Abstract Due to the influence of many environmental processes, a precise determination of the post-mortem interval (PMI) of skeletal remains is known to be very complicated. Although methods for the investigation of the PMI exist, there still remains much room for improvement. In this study the applicability of infrared (IR) microscopic imaging techniques such as reflection-, ATR- and Raman- microscopic imaging for the estimation of the PMI of human skeletal remains was tested. PMI specific features were identified and visualized by overlaying IR imaging data with morphological tissue structures obtained using light microscopy to differentiate between forensic and archaeological bone samples. ATR and reflection spectra revealed that a more prominent peak at 1042 cm-1 (an indicator for bone mineralization) was observable in archeological bone material when compared with forensic samples. Moreover, in the case of the archaeological bone material, a reduction in the levels of phospholipids, proteins, nucleic acid sugars, complex carbohydrates as well as amorphous or fully hydrated sugars was detectable at (reciprocal wavelengths/energies) between 3000 cm-1 to 2800 cm-1. Raman spectra illustrated a similar picture with less ν 2 PO 4 3−at 450 cm-1 and ν 4 PO 4 3− from 590 cm-1 to 584 cm-1, amide III at 1272 cm-1 and protein CH 2 deformation at 1446 cm-1 in archeological bone material/samples/sources. A semi-quantitative determination of various distributions of biomolecules by chemi-maps of reflection- and ATR- methods revealed that there were less carbohydrates and complex carbohydrates as well as amorphous or fully hydrated sugars in archaeological samples compared with forensic bone samples. Raman- microscopic imaging data showed a reduction in B-type carbonate and protein α-helices after a PMI of 3 years. The calculated mineral content ratio and the organic to mineral ratio displayed that the mineral content ratio increases, while the organic to mineral ratio decreases with time. Cluster-analyses of data from Raman microscopic imaging reconstructed histo-anatomical features in comparison to the light microscopic image and finally, by application of principal component analyses (PCA), it was possible to see a clear distinction between forensic and archaeological bone samples. Hence, the spectral characterization of inorganic and organic compounds by the afore mentioned techniques, followed by analyses such as multivariate imaging analysis (MIAs) and principal component analyses (PCA), appear to be suitable for the post mortem interval (PMI) estimation of human skeletal remains.

Citation: Woess C, Unterberger SH, Roider C, Ritsch-Marte M, Pemberger N, Cemper-Kiesslich J, et al. (2017) Assessing various Infrared (IR) microscopic imaging techniques for post-mortem interval evaluation of human skeletal remains. PLoS ONE 12(3): e0174552. https://doi.org/10.1371/journal.pone.0174552 Editor: Mohammad Shahid, Aligarh Muslim University, INDIA Received: July 4, 2016; Accepted: March 11, 2017; Published: March 23, 2017 Copyright: © 2017 Woess et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: A Part of this research was funded by the Austrian Science Fund (FWF) [P22880-B12]. CR was supported by the Christian Doppler Laboratory CDL-MS-MACH (funded by the Federal Ministry of Economy, Family and Youth, and the National Foundation for Research, Technology and Development). Competing interests: The authors have declared that no competing interests exist. Abbreviations: ATR, Attenuated Total Reflection; CCD, Charge coupled device; DNA, Deoxyribonucleic acid; FTIR, fourier transform infrared; HCA, hierarchical cluster analysis; IR, infrared; KMC, K Means Clustering; LM, light microscope; MCT, mercury cadmium telluride; MIAs, multivariate imaging analyses; MIR, mid infrared; PCA, principal component analyses; PMI, post-mortem interval; ROIs, regions of interest; UV, ultraviolet

1. Introduction After discovery of skeletonized human remains or individual bones, the most important apprehension for investigators and legal authorities is to distinguish forensic material from archaeological material [1],[2]. Therefore, it is crucial to determine the post-mortem interval (PMI) as precisely as possible. PMI estimation usually starts with the macroscopic examination of the bone material, together with the consideration of the gross appearance, tissue preservation and odour. Due to the fact that these features are influenced by many environmental factors (temperature, body size, accessibility for insects or animals, location of the body etc.), which can impinge on the decomposition process, the estimation of the exact PMI is very difficult [3], [4], [5]. Thus, the differentiation between forensic and anthropological material can be quite challenging and often depends on the experience of the investigator. Techniques for the investigation of the PMI include microscopic methods [6], [7], [8], chemiluminescence tests, such as the luminol reaction [2],[9],[10], radiocarbon techniques [3, 11], chemical methods, spectroscopical analysis [12], [13], macroscopic UV fluorescence [14] and the detection of various radionuclides [15], [16], [17, 18]. The usage of different reflection infrared (IR) microscopic imaging methods for PMI research might provide a useful adjunct to conventional methodologies. IR and Raman spectroscopy has already demonstrated great promise for the characterization of bone specimens [12, 19–23]. The primary aim of our study is to test the suitability of these techniques for analyzing organic and inorganic components of bone material and to subsequently differentiate between forensic and archaeological bone material. For this purpose IR microscopic imaging techniques, such as infrared (IR) reflection-, ATR- and Raman-microscopic imaging, followed by multivariate data analyses (MIAs), were combined in order to achieve a more sophisticated characterization of human skeletal remains. These modern analytical techniques enable molecular imaging of complex samples and are based on the absorption of infrared radiation by vibrational transitions in covalent bonds [24]. The major advantage of these methods is the acquisition of unique images of the in situ distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids and small molecules with high spatial resolution whilst maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification and separation steps [25]. These techniques also allow samples to be probed under native conditions and provides unique chemo-morphological information about the tissue status without the need for fixation, staining or application of additional markers [26]. With these methods it is possible to gain qualitative and quantitative information from heterogeneous samples, since the individual IR spectrum of any compound represents a unique `molecular fingerprint´ [27, 28]. IR microscopic imaging has already been utilised with great success for the characterization of biological specimens [29–42], malignancies in several tissues [25, 43–56], in environmental mapping [57], precision farming [58], food quality evaluation [59], product functionality [60, 61], for ascertaining the severity of plant diseases [62, 63], detecting defects [64] and contaminations [65–67], as well as for assessing the distribution of certain chemical components [32, 39, 68–70]. Hence there is good reason to believe that IR microscopic imaging measurements may also be applicable for PMI estimation of human bones through the evaluation of molecular distribution patterns.

2 Material and methods 2.1 Sample collection Archaeological bone samples of different ages from an excavation site covering several centuries in European medieval times (n = 2) and forensic bone samples (n = 4) were investigated. Gender identifications were confirmed by DNA analyses. For this study, the part between the upper and mid third of the femur was used. Employing an oscillating bone saw a transverse section was cut to a thickness of about 7 mm from each bone. Cross sections were polished with 1200 grit carbide paper prior to the measurements. All examined samples were anonymized before the authors had access to the specimens. Anthropological properties and place of discovery of all measured human skeletal remains are summarized in Table 1 and additionally described elsewhere [41, 71, 72]. Detailed information concerning the archaeological bone samples is given in Table 2. Archaeological bone samples were provided by the Museum of Industry and Prehistory in Wattens and permission was granted by the ‘Staatssammlung für Anthropologie und Paläoanatomie MUC ‘(SAPM, Munich, Germany). Detailed information about sex identification, specimen number, complete repository information, museum name(s) and geographic location is published in [71, 72] and stated in the supplementary (S1 and S2 Figs, S1 Table and S1 File). Forensic bone samples were provided by the Institute of Legal Medicine (Medical University of Innsbruck). Bone extraction was executed according to the specifications/standards required by public prosecution for DNA extraction and identity determination. All necessary permits were obtained for the described study in agreement with Austrian legislation, which complied with all relevant regulations. PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. Anthropological properties and place of discovery of the measured human skeletal remains. https://doi.org/10.1371/journal.pone.0174552.t001 PPT PowerPoint slide

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larger image TIFF original image Download: Table 2. Properties of archaeological bone samples. https://doi.org/10.1371/journal.pone.0174552.t002 2.2 MIR reflection and ATR microscopic imaging Mid infrared (MIR) reflection and ATR (Attenuated Total Reflection) microscopic imaging measurements were performed at room temperature using a LUMOS Fourier transform infrared (FTIR) microscope (Bruker) with an integrated FTIR spectrometer, equipped with a photoconductive MCT detector with liquid nitrogen cooling. Visual image collection was performed via a fast and highly resolving digital CCD camera. The bone specimens for PMI estimation were detected with a nominal lateral pixel resolution a) of 20 μm × 20 μm for MIR reflection and b) of 10 μm × 10 μm for ATR using the MCT detector. Measurements were done in a spectral range from wave numbers of 4000 cm-1 to 600 cm-1. Spectra were recorded with a spectral resolution of 2 cm-1 with 256 co-added scans for reflection and 128 scans for ATR. Prior to each MIR reflection and ATR sample measurement, a background spectrum of a gold-coated substrate was recorded with a spectral resolution of 2 cm-1 with 256 co-added scans. Scan number and spectral resolution were optimized in order to achieve a suitable signal to noise ratio within the recorded spectra. For detailed information about detector theory, technology and current developments see references [73–75]. After background reduction, each sample was scanned with a light microscope (LM) for histological reevaluation and comparison to the imaging results. 2.3 Raman imaging Raman imaging measurements were performed in reflection mode using a WITec ALPHA300R microscope at room temperature. For the Raman excitation in the near-infrared a Toptica XTRA laser with a nominal wavelength of 785 nm and a power of 15 mW, measured at the back aperture of the objective (Zeiss EC EPIPLAN 50x/0.7), was used. Because of the large size of the sample and the structures within, it was possible to choose parameters that reduced the measurement time for a single scan, i.e. a fibre with a core diameter of 100 μm to deliver the collected light to a spectrometer and a distance between scan points of 1 μm, which does not yield the highest possible lateral resolution of the microscope. The signal was analysed in the spectral range between 0 cm-1 and 1776 cm-1 with a spectral resolution of about 6 cm-1. The integration time for a single scan point was optimized near the scan region prior to each measurement to yield a good signal to noise ratio without causing damage to the sample. 2.4 Data processing All spectral data processing and image assembling were performed using the OPUS 6.5 software (Bruker), The Unscrambler X 10.3 (Camo, Norway, Oslo) and the CytoSpec™ software package (http://www.cytospec.com, Hamburg, Germany). Univariate chemical maps, depicting a single spectral feature and multivariate imaging analysis (MIAs) were generated by using the OPUS 6.5 (for MIR reflection and ATR microscopic imaging) and CytoSpec™ (Raman) software. Principal Component Analyses (PCA) Noise and atmospheric absorptions were removed using the CytoSpec™ software in the run-up to principle component analyses (PCA) and image analysis. Subsequently PCA models were generated with The Unscrambler X 10.3 software. For PCA model generation tissue type-associated spectra were selected with the CytoSpec™ software and then ROIs (regions of interest) were assigned. Selected spectra of ROIs were imported into The Unscrambler X 10.3 software and underwent several data pretreatments (e.g., baseline correction, normalization) prior to PCA model generation. Image analysis Initially, atmospheric correction and noise reduction were performed: a) by using the OPUS 6.5 software (Bruker) for MIR reflection and ATR microscopic imaging and b) by using the WITec Control software. After spectral refinement sample specific data sets were loaded in the CytoSpec software. Spectra of MIR reflection and ATR microscopic imaging were vector normalized in the wave number range 4000 cm-1 to 600 cm-1. This procedure led to more pronounced peaks, eliminated background slopes and reduced the influence of intensity changes caused by differences in tissue density and tissue roughness [76]. The processed spectral datasets were used for subsequent MIAs. Furthermore, the imaging results were assembled and compared directly with the LM images captured from the same samples.

4. Conclusion In order to further develop the current state of the art in PMI estimation we tested the suitability of various infrared (IR) microscopic imaging techniques regarding their ability to make conclusions about the PMI of human skeletal remains by analysing their organic and inorganic components. Various IR microscopic imaging techniques such as infrared (IR) reflection-, ATR- and Raman- microscopic imaging evaluated by multivariate data analyses (MIAs), were combined together as a means for a more sophisticated characterization of human skeletal remains. Spectra (reflection and ATR) at 1042 cm-1 (indicator for bone mineralization) indicated a more prominent peak in archeological bone material compared to forensic ones. Furthermore, a reduction in phospholipids, proteins, nucleic acid sugars, complex carbohydrates as well as amorphous or fully hydrated sugars at 3000 cm-1 to 2800 cm-1 in archaeological bone material could be demonstrated. Raman spectra illustrated a similar picture with less ν 2 PO 4 3− at 450 cm-1 and ν 4 PO 4 3− from 590 cm-1 to 584 cm-1, amide III at 1272 cm-1 and protein CH 2 deformation at 1446 cm-1 in archeological specimens. Chemical maps from reflection and ATR imaging determined (highlighted) that the levels of carbohydrates 1185 cm-1 and complex carbohydrates together with amorphous or fully hydrated sugars 3000 cm-1 to 2800 cm-1 are higher in forensic bone samples than in archaeological bones. It can be assumed that this observation is due to diagenetic decomposition and aging processes. Therefore, the semi-quantitative analysis of these biomolecules could serve as an estimation of PMI. Results from Raman microscopic imaging exhibit less B-type carbonate at 756 cm-1 and also a lower amount of protein α-helix at 1272 cm-1 in bones with a PMI of more than 3 years, indicating a decrease in these biomolecules within the first years post mortem. Findings concerning bone mineral compounds were inconclusive, most likely due to mineral uptake during diagenesis from the surroundings. To gain deeper insight into the histo-anatomical features of bones, cluster analyses were performed. Results from reflection and ATR measurements did not reveal any relations to histological structures, however, spectral clusters from Raman microscopic imaging corresponded well to histo-anatomical features. Nonetheless, an in-depth differentiation could not be achieved in general. Furthermore, the mineral content ratio and the organic to mineral ratio were calculated from the obtained reflection, ATR and Raman spectra. Results are similar to the findings published by Creagh et al.[22]: during the age-related degradation, the mineral content ratio increases while the organic to mineral ratio decreases with time. For a complete characterization of spectral variations in correlation with the PMI, PCAs were carried out, suggesting that most of the significant information can be found in the wavelength number region of 1700 cm-1 to 750 cm-1 (reflection- and ATR imaging) and 1700 cm-1 to 300 cm-1 by Raman imaging. The most promising segregation of forensic and archaeological bone samples was achieved with Raman imaging. Taken together, our results show that the differentiation of forensic and archaeological bone material is possible by the use of the aforementioned techniques. In the context of forensics this aspect is most important because of the various legal implications. We therefore suggest to use IR/Raman microscopic imaging techniques as another important tool in the field of PMI estimation. We hope that this study will serve as a basis for additional developments in the field of PMI estimation. A number of questions such as the impact of environmental influences still need to be investigated in more detail. We anticipate that future studies will enable the PMI to be narrowed down further by analysing a bigger sample size taken from different decades, although obtaining an adequate and comparable sample size is still a major challenge.

Acknowledgments The authors would like to thank Prof. Richard Scheithauer from the Institute of Legal Medicine (Medical University of Innsbruck) and Stefano Longato for their support. A Part of this research was funded by the Austrian Science Fund (FWF) [P22880-B12]. CR was supported by the Christian Doppler Laboratory CDL-MS-MACH (funded by the Federal Ministry of Economy, Family and Youth, and the National Foundation for Research, Technology and Development).

Author Contributions Conceptualization: JP CW SU WP PHG MRM NP CR JCK. Data curation: JP CW. Formal analysis: JP CW SU CR. Funding acquisition: WP MRM. Investigation: JP CW SU. Methodology: JP CW. Project administration: JP CW. Resources: WP PHG MRM SU NP JCK. Software: JP CW SU NP. Supervision: JP CW WP PHG JCK. Validation: JP CW WP PHG NP SU. Visualization: JP CW SU CR. Writing – original draft: JP CW SU WP PHG MRM NP CR JCK. Writing – review & editing: JP CW SU WP PHG MRM NP CR JCK.