Detecting bacteria contamination on medical device surfaces using an integrated fiber-optic mid-infrared spectroscopy sensing method

Sensors and Actuators B 231 (2016) 646–654

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Sensors and Actuators B: Chemical journal homepage: www.elsevier.com/locate/snb

Detecting bacteria contamination on medical device surfaces using an integrated fiber-optic mid-infrared spectroscopy sensing method Moinuddin Hassan a,∗ , Elizabeth Gonzalez b , Victoria Hitchins b , Ilko Ilev a a Optical Therapeutics and Medical Nanophotonics Laboratory, Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA b Division of Biology, Chemistry, and Material Sciences, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA

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Article history: Received 6 October 2015 Received in revised form 6 February 2016 Accepted 11 March 2016 Available online 21 March 2016 Keywords: Bacteria contamination Fourier transform infrared (FTIR) spectroscopy Fiber-optics sensors Medical devices Principal component analysis

a b s t r a c t Bacterial contamination on medical device surfaces is a critical public health concern. In order to detect bacteria on medical device surface, alternative methods for quantitative, accurate, easy-to-use, and real-time detection and identification of microorganism contamination are needed. We have recently presented a novel proof-of-concept platform for non-contact, label-free and real-time detection of surface contamination employing a fiber-optic Fourier transform infrared (FO-FTIR) spectroscopy sensing methodology in the mid-infrared (mid-IR) spectral range of 1.6–12 ␮m. In the present study, we demonstrate the detection capability and sensitivity of the integrated FO-FTIR approach using four species of commonly encountered bacteria: Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pneumoniae. FO-FTIR combined with multivariate approaches such as hierarchical clustering and principal component analysis provided specific mid-IR spectral differentiation of the four microorganisms including when the sample contained mixtures of bacteria types. To assess the sensitivity of the FO-FTIR platform, bacteria samples were prepared at 109 colony forming unit (CFU)/␮L and then serially diluted 1:10 eight times. The salient findings of this investigation showed that the integrated FO-FTIR based sensor can detect the presence of the bacteria at concentrations between 103 and 104 CFU/2 ␮L, producing unique bacteria signatures with high reproducibility. The advanced features of this sensing method in terms of sensitivity, specificity and repeatability employing non-contact, labelfree, and real-time approaches, demonstrate its potential use as an alternative effective screening tool for routine monitoring of bacterial contaminated surfaces. Published by Elsevier B.V.

1. Introduction Medical device surface contamination is a major public health concern and risk for transmission of infections as the reusable medical devices are being extensively used for diagnostics as well as for therapeutic purposes. In the USA approximately 1 in every 24 inpatients has an infection related to clinical and hospital healthcare due to contaminated medical devices [1,2]. Numerous studies have shown that health care infections are frequently caused by unwanted microorganisms growing on the surface of medical devices. Bacterial contamination is one of the major public health concerns, not only when it occurs in the healthcare facilities,

∗ Corresponding author at: Bldg. 62, Rm. 1115, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. E-mail address: [email protected] (M. Hassan). http://dx.doi.org/10.1016/j.snb.2016.03.044 0925-4005/Published by Elsevier B.V.

but also with the high risk of bacterial growth and contamination within food, pharmaceutical and biotech processing facilities. In order to prevent transmission of infections, the Center for Disease Control (CDC) and the U.S. Food and Drug Administration (FDA) along with professional organizations have published various guidelines and standards for healthcare professionals [3]. Currently, routine cleaning and disinfection monitoring is based on widely applied visual inspection or ultraviolet (UV) light [4]. However, these techniques are not sensitive enough to detect contamination effectively [4]. Furthermore, the most reliable and sensitive method to detect bacteria contamination on a surface is to extract the cells from the contaminated surface and culture them overnight. However, this technique has several limiting factors. One is that recovery efficiency can vary greatly due to variations in the surface material, recovery method, and type of microbes present. Another concern is that the cultured microbe species will

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Fig. 1. (A) The measurement setup of the fiber-optic FTIR (FO-FTIR) sensor systems. Fiber-optic based sensors: (B) reflection (RS) [24] and (C) grazing incidence angle (GIAS) [23].

depend on the media used, the concentration of oxygen (aerobic, facultative aerobic and anaerobic bacteria), and the time and temperature at which the microbes are grown. Lastly, microbes need time to grow, which means that this technique requires incubation times ranging from several hours to weeks. Therefore, in order to detect and monitor bacterial presence on medical device surfaces, alternative methods for quantitative, accurate, easy-to-use, and real-time detection and identification of microorganism contaminant are critically important. During the last several decades, Fourier transform infrared (FTIR) spectroscopy has been extensively used as an effective tool to identify the chemical and molecular structure of different materials without the use of reagents [5–13]. Recently, advanced FTIR such as mid infrared (mid-IR) mercury cadmium telluride (MCT) detectors that provide high sensitivity and fast response time in a broad IR spectral range, have made it possible for the FTIR technique to detect and classify microorganisms in a rapid, reliable, and sensitive manner [5,6,8,14–16]. FTIR is a vibrational absorption spectroscopic technique in the mid-IR region, which can yield a fingerprint-like spectrum representing the structure and composition of different kinds of contaminants. Mid-IR FTIR allows the identification of bacteria at distinct taxonomic levels based on differences in the IR absorption pattern of different bacteria species [11,17,18]. Advances in FTIR microscopic and macroscopic techniques have enabled the detection of pathogenic microorganisms in clinical applications [12,19,20] as well as in the food industry

[6,21,22]. However, FTIR technology has not been routinely applied to monitor bacterial presence and identification on the surface of medical devices. Recent advancements in mid-IR fiber-optic technology such as the development of hollow-core and polycrystalline waveguides integrated to FTIR have allowed the development of mid-IR reflection measurement techniques to remotely sense bacteria at any target area on medical device surfaces in real time. In our recent proof-of-concept studies [23,24], we have demonstrated the feasibility of using fiber-optic based sensors (reflection and grazing-incidence angle) integrated with FTIR technology (FOFTIR sensing approach) for label-free, remote and in-situ detection of biochemical contaminants on reflective, partially-reflective and non-reflective surfaces in the mid-IR spectral range (1.6–12 ␮m) [23,24]. The FO-FTIR sensing technology is not intended to validate sterilization. Sterile instruments have been validated through terminal sterilization process that has been calculated to have a sterility assurance level (SAL) of 10−6 [25]. There is a probability that one device out of one million has one viable organism. In addition, not all reusable devices need to be disinfected or sterilized. However, there are some devices that are simply cleaned and reused on the next patient (such as bedpans, anesthesia machines etc.). The FOFTIR sensing technology is aiming to be used rather for detecting and monitoring the presence and type of bacteria on hospital surfaces or non-critical reusable devices in order to screen for bacteria species.

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confirmed through plating on trypticase soy agar (BD Biosciences, San Jose, CA) for S. aureus, nutrient agar (BD Biosciences) for E. coli and P. aeruginosa, and blood agar (BD Biosciences, San Jose, CA) for S. pneumoniae. S. aureus, E. coli, and P. aeruginosa were incubated aerobically overnight at 37 ◦ C. S. pneumoniae were incubated overnight at 37 ◦ C in an anaerobic growth chamber with a GasPak EZ (BD Biosciences, San Jose, CA).

2.3. Fiber-optic FTIR (FO-FTIR) sensor systems

Fig. 2. Representative absorbance spectra of Gram-negative bacteria, (A) E. coli, (B) P. aeruginosa; and Gram-positive bacteria: (C) S. aureus, (D) S. pneumoniae. Spectral regions are assigned according to cells components: I—lipid and fatty acid region (3000–2800 cm−1 ), II—protein or amide region (1700–1500 cm−1 ), III—mixed region (1500–1200 cm−1 ) and IV—polysaccharide region (1200–900 cm−1 ).

Our present work extends the FO-FTIR capability of detecting and analyzing various single and mixed bacterial samples including investigation of sensitivity, reproducibility and discrimination potential of this approach. In this study, we have selected four representative reference bacteria relevant to medical device surface contamination: Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Streptococcus pneumoniae. The goal is to determine whether the integrated FO-FTIR approach could be used to detect and monitor microbial contamination on medical device surfaces.

The schematic diagram of the FO-FTIR spectroscopy sensor systems is shown in Fig. 1A. It consists of a FTIR spectrometer (Vertex 70, Bruker Optiks, Ettlingen, Germany) and fiber-optic based measurement heads built into an external compartment of the FTIR to collect mid-IR absorption spectra in the wavelength range of 1.6–12 ␮m. The fiber-optic sensor heads consists of two mid-IR hollow optical fibers (Hollow Waveguide with Acrylate Buffer, HWEA7501200, Polymicro Technologies, Phoenix, AZ) with inner diameters of 750 ␮m and a numerical aperture of 0.05, which are fashioned in reflection geometric arrangements as shown in Fig. 1A. Two reflection sensing probes were developed to detect pathogen from different types of surfaces: highly reflected surfaces such as mirror, stainless steel, glossy metals etc.; partially-reflective surfaces such as diffuse reflective rough metals, plastic etc.; and non-reflective surfaces such as highly absorbing (absorbing coatings or materials) or highly transparent (polyvinyl or clear glass) surfaces in the mid-IR spectral range. In one of the sensor heads for highly reflected surfaces named ‘reflection sensor’ (RS, Fig. 1B), the detector and light delivery fibers are arranged at 22◦ as compared to normal incidence angle [24]. In another sensor head used for nonor partially-reflective surfaces named ‘grazing incidence angle sensor’ (GIAS, Fig. 1C), the fibers are arranged at 85◦ as compared to normal incidence angle [23]. One of the sensor fibers delivers light from a halogen source to the sample, while the other fiber transmits light to the detector (liquid Nitrogen cooled MCT) after absorption by the sample. Prior to the experiment, the working distance of the corresponding sensor was defined by placing the sensor head above the test sample surface at a distance where the signal intensity was maximal. Each spectrum was averaged over 256 scans in the range of 900–4000 cm−1 at a 4 cm−1 resolution. Prior to sample measurement, the background signal was collected from a gold mirror without a sample to remove instrumental characteristics and atmospheric water vapor.

2. Material and methods 2.1. Reagents and chemicals S. aureus (ATCC 6538,) E. coli (ATCC 35378,) P. aeruginosa (ATCC BAA-47) and S. pneumoniae (ATCC BAA334) were purchased from American Type Culture Collection (Manassas, VA.). 2.2. Bacteria preparation Bacteria were grown overnight in trypticase soy broth (Oxoid, Thermo Scientific, Waltham, MA) at 30 ◦ Cfor S. aureus, nutrient broth (BD Biosciences, San Jose, CA) for E. coli, and P. aeruginosa, and brain heart infusion medium (BD Biosciences, San Jose, CA) for S. pneumoniae. S. aureus, E. coli, and P. aeruginosa were incubated in a shaking incubator at 225 rpm, while S. pneumoniae was grown statically. Bacteria were then sub-cultured in the appropriate liquid media for 2–4 h and re-suspended at approximately 109 CFU/mL in sterile phosphate buffered saline (PBS). The bacteria were serially diluted 1:10 in sterile PBS to 102 CFU/mL. Bacteria counts were

2.4. Experiment 2.4.1. Recording bacteria spectra A sterile loop was used to collect four bacteria colonies of one species from a culture plate in moist form and was smeared on approximately 4 mm2 on a mirror surface. The RS was used to collect spectra at different locations over the sample. After systematically examining the quality of the IR spectra in terms of absorbance for detector linearity and signal-to-noise ratios, the best spectral signals were used for analysis. Each bacteria species was measured in triplicate on different days. Spectral data processing was performed using the software OPUS 7 (Bruker Optiks, Ettlingen, Germany). The first and second derivatives of the spectra were calculated using a 9-point Savitzky-Golay filter to enhance the resolution of the bands and to minimize the effect of baseline shifts. Afterwards, the data were analyzed using hierarchical cluster analyses employing the Ward algorithms and utilizing ‘scaling to first scale range’ method to calculate spectral distances [26–28]. Four regions (from 1200 cm−1 to 900 cm−1 , 1500 cm−1 to 1200 cm−1 ,

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1500 cm−1 to 1700 cm−1 , and 3000 cm−1 to 2800 cm−1 ) were used to get the best separation between species of bacteria. 2.4.1.1. Principal component analysis (PCA). PCA is a multivariate procedure that rotates data to maximize the variability projected onto axes. A set of correlated variables is thus transformed to a set of uncorrelated variables ranked by variability in the descending order. PCA is used mainly to reduce the dimensionality of a data set while retaining as much information as possible by computing a compact and optimal description of the data set [29]. The data were analyzed using MATLAB. The experiment was repeated to collect IR spectra of bacteria using a GIAS on different types of partially-reflective or nonreflective surfaces such as stainless steel, rough stainless steel, aluminum, and polyvinyl. The spectra obtained from different reflection configurations were compared. 2.4.2. Testing sensitivity of the measurement method. To evaluate the sensitivity of the sensor system for the detection of bacteria, bacteria at different cell concentrations were placed on a mirror surface in 2 ␮L drops of equal size (∼4 mm diameter) and allowed to dry for ∼20 min at room temperature (approx. 20 ◦ C) under a covered area to decrease the chances of dust landing on

the plates. The RS system was used to measure mid-IR spectra. Before each measurement, the background spectrum of the clean surface was collected prior to sample deposition. Three trials were recorded for each sample at different positions on the sample. The spectral data were processed using OPUS 7 (Bruker Optiks, Ettlingen, Germany). The experiment was repeated three times for each bacteria species to test reproducibility of the spectral signature on different days. 3. Results and discussion 3.1. General characteristics of FTIR signatures of bacteria In this study, four reference bacteria species known to be associated with medical device related HAIs were used: two Grampositive (S. pneumoniae and S. aureus) and two Gram-negative (E. coli and P. aeruginosa) bacteria. Fig. 2 shows typical mid-IR spectral signatures of the four bacteria species using the FO-FTIR based RS. The defined regions in Fig. 2 demonstrate the feature characteristics for each bacteria species. The regions can be classified according to cell components [11]: I—fatty acid region (3000–2800 cm−1 ), which are mainly due to CH2 and CH3 asymmetric stretches of fatty acid; II—protein or amide region (1700–1500 cm−1 ), which corresponds

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to protein and amide components such as amide I and amide II; III—mixed region (1500–1200 cm−1 ) containing carboxylic group of proteins, free amino acids, RNA/DNA and phospholipids [30]; and IV—polysaccharide region (1200–900 cm−1 ), which includes the stretching vibrations of phosphate groups and vibrations of polysaccharides [31]. The regions can be used to differentiate and classify the bacteria species. There is also a prominent absorption peak around 1745 cm−1 , which is likely from the >C O stretch of esters or fatty acids [31]. Absorption at this frequency is clearly observed more strongly in S. aureus (Fig. 2) as compared to other three species that were evaluated. Specific absorbance peaks associated with individual species can also be observed in the mixed spectral region (1500–1200 cm−1 ) and the polysaccharide region (1200–900 cm−1 ). The regions are identical within each bacterial species and can play an important role for the classification and identification of bacteria.

The fundamental requirement for the classification of bacteria is the reproducibility of spectral features of the same bacteria species. In general, FTIR spectra are highly reproducible and unique for different bacteria [11]. However, in some cases when the same species is repeatedly measured, variations in relative spectral peak intensities may occur due to number of bacteria counts under the detection area of the sensor. The composition of the growth medium and growth phase (e.g. lag, exponential, stationary) of the bacteria may also affect the relative peak intensities but not the peak position [32]. In this study, the sample preparation and measurement conditions were strictly maintained to minimize these influences and generate consistent and reproducible spectral signatures. Reproducibility of the sensor system was tested by measuring the bacteria spectra at different positions within a sample, in different samples from the same culture plate, and in different experiments using the same bacterial species on a mirror surface as shown in Fig. 3. There were no significant variations in spectra observed between measurements within a sample (Fig. 3a) nor between measurements of multiple samples taken from a single culture plate (Fig. 3b). However, there were some spectral differences in peak intensity (but not in peak position shift) between different measurements run on different samples of the same bacteria species (Fig. 3c). As mentioned earlier, these variations may occur due to small variations in the number of bacteria under the detection area of the sensor. While these experiments were all conducted using mirror like surfaces, we also tested partially-reflective or non-reflective surfaces since medical device surfaces can be comprised of a variety of materials with significantly different surface characteristics. Representative spectra from reflective and partially-reflective surfaces are shown in Fig. 4. However, spectra from a non-reflective surface (transparent surface) such as transparent polyvinyl or clear glass (data not shown) are distorted and high absorption effects are observed in the spectral range lower than 1500 cm−1 in comparison with reflective or partially-reflective surfaces [23,33–35]. At wavenumber greater than ∼1500 cm−1 non-reflective surface material are transparent to IR radiation. However, there are strong absorption bands that are associated with material’s refractive index at longer wavelength. Since the refractive index changes significantly over the mid-IR region, the wavelength dependence effects of the

Fig. 5. FTIR spectral signatures of S. aureus, S. pneumoniae, E. coli, P. aeruginosa and mixed bacteria (E. coli and S. pneumoniae): (a) original spectra; (b) first-order derivative; and (c) second-order derivative.

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Fig. 6. Classification of E. coli, S. pneumoniae, S. aureus and P. aeruginosa. (a) Hierarchical cluster analysis was performed using the second order derivative of the spectra considering a range of 1200–900 cm−1 . The “scaling to first range” option of Bruker Optics software was used to calculate the spectral distances and the Ward’s algorithm was used to calculate the dendogram. Species specific subcultures were formed for each species from repeated measurements. (b) Score plot of the first two principal components based on second derivative in the spectral range of 1200–900 cm−1 by MATLAB.

reflectance-absorbance properties of non-reflective surface materials are more complicated than those for metals [34]. A similar effect was observed in our previous study [23]. 3.3. Classification of bacteria This section was to test whether the fiber-optic based sensor can be used to classify bacteria according to mid-IR spectral signatures and also tested whether a new spectrum displaying an unknown species can be identified as a known spectral class. To classify spectra, a suitable quantitative technique needs to be employed to identify similarity or dissimilarity among the spectral data. In this study we tested two correlation techniques: hierarchical clustering using Ward algorithm [12,27,28,36] and Principal Component Analysis (PCA) [32,37]. Both methods consider the shape or contours of the spectra rather than band numbers, frequencies or intensities. The spectral regions can subsequently be used to identify bacteria species.

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First- and second-order derivative transformations make unique spectral features of the different bacterial species more prominent [22]. Fig. 5 shows representative original raw spectra (Fig. 5a), first-order derivative (Fig. 5b) and second-order derivative (Fig. 5c) transformed spectra from four different bacteria species and one mixed with two different species: E. coli and S. pneumoniae. Second-order derivative transformation of spectra separated and cleared the overlapping absorption bands and thus, increased the apparent spectral resolution as compared to firstorder derivative transformation. Fig. 5c shows that the spectral features between the bacteria species are distinct. Furthermore, absorption bands of corresponding bacteria in the mixed and polysaccharide region (1500–900 cm−1 ) are clear and identical within each bacteria species. For classification of bacteria according to their spectral region, hierarchical cluster analysis and PCA were performed for four species of bacteria from three independent cultivations as shown in Fig. 6. Cluster analyses were performed independently using first- and second-order derivatives of spectra considering the spectral ranges 3000–2800 cm−1 , 1700–1500 cm−1 and 1500–1200 cm−1 (data not shown), as well as 1200–900 cm−1 . While each spectral window was considered separately to optimize species discrimination, it was observed that the second-order derivative of spectra in the spectral range 1200–900 cm−1 (polysaccharide region) provided the best separation of the different bacteria species (Fig. 6a). The dendrogram in Fig. 6a clearly demonstrates that distinct clusters are formed for the different bacteria species. This dendrogram also suggests that species specific sub-clusters are formed resulting from repetitive measurements of each species. However, hierarchical cluster analysis proved to be unable to discriminate between Gram-positive and Gram-negative bacteria. To validate the above cluster analysis result, PCA was employed to differentiate the bacteria samples using their spectral features by taking the second-order derivative spectrum in the wavelength range 1200 cm−1 to 900 cm−1 . Fig. 6b shows the first two components of the PCA results in clear segregation of the four bacteria into distinct clusters. In addition, the score plot of PC1 and PC2 revealing two distinct clusters corresponding to groups of Gram-positive and Gram-negative bacteria. These results suggest that the PCA analysis of FO-FTIR data can discriminate between different species of bacteria as well as classify them as Gram-positive or Gram-negative bacteria species. Medical devices are more likely to be contaminated with a variety of microbial species rather than just one [38]. In this study, 2 colonies from each species (E. coli and S. pneumoniae) were mixed together to form a mixed culture sample. To classify the mixed sample, PCA was performed based on second-order transformed spectra in the spectral range of 1200–900 cm−1 . The resulting PCA plot is shown in Fig. 7. The score plot of PC1 and PC2 placed the mixed bacteria in between E. coli and S. pneumoniae clusters as shown in Fig. 7a. We also tested PCA analysis on different combination of mixed bacteria samples by combining 2, 3 and 4 bacteria samples (data not provided). In these cases, however, we observed new type of bacteria signature which includes all the signatures from individual bacteria in the mixture. The reason is that the current configuration of our probe’s detection window is approximately 0.5 mm which can cover millions of bacteria in the mixture since the size of the bacteria is approximately 0.5–5␮m. To detect individual bacteria in the mixture, an FTIR microscopic detection window using a precisely scanning probe to identify individual bacteria in the mixture can be employed. However, this process could be very slow and not practical to be used in clinical settings for the detection of bacteria on the surface of reusable medical device or hospital surface. Moreover, bacteria grow as a colony on solid media surfaces of the same species.

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Fig. 7. Score plot for: (a) the first two principal components; (b) the first three principal components, obtained from four bacteria, mixed bacteria (E. coli and S. pneumoniae) and a test microorganism spectra based on second-order derivative in the spectral range of 1200–900 cm−1 by MATLAB.

A 3D score plot of the first three PC’s provides a greater clarity in discriminating amongst each bacteria species, in which the same species are found to be more tightly bound in their cluster. It was also verified that the current method could identify an unknown spectrum (test microorganism). A spectrum was randomly selected from spectra of four known bacteria species and tested. Score plot of PC’s (Fig. 7) correctly identified the unknown spectra as S. aureus. The preliminary study demonstrated that a combination of the FOFTIR method and the PCA analysis using second-order derivation of region IV (1200–900 cm−1 ) could successfully classify different species of bacteria in real-time. However, in this study we haven’t applied the proposed sensing technology on non-viable bacterial contamination, since bacteria cannot cause an infection when dead although non-viable bacteria can still cause an inflammatory response especially in the case of Gram-negative bacteria (e.g E. coli), which contain endotoxins in their cell walls. Therefore,

it is still important to determine whether or not non-viable bacteria residues remain on healthcare associated surfaces. 3.4. Testing sensitivity Sensitivity of the FO-FTIR based sensing approach was tested by diluting the bacteria sample with PBS. Bacteria was pelleted via centrifugation and resuspended in PBS at an initial concentration of approximately 109 CFU/mL. Eight 1:10 serial dilutions were performed in PBS resulting in an approximate minimum concentration of 102 CFU/mL. 2 ␮L of the bacteria solution was placed on a highly reflective surface for the spectral measurement. To avoid strong water absorption of FTIR signal, all samples in this study were dried for 20 min at ∼20 ◦ C room temperature on the reflective surface to reduce the water content before spectral readings were obtained. Representative spectra of P. aeruginosa at different concentrations

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and two are Gram-positive bacteria. The present study revealed that the FO-FTIR approach can be employed to detect a minimum of 103 CFU/2 ␮L, producing a quality bacteria signature with excellent reproducibility. The bacterial signature with multivariate analysis can detect and classify the bacteria species. In addition, using a combination between the FO-FTIR approach and the PCA analysis may provide the potential for grouping the bacteria into Gram-positive and Gram-negative groups. However, further study is required to confirm this possibility by adding a new class of bacteria that is neither Gram-positive nor Gram-negative, such as the non-tuberculosis mycobacteria and non-viable bacteria. It may also be possible to improve the sensitivity of the system by adjusting the dimension of detection area. Furthermore, the FO-FTIR sensing approach could be additionally improved to support the development and implementation of a miniature or handheld device that will focus on measuring a few spectral features of significant interest instead of the whole range of cell spectra. Disclaimer

Fig. 8. Typical absorbance spectra of different concentrations of P. aeruginosa ranging from 106 CFU/2 ␮L to 102 CFU/2 ␮L. An absorption spectra of PBS on a mirror surface was used as a background control. I—lipid and fatty acid region; II—protein or amide region; III—mixed region; and IV—polysaccharide region.

are shown in Fig. 8. While the minimum sensitivity observed was between 102 –103 CFU/2 ␮L, most samples had a sensitivity 1 log higher, between 103 –104 CFU/2 ␮L. At lower concentrations, spectra in the mixed (III) and the polysaccharide (IV) regions were distorted. We considered three samples for each bacteria species. The current configuration of the FO-FTIR sensor system provides a detection area much smaller than the target area because the sensor system is designed to use a single detecting hollow-core fiber with a small numerical aperture of 0.05 which can cover a detection spot size of approximately 0.5 mm in diameter. However, 2 ␮L of solution can cover approximately 4 mm2 on a mirror surface. Bacteria were not homogeneously distributed and were sometimes concentrated within different regions of the sample area. At a low cell concentration, it was always a challenge to find the bacteria within the range of the detection spot of the fiber. As compare to the conventional swab sampling approach, the non-contact, labelfree fiber-optic based sensing approach needs around 103 bacteria in a small area to be detected. For comparison, the swabbing techniques could only picking up <1% of bacteria from that area [39]. Thus, there is potentially a significantly lower efficacy to recover bacteria from that area by using conventional methods. Moreover, the current configuration was designed to fit to any small target area on a device in order to detect bacteria contamination. Depending on the area of interest, the minimum detection area could be improved by adjusting the spot size of fiber-optic sensors system to increase detection threshold. Using the current configuration, the data acquisition time for each point requires 60 s and 30 s for data analysis using an off-line computer. However, for clinical application, we will build an integrated system for data acquisition and analysis with visual confirmation of bacterial contamination. 4. Conclusion We have demonstrated the feasibility of implementing an integrated mid-IR FO-FTIR spectroscopy sensor system for real-time in-situ detection and analysis of four reference bacteria, which are known to cause nosocomial infections. Two are Gram-negative

The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the U.S. Food and Drug Administration (FDA), Department of Health and Human Services. Acknowledgements This study is supported by the intramural research program of Medical Counter Measure initiative (MCMi) of Center for Devices and Radiological Health (CDRH), U.S. Food and Drug Administration (FDA). The authors have no conflicts of interest or financial ties to disclose. References [1] (DHHS) USDHHS. Health Care-Associated Infections (HAI), 2014. www.hhs. gov/ash/initiatives/hai/. [2] S.S. Magill, J.R. Edwards, W. Bamberg, Z.G. Beldavs, G. Dumyati, M.A. Kainer, et al., Multistate point-prevalence survey of health care-associated infections, N. Engl. J. Med. 370 (2014) 1198–1208. [3] W.A. Rutala, J. David, D.J. Weber, Guideline for Disinfection and Sterilization in Healthcare Facilities, 2008, Centers for Disease Control and Prevention (CDC), 2008. [4] K. Arias, Infection Prevention: Contamination and Cross Contamination on Hospital Surfaces and Medical Equipment, Initiatives. p. 1–8. http://www. initiatives-patientsafety.org/assets/initiatives43.pdf. [5] K.K. Chittur, FTIR/ATR for protein adsorption to biomaterial surfaces, Biomaterials 19 (1998) 357–369. [6] Y. Etzion, R. Linker, U. Cogan, I. Shmulevich, Determination of protein concentration in raw milk by mid-infrared fourier transform infrared/attenuated total reflectance spectroscopy, J. Dairy Sci. 87 (2004) 2779–2788. [7] Z. Filip, S. Herrmann, J. Kubat, FT-IR spectroscopic characteristics of differently cultivated Bacillus subtilis, Microbiol. Res. 159 (2004) 257–262. [8] P.I. Haris, D. Chapman, Does Fourier-transform infrared spectroscopy provide useful information on protein structures, TIBS 17 (1992) 328–333. [9] A.O. Janbu, T. Moretro, D. Bertrand, A. Kohler, FT-IR microspectroscopy: a promising method for the rapid identification of Listeria species, FEMS Microbiol. Lett. 278 (2008) 164–170. [10] M.A. Mackanos, C.H. Contag, Fiber-optic probes enable cancer detection with FTIR spectroscopy, Trends Biotechnol. 28 (2010) 317–323. [11] D. Naumann, D. Helm, H. Labischinski, Microbiological characterizations by FT-IR spectroscopy, Nature 351 (1991) 81–82. [12] N.A. Ngo-Thi, C. Kirschner, D. Naumann, Characterization and identification of microorganisms by FT-IR microspectrometry, J. Mol. Struct. 661–662 (2003) 371–380. [13] O. Preisner, J.A. Lopes, R. Guiomar, J. Machado, J.C. Menezes, Fourier transform infrared (FT-IR) spectroscopy in bacteriology: towards a reference method for bacteria discrimination, Anal. Bioanal. Chem. 387 (2007) 1739–1748. [14] J. Grdadolnik, Y. Marechal, Hydrogen-deuterium exchange in bovine serum albumin protein monitored by fourier transform infrared spectroscopy, part I: structural studies, Appl. Spectrosc. 59 (2005) 1347–1356.

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Biographies

Dr. Moinuddin Hassan received his Ph.D in Medical Science (physiology) from Tokyo Medical and Dental University, Tokyo, Japan in 2001. Dr. Hassan has been working in the areas of biomedical imaging and optics for more than 15 years. His current research interests on multi-modality imaging and optical sensors to detect microorganism and cell components. He has authored or coauthored over 120 scientific publications, including five book chapters, on subjects that include medical imaging and instrumentation for breast cancer therapy and efficacy of treatment, multimodality approach for different types of cancer detection, spectroscopy and optical sensors to detect bio-contaminations. Dr. Elizabeth Gonzalez is a Staff Fellow (Microbiologist) in the Division of Biology, Office of Science and Engineering Laboratories, CDRH. Dr. Gonzalez received her Ph.D. at the University of Maryland, College Park in Cell Biology and Molecular Genetics in 2012. In August 2012, she started working at CDRH in the Infection Control Lab, which has been focused how device design influences infection control. The lab has specifically worked on cleaning and disinfecting surrogate select agent pathogens and surrogates of chemical toxins from reusable medical devices and equipment such as pulse oximeters, EKG leads, bed rails, anesthesia machines and ventilators. Dr. Victoria M. Hitchins is a Research Microbiologist at the Center for Devices and Radiological Health, U.S. Food and Drug Administration. She received her B.A. in Biology from Wellesley College, and her M.S. and Ph.D. in Microbiology from Michigan State University. She was a NIH Postdoctoral fellow in Biochemical Sciences in Princeton University and Biochemistry at the University of Kentucky. She has over 36 years of experience at the FDA in the fields of optical radiation and infection control. She is the Leader of the Microbiology and Infection Control Laboratory. She has over 50 publications in peer-reviewed journals and book chapters. Dr. Ilko K. Ilev is the Leader of the Optical Therapeutics and Medical Nanophotonics Laboratory at the U.S. Food and Drug Administration (FDA) and he was appointed to the HHS/FDA Senior Biomedical Research Service (SBRS) in 2012. He has over 25 years of extensive experience in the United States, Europe and Japan, with more than 385 publications in the field of laser medicine, biophotonics, nanobiophotonics, high-resolution imaging and sensing, biomedical and fiber optics, and laser safety. Along with multiple original publications, he holds 12 patents. Dr. Ilev was elected as Fellow of IEEE (2014), OSA (2016) and SPIE (2016).