella spp., ham—L. monocytogenes, powdered infant formula—
the first step we found that two principal components (PC-1,
PC-2) are the most diagnostically significant and explain 84%
and 95% of the variance in the data, for bacteria grown on
XLD and Chrom Sal agar, respectively (Fig. 3a). The loadings
of the PCs provide information on the variables (wavenumber
of the spectrum) that are important for group separation.
Figure S2 in the ESM displays the loadings plot of PC-1 for
the whole wavenumber region. By analyzing these plots one
can indicate the most important diagnostic variables in the
analyzed data set. Variables with high loading values are the
most important for diagnostic purposes. Moreover, the
calculation of PCA in the area of the most pronounced marker
bands at 649 cm−1 was performed. The PC-1 scores calculated
for the region of the chosen marker give values of 82% and
96% of total variance and together with calculated PC-2 give
Values of PCA scores calculated for analyzed bacteria species
95% and 98% of total variance in respect to the studied
samples (Fig. 3a and Table 2). These percentage values clearly
discriminate the Salmonella species from other bacteria that
grow independently in the same medium (XLD or Chrom Sal
PCA performed for L. monocytogenes and L. ivanovii (in the
region of 500–1650 cm−1) gives the value of PC-1 equal to
83% of total variance (ESM Fig. S3b). In the next step the
PCA calculation was performed in the chosen region, in the
area of the most pronounced marker band at 734 cm−1 (ESM
Fig. S3c). The PC-1 scores calculated for this region gives the
value of 98% of total variance (Fig. 3b, ESM Fig. S3c, and
Table 2). This result shows that PCA enables one to identify
L. monocytogenes and L. ivanovii species with very high
As in the case of Salmonella and Listeria species, PCA was
performed for all the collected SERS data in the whole region
(Fig. 2c) and in the areas of the most pronounced marker
bands (ESM Fig. S4). The obtained PC-1 and PC-2 values
Fig. 4 SERS spectra of a Salmonella spp., b Listeria spp., and c Cronobacter spp. obtained from path II, reference paths, and reference precultures
yield 94% of total variance for Cronobacter spp. and
Enterobacteriacae (Fig. 3c and Table 2). These percentage
values clearly discriminate the Cronobacter species from
other bacteria that grow independently on the same ESIA
To validate the SERS discrimination among the tested
bacteria, an additional step based on the reference SERS spectra
was applied. Figure 4 displays the comparison among the
SERS spectra of the analyzed bacteria obtained from path II,
reference path, and reference precultures.
These results show no differences among the SERS
spectra of particular bacteria species and confirm the
significance of the proposed, simplified to 48 h, ISO-SERS
method (path II). Additionally, using multivariate
analysis we demonstrate the impact of SERS technique
introduced into ISO standards. PCA was performed on a data
set containing all references and path II SERS spectra of
analyzed bacteria. PC scores obtained for reference
SERS spectra are marked by asterisks in Fig. 3. As can
be seen, the positions of these asterisks are in the area of
PC clusters of Salmonella, Listeria, and Cronobacter
species from path II. This demonstrates the ability to
use SERS in identification and discrimination of these
food-borne bacteria in the food industry.
To check the utility of the ISO-SERS-based method for
simultaneous detection and identification of three food-borne
bacteria—S. enterica, L. monocytogenes, and C. sakazakii — in one
sample test, PCA was performed. Figure 5a shows the spectral
comparison of all three food-borne bacteria which are the subject
of this study. These SERS spectra exhibit the same common
spectral features for the majority of bacteria species, but with
some differences in the band positions, relative intensity ratios,
and/or appearance of new bands. These differences allow one to
identify the particular bacteria species in different food samples.
The loading plot of PC-1 in relation to variables (Raman shift)
calculated for these three bacteria indicates the most pronounced
marker bands (650, 725, 1030 cm−1) which may be used in
differentiation analysis (Fig. 5b). The resulting PC-1 vs. PC-2
scores calculated for the region of the most intensive loadings
(at 1030 cm−1) give 98% of total variance (PC-1 plus PC-2) of
the analyzed data set. This demonstrates excellent separation of
the studied bacteria, in one sample test, into three separated
clusters corresponding to the S. Typhimurium, L. monocytogenes,
and C. sakazakii, respectively (Fig. 5c and ESM Fig. S5) and
the ability of the SERS technique combined with PCA to identify
the bacteria species according to ISO standards. Moreover, the
validation of the PCA method used for identification of
foodborne bacteria for five food matrices was performed. In the first
step PCA for Salmonella Typhimurium, L. monocytogenes, and
C. sakazakii from a selected food medium (five food samples
were studied and a total 600 SERS spectra were collected — 40
SERS spectra for each bacterial species) was used to build the
PCA model. Then the additional data of the test sample (external
Fig. 5 SERS spectra of Salmonella Typhimurium, L. monocytogenes,
and C. sakazakii (a). Loadings plot of the first principal component
showing the most prominent marker bands and b plot of the PC-1 versus
PC-2 for the selected marker band at 650 cm−1 (c). Asterisks represent the
scores calculated for test samples (smoked salmon—Salmonella spp.,
ham—L. monocytogenes, and powdered infant formula—Cronobacter
food sample with known bacterium identified by ISO method)
was introduced into this model. The calculated PCA scores are
included in Fig. 5 as asterisks. Three test samples are located in
the clusters of the model PC scores corresponding to particular
bacterial species. These results highlight the analytical potential
of the SERS technique combined with PCA for food-borne
Additionally, it should be noted that all calculated PC
scores are clustered with large distances among particular
clusters (S. Typhimurium, L. monocytogenes, and C. sakazakii,
e.g., see Fig. 5). At the same time the distances between the
calculated scores in each cluster are very short. There are no
scores with wrong assignments. Thus, the sensitivity and
specificity of the combined SERS and PCA methods are very high
(for more information, see ESM).
The results obtained in the present study demonstrate that SERS
is a powerful technique for the detection and identification of
pathogenic bacteria in food samples and can be introduced into
ISO standards as an alternative method. This strategy enables one
to avoid or skip the time-consuming methods routinely used in
the laboratory and reduces the time of analysis from 6 to just
2 days. In the presented SERS technique the long,
timeconsuming incubation required by standard ISO procedures
was reduced and the direct SERS analysis of bacteria colonies
cultured on agar with selective media was performed. PCA
calculations were used to demonstrate the impact of this new
approach of the SERS strategy for food-borne bacteria, namely
S. enterica, L. monocytogenes, and C. sakazakii identification
in selected food matrices (salmon, eggs, powdered infant formula
milk, mixed herbs) with 98% of accuracy in only 48 h. The
research presented here should open a new path in
microbiological diagnostics. It is believed that the proposed SERS-based
method can in the future become a robust tool for identification
of pathogens in the food industry.
Acknowledgements National Institute of Public Health - National
Institute of Hygiene (Warsaw, Poland).
The research was supported by the National Centre for Research and
Development (NCBiR) under grant PBS2/A1/8/2013.
Compliance with ethical standards
Open Access This article is distributed under the terms of the Creative
C o m m o n s A t t r i b u t i o n 4 . 0 I n t e r n a t i o n a l L i c e n s e ( h t t p : / /
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
1. Noble RT , Weisberg SB. A review of technologies for rapid detection of bacteria in recreational waters . J Water Health . 2005 ; 3 ( 4 ): 381 - 92 .
2. Corless CE , Guiver M , Borrow R , Edwards-Jones V , Fox AJ , Kaczmarski EB . Simultaneous detection of Neisseria meningitidis, Haemophilus influenzae, and Streptococcus pneumoniae in suspected cases of meningitis and septicemia using real-time PCR . J Clin Microbiol . 2001 ; 39 ( 4 ): 1553 - 8 . doi:10.1128/jcm.39.4. 1553 - 1558 . 2001 .
3. Deutch S , Moller JK , Ostergaard L. Combined assay for two-hour identification of Streptococcus pneumoniae and Neisseria meningitidis and concomitant detection of 16S ribosomal DNA in cerebrospinal fluid by real-time PCR . Scand J Infect Dis . 2008 ; 40 ( 8 ): 607 - 14 . doi:10.1080/00365540801914833.
4. Hedberg ST , Olcen P , Fredlund H , Molling P. Real-time PCR detection of five prevalent bacteria causing acute meningitis . Apmis . 2009 ; 117 ( 11 ): 856 - 60 . doi:10.1111/j.1600- 0463 . 2009 .02539.x.
5. Abdeldaim GMK , Stralin K , Olcen P , Blomberg J , Herrmann B. Toward a quantitative DNA-based definition of pneumococcal pneumonia: a comparison of Streptococcus pneumoniae target genes, with special reference to the Spn9802 fragment . Diagn Microbiol Infect Dis . 2008 ; 60 ( 2 ): 143 - 50 . doi:10.1016/j. diagmicrobio. 2007 .08.010.
6. Chan JW , Esposito AP , Talley CE , Hollars CW , Lane SM , Huser T. Reagentless identification of single bacterial spores in aqueous solution by confocal laser tweezers Raman spectroscopy . Anal Chem . 2004 ; 76 ( 3 ): 599 - 603 . doi:10.1021/ac0350155.
7. Pellegrino PM , Fell NF , Gillespie JB . Enhanced spore detection using dipicolinate extraction techniques . Anal Chim Acta . 2002 ; 455 ( 2 ): 167 - 77 . doi:10.1016/s0003-2670(01)01613- 0 .
8. Thompson SE , Foster NS , Johnson TJ , Valentine NB , Amonette JE . Identification of bacterial spores using statistical analysis of Fourier transform infrared photoacoustic spectroscopy data . Appl Spectrosc . 2003 ; 57 ( 8 ): 893 - 9 . doi:10.1366/000370203322258832.
9. Campion A , Kambhampati P. Surface-enhanced Raman scattering . Chem Soc Rev . 1998 ; 27 ( 4 ): 241 - 50 . doi:10.1039/a827241z.
10. Camden JP , Dieringer JA , Wang YM , Masiello DJ , Marks LD , Schatz GC , et al. Probing the structure of single-molecule surfaceenhanced Raman scattering hot spots . J Am Chem Soc . 2008 ; 130 ( 38 ): 12616 . doi:10.1021/ja8051427.
11. Stiles PL , Dieringer JA , Shah NC , Van Duyne RR . Surfaceenhanced Raman spectroscopy . Ann Rev Anal Chem . 2008 ; 1 : 601 - 626 . doi:10.1146/annurev.anchem.1.031207.112814.
12. Ru ECL , Etchegoin PG . Single-molecule surface-enhanced Raman spectroscopy . Annu Rev Phys Chem . 2012 ; 63 ( 1 ): 65 - 87 . doi:10.1146/annurev-physchem- 032511 - 143757 .
13. Kneipp K , Kneipp H , Kartha VB , Manoharan R , Deinum G , Itzkan I , et al. Detection and identification of a single DNA base molecule using surface-enhanced Raman scattering (SERS) . Phys Rev E . 1998 ; 57 ( 6 ): R6281 -4. doi:10.1103/PhysRevE.57.R6281.
14. Stokes RJ , McBride E , Wilson CG , Girkin JM , Smith WE , Graham D. Surface-enhanced Raman scattering spectroscopy as a sensitive and selective technique for the detection of folic acid in water and human serum . Appl Spectrosc . 2008 ; 62 ( 4 ): 371 - 6 . doi:10.1366/000370208784046812.
15. Faulds K , Smith WE , Graham D , Lacey RJ . Assessment of silver and gold substrates for the detection of amphetamine sulfate by surface enhanced Raman scattering (SERS) . Analyst . 2002 ; 127 ( 2 ): 282 - 6 . doi:10.1039/b107318b.
16. Sivanesan A , Witkowska E , Adamkiewicz W , Dziewit L , Kaminska A , Waluk J. Nanostructured silver-gold bimetallic SERS substrates for selective identification of bacteria in human blood . Analyst . 2014 ; 139 ( 5 ): 1037 - 43 . doi:10.1039/c3an01924a.
17. Alexander TA , Le DM . Characterization of a commercialized SERS-active substrate and its application to the identification of intact Bacillus endospores . Appl Optics . 2007 ; 46 ( 18 ): 3878 - 90 . doi:10.1364/ao.46.003878.
18. Meyer SA , Le Ru EC , Etchegoin PG . Quantifying resonant Raman cross sections with SERS . J Phys Chem A . 2010 ; 114 ( 17 ): 5515 - 9 . doi:10.1021/jp100669q.
19. Fell NF , Smith AGB , Vellone M , Fountain AW . Optimization of substrates for surface-enhanced Raman spectroscopy of bacteria . In: Christesen SD, Sedlacek AJ , editors. Vibrational spectroscopy-based sensor systems, Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) , vol. 4577 . Bellingham: SPIE; 2002 . p. 174 - 81 .
20. Grow AE , Wood LL , Claycomb JL , Thompson PA. New biochip technology for label-free detection of pathogens and their toxins . J Microbiol Methods . 2003 ; 53 ( 2 ): 221 - 33 . doi:10.1016/s0167-7012 (03)00026- 5 .
21. Guzelian AA , Sylvia JM , Janni JA , Clauson SL , Spencer KM . SERS of whole cell bacteria and trace levels of biological molecules . In: Christesen SD, Sedlacek AJ , editors. Vibrational Spectroscopy-Based Sensor Systems, Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) , vol. 4577 . Bellingham: SPIE; 2002 . p. 182 - 92 . doi:10.1117/12.455736.
22. Jarvis RM , Goodacre R. Discrimination of bacteria using surfaceenhanced Raman spectroscopy . Anal Chem . 2004 ; 76 ( 1 ): 40 - 7 . doi:10.1021/ac034689c.
23. Sockalingum GD , Lamfarraj H , Beljebbar A , Pina P , Allouch P , Manfait M. Direct on-plate analysis of microbial cells: a pilot study by surface-enhanced Raman spectroscopy . In: Greve J , Puppels GJ , Otto C, editors. Spectroscopy of biological molecules: new directions . Dordrecht: Springer; 1999 .
24. Zeiri L , Bronk BV , Shabtai Y , Czege J , Efrima S. Silver metal induced surface enhanced Raman of bacteria . Colloid Surf A-Physicochem Eng Asp . 2002 ; 208 ( 1-3 ): 357 - 62 . doi:10.1016/s0927-7757(02)00162- 0 .
25. Efrima S , Bronk BV , Czege J. Surface enhanced Raman spectroscopy of bacteria coated by silver . In: Lakowicz JR, Soper SA , Thompson RB, editors. Advances in fluorescence sensing technology IV. Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) , vol. 3602 . Bellingham: SPIE; 1999 . p. 164 - 71 . doi:10.1117/12.347519.
26. Efrima S , Bronk BV. Silver colloids impregnating or coating bacteria . J Phys Chem B . 1998 ; 102 ( 31 ): 5947 - 50 . doi:10.1021/jp9813903.
27. Benecky M , Yu TY , Watters KL , McFarland JT . Metal-flavin complexation. A resonance Raman investigation . Biochim Biophys Acta . 1980 ; 626 ( 1 ): 197 - 207 . doi:10.1016/ 0005 -2795(80)902 1 .
28. Jarvis RM , Brooker A , Goodacre R. Surface-enhanced Raman scattering for the rapid discrimination of bacteria . Faraday Discuss . 2006 ; 132 : 281 - 92 . doi:10.1039/b506413a.
29. European Food Safety Authority. EFSA J . ( 2015 ); (13).
30. http://www.cdc.gov/salmonella/general/index.html. Accessed 17 Nov 2015 .
31. Assaf A , Cordella CBY , Thouand G . Raman spectroscopy applied to the horizontal methods ISO 6579:2002 to identify Salmonella spp . in the food industry . Anal Bioanal Chem . 2014 ; 406 ( 20 ): 4899 - 910 . doi:10.1007/s00216- 014 - 7909 -2.
32. Ryser ET , Marth EH. Listeria, listeriosis, and food safety, 3rd edn. Boca Raton: CRC ; 2007 .
33. Munoz P , Rojas L , Bunsow E , Saez E , Sanchez-Cambronero L , Alcala L , et al. Listeriosis: An emerging public health problem especially among the elderly . J Infect . 2012 ; 64 ( 1 ): 19 - 33 . doi:10.1016/j. jinf. 2011 .10.006.
34. http://www.cdc.gov/cronobacter/definition.html. Accessed 17 November 2015 .
35. ISO 6579:2002 Microbiology of food and animal feeding stuffs - Horizontal method for the detection of Salmonella spp .
36. ISO/TS 22964: 2006 (IDF/RM 210: 2006 ): Milk and milk products - Detection of Enterobacter sakazakii .
37. Notermans SHW , Dufrenne J , Leimeisterwachter M , Domann E , Chakraborty T. Phosphatidylinositol-specific phospholipase-C activity as a marker to distinguish between pathogenic and nonpathogenic Listeria species . Appl Environ Microbiol . 1991 ; 57 ( 9 ): 2666 - 70 .
38. Ottaviani F , Ottaviani MO , Agosti M. Differential agar medium for Listeria monocytogenes . In: Quimper Froid Symposium Proceedings ; 1997 .
39. Mobili P , Londero A , De Antoni G , Gomez-Zavaglia A , AraujoAndrade C , Avila-Donoso H , et al. Multivariate analysis of Raman spectra applied to microbiology: discrimination of microorganisms at the species level . Rev Mex Fis . 2010 ; 56 ( 5 ): 378 - 85 .
40. Luna-Pineda T , Soto-Feliciano K , De La Cruz-Montoya E , Pacheco Londoño LC , Ríos-Velázquez C , Hernández-Rivera SP . Spectroscopic characterization of biological agents using FTIR, normal Raman and surface-enhanced Raman spectroscopies . In, 2007 . pp 65540K - 65540K - 65511
41. Walter A , Marz A , Schumacher W , Rosch P , Popp J. Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device . Lab Chip . 2011 ; 11 ( 6 ): 1013 - 21 . doi:10.1039/c0lc00536c.
42. Fan C , Hu ZQ , Mustapha A , Lin MS . Rapid detection of food- and waterborne bacteria using surface-enhanced Raman spectroscopy coupled with silver nanosubstrates . Appl Microbiol Biotechnol . 2011 ; 92 ( 5 ): 1053 - 61 . doi:10.1007 /s00253- 011 - 3634 -3.
43. Turner RD , Vollmer W , Foster SJ . Different walls for rods and balls: the diversity of peptidoglycan . Mol Microbiol . 2014 ; 91 ( 5 ): 862 - 74 . doi:10.1111/mmi.12513.
44. Demirel MC , Kao P , Malvadkar N , Wang H , Gong X , Poss M , et al. Bio-organism sensing via surface enhanced Raman spectroscopy on controlled metal/polymer nanostructured substrates . Biointerphases . 2009 ; 4 ( 2 ): 35 - 41 . doi:10.1116 /1.3147962.
45. Guicheteau J , Argue L , Hyre A , Jacobson M , Christesen SD . Raman and surface-enhanced Raman spectroscopy of amino acids and nucleotide bases for target bacterial vibrational mode identification . Proceedings of SPIE , 62180O- 62111 , 2006 .
46. Kahraman M , Zamaleeva AI , Fakhrullin RF , Culha M. Layerby-layer coating of bacteria with noble metal nanoparticles for surface-enhanced Raman scattering . Anal Bioanal Chem . 2009 ; 395 ( 8 ): 2559 - 67 . doi:10.1007/s00216- 009 - 3159 -0.
47. Yang X , Gu C , Qian F , Li Y , Zhang JZ . Highly sensitive detection of proteins and bacteria in aqueous solution using surface-enhanced Raman scattering and optical fibers . Anal Chem . 2011 ; 83 ( 15 ): 5888 - 94 . doi:10.1021/ac200707t.